Quality Management In The Automobile Industry

4

Quality Management in the Automobile Industry Quality Assurance in the Process Landscape

– General, risk analyses, methods, process models –

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Verband der Automobilindustrie

Quality Management in the Automobile Industry

4

Quality Assurance in the Process Landscape - General, risk analyses, methods, process models -

DFMA

st

1 edition 2008 English version published in 2009

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Foreword to first edition

This description of the DFMA approach was drawn up as a part of the work of the DFMA Automotive Forum and is made available to the VDA for publication in Volume 4 "Quality Assurance in the Process Landscape – General, risk analyses, methods, process models". We thank the participating companies and their staff members for the work put into preparing this chapter. The following companies collaborated in its preparation: Adam Opel AG Audi AG BMW Group Daimler AG Fachhochschule Rosenheim, University of Applied Science MAGNA STEYR Fahrzeugtechnik AG & CO KG Volkswagen AG Thanks also go to all who gave us their suggestions for its preparation and improvement.

Oberursel, August 2008

Verband der Automobilindustrie e.V. (VDA)

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Contents

Page

Foreword to first edition

1

1

Introduction

3

2

Approach

4

3

Principles

5

3.1

Principle of Design for Manufacture (DFM)

5

3.2

Principle of Design For Assembly (DFA)

5

3.3

Principle of Design for Service (DFS)

6

3.4

Principal of Design for Environment (DFE)

7

4

Objectives, point of application, pre-requisites, approach

7

4.1

Objectives

7

4.2

Point of application

8

4.3

Pre-requisites

8

4.4

Approach -workflow and process steps

9

4.4.1

Selection of the system to be considered

10

4.4.2

Analysis of the current situation

10

4.4.3

Development, evaluation and selection of ideas

11

4.4.4

Checking proposed solutions

11

4.4.5

Decision making

11

4.4.6

Monitoring implementation, effectiveness of the approach

11

5

Duties and requirements, DFA Moderator

12

6

Possible support with aids /tools

12

7

Example

13

8

Literature

16

2

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Introduction

It is a fact that, in the product development process, the product costs are defined at a very early phase while the actual costs do not arise until much later. Product refinement is thereby often neglected in the early phases, since the concrete design for manufacturing and assembly is still very difficult at this stage. The more concretely the product is defined as the development process continues, the harder it becomes to take account of the concerns of manufacturing and assembly and the higher the costs in time and money of any product changes required (the "rule of ten"). The objective is, in the early phase of the product development process, to reach agreement with all departments involved in the product (i.e. the required technical changes should be made at this stage so that no further changes are needed at the start of series production) so that the requirements of all involved in the product can be taken into account in the product design. In practice, however, during the early phases of the product development process, there are often insufficient resources available for intensive design of the product for manufacture and assembly, as work is needed on earlier products (shortly before start of production) and the necessary changes can only be implemented in part and at high cost due to pressure of time. An attempt will then be made, shortly before start of production, to fit the changes in "artificially" (e.g. with a "design stop"), which is, however, not possible as the product would not work like that. As a result, very time and cost-intensive product adjustments will be needed after production start. This vicious circle is represented in fig. 1. Number of technical changes

„Practice“

„Objective“

Start of production Fig. 1:

Time

Number of technical changes during product development: desired curve and curve often encountered in practice

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In Simultaneous Engineering Teams (Simultaneous Engineering
SE, is represented in fig. 2) and with suitable methodological support, this vicious circle can be broken. An effective methodology can be the "Design for Manufacture and Assembly (DFMA)" approach. Simultaneous Engineering Design & development Entwicklung / Konstruktion

Sequential workflow sequentieller Ablauf

Production planning Procurement

Tool design‘ & manufacture Q-manag.

Fertigung Production

Time saving Zeitersparnis

Design & development Production planning

Parallel workflow paralleler Ablauf

Procurement Einkauf Tool design & man. ./--bau Wzg ‘konstr

Production Fertigung

Quality management

TimeZeit

Fig. 2:

2

Principle of Simultaneous Engineering (SE) /2/

Approach

DFMA embodies a number of approaches, often described as Design for X. The best know "Design for X" approaches are (with reference to BDI /3, 4/): a. Design for Manufacture (DFM) The product design is optimised for manufacturability. The parts manufacturing cost and manufacturing process and tooling costs are optimised by design. b. Design for Assembly (DFA) The product design is optimised for assembly. This means that, even in the early phases of the product development process, the assembly costs are minimised while keeping manufacturing costs to a minimum. The assembly requirements are given special consideration in the design of the finished product.

4

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c. Design for Service (DFS) This approach considers the customer's demand for repair, service, spare parts etc. to be kept as low as possible or avoided altogether. At the same time, the manufacturer's guarantee costs are reduced. d. Design for Environment (DFE) DFE considers environmental compatibility and, in particular, product recycling. The product is so designed that, at the end of its life, its disposal costs are minimised. In the German automotive industry, product design for recycling has been raised to a high level of importance with the product take-back order. 3

Principles

3.1

Principle of Design for Manufacture (DFM)

Design for Manufacture is based on a few underlying principles: a) b) c) d)

determination of the tooling costs, determination of the manufacturing costs, conclusions/opinions on design alternatives, determination of the theoretically possible minimum tooling and manufacturing costs through benchmark comparisons with similar products, e) determination of an indicator number for the manufacturability by setting the actually required tooling and manufacturing costs for a product against those for an ideally designed product. Manufacturing Index = (actual tooling costs / ideal tooling costs) + (actual manufacturing costs / ideal manufacturing costs). 3.2

Principle of Design For Assembly (DFA)

Design for Assembly is based on a few underlying principles: a) determination of the assembly time from the time for handling and the time for jointing, b) determination that an ideally designed part can be assembled in three seconds (insertion of an appropriately dimensioned ball in a hole with guide countersink), c) conclusions/opinions on design alternatives, d) determination of the theoretically possible minimum number of parts,

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e) determination of an indicator number for the ease of assembly by setting the time actually required to assemble a product against that for an ideally designed product. Further parameters can be determined according to the various methods and levels of detail (e.g. assembly costs, product design proposals, statements about tool and equipment etc.). Fig. 3 shows a representation of this approach. 3. Parts handling (Th) Assembly time (Ta)

Jointing

Design Index DI =

3 x Mq x 100 Ta

estimated min number of parts (Mq)

min Assembly time 3 s

(Tj)

Time to assemble an ideally designed part

• Movement relative to other parts required? • Different material or separation required? • Separation required on assembly/disassembly grounds?

Fig. 3:

3.3

The principal of Design for Assembly (manual assembly), with reference to BDI /3/ Principle of Design for Service (DFS)

Design for Service is based on a few underlying principles: a) b) c) d)

determination of service/repair time, determination of materials / spare parts costs, conclusions/opinions on design alternatives, determination of the theoretically possible minimum service time and materials / spare parts costs through benchmark comparisons with similar products, e) determination of an indicator number for the serviceability by setting the actually required and actual materials / spare parts costs for the service/repair of a product against the time and costs for an ideally designed product. Service index = (actual time required / ideal time required) + (actual materials / spare parts costs / ideal costs).

6

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3.4

Principle of Design for Environment (DFE)

Design for Environment is based on a few underlying principles: a) determination of the disposal costs, b) conclusions/opinions on design alternatives, c) determination of the theoretically possible minimum disposal costs through benchmark comparisons with similar products. d) determination of an indicator number for the recyclability by setting the actually required disposal costs for a product against those for an ideally designed product. Environment Index = (actual disposal costs / ideal disposal costs)

4

Objectives, point of application, pre-requisites, approach

4.1

Objectives

Experience shows that products optimised for costs, materials and parts have smoother running production and higher reliability in use. The DFMA approach, through the involvement of specialists from development, planning, production, quality management, marketing, purchasing and other departments affected, uses the whole knowledge base. This avoids misunderstandings and ensures that information is transferred in good time, specifications are met, product adjustment costs are reduced, development time is shortened and that valuable development and planning capacities are thereby used correctly. In the concept phase, decision-making is supported and accelerated through the systematic development and evaluation alternatives and the speedy and objective assessment of their costs. In the development phase, reduction of the number of parts, the simplification of their design and product design for manufacture lead to optimised production costs (consisting of parts costs, production costs, tool and equipment investments) for the quantities in question. In addition, assembly costs are reduced and the demands of ergonomics and workplace organisation are taken into account. Through uniform and comprehensible documentation of the use of the approach, information exchange is improved in the SE process and adjustment costs are reduced. The overall result is an improvement of quality and a reduction of complexity and failure possibilities.

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4.2

Point of application

From the objectives of the DFMA approach, its use for the design or optimisation of products and processes is possible at all stages of the product life cycle. The DfX group of approaches makes it possible to design or optimise a product for manufacture, assembly, service and recycling at all stages of the product life cycle. Reasons for its use are, for example • the concept decision must be confirmed by systematically produced and evaluated alternatives • the weight must be reduced • there is a target cost conflict • we have been found to be behind the competition • the product cannot be produced as thought, e.g. feedback from suppliers, simulation or FMEA etc. • high repair cost • ergonomic problems • reduction of rework • design does not meet assembly targets It is most effective/beneficial, however, in the early phase of the product development process (concept phase).

4.3

Pre-requisites

For effective and reasonable use of the DfX group of approaches some pre-requisites of a system technical or organisational nature must be fulfilled. On the system side, a description of the product concept, product or process must be available in as much detail as possible (quantities, variants, boundary conditions and so on). Cost information (investments, product and assembly costs), so far as available, or costs of earlier or similar systems must be brought into the discussion. From the objectives and reasons for use set out above, concrete and measurable objectives of the use of the approach must be derived and a task to be performed must be agreed.

8

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On the organisational side, an interdisciplinary team, with its members drawn from the departments involved (e.g. development, planning, production, purchasing, quality assurance, controlling, customer service, supplier), is to be formed and its capacities planned for the work on the design approach. For a systematic and methodical workflow, a trained moderator, internal or external, is required. Support from "lateral thinkers" from outside the project raises the chances of success.

4.4

Approach -workflow and process steps

The approach involves the following essential steps: 4.4.1 Selection of the system to be considered 4.4.2 Analysis of the current situation 4.4.3 Development, evaluation and selection of ideas 4.4.4 Checking proposed solutions 4.4.5 Decision making 4.4.6 Monitoring implementation, effectiveness of the approach

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DFM/A TI-410, Naujocks 26/03/03 page 10

DFM/A in the automotive industry Workflow according to BMW

Preparation

Workshop

Rework

Definition

Formulation

Analysis

Solution

Formulating the problem

Check definition in the group

Collect, list and weigh information

Collect, complete and evaluate proposals

 Identify and define scope of investigation  Work out target contact (clear objectives!)  Define team  Build up DFA baseline

 Explain procedure  Set out targets; if appropriate, define team target  Work through DFA baseline with the team and complete if necessary

 Collect first ideas on working through

Decision Measures, those responsible, deadlines

 Classify ideas (A/B/C)

 Work out one final team proposal

 possible solutions (A/B)  Identify potential fields from software (candidates)


work out
evaluate
agree

 Brainstorming  Different solutions

 Agree next steps, those responsible and deadlines  Draw up and distribute workshop report  Monitor implementation

 Answer open DFA questions

Fig. 4: 4.4.1

DFMA process Selection of the system to be considered

In a preparatory phase, the system to be considered is defined with regard to its scope, interfaces and depth of consideration and available documents (sketches, descriptions, drawings, cost information etc.) are collected. Objectives and the scope of work are agreed, team members selected, deadlines are set and other organisational aspects are clarified. 4.4.2

Analysis of the current situation

Depiction of the system to be worked on (concept, design progress, manufacturing process and the like) in a structural diagram. Determination of functions and allocation of individual costs and investments to the elements of the structural diagram (parts and process steps). Note: can be prepared with a reduced team.

10

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4.4.3

Development, evaluation and selection of ideas

Systematic discussion of the structure depicted and scrutiny of function, individual parts and process steps with a catalogue of questions targeting the desired direction of optimisation. In the process, new ideas, proposed solutions and alternative solutions should be developed using creative techniques. The ideas or proposed/alternative solutions are sorted, prioritised and passed to those responsible with requests for testing and deadlines. Note: takes place with whole team

4.4.4

Checking proposed solutions

A detailed investigation of the selected ideas, proposed solutions and alternative solutions in respect of the functions, manufacturability, costs, deadlines, weight, service and other boundary conditions (safety, laws etc.) by the appropriate departments leads to new, realisable solutions (with possible modifications of the proposed solutions). Note: by the appropriate specialist experts 4.4.5

Decision making

Joint discussion of the possible alternatives from 4.4.3, their depiction in the structural diagram and comparison with the starting situation show the potential for improvement or savings. Recommendations to implement, not implement or to test further facilitate the selection of optimised systems for the decision-maker. 4.4.6

Monitoring implementation

After costing and identification of the improvements, the measures found and agreed upon are to be implemented. Their realisation is to be monitored and the success evaluated. The last step is to put together all the documents (e.g. final report) in order to document the effectiveness and benefit of the use of the approach and provide arguments for further DfX projects.

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5

Duties and requirements, DFA Moderator

To guarantee effective and methodical application of the DfX approaches, the use of a moderator is recommended. His duties are the preparation of the use of the approach, the selection of the team in discussion with the project leader, the organisation and conduct of the team meetings and to ensure that approach is applied systematically and documented (including final report). To be able to carry out these duties, a moderator needs sound knowledge and experience of the approach. He must have command of the aids (software) to be used. In addition to the training as moderator, a basic technical and commercial understanding is required.

6

Possible support with aid /tools

Additional aids for successful use of the approach are: - visualisation tool for product structure, including associated costs and in vestments - system for determination of production times - system for identification of potentials for optimisation of parts and process steps - system for identification of parts and process steps to be saved - CAD simulation of assembly - method for the evaluation and prioritisation of ideas - documentation tools - database for standard parts, materials, operations, tools and machines - good/bad example of product design for manufacture - teaching program for DFMA workshop participants.

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Example:

Taking a ballpoint pen as an example, the principle and procedure of DFA can be explained. Here, only the basic and, in part, further simplified, principles of DFA were used, without going further into the individual methods. An exploded drawing of a ballpoint pen is shown in fig. 5. Its individual parts can be seen. Example ballpoint pen with 10 parts

Tip Ink tube

Rubber grip

Spring 2

Spring 1

Upper body Button

Lower click part Upper click part Clip Fig. 5:

Exploded drawing of the ballpoint pen to be analysed, as made available by Volkswagen Coaching GmbH

The ballpoint pen is first dismantled to identify and number the separate parts. The separate parts are then analysed in the order of assembly with regard to: • •

assembly time, theoretical minimum number of parts.

The result for this ballpoint pen is shown in the following table1. An assembly time of about one minute is determined and the information is obtained that this design, with its ten separate parts, could, in theory, be realised with only four parts. The parts marked with "=" in the Qpm column must now be checked to see whether their function could be incorporated in other parts.In addition, the indicator "DI" gives an indication of the fitness for assembly of the design carried out in comparison with the design improvements that now become necessary. Dokument wurde bereitgestellt vom

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Analysis ballpoint pen with 10 parts Time calculation

N

N

Parts Parts 4.4. Upper body 4.3 Clip 4.2.3 upper click part 4.2.2 Spring 2 4.2.1 lower click part

Th

Assemblies

[Quantity] 1 1

t (handling)

[Quantity]

1 1 1 1

4.2 Click mechanism

Tj

Ta Assembly time

t (jointing)

[s] 1,2 2,3

[s] 1,5 5,0

[s] 2,7 7,3

1,2

1,5

2,0 1,2

3,5 2,5

2,7 5,5 3, 7

1,2

3,5

4,7

Mq min.Parts

[Quantity] 1 0 1 0 0

1.2 Tip

1

1.1 Rubber grip

1

1,2 1,1

1,5 5,0

2,7 6,1

0 0

3 Spring 1 2 Ink tube

1 1

2,0 1,2

3,5 1,5

5,5 2,7

1 1

1,2

8,5

9,7

1

1,2

2,1

3,3

10

17,0

39,6

1

1 Ink tip 4.1 Button Button

Legend: Th Time for handling Ta Assembly time

0

56,6

4

Design Index:

21

Mq min number of parts N

Number of parts/assemblies

Tj Time for jointing Tab. 1:

Analysis of a ballpoint pen with regard to fitness for assembly according to DFA

After carrying out DFA, there is a proposed solution with only 4 parts, as shown in fig. 6. Example ballpoint pen with 4 parts Integral part

Ink tube

Body Spring Fig. 6: Design implemented after DFA of a ballpoint pen with 4 parts (Study, FH Rosenheim)

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Analysis of ballpoint pen with 4 parts Time calculation

N Parts

Parts

[Quantity]

N

Th

Assemblies

t (handling)

[Quantity]

Tj t (jointing)

Ta

Mq min parts [Quantity]

[s]

[s]

Assembly time [s]

Ink tube

1

1,2

1,5

2,7

1

Spring

1

2,0

3,5

5,5

1

Body

1

1,5

1,5

3,0

1

Integral part

1

1,7

2,5

4,2

0

4

6,4

9,0

15,4

3

Design Index:

58

Total

Legend:

Th Time for handling

Mq min number of parts

Ta Assembly time

N

Number of parts/assemblies

Tj Time for jointing

Tab. 2:

Analysis of the new ballpoint pen with regard to fitness for assembly according to DFA

The analysis shows a reduction of the number of parts from 10 to 4 and a reduction of the assembly time from 56.5 s to 15.5 s, however, it should be noted that one part (integral part) is rather more complicated. In all, however, not only does the reduction in the number of parts save assembly time, it also reduces the potentially possible failure modes and thus improves quality.

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15

8

Literature:

/1/

Bäßler, R.

/2/

Andreasen,M.;

/3/

Kähler, S.; Lund, T.: Boothroyd, G.:

/4/

16

Boothroyd, G.; Dewhurst, P.:

Integration der montagegerechten Produktgestaltung in den Konstruktionsprozess [Incorporation of product design for assembly in the design process]. Berlin, Heidelberg, New York, London, Paris, Tokyo: Springer, 1988. Design for Assembly. Berlin, Heidelberg, New York, Tokyo: Springer, 1983. Design for Assembly. Amherst, University of Michigan, Department of Mechanical Engineering, 1983. DFA – Design for Assembly; Handbook for Users Analysis and Optimization of Manually Assembled Products and Circuit Boards. University of Rhode Island, Kingston, Department of Industrial and Manufacturing Engineering, 1993.

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Verband der Automobilindustrie

Quality Management in the Automobile Industry

4

Quality Assurance in the Process Landscape - General, risk analyses, methods, process models -

DMU (Digital Mock-Up)

st

1 edition 2008 English version published in 2009

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We are grateful to the participating companies and their employees for their commitment in writing this chapter. The following companies participated in the drafting:

BMW Group Daimler AG Siemens VDO Volkswagen AG Karmann GmbH ZF Sachs AG Bosch GmbH automotive.business.support, H.Füller

We also wish to thank everyone who made suggestions for improvements.

Oberursel, August 2008

Verband der Automobilindustrie e. V.

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1

DMU (Digital Mock-Up)

DMU is the full structural and spatial description of a vehicle in a CAD system. DMU permits the virtual assembly of components with the corresponding assembly structures and can be used for analysis and safeguarding of the completed vehicle with regard to freedom from collisions, minimum clearances and fitness for assembly. The purpose of DMU is to reach cross-boundary agreement between the departments and development partners by means of structural space management. This guarantees short information paths and rapid reactions in the development phases. The objective is to fully secure the development process through the virtual construction and simulation of all 3-D geometric data. Through the parameterised administration of all 3-D geometric data in VPM (Virtual Product Management), specific structural spaces or vehicles can be viewed on the computer with the aid of CAD systems.

Complete Vehicle Structure Gesamtfahrzeug Struktur functional parts Funktionsteile

doors / lids Tür / Deckel seats / belts Sitze /Gurte

Dachsystem roof system

motor Motor // underbody Fahrwerk

cockpit Cockpit

Rohbau body in white

interior Interieur Elektrik electricity

Fig. 1:

2

Division of a car into work packets

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In VPM, all information about the components is administered, such as 3-D data, structural information, kinematic mechanisms, materials information, administrative attributes (e.g. development status, entitlements) etc. In combination with the VPM Configuration Management and the Product Structure Navigator (PSN), configured vehicles, for example, different equipment variant or national types, can be accessed at any development status and shown on the computer with the latest CA technologies. In the Product Structure Navigator (PSN), the data for a configured vehicle are selected and uploaded to a CAD system. Extensive structural space investigations, simulations and collision checks can then be carried out for virtual safeguarding.

Display of the possible configurations

Fig. 2:

Selection of the desired configuration e.g. Coupe, Standard, USA, LL, Auto., Cd

PSN of the desired configuration

Collision analysis that, after appropriate evaluation, will be documented and communicated in VPM.

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Fig. 3:

Areas with interferences or collisions are shown with red lines (see arrow).

This leads to high quality and a high level of maturity of the components. Development times become shorter and costs are reduced. The objective is to build trial vehicles that are close to production-ready. Advantages of DMU

• • • • •

Visualization of structural spaces for all departments and development partners in the structural space management. Early detection and solution of problems. Shortened development times. High quality of the products developed. Early consideration of production and customer service requirements. Cost reduction.

4

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•

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Verband der Automobilindustrie

Quality Management in the Automobile Industry

4

Quality Assurance in the Process Landscape - General, risk analyses, methods, process models -

Design of Experiments (DoE)

nd.

2 revised edition 2008 English version published in 2009

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We are grateful to the participating companies and their employees for their commitment in writing this chapter. The following companies participated in the drafting: AUDI AG BMW AG Robert Bosch GmbH Continental AG DGQ (Deutsche Gesellschaft für Qualität) Fichtel & Sachs AG Ford-Werke AG GETRAG Getriebe- und Zahnradfabrik Hermann Hagenmeyer GmbH & Cie Kolbenschmidt AG Mercedes Benz AG Adam Opel AG Dr. Ing. h. c. F. Porsche AG Siemens AG Steyer-Daimler-Puch AG ITT Automotive Europe GmbH VDO Adolf Schindling AG Wabco Fahrzeugbremsen Volkswagen AG ZF Friedrichshafen AG

We also wish to thank everyone who made suggestions for improvements. Frankfurt/Main, May 1996 The chapter has been editorially revised. Oberursel, August 2008 Verband der Automobilindustrie e.V. (VDA)

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1

Contents

Page

1

Design of Experiments (DoE)

3

1.1 1.2 1.2.1 1.2.2 1.2.3 1.2.4 1.2.5 1.2.6 1.2.7 1.3

Introduction Problem description and problem analysis Problem Formulation and Defined Goals and Objectives Survey Target quantity Input Variables and Data Collection and Preparation Recording, evaluation and selection of input variables Interactions Example Reducing the Number of Input Variables, Selecting Test Factors Reproducibility and Independence Evaluation Criteria and Evaluation Scale Input Variable Weighting Impact Matrix (after Scheffler) Interactions Factor levels Factor selection summary in a flow chart Example Selection of an Experiment Strategy Single-Factor Experiment Full Factorial Experiment Fractional Factorial Experiment Factor Search after D. Shainin Design of Experiments according to G. Taguchi Development of Robust Products and Processes Development of Robust and Sensitive Products and Processes Example Evaluation of the Experiment Results Representation of the Measurement Results Calculating the Effects Statistical Analysis Method Example Computer Support Bibliography

3 3 4 4 4 4 5 5 6

1.3.1 1.3.2 1.3.3 1.3.4 1.3.5 1.3.6 1.3.7 1.3.8 1.4 1.4.1 1.4.2 1.4.3 1.4.4 1.4.5 1.4.5.1 1.4.5.2 1.4.6 1.5 1.5.1 1.5.2 1.5.3 1.5.4 1.6 1.7

2

8 9 9 9 10 10 10 11 12 13 13 14 15 17 19 19 21 22 23 23 26 28 29 32 33

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Design of Experiments (DoE)

1.1

Introduction

With the objective of ensuring that the products entering into production are reliable and already correctly manufactured and ready for market, right from the start of the series, it is crucial for design and product alternatives to be tested even before series production is launched. This involves conducting practical experiments or theoretical calculations (simulations) to examine the effects that changes in parameters bring about and to evaluate these effects. Statistical experiment design is an effective method for examining the effects that modified input variables (factors) have on the characteristics of the target quantities. This is a process for examining alternative designs or products with regard to interactions and the influence exerted on the target quantities. It is advantageous that the scope of the experiment and depth of information are precisely defined before starting the experiment, and the result is statistically substantiated. The following sections present possibilities for designing and conducting experiments that can be used in each phase of production planning, production preparation, development and manufacturing. Just as for practical trials, theoretical calculations and simulations can be carried out with the methods proposed, in order to obtain systematic findings about the target quantities and cut costs. 1.2

Problem description and problem analysis

It is necessary to work meticulously in problem description and problem analysis. Initially, this is time-consuming. However, that time can made up easily because it means that only the experiments that are actually necessary have to be carried out. It is often the case that the desired result in terms of task formulation has already been obtained after the problem analysis.

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1.2.1

Problem Formulation and Defined Goals and Objectives

The problem formulation and the defined goals and objectives must indicate what the objective is: an improvement, attainment of one relative optimum or the absolute optimum, or another objective. For this purpose, it is necessary to define quality characteristics and evaluation criteria, by means of which the degree to which the defined goals and objectives are attained can be assessed. 1.2.2

Survey

Product: In addition to a drawing or sketch, complete details on the functions that are to be fulfilled are also required for a precise product description. These can be prepared with the help of forms, similar to those used in the FMEA. Process: A process is deemed to include a manufacturing sequence and a functional technical sequence. The process can be represented using a flow chart |1| or flow schedule. If hierarchies or procedures with common dependencies are to be shown, block diagrams |1| are also suitable. Environment: When describing the environment, important boundary or peripheral conditions for the problem solution and noise variables that are already known or assumed must be given for the problem solution. In practice, these are usually system influences that cannot be controlled. 1.2.3

Target quantity

A fundamental step in problem analysis is the definition of the target quantity or quantities. Target quantities are directly or indirectly measurable physical quantities that are suitable for evaluating the achievement of the objectives in the experiment. 1.2.4

Input Variables and Data Collection and Preparation

This step requires data acquisition that extends across the general field. This includes data and facts from current production as well as results of previous analyses.

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The information about the ACTUAL situation is structured, for which a whole range of simple and proven tools are available: -

Error check sheet, error location diagram |1| Pareto analysis (also known as ABC analysis) |1| Graphic representation (line or column graph, pie chart) |1| Stratification, histogram, correlation diagram, box plots z |1|, Probability network |2|.

For new developments, theoretical findings or findings derived from simulation results should be taken into account. 1.2.5

Recording, evaluation and selection of input variables

Brainstorming sessions are held to derive and define the possible input variables. The following aids for collecting and structuring assumed input variables are employed: -

Metaplan technique |3| Cause and effect diagram (also known as a herringbone or Ishikawa diagram) |1| fault tree diagram |4|.

The input variables are then classified and grouped into a list. A distinction is made between -

-

1.2.6

individually adjustable and influenceable input variables, e.g. technical or physical variables, such as size, pressure, rotational speed, input variables that can hardly be influenced, or not at all, e.g. ambient temperature, air humidity, customer behaviour, usage profile. Interactions

It is necessary to consider any interactions. An interaction between input variables is present when the effect of one input variable is dependent on the level to which the other input variables(s) is/are set.

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1.2.7

Example

A demonstration experiment |5| is discussed as an example for explaining and illustrating the procedure in the framework of the design of experiments. This concerns the driveshaft thrust bearing of an automatic gearbox as shown in Figure .

Figure 1:

Driveshaft thrust bearing of an automatic gearbox, ∅ 31 mm

The survey and discussion of the initial situation determined that the objective of the examination was improvement of the thrust bearing surface quality. The target quantities for evaluating this process were set as follows: 1. Surface roughness Rz, 2. Chip form. The surface roughness Rz with a diameter of 31 mm was measured in accordance with DIN 4768, see. The evaluation of the chip form was carried out according to the modified standard series INFOS (after W. König and W. Eversheim 1977).

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Figure 2:

Determining the Surface Roughness RZ according to DIN 4768

After leaving out the start-up length and the trailing length, the measurement length is divided into five equal lengths for separate measurement. The averaged surface roughness RZ is determined as the arithmetic mean of the surface roughness values for the five consecutive separate measurement lengths. The surface roughness Rmax is the largest surface roughness of the five measured values.

The result of a detailed survey in the form of brainstorming for the input variables that possibly determine the surface quality is shown in the causeeffect diagram in

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1.3

Reducing the Number of Input Variables, Selecting Test Factors

For expediency, the workgroup that performed the problem analysis also carries out the planning step to reduce the possible input variables determined during the problem analysis, so that the number of input variables is at a level that is manageable with respect to the experiment. This ensures that all existing practical and technical expertise can be utilized in selecting the factors. Particular reference to Scheffler |6| is made with regard to the procedure treated here.

Tool F G

Material/workpiece

Cutting edge angle

E Carbon content Primary structure Alloying elements

Chip form groove

Tool material

Hardness (HRC)

positive/negative cutting geometry cutting radius

H

Pretreartment.g workpiece

Ceramic HSS PCD

Gripping workpiece

Lot size CBN Hard Hard metal metal coated

Z

Feed rate

Positioning Tool/workpiece

D Employee

Cutting speed

with w/o Composition Ground floor Installation Floor Impurity small

Time Time of day

trained

Feed device C Cutting depth

Rz (µm) Chip form

untrained

B

Shift

Attention

Lightning Temperature

A Lubricant

Human/environment

large

Machine

Figure 3:

8

Cause-Effect Diagram for the Target Quantities Surface Roughness and Chip Form of the Driveshaft Thrust Bearing for an Automatic Gearbox

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1.3.1

Reproducibility and Independence

It must be possible to set the selected input variables at defined levels with known repeating accuracy. A change in the setting of one input variable is not permitted to have any effect on the setting of other input variables. 1.3.2

Evaluation Criteria and Evaluation Scale

Depending on the exact situation, evaluation criteria are defined for selecting the input variables, and a suitable, multiple level scale is devised for these criteria. Each favourable case is rated with a high value; the more unfavourable the assignment to the evaluation criterion, the lower the ranking. For example: Effort for setting the input variable 1 large ... 10 small Assumed factor influence 1 small ... 10 large Costs for changing the setting of the input variable 1 high ... 10 low The multiplication of the means determined for each evaluation criterion yields a statistical parameter for the priority of an input variable. The input variables are ordered according to their priority. 1.3.3

Input Variable Weighting

All team members work together to evaluate and weight the input variables. The initial result is an average ranking for each of an input variable's evaluation criterion. Subsequently, all of an input variable's average rankings are multiplied together. The result is a statistical parameter for this input variable's priority. Finally, the input variables are ordered according to their priority and thereby yield the weighting.

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1.3.4

Impact Matrix (after Scheffler)

An impact matrix is a tabular presentation of the changes in a target quantity or target quantities when input variables are varied. The wave form and symbols characterize the assumed or known change.

Effect known Effect known and non-linear Effect Effect unknown

X1 – Xn = input variables Y1….yn = target quantities

Figure 4:

1.3.5

Impact Matrix after Scheffler

Interactions

In selecting a suitable and economical experiment design, it is particularly important to acquire preliminary information on possible interactions between the input variables. If interactions that actually exist are unintentionally not taken into account when the experiment design is defined, the experiment results may lead to incorrect statements. 1.3.6

Factor levels

Those input variables that are considered in the experiment design are called factors. In the simplest case, two factor levels are assumed. The two levels of the separate factors are selected with "suitable" separation from each other, on the basis of technical considerations, boundary conditions and experimental feasibility. For qualitative factors, the imprecision of the setting must be negligibly small compared to the difference between the intervals.

10

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1.3.7

Factor selection summary in a flow chart

1

Input variable reproducible independent ?

no

Unsuitable for experiment design

yes 2

Input variable evaluation evaluation criteria and evaluation scale

3

Input variable weighting mathematic combinations from 2

4

Set up factor impact matrix to target quantities

5

Obtain preliminary information on possible interactions among the factors

6

Define factor levels check compatibility

Figure 5:

Flow Chart for Factor Selection and Factor Levels

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1.3.8

Example

There is sufficient basic knowledge about the principle effects of the diverse input variables for the machining process. With the help of this existing knowledge, the following eight significant input variables are taken from Figure 3 and defined as factors for the examination: A = Cooling lubricant S = Cutting speed C = Cut depth D = Feed rate E = Material F = Cutting edge angle G = Chip format H = Cutting radius To obtain the simplest possible experiment plan, each of these factors was examined at two levels. With the help of existing experience with the process, the team defines the following factor levels:

Factor levels

Figure 6:

Factor

-

+

A B C D E F G H

no 100 m/min 1 mm 0.2 mm/h Material 2 45° small 0.8 mm

yes 150 m/min 2 mm 0.3 mm/h Material 1 75° large 1.2 mm

Factors and Factor Levels in the Machining Process

To the extent that knowledge or assumptions are available regarding the effects of these input variables on the target quantities, they are shown in the impact matrix shown in Figure 7.

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Influencing A factors Cooling lubricant Target quantity - +

B Cutting speed

C Cutting depth

-

-

+

+

D E F Cutting Feed rate Material workpiece edge angle - + - + - +

G Chip form groove - +

H Cutting radius -

+

large Surface roughness

small unfavorable Chip form favorable

Figure 7:

1.4

Impact Matrix for Known and Assumed Effects of the Eight Selected Factors for the Target Quantity

Selection of an Experiment Strategy

Several especially frequently applied experiment designs will be described in brief. They represent only a small extract from the known experiment strategies. More comprehensive information is given by Juran |7|, for example. 1.4.1

Single-Factor Experiment

A single-factor experiment is an examination of the effect that one quantitative or qualitative factor has on one or more target quantities. The factor is set to two or more levels. The examination is carried out on all levels equally often (n > 1). The effects of input variables that are not included in the experiment plan must be eliminated. This is done either by keeping them constant during the experiment or by randomly assigning the experiment units to the levels.

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The single-factor experiment supplies statements on the effect this factor has on one or more target quantities under fixed, defined conditions. Furthermore, the residual or experimental variance can be estimated. Typical application: Examination of one factor's effect.

1.4.2

Full Factorial Experiment

Examination of the effects that multiple quantitative or qualitative factors have on one or more target quantities. Each factor is set to two or more levels. All possible factor level combinations are examined equally often (n > 1 if possible) The effects of input variables that are not included in the experiment plan must be eliminated. This is done either by keeping them constant or by randomly assigning the experiment units to the factor level combinations. The full factorial experiment supplies statements on the effects the examined factors have on the target quantity or quantities, and on the interactions among the factors examined. Furthermore, the residual or experimental variance can be estimated. Typical application: Examination of the effects of a small number of factors, if interactions are expected among them or if interactions cannot be ruled out.

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Example: Examination of the effects that four factors (A, B, C and D) have on one target quantity when each factor is set to two levels, ' - ' and ' + '. Term for 4 this experiment design: 2 . No.

A

B

C

D

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

+ + + + + + + +

+ + + + + + + +

+ + + + + + + +

+ + + + + + + +

This experiment supplies statements on: - the main effects: A, B, C and D - the two-factor interactions AB, AC, AD, BC, BD and CD - the three-factor interactions ABC, ABD, ACD and BCD and - the four-factor interaction ABCD. Further literature: E. Scheffler |6|.

1.4.3

Fractional Factorial Experiment

Examination of the effects that multiple quantitative or qualitative factors have on one or more target quantities. Each factor is set to two or more levels. The examination is carried out equally often with a selected portion of the possible factor level combinations.

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The effects of input variables that are not included in the experiment plan must be eliminated. This is done either by keeping them constant or by randomly assigning the experiment units to the selected factor level combinations. The fractional factorial experiment supplies statements on the effects that the examined factors have on the target quantity or quantities, and on the interactions among the examined factors. Depending on the selection of the factor level combinations, however, mixing can occur. Interaction effects are mixed among each another and also with main effects. The level of mixing depends particularly on the experiment plan that is selected. Typical application: Examination of the effects of a larger number of factors, if at least a portion of the possible interactions can be shown to be nonexistent or can be justifiably ruled out. Often it is assumed that higher level interactions, meaning interactions among more than two factors, are negligible. Example: Examination of the effects of four factors (A, B, C and D) on one target quantity when the factors are each set to two levels, ' - ' and ' + '. Performance of the examination with half of the possible factor level combinations of the full factorial experiment. Term for this experiment design: (1/2 . 24 =) 24-1.

16

No.

A

B

C

D

1 2 3 4 5 6 7 8

+ + + +

+ + + +

+ + + +

+ + + +

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This experiment provides statements on the following effects, which are mixed: A B C D AB AC AD

+ + + + + + +

BCD, ACD, ABD, ABC, CD, BD, BC.

This experiment plan is therefore expedient if at least one of the four factors does not have any interactions with the remaining factors and, in addition, if higher level interactions can justifiably be shown to be negligible. Further literature: E. Scheffler |6|.

1.4.4

Factor Search after D. Shainin

Refers to finding those factors that exercise the strongest effect on the target quantity or quantities. The most important input variables are selected for the examination and sorted according to their assumed significance from the technological point of view: A, B, C, D, E, ... . . . . Then two levels are assigned to each of these factors, a level ' + ', from which the better effect would be expected for the target quantity from a technological point of view, and a level ' - ', from which the poorer effect would be expected on the target quantity from a technological point of view. In the first experiment phase, two experiments are performed with equal frequency (at least twice). In the first experiment, all factors are set to the ' + ' level, and in the second experiment, all factors are set to the ' - ' level. If the difference between the two settings is significant, the second experiment phase can be started.

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If the difference between the two settings is not significant, the cause for this must be determined. Either important input variables were overlooked and not included in the examination as factors, or the levels "+" and "-" were incorrectly assigned to some extent. In a further brainstorming session by the experts, the input variables are discussed again and the definition of the levels is verified with the help of single-factor experiments. In the second experiment phase, the following four experiment results are compared to one another: A A A A

+ +

B B B B

-, -, +, +,

C C C C

-, -, +, +,

D D D D

-, -, +, +,

E E E E

-, -, +, +,

... ... ... ...

Depending on how strong the effect of A is in comparison to the effect of the remaining factors, A can be classified as the single significant factor, as one of several significant factors, or as a non-significant factor. The analogous examination is then carried out on the other factors B, C, etc., until all significant factors have been found. Working with a large number of factors, this method selects those factors that exercise the strongest influence on the target quantity or quantities. Furthermore, the residual or experimental variance can be estimated. Typical application: As a preliminary examination, if it is expected that very few factors exercise a strong effect on the target quantity or quantities, while the majority of the remaining factors show only a small effect on the target quantities (Pareto principle). Subsequently, the full factorial experiment can be conducted on the factors that have been identified as significant. Further literature: K. Bhote |8|.

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1.4.5

Design of Experiments according to G. Taguchi

Development of products and processes, that are robust over a wide range of use profile quantities. This is a development tool for achieving the following objectives: -

-

Development and improvement of products and processes that should be robust over a wide range of use profile quantities (robust design). Development and improvement of products and processes that should be robust over a wide range of use profile quantities and simultaneously react sensitively to one or more selected input variables (dynamic characteristics).

Taguchi employs traditional experiment plans for his procedure. In twolevel experiment plans, however, he uses the numbers '1' and '2' to identify the levels, instead of ' - ' and ' + '.

1.4.5.1

Development of Robust Products and Processes

If there is a use profile with a temperature range from -10°C to +40°C, as shown in Figure and if constant product characteristics are demanded in this range, material A2 would be preferred to Material A1. When developing robust products and processes, both factors specific to the product, meaning factors that determine the product characteristics and factors specific to the process and use profile must be considered. The former are sometimes called control or design factors, and the latter are referred to as noise factors.

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Tensile strenght A1

A2

-10 °C

Figure 8:

Temperatur

40 °C

Tensile strength of two materials in a temperature range

Product-specific factors and noise factors are assigned to different, mutually independent experiment plans see Figure Each factor level combination of the product-specific factors is examined with all factor level combinations of the noise factors.

D

E

F

G

1 2 3 4

1 1 1 1

1 1 2 2

1 1 2 2

1 2 1 2

1 2 1 2

1 2 2 1

1 2 2 1

5 6 7 8

2 2 2 2

1 1 2 2

2 2 1 1

1 2 1 2

2 1 2 1

1 2 2 1

2 1 1 2

Figure 9:

20

221

C

212

B

122

A

111

Factor

xyz

Noise parameter matrix

Results

Schematic Experiment Arrangement for Product-Specific Factors and Noise Factors for Developing Robust Products and Processes

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1.4.5.2

Development of Robust and Sensitive Products and Processes

A vehicle's steering should be designed in such a way that it: -

responds to changes the driver makes in the steering angle (signal factors) over a wide range as well and uniformly as possible; reacts as independently as possible of outside conditions (noise variables), meaning that it ensures constant steering conditions under the widest range of road conditions.

Measurement procedures should be designed in such a way that they: -

react sensitively to changes in the quantity that is to be measured (signal factor); and supply reproducible results independently of outside conditions (noise factors).

The development objective is therefore a product or measurement method that, on the one hand, reacts sensitively to an input quantity, a so-called manipulated factor (such as steering angle) but, on the other hand, that does not react to the use profile quantities (such as road conditions). As can be seen in Figure, it is possible to expand the types of factors by an additional group, namely the manipulated factors.

1 1 1 1

1 1 2 2

1 1 2 2

1 2 1 2

1 2 1 2

1 2 2 1

1 2 2 1

5 6 7 8

2 2 2 2

1 1 2 2

2 2 1 1

1 2 1 2

2 1 2 1

1 2 2 1

Figure 10:

2 1 1 2

Results

221

1 2 3 4

212

G

122

F

111

E

221

D

Noise parameter matrix

C

212

B

122

A

111

Factor

Noise parameter matrix xyz

Noise parameter matrix S1 S2

Results

Schematic Experiment Arrangement for the Development of Robust and Sensitive Products and Processes Further literature: M. Phadke |9|.

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1.4.6

Example

In the example for the "Examination of the Thrust Bearing Surface Quality", eight factors, each at two levels, are to be included in the experiment plan (see Section 1.3.8). Consequently, this case involves factorial examinations, each with two levels for each factor. In order to minimize the experiment effort (number of sub-experiments to be carried out differently), a fractional factorial experiment was selected, in which the main effects (A, B, C, etc.) are not mixed with two-factor interactions (AB, AC, BC, etc.), but instead only with higher interactions, while the two-factor interactions are mixed together with each other and with higher interactions. This is called an experiment of resolution IV. Such experiments are characterized by the fact that it is possible to obtain a good approximation for the main effects in connection with low experimenting cost. On the other hand, the price that must be paid for low experimenting cost is the fact that it is not possible to determine interactions1. Because the experiment includes a total of 16 sub-experiments, which cannot be conducted in one stage with different settings with unchanged boundary conditions, an experiment in four blocks was planned, with each of these blocks being conducted on four different days:

1 For this reason, these experiment plans are also often used as a starting point for an examination. Initially, those factors causing significant main effects are determined. Subsequently – if necessary - experiments that also allow interactions to be determined can be conducted with these (normally few) factors.

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Block

I

II

III

IV

Subexperim ent 2 3 14 15 5 8 9 12 6 7 10 11 1 4 13 16

Setting of factors A + + + + + + + +

B + + + + + + + +

C + + + + + + + +

D + + + + + + + +

E + + + + + + +

F + + + + + + + +

G + + + + + + + +

H + + + + + + + +

Matrix

(5)

Sample size for each sub-experiment n = 10

1.5

Evaluation of the Experiment Results

The evaluation of the experiments is explained by way of example, using k factorial experiment plans with k factors, each at two levels (2 ). 1.5.1

Representation of the Measurement Results

The simplest, trivial case k = 1 corresponds to a single-factor experiment, in which the dependency of a target quantity y is examined by only one influencing factor A. The dependency is shown in Figure . The change in the target quantity y when changing from A- to A+ is called the effect of factor A. The size of the effect depends on the selection of the A- and A+ settings. The above fundamental observations can be transferred to full and fractional experiments with two or more input variables.

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yy-

2

Effect (A)

y

1

A-

Figure 11:

A+

A

Graphical Representation of one Effect in a Single-Factor Experiment

In the two-factor experiment, factors A and B are changed to two levels according to the following plan matrix at two levels: No.

A

B

y

1

-

-

y1

2

+

-

y2

3

-

+

y3

4

+

+

y4

Matrix (6)

Column y holds the means y1, ..., y4 of the results of the four experiment rows. They can be represented in the following form:

24

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B B+

B-

Figure 12:

y-

y-

y1

y-

A-

A+

3

4

2

A

Graphical Representation of the Results of a Two-Factor Experiment

y-

y 4 y-

B+

3

y-

2

y-

1 B-

A-

Figure 13:

A+

A

Graphical Representation of a Two-Factor Experiment with the Factors A and B

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This form of representation can also be used if one (or more) of the factors being examined is not a quantitative, adjustable variable, but instead a qualitative variable with fixed levels (such as Material 1 - Material 2). Naturally, interpolation of intermediary values is not meaningful in this case. 1.5.2

Calculating the Effects

A factor's effect yields the change in the target quantity y when there is a change in the setting from level - to level +, averaged over the settings of all remaining factors. Of course, the effect depends on the explicit choice of levels. The graphical determination of the effects for the two-factor experiment example is shown in Figures 14 to 16. If the factors behave additively, the result is two straight lines running parallel, as shown in Figure In contrast, if the effect of one factor depends on the setting (level) of another, these two factors interact, and they do not behave additively. In addition to the columns for the factors A and B, the analysis matrix for the two-factor experiment contains a column AB for the interaction of these factors.

26

No.

A

B

AB

y

1

-

-

+

y1

2

+

-

-

y2

3

-

+

-

y3

4

+

+

+

y4

(7)

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yy- 4 y

B+

3

Effect (A)

-y 2 y-1

BA-

A+

A

yy-4 y-2

B +

Effect (B)

y3 y-

B-

1

B-

B+

B

yy-4

Effect (A) with B+

B +

y-3 y2

B-

Effec t (A) with B-

-y

1

A-

A+

A

Effect(AB) = (Effect(A) with B+ - Effect(A) with B-)/2 Figures 14, 15, 16: Graphical Representation of the Main Effects A and B and the Interaction Effect AB Dokument wurde bereitgestellt vom

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The effect of factor X is calculated as the difference between the mean of all results y in which X has the level + and the mean of all events in which X has the level -. This computation rule similarly applies to interactions and can be applied in general to orthogonal experiment plans with k factors. Consequently, the following applies to the example being considered: =

y2 + y4 2

Effect (B)

=

y3 + y4 2

-

y1 + y 2 2

Effect (Ab)

=

y1 + y 4 2

-

y2 + y3 2

Effect (A)

Matrix (8)

In fractional factorial experiment plans a mixing of factors may occur, resulting in interactions. The effects of the mixed quantities can then no longer be considered separately. The effects of the combined variables can then no longer be calculated separately. 1.5.3

Statistical Analysis Method

Based on the experiment repetitions, it is expedient to add corresponding columns to the right of the experiment plan analysis matrix. No.

A

B

AB

Results

y

si

1

-

-

+

y11, ...,y1m

y1

s1

2

+

-

-

y21, ...,y2m

y2

s2

3

-

+

-

y31, ...,y3m

y3

s3

4

+

+

+

y41, ...,y4m

y4

s4

28

Matrix (9)

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The determined statistical parameters make it possible to assess the significance of the calculated effects by a comparison with the experimental variance. Further considerations or experiments are limited to those factors that demonstrate significant effects and/or interaction effects. The significance test is expediently conducted with the help of computer support. 1.5.4

Example

The experiment was conducted as defined in matrix (5). The following describes only the results for the surface roughness in detail. For each of these sub-experiments, the mean x and standard deviation s were calculated for the 10 measured values. We dispense with a tabular representation of these results here. The graphical representation in Figure 17 is clearer.

25

20

xin µm

= x = 14.36 µm

15

10

5

0 1

Figure 17:

2 3

4

5

6

7

8

9 10 11 12 13 14 15 16

Sub-experiment No.

Mean x of the 16 Sub-Experiments

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In the figure, it is already possible to see clear differences between the results of the different sub-experiments, which are caused by the factor settings. The required information about the impacts of the different factors is obtained by determining the associated effects, see Sections 1.5.2 and 1.5.3. The calculation of the separate effects and their significance is dispensed with in this context. The results are summarized in Figure18 . In this figure, the effect D = 4.5 µm means that the surface roughness increases by 4.5 µm when the feed rate is increased from the lower level = 0.2 mm/h to the upper level = 0.3 mm/h. 6 Feed rate 4 Surface roughness effect 2 in µm

D

Significant

Cutting ed ge angle

0

F Cu tting d epth B Cutting speed

-2

A Cooling lubricant E Chip format Material Cutting radius H

-4 -6

Figure 18:

G

C

Not significant

Significant

Effects of all Factors, Ordered by Absolute Value, and Their Significance

At this point, we would like to point out again that this experiment plan only allows the analysis of the main effects; statements about interactions are not possible. The chip form was analyzed in a manner similar to the surface roughness. The result is represented in Figure19 . The smaller the chip characteristic number is, the better the chip form evaluation.

30

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2,0 Chip form groove 1,5 Chip characteristicnumber effect

Significant

G

1,0 0,5 0,0 -0,5

Cut depth Cutting radius C Cooling lubricant H A Material E E B D F Cutting speed Feed rate

Not significant

Significant

Cutting edge angle

Figure 19:

Effects of all Factors, Ordered by Absolute Value, and Their Significance

The best possible settings from the point of view of the two target quantities, surface roughness and chip form, can be read off of Figures 18 and 19 as follows: Factor A

B

C

D

E

F

G

H

Surface roughness

+

(+)

(-)

-

+

(+)

+

+

Chip form

-

(+)

-

(+)

(-)

+

-

-

The statements in parentheses are based on effects that are not significant. The primary objective here is low surface roughness with a chip form that is acceptable for manufacturing. At the same time, it is evident that the input variables act on these two target quantities differently, and therefore a compromise must be sought.

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A follow-up experiment with the optimal setting with respect to the surface roughness A

B

C

D

E

F

G

H

+

+

-

-

+

+

+

+

resulted in excellent (slight) roughness, but a chip form that was not acceptable for manufacturing. As a result, it was necessary to search for a modified setting that both favoured a better chip form and interfered with the roughness as little as possible. This is apparently possible with the help of the factor G, which has a relatively small effect on the surface roughness, but, on the other hand, the largest influence on the chip form. As a result, the following was tried as the modified setting: A

B

C

D

E

F

G

H

+

+

-

-

+

+

-

+

The experiment result supplied approximately the same roughness, with a chip form that was considerably improved and acceptable for manufacturing. 1.6

Computer Support

There is a wide range of software available for supporting users in statistical experiment design. This includes both stand-alone, closed programs and programs that are an integral part of modularly constructed, comprehensive statistical software. The range of submenus offered by these programs, either as standard or optional features, is correspondingly diverse. The majority of these programs support statistical and graphical experiment analysis, but not the planning phase.

32

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1.7

Bibliography

|1|

Juran Institute: Quality Improvement Tools

|2|

DGQ Document 18-19: Formblätter mit Wahrscheinlichkeitsnetz

|3|

K. Nagel: Erfolg, Oldenbourg-Verlag

|4|

DIN 25 424: Fehlerbaumanalyse

|5|

S. Häffner: Statistische Versuchsplanung in der betrieblichen Praxis, Betriebsversuch Drehen, FHT Esslingen, Fachbereich Feinwerktechnik, 1992

|6|

E. Scheffler: Einführung in Versuchsplanung, 2.Auflage Grundstoffindustrie, 1984

|7|

J. Juran: Quality Control Handbook, McGraw Hill Book Company, New York, 1988

|8|

K. R. Bhote: Der Weg zur Weltspitze, Institut für Qualitätsmanagement, Großbottwar, 1990 (Translation of World Class Quality, American Management Association)

|9|

M. S. Phadke: Quality Engineering Using Robust Design (in German translation), Gfmt

|10|

G. Box, W. Hunter, J. Hunter: Statistics for Experimenters, Wiley & Sons, New York, 1978

|11|

Retzlaff, Rust, Waibel: Statistische Versuchsplanung, Verlag Chemie, Weinheim, 1978

|12|

D. Wheeler: Understanding Industrial Experimentation, Statistical Process Controls, Inc., Knoxville, 1988

|13|

E. Spenhoff: Prozesssicherheit durch statistische Versuchsplanung in Forschung, Entwicklung und Produktion, gfmt-Verlags KG, 1991

|14|

B. Gimpel: Qualitätsgerechte Optimierung von Fertigungsprozessen, VDI-Verlag Düsseldorf, 1991

die Praxis der VEB Deutscher

statistischen Verlag für

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33

Verband der Automobilindustrie

Quality Management in the Automobile Industry

4

Quality Assurance in the Process Landscape - General, risk analyses, methods, process models -

Manufacturing feasibility analysis

st

1 edition 2009 English version published in 2010

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7[

Verband der Automobilindustrie

Quality Management in the Automobile Industry

4

Quality Assurance in the Process Landscape - General, risk analyses, methods, process models -

QFD Supplement

st

1 edition 2009 English version published in 2010

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"Copy the Spirit, not the Form" (AKAO) The QFD model (often referred to as "ASI model") used in the previous sections, with the "House of Quality" and the 4 phases of application, is particularly suitable for illustrating the QFD method. However, the method and its elements provide a wide spectrum of possible applications. In accordance with VDA 6.1, Question 02.4 a project-specific application of the method and its elements is recommended : "Quality techniques (such as QFD, System FMEAs, DOE,...) must be applied on a project-specific basis". As examples, a number of other QFD approaches are explained.

Different QFD approaches The Akao approach Professor Yoji Akao, the spiritual creator of QFD, is in favour of a companywide approach. The Akao approach is based on the concepts of determining the needs of the market and the customer and using these to generate the technical requirements for products and processes. However, Akao does not deal merely with the development of new products and their quality assurance; it also covers the planning of technology, costs and reliability, as well as the coordination of other methods such as FMEA, FTA and value analysis. The Akao approach offers the user a wide range of tables and matrices which can be assembled in a flexible manner.

The Bob King approach Bob King places the emphasis on the development of matrices and the planning activities which can be implemented with their use. The concept is based on a matrix of matrices, in which various sample matrices are available for use as required. The King approach also integrates further methods such as FMEAs and FTA.

The Zultner approach This approach is also referred to as "lightning QFD". Zultner sets out a clearly structured procedure and places the emphasis on the implementation of information from the customer in determining the most important customer requirements regarding the product and its creation. The customer's requirements are derived from the customer's Dokument wurde bereitgestellt vom

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comments with the aid of a "Customer Voice Table". A "Maximum Value Table" is drawn up to establish the correlations between the customer's requirements and the advantages for the customer. The relationships between the most important customer requirements and the technical features are determined.

The Saatweber approach Saatweber expands the 4-phase model (the ASI model) to include phases 0 and 5. Phase 0 covers the use of methods for the analysis and structuring of customer requirements while in phase 5 a check is made to determine whether the objectives defined for customer satisfaction have also been achieved.

2

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Verband der Automobilindustrie

Quality Management in the Automobile Industry

4

Quality Assurance in the Process Landscape - General, risk analyses, methods, process models -

TRIZ

st

1 edition 2008 English version published in 2009

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Foreword to the first edition This description of the TRIZ method has been produced as part of the work of VDA Working Group 4 “Quality Assurance in the Process Landscape– General, risk analyses, methods and procedure models”. We are grateful to the participating companies and their employees for their commitment in writing this chapter. The following companies participated in the drafting: Audi AG automotive.business.support, Mr Füller BMW AG Daimler AG Karmann GmbH Horst Ried Methodenberatung Robert Bosch GmbH Siemens VDO Trisolver GmbH & Co. KG Volkswagen AG ZF Sachs AG We also wish to thank everyone who made suggestions for improvements.

Oberursel, August 2008

Verband der Automobilindustrie e. V. (VDA)

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Contents

Page

Foreword to the first edition

1

1 1.1 1.2

General information about TRIZ Background and definitions Technical contradiction

4 4 5

2 2.1

5

2.3.3 2.4 2.4.1 2.4.2 2.4.3 2.4.4

Principles 40 basic innovation principles for overcoming technical contradictions 76 standard solutions for inventive problems TRIZ standards TRIZ for innovative inventions (ARIZ) ARIZ Database of technological effects - a link to physics Solving a problem with ARIZ TRIZ for problem solving Analysis Abstraction Guided problem solving Prioritising and fleshing out the solution ideas

10 11 13 14 15 17 18

3 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 3.10

TRIZ tools Technical contradiction Contradiction table (39 parameters) Substance-field analysis Object modelling Function modelling Separation principles Bionics Material, cost, time (MCT) operators Innovation situation questionnaire Resources checklist

20 21 21 23 26 27 28 29 30 31 31

2.2 2.2.1 2.3 2.3.1 2.3.2

2

7 8 8 9 9

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3.11 3.12 3.13

Anticipatory error detection Eight evolutionary principles Trimming

32 33 38

4 4.1 4.2 4.3

Implementation TRIZ software Using software successfully Success potential of TRIZ & CAI

41 41 41 42

5 5.1 5.2

Implementation of TRIZ and CAI in your company Overcoming hurdles Platform for innovation and quality management

43 43 45

6 6.1 6.2 6.3 6.4

Annex - tables 39 technical parameters which describe a contradiction 40 innovation principles TRIZ – the 76 standard solutions for substance-field analysis Contradiction matrix

46 46 47 48 58

7

Bibliography

59

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1

General information about TRIZ

1.1

Background and definitions

TRIZ is the internationally used Russian acronym for the Theory of inventive problem solving (Russian: Algoritm Rešenija Isobretatelskih Zadač; it is pronounced “trees”), and is also known in the USA using the abbreviation TIPS (Theory of Inventive Problems Solving). TRIZ was developed between the sixties and the eighties by the Russian scientist, Genrich Altschuller (1926-1998), and his staff (his first publication was in 1956). In contrast to the normal practice of “trial and error” problem solving methods such as brainstorming, synectics, morphological analysis, etc. TRIZ uses the objective development laws of technical systems and therefore makes it possible to complete a much more focused search for solutions. These laws and other components of TRIZ were discovered and made into a systematic approach by many years of analysis and evaluation in Russia in the form of patent information. During his work he found that the origin of a patent was always a technical or physical contradiction. On this basis he developed 39 technical parameters with which the conflicts of contradictions can be described and the 40 innovative solution principles which contribute to their solution. After the introduction of TRIZ in the USA at the beginning of the nineties, TRIZ techniques and databases were used to provide the basis for invention software and modified for computer-based applications. This resulted in a new type of technological engineering work – CAI (computer-aided innovation) which is already used widely and successfully by a large number of well-known companies around the world.

4

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1.2

Technical contradiction

The main feature of problem solving with TRIZ is the identification, reinforcement and elimination of technical and physical contradictions in technical systems instead of the search for compromises, in other words the middle ground. The term “technical contradiction” plays one of the key roles in the TRIZ concept. A technical contradiction represents two controversial properties of a technical system: If a part or a property of a machine (for example the power output of an engine) is improved, another property (for example its weight or fuel consumption) is worsened. A problem is deemed to have been solved by TRIZ when a technical contradiction has been detected and eliminated. So-called operational blindness, psychological inertia and various uncertainties are consistently overcome with TRIZ. This also means that even in very difficult cases the search area is considerably extended and people think “outside the box”. 2

Principles

Innovations are a major key element for the long term success of a company. TRIZ is a pendant for the more or less chance-controlled creation of ideas, an empirical and highly systemic innovative approach. The TRIZ method provides procedures which enable invention to take place on a systematic basis. In contrast to normal problem solving methods such as trial and error or brainstorming, TRIZ uses empirical first principle laws of technological evolution and provides a wide range of tools for systematic problem solving (see Gimpel, Herb, Herb 2000, pg. 1). In the past the TRIZ method was used exclusively in technical fields and to find product innovations. In addition TRIZ also constitutes an important tool for innovative problem solving.

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The components of the theory for innovative problem solving are set out in Table 1. Simple applications such as the principles for solving technical contradictions can be made into active tools more quickly but are limited in their effectiveness for tackling difficult problems. However, an experienced TRIZ user will use various tools to suit his needs - there is equally little point in shooting a canon at sparrows as there is in hunting elephant with a club. No.

TRIZ component, tool

Application

1

Contradiction analysis, 39 technical parameters and 40 innovation principles for overcoming technical contradictions; application system in the form of the socalled contradictions table.

Inventive problems which range from simple to medium difficult, recommended for TRIZ beginners.

2

System of abstractive standard solutions for inventive problems: Five classes / 76 standards; method for using the standards.

Simple to difficult inventive problems.

3

Algorithm or step-by-step process to solve the inventive problems (acronym: ARIZ). Universal tool for solving all problems.

Particularly difficult inventive problems.

Application of TRIZ for solving innovative problems which are either derived from other method applications or are related to existing systems.

Solving problems (with the aid of abstraction) – in practice this is the most common application.

Table 1:

6

Possible procedures for using TRIZ [1]

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2.1

40 basic innovation principles for overcoming technical contradictions

An evaluation of several hundred thousand patents shows that the inventive work and technical contradictions from various fields of industry can be solved by a limited number of elementary principles (methods). Modern TRIZ contains 40 basic innovation principles (there are over 100 if we include sub-methods). Here are a few examples: 3. 10. 11. 18. 22. 28. 35. 40.

Principle of local quality. Principle of prioritised action. Principle of “placing a cushion underneath first” (preventive action). Principle of using mechanical oscillations. Principle of converting harmful things into useful things. Substitution of the mechanical system. Changing physical and chemical properties Principle of using composite materials.

27 Cheap short life

1 Segmentation and dismantling

14 Curving

2 Separation

15 Dynamisation

28 Replace mechanism

3 Local quality

16 Partial or surplus effect

29 Pneumatics and hydraulics

4 Asymmetry

17 Higher dimension

30 Flexible sheathing and films

5 Joining

18 Mechanical oscillations

31 Porous materials

6 Universality

19 Periodic effect

32 Colour changes

7 Nesting

20 Continuity

33 Homogeneity

8 Counterweight

21 Hurry through and omit

34 Elimination and regeneration

9 Preferred opposite action

22 Convert harmful into useful

35 Change of properties

10 Preferred action

23 Recoupling

36 Phase transition

11 Preventive action

24 Mediator, broker

37 Heat expansion

12 Equipotent

25 Self-supply, self-service

38 Strong oxidising agent

13 Reversal

26 Copy

39 Inert medium 40 Composite material

Table 2:

40 basic innovation principles

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2.2

76 standard solutions for inventive problems

The principles and the table (see annex) of their application are among the simplest TRIZ tools. The analysis of complicated problems showed, however, that they can only be solved by the simultaneous use of several elementary principles using physical effects. These particularly strong and stable combinations of the principles and effects for the system for the standard solution of inventive problems. 2.2.1

TRIZ standards

TRIZ standards are general rules for the synthesis and conversion of technical systems. They are based on the development laws of technical systems. Several standard solutions directly represent the practical application of these laws. The modern system of standard solutions involves a structured and highly systematic procedure and can also be used to forecast technical product development. It consists of five groups of a total of 76 standards: •

Group 1:

Building and destruction of complete substance-field models

•

Group 2:

Improving substance-field models

•

Group 3:

Transition into super and sub systems (Macro and micro levels)

•

Group 4:

Detection and measurement

•

Group 5:

Auxiliary methods for using standards

8

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2.3

TRIZ for innovative inventions (ARIZ)

2.3.1

ARIZ

Algorithm or step-by-step process to solve the inventive problems (Russian: Algoritm Rešenija Isobretatelskih Zadač, abbreviated to ARIZ) is a TRIZ tool for solving all problems, from the analysis of the problem situation and system resources to the evaluation of potential solutions. It is generally used if the 40 innovation principles or standards do not provide a satisfactory result. ARIZ will only help an experienced user to solve a problem, detect the technical contradiction, formulate the objective of the solution search (socalled idea end result) and then to summarise and overcome the basic physical contradiction. These main ARIZ steps (Figure 1) are explained below in more detail using an example. A complete ARIZ contains nine parts with approx. 70 steps. Solution

Problem Model of the problem

Figure 1:

Ideal final result Technical contradiction - Matrix of contradictions - Standards

Physical contradiction - Database of effects - Class 5 standards - Separation principles

Main stages of the ARIZ method [1].

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2.3.2

Database of technological effects - a link to physics

Many highly technical solutions are based on the physical, chemical, geometric and other phenomena and effects. However, engineers frequently do not have a reliable link between a problem and physics. This deficiency is eliminated in TRIZ by the database of effects. This database contains a range of effects which depend on the effect or property required by the physical contradiction with examples of their applications, as the examples in Table 3 show. Almost every effect is described in the database with its input and output fields, for example thermo-mechanical effect or mechanoelectrical effect. This permits a range of effects to be coupled in new combinations so that they can be used to solve more complex problems. Required effect

Physical effects, methods

Increase in temperature

Electromagnetic induction, eddy currents, skin effect, dielectric heating, thermo-electric phenomena, radiation absorption, exothermic reactions, etc.

Mixing substances; formation of solutions

Ultrasonics, cavitation, diffusion, electrolysis, electrophoresis, magnetic field relating to a ferromagnetic substance, electric fields, geometric effects, etc.

Changing the dimensions of an object

Thermo-mechanical effects (heat expansion, metal memory effect, etc.), deformation, magneto-electric striction, piezo-electric effect, phase transitions, chemical reactions, etc.

Table 3:

10

Fragment from the database of physical effects and phenomena [1].

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2.3.3

Solving a problem with ARIZ

The ARIZ method can best be explained using an example Blank

Tool

Figure 2:

Illustration of the system for an ARIZ example [1].

Level 1: Formulation of the problem model. A heated cylindrical blank (cylinder) with is penetrated by a pointed forming tool (needle) to produce seamless pipes. For this purpose the blank is held from the outside in rollers and turned (Figure 2). After the needle tool has been inserted into the hot blank, the needle often starts to run off centre in the cylinder. The problem becomes all the more pronounced as the length of the pipe or cylinder increases, with lengths of up to 6 metres being possible. How can the undesirable movement of the tool be eliminated? Level 2: Formulation of the technical contradiction. As the length of the cylinder increases, the support for the needle falls and this reduces the dimension and production accuracy of the pipe. (At this stage it is also possible to attempt to overcome the technical contradiction with the help of simple tools such as the contradiction table or the system of standard solutions.)

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Level 3: Formulation of the ideal end result (IER) for the problem. The infinitely long cylinder provides its own support for itself and guides the needle precisely along its centre. The technical contradiction is thrown into even greater focus by this formulation. The IER in this case acts as a pointer towards a perfect solution. Every technical contradiction has concrete physical causes which must be clarified in the next stage. Level 4: Formulation of the physical contradiction. To achieve the IER the cylinder must have two mutually exclusive physical states: The cylinder must be rigid so that it can support both itself and the needle and it must also be soft so that the needle can pass through its centre. This is a physical contradiction. To overcome it, ARIZ has the separation principles for overcoming physical contradictions, class 5 standards and a database of more than 1000 physical, chemical and geometric effects. One of the simplest methods of solving a physical contradiction is to separate the controversial properties in time or space. In fact: The cylinder must be hard, above all on its outside and softer towards its centre. The aim now is to find a physical effect in the database which can influence the material hardness. But before any “external” effects are used, the existing internal resources (substances, fields, time, space, information and functions) should be studied thoroughly and mobilised to find the solution. This type of analysis is one of the main stages of ARIZ. Overcome physical contradiction The cylinder is heated anyway so that the needle can penetrate it more easily. Material hardness is a function of various variables, including temperature. All the system is missing is the required distribution of the heat field: Colder on the outside and hotter towards the centre. This condition can be satisfied without any great effort by simply spraying the cylinder with cold water. The needle will then find the easiest route – the hottest or softest part in the cylinder, which will be precisely at the centre.

12

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2.4

TRIZ for problem solving

The four stages and the innovative problem solving tools using TRIZ can be shown in the logic chain: Concrete problem – abstraction – standard problem – TRIZ tool – standard solution – application/implementation – innovative solution.

Principles of the TRIZ method The systematic analysis of a problem Standard problem

The contradiction is the central, innovationprovoking element of technical problems Many problems have already been solved in other fields and sectors under different names, but comparable in principle. The advanced development of technical systems to form an ideal product follows certain (evolutionary) laws.

Figure 3:

TRIZ tool

Abstraction

Concrete problem

Standard solution

Application

Innovative solution

Procedure for using TRIZ for problem solving

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2.4.1

Analysis

The project history and the current condition are described, information is collated, marginal conditions (environment and resources) are recorded and the technical conflict is detected. The TRIZ tools which can be used are as follows: Innovation situation questionnaire The system being considered is described in the form of a questionnaire. The current system structure is recorded in its environment (higher level system, lower level system and parallel systems). The functions of the system are described and the primary benefits for the customer are set out. The function of the system is queried. In addition the system environment (type and form, resources) is recorded. In many cases the history of the current system structure and the desired ideal end result are queried. Resource checklist This checklist is used to record the available resources in the system and its environment (material resources, field-type resources, information resources, time resources, space resources and functional resources) and their usage defined. Topics to solve the problem are found by querying the usage of the resources to solve the problem so as to prevent harmful functions or to fulfil useful functions so as to allow a clear problem formulation to be achieved. Anticipatory error detection Information about the technical conflict is obtained from the following questions: − What do I have to do to make the product, process or system fail? − What activities during the development, production or transport, at the customer’s premises or by the customer result in the failure of the system?

14

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Object modelling Presentation of the positive and negative dependents (network structure) between the objects involved (system elements) and highlighting the focal points of particularly good or poor elements. One side-effect is to make the technical and/or physical contradictions visible. Additional tools − − − − − − − −

ABC analyses (cost structures, weight structures) 6W questions Obtaining knowledge from documents, literature, patents, internet, etc. Cause-effect diagram (Ishikawa diagram) FMEA structure Error tree analysis Mind mapping Black box.

Results of the analysis phase − − − − −

2.4.2

General understanding of the function and problem (cause of the problem) The same knowledge is held by all team members Technical conflict and physical contradiction detected Previous activities and history Identification of the obstacles.

Abstraction

Describe the problem in general terms and thus make is comprehensible regardless of expert knowledge. The problem is viewed from various angles and understood better. The TRIZ tools which can be used are as follows:

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Contradiction analysis By listing contradictions it is possible to formulate a problem in precise terms. The contradictions tool is used to analyse a technical system with its functions. The contradiction analysis is based on the knowledge that innovative solutions are generally only created if two situations obviously contradict each other. 39 technical parameters (see annex) The principles can be used in the form of a matrix, known as a contradiction table, with 39 lines and 39 column entries. These 39 entries are the most important characteristics of technical systems: Weight, length, volume, speed, temperature, material losses, dimension accuracy, production quality, ease of operation, etc. They help to describe a technical system using standardised terms and appear in the boxes in the table in the form of controversial properties of a technical contradiction: For example weight-strength or temperaturedimension accuracy, etc. Substance-field model Every technical system can be presented in the terms of the existing substances, fields and their interaction. “Substances” is the term used to designate objects or parts of the system regardless of their level of complexity. The term “field” not only includes the four classic physical fields such as electromagnetic field, gravitational field and fields of strong and weak interaction. In TRIZ, the term “field” also includes all the possible “technical” fields such as the temperature field (heat field), centrifugal forces field, pressure field, acoustic field, etc. Once a substance-field model has been formulated for a system, a suitable standard solution can quickly be proposed. MCT operators The theory of MCT (material-cost-time) operators confronts the problem solver with the hypothetical situation of having an “arbitrary quantity or absolutely no” materials, money and time available. This extreme type of thought can result in revolutionary ideas and solutions.

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Additional tools − − − −

Stimulus word analysis Anticipatory error detection Bionics Mathematical model (simulation calculations).

Results of the abstraction phase − −

2.4.3

Possible solutions are found Further steps are specified.

Guided problem solving

The use of specific TRIZ tools and therefore using knowledge from other areas. The TRIZ tools which can be used are as follows: 40 basic innovation principles for overcoming technical contradictions Inventive problems and technical contradictions from various sectors can be solved by a limited number of elementary principles (methods). 76 standard solutions for inventive problems (see annex) The principles and the table of their application are among the simplest TRIZ tools. The analysis of complicated problems showed, however, that they can only be solved by the simultaneous use of several elementary principles using physical effects. These particularly strong and stable combinations of the principles and effects for the system for the standard solution of inventive problems.

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Separation principles Contradictions can be solved by separating the requirements. TRIZ provides four separation principles for this purpose: − − − −

Separation in space Separation in time Separation within an object or its parts Separation by changing conditions.

Additional tools − − −

Creativity methods (635 method, brainstorming, bionics, etc.) Mind mapping Morphological box.

Results of the solution finding phase −

2.4.4

Collation of non-evaluated solutions.

Prioritising and fleshing out the solution ideas

Systematically evaluate the solutions that have been found and select the most suitable solution. The TRIZ tools which can be used are as follows: Evolution principles and evolution laws The development patterns of technical systems can be described with the so-called S curve according to Altschuller. This represents the level of maturity over the life cycle (childhood, adult, mature, old). Level of achievement of the ideal solution All systems develop towards increasing idealness. Idealness means the reduction of the use of substance, energy, space, time and costs whilst also improving the desired function. The basic idea behind this is that a technical system is not the objective of a development, but the fulfilment of the function. An ideal system fulfils its function without actually being there.

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Additional tools − − − − −

Individual evaluation matrices Portfolio Benefit analysis Target costing Customer benefit analysis QFD

Results of the prioritisation phase − −

Possible solutions are evaluated The solution has been selected for implementation.

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3

TRIZ tools

The components of the theory for innovative problem solving are set out in Table 4. Simple applications such as the principles for solving technical contradictions can be made into active tools more quickly but are limited in their effectiveness for tackling difficult problems. However, an experienced TRIZ user will use various tools to suit his needs - there is equally little point in shooting a canon at sparrows as there is in hunting elephant with a club. No. TRIZ tool

Application for

1

Technical contradiction

Listing contradictions

2

Contradiction table

Overcoming technical contradictions

3

Substance-field analysis

Abstract models of systems consisting of substances and fields

4

Object modelling

Positive and negative dependents of the objects in the network structure of the system, highlighting technical and physical contradictions

5

Function modelling

Linking the individual functions in the complete system

6

Separation principles (physical contradiction)

Solution of contradictions by separating the requirements

7

Bionics

Transferring biological principles to engineering

8

MKZ operators

Viewing hypothetical situations relating to material, costs and time

9

Innovation situation questionnaire

Analysis of the current situation, the creation history and the ideal condition

10

Resource checklist

Recording the available resources

11

Anticipatory error detection

Theoretical considerations for causing the problem (the disaster) intentionally

12

8 evolution principles

Derivation of principles from the development patterns of technical systems

13

Ideal system

Use of development trends to achieve the ideal condition

14

Trimming

Value analysis consideration of object models

Table 4:

20

List of the main TRIZ tools [1]

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3.1

Technical contradiction

By listing contradictions it is possible to formulate a problem in precise terms. The contradictions tool is used to analyse a technical system with its functions. The contradiction analysis is based on the knowledge that innovative solutions are generally only created if two situations obviously contradict each other. A technical system can provide both useful effects (so-called desirable functions) and also detrimental effects (so-called harmful factors). If a desirable function is provided in a conventional manner and a harmful factor also takes effect, a contradiction in the sense of methodical invention exists. The contradictions can be distinguished in two ways: -

Technical contradiction: The desirable function has an effect on another parameter of the system in the form of the harmful factor. In general two parameters of a technical system are involved in a technical contradiction.

-

Physical contradiction: The desirable function and the harmful function affect the same parameter of the system, in other words the parameter should exist in two contradictory states. An object should be hot and cold or soft and hard or large and small.

The following contradiction is an example from the automotive industry. The customer wants a safe vehicle (thick metal, strong frame
high weight) which has low fuel consumption (low weight
thin metal, lightweight frame). 3.2

Contradiction table (39 parameters)

The use of the 40 innovative principles can also be used in the form of a matrix, known as a contradiction table, with 39 lines and 39 column entries. These 39 entries are the most important characteristics of technical systems: Weight, length, volume, speed, temperature, material losses, dimension accuracy, production quality, ease of operation, etc. They help to describe a technical system using standardised terms and appear in the boxes in the table in the form of controversial properties of a

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technical contradiction: For example weight-strength or temperaturedimension accuracy, etc. With the figures in the fields at the point of intersection of two controversial properties the table shows which principles offer particularly good prospects of success for overcoming technical contradictions. Although not all of the fields in the contradiction table are completed, it offers solution principles for more than 1200 types of technical contradictions and can also considerably reduce the search for suitable solution ideas in some cases.

1

28

...

3, 11 1, 27

28, 27 35, 26

28, 32 1, 24

Speed

2, 28 13, 38

11, 35 27, 28

10

Force

8, 1 37,18

3, 35 35,10 13, 21 23,2 4

39

...

39

35, 3 24, 37

...

9

...

...

Productivity

27

Weight of the moving objects

...

Figure 4:

...

Measuring precision

What is changed in ´the system?

1

Reliability

What deteriorates this point?

Weight of Moving objects

The contradiction table is now part of TRIZ history. Generally the 40 innovation principles are very well suited for solving problems of a simple to medium difficulty level. For more complex problems it is recommended that the system of 76 standard solutions or the ARIZ invention algorithm be used.

... Productivity

35, 26 24, 37

1, 35 10, 38

3, 28 35, 37

1, 10 34, 28

Search for the solution principles using the contradiction table [1] (As a result of the size of the complete contradiction matrix, it has not been printed in full and instead we refer you to various internet sites, for example http://www.trizonline.de/triz_tools/analogien/widerspruchsmatrix.htm)

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Example As a vehicle’s speed increases over 100 km/h, the risk of a serious accident if a tyre bursts rises sharply. A technical contradiction is already formulated in this and the contradiction table can be used. The increase in the speed of a vehicle (line 9) has a negative effect on the reliability of its chassis (column 27). Table field 9/27 contains the following methods in the recommended order: 11, 35, 27, 28 (Figure 4). According to principle No. 11 the inadequate reliability should be compensated by the means of damage prevention provided previously. One possible solution is as follows: A fixed disc is fitted behind each wheel. After the tyre bursts this keeps the vehicle horizontal and therefore reduces the risk of a serious accident (US Pat. 2879821). Example Another example of the principle of “placing a cushion underneath first” (No. 11) comes from the pharmaceuticals industry. A sleeping tablet is covered by a thin film of an emetic substance. If a patient swallows several pills at once, the volume of this substance reaches the critical mass and the dangerous tablets will be vomited out of the stomach. In practice, however, it is often very difficult to formulate a technical contradiction for a concrete problem using the contradiction table. Therefore the direct use of innovation principles is recommended. 3.3

Substance-field analysis

The standard solutions operate with the abstracted models of technical systems which are easy to produce with the aid of the so-called substance-field analysis. Every technical system can be presented in the terms of the existing substances, fields and their interaction. “Substances” is the term used to designate objects or parts of the system regardless of their level of complexity. The term “field” not only includes the four classic physical fields such as electromagnetic field, gravitational field and fields of strong and weak interaction. In TRIZ, the term “field” also includes all the possible “technical” fields such as the temperature field (heat field), centrifugal forces field, pressure field, acoustic field, etc. Once a substance-field model has been formulated for a system, a suitable standard solution can quickly be proposed. To demonstrate this let us consider one standard from the total of 76.

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Example Small metal balls are transported through a pipeline containing a large number of bends by means of a current of air (Figure 5). As a result of the surge effect of the balls the pipe walls wear very quickly at the curves. Although additional protective coatings (typical compromise solution) would produce a longer service life, they still suffer wear. What can be done? There is an undesirable interaction between two substances (balls and pipe) in a technical system. Class 1 standard solutions deal with such problems. One of these standard solutions is as follows: “If contact between two substances/objects which move relative to each other results in a negative effect, a third substance, which represents a modification of one or both of the existing substances, should be introduced between these substances/objects.”

Figure 5:

Graphic illustration of the example [1].

Figure 5 shows the result. A layer of the same balls is placed between the pipe wall and the stream of balls. The balls are either in a pocket of the pipeline or are in another versions are held by a permanent magnet. The moving balls no longer strike the wall but instead strike other balls. If one of the fixed balls is displaced, its place is taken by another one. The technical contradiction has been solved. There is no more wear in the pipe bends. A problem from another sector can be solved using the same standard.

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Example The wings of high speed boats are often exposed to the cavitation effect (hydro-dynamic erosion) of the water current. Small imploding air bubbles gradually destroy even high strength underwater wings. There is obviously a harmful interaction between two substances: Water and metal which, according to the above standard, can be eliminated by modifying one of the existing substances. First of all we look at “modified water” (for example ice or steam) and immediately find the solution. Part of the underwater wing is cooled and a thin but constantly renewable layer of ice is formed on it. These examples also show clearly why a modification of one of the existing substances should be used as the third substance to be introduced. A “foreign” substance will not generally fit into what for it is a “foreign” technical system without complications.

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3.4

Object modelling

Presentation of the positive and negative dependents (network structure) between the objects involved (system elements) and highlighting the focal points of particularly good or poor elements.

Illustration of positive and negative dependences between the objects involved

Activity 1 Object 2

There is a negative dependence between object 1 and object 2

Object 1

Activity 2

Object 3

There is a positive dependence between object 1 and object 2

fans

Example:

Forest fire

Wind

accelerates Figure 6:

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Sailing ship

Object modelling [3].

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3.5

Function modelling

Function modelling breaks down the whole system or whole problem into its individual functions (function network)) and describes the links between these functions by standardised means. A function in the sense of TRIZ is characterised by an active verb and a noun. Illustration of system links with function modelling

Function 1

Function 3

Function 5

(Active verb + noun)

(Active verb + noun)

(Active verb + noun)

Function 2

Function 6

(Active verb + noun)

(Active verb + noun)

results in

Errors are created

Improve quality

Figure 7:

Function 4 (Active verb + noun)

causes

eliminates

Use Q methods

Improve customer satisfaction

prevents

Costs incurred

Update VDA Volume 4

Function modelling [2].

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3.6

Separation principles

Contradictions can be solved by separating the requirements. TRIZ provides four separation principles for this purpose: −

Separation in space The basic concept behind this principle is to separate contradictory requirements spatially. Split the system spatially into part systems or sub-systems and assign the contradictory functions to different parts.

−

Separation in time If a system or a process has to satisfy contradictory requirements, fulfil opposed functions or work under contrary conditions, an attempt should be made to ensure that the contradictory requirement, functions or conditions are required or take place at different times.

−

Separation within an object or its parts If a system has to fulfil contradictory functions or has to work in contradictory conditions, an attempt may be made to split the system into sub-systems, assign one of the contradictory functions to one or more sub-systems and to leave the other functions to the system as a whole as before.

−

Separation by changing conditions The basic concept in this case is the separation of contradictory requirements by modifying the conditions in which a useful and an unnecessary or harmful process occur at the same time. The system or environment can be modified in such a way that only the useful process can occur.

Example Contradiction: A chair lift should move quickly so that the travel time is show, but it should move slowly so that the passengers can climb in and out safely and in comfort. Solution by separation in time: Transfer of the chair from the transport cable (high speed) to a loading cable (low speed).

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Example Contradiction: The hull of a boat should be narrow so that its water resistance is low, but it should be wide so that it is stable and offers lots of space. Solution by the separation of an object: By the combination of two narrow hulls to form a catamaran it is possible to satisfy both requirements.

3.7

Bionics

The bionics method is particularly suitable for finding ideas when faced with technical problems. It checks how nature has solved material or construction problems. Bionics is designed to transfer structures, mechanisms of systems taken from nature specifically on to technical solutions. Example Problem:

Pneumatic cylinders for short, finely metered translatory movements are very heavy, move in a jerky fashion (stick-slip effect) and often leak (piston rod/cylinder)

Question:

How does nature perform translatory movements?

Solution:

Muscles.

Implementation:

Production of the muscle principle by using a diamondshaped, woven, flexible pressure hose.

Pneumatic muscle from Festo

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3.8

Material, cost, time (MCT) operators

The theory of MCT operators confronts the problem solver with the hypothetical situation of having “an arbitrary quantity or absolutely none” of the following: Available materials, such as raw materials, working materials, components, energy, etc. − Costs relating to money for materials, technologies, systems, humans, etc. − Available time to fulfil the function, for activities, for thinking, etc. −

This extreme type of thought can result in revolutionary ideas and solutions.

I m a g i n e yo u h a ve all the mone y in the world all the experts in the world all the time in the world t o s o l ve yo u r p r o b l e m

D e s c r i b e yo u r i d e a s / s o l u t i o n s I m a g i n e yo u h a ve n o t h i n g no money no experts / no help from others no time t o s o l ve yo u r p r o b l e m D e s c r i b e yo u r i d e a s / s o l u t i o n s

Figure 8:

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MCT operators [3].

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3.9

Innovation situation questionnaire

The innovation checklist (IC) or innovative situation questionnaire (ISQ) is a type of questionnaire which, if completed systematically, provides an extensive analysis of the current situation and may create original ideas even for this analytical descriptive process. The basic structure (headings on a checklist) for formulating a problem may be as follows: − − − − − − − − − − − −

3.10

System structure Primary useful functions of the system How the system works Available resources Problem situation, primary harmful functions Mechanism or effect of the error History of the problem Limits of the system change Selection criteria for solution concepts Spontaneous ideas Analogous problems and ideas Ideal current situation.

Resources checklist

This checklist is used to record the available resources in the system and its environment (material resources, field-type resources, information resources, time resources, space resources and functional resources) and their usage defined. Topics to solve the problem are found by querying the usage of the resources to solve the problem so as to prevent harmful functions or to fulfil useful functions.

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Example a)

Material resources Waste Raw materials and products System components Low cost substance Substance flow Substance properties

b)

Field-type resources Energy in the system Energy from the environment Build on possible energy sources System waste becomes a source of energy

c)

Spatial resources Empty space Different dimension Vertical layout Nesting

Figure 9: 3.11

d) Time resources Work in advance Cycle work Parallel work Rework e) Information resources Information provided by the substance itself Information is an inherent property Mobile information Temporary, volatile information Information about a change of state f)

Functional resources Primary function provides resources Use harmful function Use secondary and auxiliary functions

Resources checklist [2]. Anticipatory error detection

From the questions -

What do I have to do to make the product, process or system fail? What activities during the development, production or transport, at the customer’s premises or by the customer result in the failure of the system?

it is possible to acquire information on the technical conflict.

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What do I have to do to make the following fail: The product The process The system properties List of activities during development during production during transport at the customer’s premises by the customer which result in a failure of the system Description

Activity

Get the pizza to the customer whilst it is still warm and crispy

Transport the pizza to the customer without a box Transport pizza in a cool box

Figure 10:

3.12

Anticipatory error detection [3].

Eight evolutionary principles

The development patterns of technical systems can be described with the so-called S curve according to Altschuller. This represents the level of maturity over the life cycle (childhood, adult, mature, old). The innovations (inventions) which take place as part of the S curve can occur on five levels Level 1:

Conventional solutions which use existing technologies, for example improve stability – increase wall thickness

Level 2:

Improve existing systems by minor improvements, for example protect from dirt – cover cap.

Level 3:

Major improvement using existing technology, for example motorcycle chain – shaft drive.

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Level 4:

Use of new technologies which have previously been used in other sectors, for example the Lotus effect for ceramic parts – use for car paints.

Level 5:

Completely new technology invention, for example the technical use of the Lotus effect.

The TRIZ methods are essentially aimed at levels 3-4 (in some cases level 5). The basis for inventions and improvements is provided by the two basic laws of development: Law 1:

Increase the level of idealness of a system All systems develop towards increasing idealness. Idealness means the reduction of the use of substance, energy, space, time and costs whilst also improving the desired function. The basic idea behind this is that a technical system is not the objective of a development, but the fulfilment of the function. An ideal system fulfils its function without actually being there.

Law 2:

Inexhaustibility of technical development Every system can be improved. It is always worthwhile thinking about improvements.

TRIZ provides two main tools for making inventions on the basis of these two laws: I.

Eight development patterns of technical evolution

If we study the development of systems on the basis of the S curves it is possible to observe eight patterns of development: 1.

Non-standard development of the system parts Technical systems generally consist of sub-systems which are developed individually. Different sub-systems therefore reach their limit at different times. The components which reach the maturity phase last therefore slow down the system as a whole and therefore become the weakest link. If, for example, the engine in the automobile system is already very quiet but the gearbox is not, sound insulation must be concentrated on the gearbox.

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2.

Evolution to higher dynamic, controllability and regulation capacity The basic system is improved, weaknesses are detected and eliminated. The level of the dynamic of the system is increased (example of the steering wheel – see Figure 11)

Efficiency / Level of maturity of the system

Fluidactuated system

Field-based system

Elastic system Rigid system

System with joints

Simple adjustment in any position (electric) Adjustment in any position (hydraulic) Height adjustable

No possible adjustments

Limited possible adjustments

Youth age

Figure 11: 3.

Time axis / Level of segmentation Mature

Old

Development towards greater dynamic and control lability [2].

The course of evolution using initially complicated and then superbly simple systems This course of development can be explained using the example of the instrument panel in a car (previously there were lots of individual instruments and now the displays are projected on to the windscreen).

4.

Increasing integration The systems are given more and more properties which are integrated into the system. One example from the automotive industry is the integration of a child seat in the rear seat which grows with the child.

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5.

Evolution using suitable and specifically unsuitable components Expansion of the properties of the system by extending the core functionality or by properties which have nothing to do with the original function of the system. An example of this is the expansion of filling stations supplying fuel to now also selling accessories and items for travellers.

6.

Miniaturisation and the increased use of fields Systems become smaller and smaller, a point which can by illustrated very well by mobile phones. Fields are used to fulfil functions (see Figure 12). Efficiency / Level of maturity of the system

Rigid system without ground Rigid system station with ground station

Field-type system Elastic system

System with joints Voice-controlled system Extending touchsensitive keypad (Sony) 1 joint (Motorola) More than one joint (Nokia)

Youth

Figure 12:

36

Time axis / Level of segmentation

Mature

Old age

Development towards miniaturisation and the increased use of fields [2].

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7.

Increasing segmentation and separation Construction of module systems which can be specifically combined. This can be illustrated very well by notebooks whose hard disc drives are external to save space and weight.

8.

Evolution towards reduced human interaction Operation by humans is no longer required since the system controls itself. One example is the rain sensor which controls the wipers if it starts to rain.

II.

Seven development trends en route to the ideal system

An ideal system is a system which performs its function without actually being there. In TRIZ there are seven development trends en route to this ideal state. The seven trends are as follows (see Figure 13): − − − − − − −

Dimension – from the rectangular fuel tank to the tank which fits into the vehicle’s contours. Components - from producing text with a pen to the multi-colour printer cartridge. Segmentation – from ball bearings to magnetic field bearings. Dynamic – from the single leaf door to the air curtain. Frequency – from the vacuum cleaner which sucks constantly to the resonance beater. Function – from the chocolate bar to the Kinder Surprise egg. “Voidness” – from aluminium cast to magnesium foam.

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1. Dimension 7. “Voidness” Monolith system A large void Lots of voids Porous system Dynamic voids

6. Function system properties Bi/Poly-system Mono-function

Point Line Surface 3-D

2. Components Mono-system Bi-system Poly-system

3. Segmentation Solid Pseudo-solid Liquid Gaseous

Ideal system

Mono-function with influencing properties Poly-function Poly-system with contradictory properties

4. Dynamic 5. Frequency Continuous Vibrations Resonance Immobile shaft

Figure 13: 3.13

Rigid 1 node/joint Lots of nodes/joints Elastic Field-based

Development trends en route to the ideal system [2].

Trimming

The object modelling process is followed by so-calling trimming, in other words improving by routing out potential improvements in the existing model by means of a value analysis consideration. The aim of this is to expand/improve the useful functions by removing or modifying problematic components (objects) and to avoid/bypass/eliminate the harmful functions and therefore to significantly and verifiably improve the system.

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Three core values describe this improvement process: Function ranking – specifies how close an object is to the useful product: Objects which are directly connected to the product have a higher function ranking than objectives which are only indirectly linked to the product (via several intermediate stages). Problem ranking – represents the number of harmful interactions which are linked to an object. Cost ranking – documents how expensive an object is. This is used to determine the trimming factory, in other words the priority with which a component should be trimmed, or in other words improved, modified or eliminated:

Function ranking x Function ranking

Trimming factor = Problem ranking + Cost ranking The trimming factor states for which object trimming is most required (highest ranking = 1). The considered ranking values (function ranking, problem ranking and cost ranking) are normalised on a relative scale from 1 to 10 so that they can be compared to each other and are summarised in Table 5. The second step for the trimming procedure is based on the following four standard questions: 1. Can a different object (1) take over the function/task? Which? 2. Can the object (2) in the function on which the function/task acts carry out the function/task itself? 3. Can the object (2) in the function be eliminated? 4. Can the function/task be eliminated? These questions are designed to encourage problem solvers or inventors to think systematically about eliminating elements of the system and thus to find new, improved solutions.

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Evaluation (ranking)

Functionality F

Harmful influence Problem P

1

Extremely useful, absolutely essential

Absolutely harmful / absolutely undesirable (PSF)

Unaffordable

2

Very very useful

Very very harmful / very very undesirable

Extremely expensive

3

Very useful

Very very harmful / very very undesirable

Very very expensive

4

Useful

Very harmful / very undesirable Very expensive

5

Somewhat useful / useable

Harmful / undesirable

Rather expensive

6

Moderately useful

Slightly harmful / somewhat undesirable

Expensive

7

Slightly useful

Hardly harmful / very unwelcome

Not very expensive

8

Hardly useful

Detrimental, negative/unwelcome

Cheap, low cost

9

Very little use

Not significantly harmful / almost no effect

Very cheap

10

Absolutely not useful, indifferent

Non-harmful, indifferent / no ef- Extremely fect, indifferent cheap

Table 5:

Costs K

Ranking evaluation catalogue for the trimming process

An example of a trimming process is the development of displays on an instrument panel in a vehicle. Starting from lots of individual instruments, a few combination instruments were then used. The next step was a single, multi-function display which also only requires one screen. The last step is the projection of the information on the windscreen directly in the driver’s line of vision (head-up display).

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4

Implementation

4.1

TRIZ software

The contradiction “strong TRIZ tools are more complicated in theory and application” can be solved in the TRIZ manner by using modern computer programs. Although there is still not an invention at the touch of a button, a TRIZ software package can considerably accelerate the innovation process and considerably improve the quality of possible solutions. It demands at least minimal TRIZ knowledge from the user and helps him to analyse and solve his problem with various TRIZ tools and examples. One example of this is the German software for innovations, “Tri-Solver”, which uses the full potential of TRIZ and also proves to be a convenient tool for idea and knowledge management. The software supports both a simple idea search for the solution of every day problems and also a systematic and complete idea identification process. 4.2

Using software successfully

Different TRIZ components must be used sensibly to suit the level of difficulty of the problem. Practice has also shown that the verifiable results can only be achieved if the users master at least 10% - 15% of the total TRIZ potential without having to turn to a textbook or an electronic thesaurus. Qualified use of contradiction-based thought methods and various TRIZ tools with software support make it possible to solve problems quickly and develop pioneering products. There is also no doubt that computer-aided innovation (CAI) will be a standard engineering activity in the future, similar to CAD and CAM.

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4.3

Success potential of TRIZ & CAI

The benefits and future potential of TRIZ and CAI have technical and psychological aspects. Technical aspects •

•

•

•

•

•

Faster and more effective problem solving and innovation by consistently ignoring the trial and error method, reducing the development risk and lowering development costs. Forecasting the development direction of technical systems, creating patent shields, discovering product niches which have not yet been filled. Quality assurance and product improvement by the targeted search for outstanding alternative solutions and the use of anticipatory error detection. Generating ideas in almost hopeless situations where practically every conceivable method appears to have been covered by patents held by competitors. The perfect use of system resources: For example eliminating a negative effect in a finished machine yet only making minimal changes to a technical system. Possible integration with DFMA (design for manufacturer and assembly), FMEA (failure mode and effect analysis), QFD (quality function deployment), DoE (design of experiments), Taguchi (robust design) and other methods.

Psychological aspects • • • •

Encourages the creativity and innovative spirit of engineers and project teams. Suppresses slowness of thought and so-called operational blindness. Supports systematic thinking and the will to search for non-trivial solutions outside one’s own area of expertise. Improves team work; invention becomes a controllable process and is not just an act of pure inspiration.

TRIZ is not a magic wand and we are a long way from creating inventions at the touch of a button. Furthermore, not every “hard nut” can definitely be cracked by TRIZ tools on the spot. However, it gives invention technology a solid, convincing procedure for getting as close as possible to the core of the problem and provides all types of engineering expertise from almost every field of technology and science. For this reason at least it is worthwhile using TRIZ.

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5.

Implementation of TRIZ and CAI in your company

5.1

Overcoming hurdles

From a practical point of view TRIZ and CAI beginners have two hurdles to overcome - one technical and one psychological. The well-known parallels with the relatively recent introduction of CAD into every day design practice appear to be particularly helpful in this respect. Firstly: A modern CAD workstation will hardly be of any use if its users are not familiar with design theory. Exactly the same is true for computer-aided innovation which should not begin at the PC but with studying the TRIZ principles and practising new working methods. Secondly: TRIZ requires unconventional, contradiction-based thought processes from its users and the consistent rejection of the trial and error method for solving technical problems. Changing thought processes which have been engrained over a period of years is no easier for many designers and developers than, for example, moving from the drawing board to the CAD device. •

Industrial experience

The experience of a large number of TRIZ users confirms that TRIZ (which is not known as the “theory of invention” for nothing) is much too extensive to be introduced successfully whilst continuing day to day business at the same time. Systematic support by method experts allows TRIZ working techniques with their PC-based idea databases to be integrated in the company processes successfully and with good results. Depending on the needs of the company, TRIZ expertise is provided by practical training courses or by the moderation of pilot projects which involve the solution of current problems.

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•

Internal training

The wish to equip management staff and development teams with the techniques of systematic, inventive problem solving often conflicts with high workloads and a general lack of time for day to day business. A multi-level internal training programme will help to overcome this contradiction. This enables companies to do the following: • •

Train its own TRIZ experts by having them attend all the levels Qualify a large number of staff in different levels using the rotation principle.

The contents, number and duration of the levels can be tailored to the company’s specific needs. The entire training cycle can therefore be completed within two to 12 months depending on the available time resources. •

Building TRIZ expertise

The acquisition of TRIZ expertise and experience is certainly not a matter of a few hours. Figure 14 shows the recommended structure of a TRIZ expertise pyramid within the company. The bottom levels such as “Contradictionbased thought” and “Innovation principles” should become as widespread as possible at all levels of the company. The use of special TRIZ tools, on the other hand, should be reserved for internal method specialists. The route offered by simple to more complex tools and methods should also be chosen since this is definitely worthwhile and will not create disappointments.

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Standards Technological effects

Development patterns of technical systems, principles, standards, etc.

TRIZ innovation principles Contradiction-based technical thought

Figure 14:

5.2

Structure of TRIZ expertise within the company

Platform for innovation and quality management

TRIZ is made up of several methods and tools and therefore occupies a much higher level than conventional methods of quality management and product development, such as QFD, FMEA, DFMA, DoE and Taguchi, etc. It will prove to be a platform of internal innovation and quality management which can be developed and extended. In the face of dynamically growing global competition, the fast adoption of TRIZ in every company cannot only be refreshing and encouraging but may even prove vital for survival.

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6

Annex - tables

6.1

39 technical parameters which describe a contradiction

1

Weight of a moving object

2

Weight of a stationary object

3

Length of a moving object

4

Length of a stationary object

5

Length of a moving object

6

20 21

Power

22

Waste of energy

23

Waste of material

24

Loss of information

Surface area of a moving object

25

Wasted time

7

Volume of a moving object

26

Material quantity

8

Volume of a stationary object

27

Reliability

9

Speed

28

Measuring precision

10

Force

29

Production precision

11

Pressure or tension

30

External negative influences on the object

12

Form

31

Negative side-effects of the object

13

Stability of an object

32

Ease of production

14

Strength

33

Ease of use

15

Durability of a moving object

34

Ease of repair

16

Durability of a stationary object

35

Ability to adapt

36

Complexity in structure

37

Complexity in control or controller

38

Level of automation

39

Productivity

17

Temperature

18

Brightness

19

Energy consumption of a moving object

Table A:

46

39 technical parameters which describe the contradiction in patents [1]

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6.2

40 innovation principles

1 Segmentation and dismantling 14 Curving

27 Cheap short life

2 Separation

15 Dynamisation

28 Replace mechanism

3 Local quality

16 Partial or surplus effect

29 Pneumatics and hydraulics

4 Asymmetry

17 Higher dimension

30 Flexible sheathing and films

5 Joining

18 Mechanical oscillations

31 Porous materials

6 Universality

19 Periodic effect

32 Colour changes

7 Nesting

20 Continuity

33 Homogeneity

8 Counterweight

21 Hurry through and omit

34 Elimination and regeneration

9 Preferred opposite action 10 Preferred action

22 Convert harmful into useful 35 Change of properties 36 Phase transition 23 Recoupling

11 Preventive action

24 Mediator, broker

37 Heat expansion

12 Equipotent

25 Self-supply, self-service

38 Strong oxidising agent

13 Reversal

26 Copy

39 Inert medium 40 Composite material

Table B:

40 innovation principles [3].

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6.3

TRIZ – the 76 standard solutions for substance-field analysis

From Prof. Dr. A. Mack “TRIZ – the 76 standard solutions for substancefield analysis according to Altschuller”. (Nomenclature: SFA = substance-field analysis; SFM = substance-field model) 1

Building and destruction of complete substance-field models

1.1

Structure of SFMs

1.1.1

Complete an incomplete SFM Centrifugation (F, centrifugal force) is used to separate air or water (S1) from solid particles (S2).

1.1.2

If additives can be added internally, complete with them Small drops of fluid can be detected easily by adding a fluorescent dyestuff.

1.1.3

If additives can be added externally, complete with them Gas leak detection using soap spray from the outside on a pipe.

1.1.4

Use resources for completion

1.1.5

Create additional resources by changing the system environment

1.1.6

Use surplus actions for completion and eliminate the surplus

1.1.7

If the surplus action is harmful, try to guide it at a different component in the system

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1.1.8

Insert locally protective substances to compensate Glass ampoules are cooled from below whilst they melt in a water bath to prevent overheating the thermally sensitive content.

1.2

Destruction of SFMs

1.2.1

Eliminate harmful interactions by introducing a third substance S3

1.2.2

Eliminate harmful interactions by introducing a third substance S3 where S3 may be a modification of the two substances S1 and/or S2. The superficial destruction of the hulls of high speed boats due to cavitation caused by water can be prevented by cooling the hull and forming a layer of ice.

1.2.3

Direct the harmful effect to a less important substance S3 Walls of containers (S1) in which water (S2) freezes are clad with elastic material (S3) to compensate for thermal expansion (F, force) and prevent the container wall being damaged.

1.2.4

Introduce a new field to compensate the harmful effects A stream of air is used to atomise flowers, but this also unfortunately results in the blossom closing. This negative effect can be eliminated by given the blossom an electrostatic charge which keeps them open.

1.2.5

Use the possibility of being able to switch magnetic fields on and off

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2

Improvement of SFMs

2.1

Transition to more complex SFMs

2.1.1

Link several SFMs Hilti principle, drive unit strikes heavy intermediate chisel which strikes the actual chisel which strikes the object to be smashed (pulse transfer and strengthening over several stages)

2.1.2

Couple an SFM

2.2

Advanced development of SFMs

2.2.1

Use fields which can be better controlled

2.2.2

Fragment S2 Knife  Knife with serration  roughened blade

2.2.3

Microwave-sharpened 

Specifically

Use capillary or porous substances Several small nozzles emit adhesive more precisely than on large aperture.

2.2.4

Increase the level of dynamic Split a door leaf into several segments, roller doors, shutters connected flexibly.

2.2.5

Structure fields

2.2.6

Structure substances

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2.3

Rhythm coordination

2.3.1

Bring the rhythm (the frequency) of the acting field into harmony (or specific non-harmony) with one of the two substances In a massage one attempts to bring the rhythm of the external effect into harmony with the pulse of the patient. Specific asymmetry in gearboxes prevents loud noise and inherent resonance.

2.3.2

Synchronise the rhythm or the frequency of fields

2.3.3

Bring independent actions into rhythmic harmony Use breaks in the completion of a useful function to complete another function.

2.4

Complexly improved SFMs

2.4.1

Use ferromagnetic substances and magnetic fields

2.4.2

Use ferromagnetic particles, granulates and powders Magnet and reed contact

2.4.3

Use ferromagnetic fluids

2.4.4

Use capillary structures in conjunction with ferromagnetism

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2.4.5

Use complex ferromagnetic SFMs, for example external magnetic fields, ferromagnetic additives, etc.

2.4.6

Improve the controllability of ferromagnetic systems by using the effects database

2.4.7

Increase the level of dynamic in a complex SFM

2.4.8

Structure and split fields and substances into complex SFMs

2.4.9

Improve the controllability of ferromagnetic systems by using the effects database

3

Transition to super and sub-system (macro and micro level)

3.1

Transition to bi and poly systems

3.1.1

Combine systems to form bi and poly systems Several panels are combined to form a stable stack so as to machine the edges of thin glass panels

3.1.2

Create or intensify the connection between the individual elements in bi or poly systems

3.1.3

Improve the efficiency of bi or poly systems by increasing the differences of individual components Apply coloured marking to similar parts during assembly to prevent confusion

3.1.4

Simplify bi or poly systems by eliminating superfluous, redundant or similar functions An LED which can light up in two or three colours

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3.1.5

Improve the efficiency of bi or poly systems by spreading mutually hindering or harmful effects among various components

3.2

Transition to micro systems

3.2.1

Miniaturise components or entire systems

4

Detection and measurement

4.1

Indirect methods

4.1.1

Bypass detection and measurement A temperature sensor must measure the temperature and initiate actions to protect an electric motor from overheating. If the poles of the motor are made from an alloy with a Curie point at precisely the temperature where the overheating starts, the motor will cut out by itself.

4.1.2

Carry out detection and measurement on a copy Measuring the length of snakes is dangerous. It is better to measure a scale photograph of the snake.

4.1.3

Replace measurement by two consecutive detection processes Thermo-chromium substances which change colour depending on temperature can be used to measure temperature.

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4.2

Structure of measurement SFMs

4.2.1

Detect or measure using an additional field An electric current is fed through water to detect that it is boiling. When it starts to boil the electrical resistance rises sharply due to the formation of steam bubbles.

4.2.2

Add additives or substances which are easy to detect or measure Luminescent dyestuff is added to the refrigerant to detect a leak in the cooling system of a refrigerator.

4.3

Improvement of measuring systems

4.3.1

Use the effects database to improve measuring systems

4.3.2

Use resonance phenomena for measuring purposes Mechanical oscillations whose frequency depends on mass are generated to find the mass of a body in a closed container (Coriolis effect).

4.3.3

Use the resonance phenomena of linked objects for indirect measurement

4.4

Transition to ferromagnetic systems

4.4.1

Use ferromagnetic substances and magnetic fields

4.4.2

Replace substances with ferromagnetic substances and detect or measure using the magnetic field

4.4.3

Create complex, linked SFMs with ferromagnetic components

4.4.4

Introduce ferromagnetic substances to the system environment

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4.4.5

Use the effects database (Curie point, Hopkins and Barkhausen effect, magneto elastic effects, etc.)

4.5

Evolution of detect and measure

4.5.1

Create bi and poly systems

4.5.2

Detect and measure the (mathematical) derivation or the integral instead of the original function

5

Auxiliary methods for using standards

5.1

Introduction of substances

5.1.1

Indirect methods Introduce voids or cavities as a substance, use highly active additives in small quantities or just locally, create the required substance only when required (in situ, for example by electrolysis, catalysis, etc.).

5.1.2

Distribute substances, use fragments

5.1.3

Use the self-limitation of substances The temporarily introduced substance decomposes after the successful action or is chemically destroyed.

5.1.4

Use substances in surplus The use of foams can be used to take account of both requirements for low mass and high substance quantity (volume) (cavity in surplus).

5.2

Introduction of fields

5.2.1

Use all the existing fields in the sense of resources

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5.2.2

Use fields from the system environment

5.2.3

Use field-generated substances Magnetic substances

5.3

Phase transitions

5.3.1

Change the aggregate state or the phase of substances

5.3.2

Use two aggregate states or phases of a substance Water and ice together fix the temperature at 0°C

5.3.3

Use the physical effects which accompany a phase transition Use the low temperature during evaporation for cooling.

5.3.4

Use effects which result from the simultaneous presence of two phases

5.3.5

Improve the interaction between two phases

5.4

Use the effects database

5.4.1

Use inherently controlled reversible physical transformations Phase transitions, dissociation – association, ionisation and recombination.

5.4.2

Use accumulation and reinforcement effects. Catalysts, enzymes

5.5

Substance particles

5.5.1

Create substance particles (for example ions) by destroying a higher level organised substance

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Molecules 5.5.2

Create substance particles (for example ions) by combining lower level organised substances Elementary particles

5.5.3

Use a substance with a similar level of classification to destroy or combine substance particles (take small steps) Powder instead of solids

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6.4

58

Contradiction matrix

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Deteriorating parameters

P Parame eters to be im mprove ved

Weight g of a moving g object j

15,, 8,29,34 , ,

Weight of a stationary object Length of a moving object

8,15,29,34

Length of a stationary object Area of a moving object

35,28,40,29 2 17 29 4 2,17,29,4

Area of a stationary object Vol me of a mo Volume moving ing object

2 2,26,29,40 26 29 40

Volume of a stationary object Speed

14 15 18 4 14,15,18,4 30,2,14,18 1 1,7,4,35 7 4 35 8,13,14 9,17,19,36

38

vity Pro oductiv

Lev vel of a autom mation

39

29,5,15,8 , , ,

26,30,36,34 , , ,

28,29,26,32 , , ,

26,35,18,19 , , ,

35,3,24,37 , , ,

19,15,29

1,10,26,39

25,28,17,15

2,26,35

1,28,15,35

14,15,1,16

1,19,26,24

35,1,26,24

17,24,26,16

1,35

1,26

26

15 30 15,30

14 1 13 14,1,13

2 36 26 18 2,36,26,18

14 30 28 23 14,30,28,23

10 26 34 2 10,26,34,2

15,16

1,18,36

2,35,30,18

23

7,10,15,17

15,29 15 29

1,26 1 26

4,26,29 4 26 29

16,24,34,35 16 24 34 35

2,6,10,34 2 6 10 34

1,31

2,17,26

10,15,26

4,10,28,34

3,16,27,34

10,18

10,14,19,35 14,19 2,13,28,38

37 Com omplex xity in c contro ol

Example / Cut-out

36

Ada daption n capac acity

ngth o Len of a mo oving objject

35

Com omplex xity of s ture structu

3

We eight of o a sta ationarry objject

Contradiction matrix 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39

2 We eight of o a mo oving objject

1

14,4,28,29 30,14,7,26

2,10,35,37

Force

1,8,18,37

1,13,18,28

15,17,18,20

10,18,26,35

10,19,36,37

2,35

Pressure or tension

10,36,37,40

10,13,18,29 10,35,36

35

1,19,35

2,36,37

24,35

3,28,35,37 10,14,35,37

Form

8,10,29,40

3,10,15,26

5,4,29,34

1,15,29

1,16,28,29

13,15,39

1,15,32

10,17,26,34 3,23,35,40

Stability of an object

2,21,35,39

1,26,39,40

1,13,15,28

2,34,35,30

2,22,26,35

22,23,35,39

1,8,35

Strength

1,8,15,40

1,26,27,40

1,8,15,35

3,15,32

2,13,28

3,15,27,40

15

10,14,29,35

Endurance of a moving object

5,19,31,34

2,9,19

1,13,35

4,10,15,29

19,29,35,39

6,10

14,17,19,35

Endurance of a stationary object

6 16 19 27 6,16,19,27

2

1

10 16 20 38 10,16,20,38

3,27,31,35

2,16,19,26

15,28,35

6,22,36,38

22,32,35

9,15,19

2,18,27

Brightness B i ht

1,19,32 1 19 32

2 2,32,35 32 35

16 16,19,32 19 32

1,15,19 1 15 19

6 6,13,32 13 32

15 15,32 32

2 2,10,26 10 26

2 2,16,25 16 25

Energy consumption of a moving object

12,18,31,28

12,28

13,15,16,17

2,27,28,29

35,38

2,32

12,28,35

, , 17,19,34

, , , 19,20,30,34

, , 16,19,35

, , 2,17,28

, , 28,34,35

7,23

3,15,23,35

2

10,28,29,35

10,24,28,35

10,13,18,35

10,18,35

10,23,28,35

33,35

35

13,15,23

Energy consumption of a stationary object Power

6,9,19,27 , , , 8,31,36,38

, , , , , , 17,19,26,27 1,10,35,37

Wasted energy

6,15,19,28

6,9,18,19

2,6,7,13

Wasted material

6,23,35,40

6,22,32,35

14,10,29,39

Loss of information

10,24,35

5,10,35

1,26 2 15 29 2,15,29

Request the complete matrix free of charge from: [email protected] Ke ord TRIZ / Contradiction matri Keyword: matrix

2,16,17

6 25 34 35 6,25,34,35

Temperature

16,19,25,35

2,10,15

1,6

Wasted time

10 20 35 37 10,20,35,37

5 10 20 26 5,10,20,26

28 35 28,35

6 29 6,29

10 18 28 32 10,18,28,32

24 28 30 35 24,28,30,35

Material volume

6,18,31,35

18,26,27,35 14,18,29,35

3,15,29

3,10,13,27

3,18,27,29

8,35

Reliabilit Reliability

3 3,8,10,40 8 10 40

3 3,8,10,28 8 10 28

8 8,13,24,35 13 24 35

1,13,35 1 13 35

27,28,40 27 28 40

11,13,27 11 13 27

1,29,35,38 1 29 35 38

Measuring accuracy

26,28,32,35

25,26,28,35 5,16,26,28

2,13,35

10,27,34,35

24,26,28,32

2,10,28,34

10,28,32,34

Production accuracy

13,18,28,32

9,27,28,35

10,28,29,37

18,23,26,28

10,18,32,39

External negative influences on the object

21,.22,27,39

2,13,22,24

1,4,17,39

11,22,31,35

19,22,29,40

19,22,29,40

3,33,34

13,22,24,35 18,22,35,39

4 4,9,14,15 9 14 15

2,18,26

3,13,27,29

Negative side-effects of the object

15,19,22,39

1,22,35,39

15,16,17,22

1,19,31

1,2,21,27

2

Ease of production

15,16,28,29

1,13,27,36

1,13,17,29

2,13,15

1,26,27

1,6,11,28

1,8,28

1,10,28,35

Ease of use

2,13,15,25

1,6,13,25

1,12,13,17

1,15,16,34

12,17,26,32

1,3,12,34

1,15,28

1,10,25,28

1,4,7,16

Ease of repair

2,11,27,35

2,11,27,35

Adaption capacity

1,6,15,8

19,15,29,16 35,1,29,2

1,11,13,35 15,29,37,28

1

Complexity of structure

26 30 34 37 26,30,34,37

2 36 35 39 2,36,35,39

1 19 26 24 1,19,26,24

29 15 28 37 29,15,28,37

Complexity in control

27,26,28,13

6,13,28,1

16,17,26,24

1,15

Level L l off automation t ti

28 28,26,18,35 26 18 35

28,26,35,10 28 26 35 10 14,13,17,29 14 13 17 29

27,4,1,35 27 4 1 35

15,24,10 15 24 10

34,27,25 34 27 25

Productivity

35,26,24,37

28,27,15,3

1,35,28,37

12,17,28,24

35,18,27,2

18,4,28,38

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15 10 37 28 15,10,37,28 15,10,37,28

7,13,34,35

1,10,32

27,34,35

35,28,6,37

15 1 24 15,1,24

12 17 28 12,17,28

34,21

35,18 5,12,35,26 5 12 35 26

5,12,35,26

7

Bibliography

(1) Livotov, Pavel, Dr.-Ing.; “Innovationstechnologie TRIZ”; paper from TriSolver GmbH & Co. KG for VDA. (2) Bernd Gimbel, Rolf Herb, Thilo Herb: Ideen finden, Produkte entwickeln mit TRIZ. Textbook, Munich: Hanser Verlag (3) Ried, Horst; “TRIZ Problemlösungsmethode, kreativ Probleme lösen” training document; http://www.horstried.de/.

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59

Verband der Automobilindustrie

Quality Management in the Automobile Industry

4

Quality Assurance in the Process Landscape - General, risk analyses, methods, process models -

Economical Process Design and Process Control

st

1 edition 2005

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We would like to thank all the companies and their staff who have contributed to this workgroup: BMW AG, Steyr Robert Bosch GmbH, Stuttgart Continental AG, Hannover DaimlerChrysler, Stuttgart GFQ Akademie, Bad Kreuznach Ford-Werke AG, Cologne Dr. Ing. h. c. F. Porsche AG, Stuttgart Siemens VDO AG, Babenhausen Volkswagen AG, Wolfsburg Our thanks also go out to all our readers who have submitted improvement suggestions. Oberursel, January 2005

VERBAND DER AUTOMOBILINDUSTRIE E.V. (VDA)

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1

Contents

Page

1

Economical Process Design und Process Safety

4

1.1

Introduction

4

2

Processes

5

2.1 2.2 2.2.1 2.2.2 2.2.3

Process Variables Definitions Process Capable Process Stable Process

5 6 6 7 7

3

Process Design

7

3.1 3.2 3.3 3.4 3.5

Consideration of new Knowledge in Quality Planning Communication Basis Tolerance Specifications and Process Dispersion Converting Product Requirements into Process Characteristics Inspection Planning

11 12

4

Process Qualification

13

4.1 4.2 4.3 4.4

Measurement Equipment Capability and Measurement Uncertainty Machine Capability Preliminary Process Capability Process Capability

13 13 15 15

5

Process Analysis

16

5.1

Distribution Time Models (corresponding to DIN 55319 and DIN ISO 21747) Process Improvement

16 20

5.2

2

7 9 10

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Process Control

21

6.1 6.1.1

21

6.2 6.3 6.4 6.5

Quality Control Chart (QCC) Quality Control Chart for Controlling the Process Location Quality Control Charts for Controlling the Process Dispersion Recording a QCC Evaluating a QCC Use of QCC Before the Series Start Response Characteristic of a QCC

25 26 26 27 27

7

Process Assessment

27

7.1 7.2

28

7.3

Quantile Method Effective Parameters (Minimum Process Capability/Process Performance) Process Capability und Tolerances

29 30

8

Appendix

32

8.1 8.2 8.3 8.3.1 8.3.2

Formulas for Calculating Control Limits Formula Symbols Examples Quality Control Chart for Moving Averages Operation Characteristic for a Shewhart Chart With Extended Limits Example for Process Analysis and Evaluation of the Long-Term (Process) Capability Operative Range for Statistical Parameters, Procedures and Aids Bibliography

32 33 35 35

6.1.2

8.3.3 8.4 8.5

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22

38 40 46 48

3

1

Economical Process Design und Process Safety

1.1

Introduction

The application of statistical methods is an essential aspect in an overall quality management system. For example, this can also be seen in relevant sections in national and international rules and regulations for QM - such as ISO/TS 16949:2002 for example - which have the monitoring and measuring of production processes as their subject. The significance of Statistical Process Control (SPC) is one issue that is always emphasized. This document describes several procedures for process qualification, process control and process evaluation that correspond with current, stateof-the art technology. Only processes with measurable (quantitative), continuous characteristics are considered. The quality characteristics that are to be assessed here are seen as inherent characteristics of a product, process or system. As a rule, quality characteristics are also inspection characteristics. Due to rising cost pressures, it is becoming increasingly important to plan processes according to economic considerations. Of particular significance to process planning are small tolerances, a design necessity which often arises due to the product's function. Furthermore, however, it can also be necessary to consider aspects such as − − − − −

Reliability/safety Exchangeability Service life Appearance Verifiability

when defining tolerances. − To achieve a product's optimal economic viability, the requirements placed on the product and the manufacturing costs must be reconciled. For production planning and execution, the emphasis is naturally on feasibility and verifiability of the product components' characteristics. As a rule, the smaller the tolerance, the more costly the associated processing and inspection processes.

4

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Processes should be effective and efficient. This means that the processes produce products that fulfill the stipulated requirements while achieving the greatest possible economic viability, meaning the least possible consumption of resources. These guidelines do not make any claims to completeness. The procedures described here are based on knowledge gained in an empirical investigation of the automotive industry (QZ, year 44, pages 761 to 765), in which previous procedures were critically analyzed in conjunction with the evaluation of manufacturing processes. In particular, these guidelines take into account current developments in national and international standardization (see Section 8.4). 2

Processes

2.1

Process Variables

A number of input variables affect each process; when these effects are added together, they cause dispersion in the respective product characteristic. Here, a distinction is made between random variables and system variables. Examples of random variables: − Temperature fluctuations in a casting mold when at the working temperature − Positioning accuracy of a machining tool when positioned repeatedly Examples of system variables: − − − − −

Temperature changes during the heating phase of a casting mold Machining tool wear and tear Replacement of a tool that cannot be adjusted. Cooling failure during a casting process Tool breakage in a machining device.

As a part of a study at DaimlerChrysler (QZ, Year 44, pages 761 to 765), it was found that only roughly 5 % of all examined processes are subject to random variables only. This means that as a rule, manufacturing processes are afflicted with system variables, and consequently that no normal distribution can be assigned to them as a distribution time model.

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With only a few exceptions, the examined processes are to be assigned to the distribution time model (see Section 5), regardless of how the corresponding mixed distributions arose. Calculation of the quality capability statistics according to the "classic" formulas for normally-distributed processes, using the mean and the standard deviation, cannot take into account the special features of such a characteristic distribution and could lead to incorrect conclusions. An alternative calculation method that is universally applicable to any distribution time model is the quantile method, which is explained in Section 7.1. For information on the quality capability parameters calculated with this method from the examination mentioned above, see Section 7.3. System process variables often attract attention by the characteristic pattern presented in the time history of a quality characteristic's values. Often these changes are caused by physical regularities or manufacturing conditions (for example, start-up behavior after standstills). Typical, for example, are trends when there is tool wear and jumps in the value development caused by readjustments, lot changes or different recesses in a die-casting mold. If the pattern is known as there has been sufficient observation, the corresponding process performance can be included in the evaluation and taken into account when calculating a quality capability parameter. The causal process characteristics then require special monitoring, for example, in the case of tool wear, by determining a maximum number of pieces that may be manufactured until the next truing or tool change. In these cases, a Shewhart chart with extended limits is used for continuous process control (see 6.2.2.). 2.2

Definitions

2.2.1

Process

A process is a "set of interrelated or interacting activities that transforms inputs into outputs." (DIN EN ISO 9000:2000).

6

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2.2.2

Capable Process

Quality capability means the suitability of a unit for producing a product in such a way that the quality requirements expected of the product are met. Process capability is therefore a process's quality capability. A process is considered to be capable when the process capability index reaches or exceeds a prescribed limit value. 2.2.3

Stable Process

A process is considered to be stable if it is only subject to random variables. In Section 3.2.2.7 of the standard ISO 3534 Part 2, there is a description of a stable process as a process under statistical control. Accordingly, only the distribution time models A1 and A2 in Section 5.1 are stable. 3

Process Design

3.1

Consideration of new Knowledge in Quality Planning

Quality planning is a part of quality management that is aimed at defining the quality objectives, the necessary execution processes and the associated resources for fulfilling the quality objectives. This involves the selection and weighting of quality characteristics for a product or a service, as well as the translation of these characteristics into requirements and specifications. In summary, quality planning means developing products and processes in such a way that they meet the customers' needs. Consequently, quality planning includes the following fundamental activities: − Identify the customers and their needs − Develop a product that meets these needs − Develop a process that is capable of generating this product

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The objective of quality planning is the conversion of the functions that external and internal customers require into internal company processes, so that the customers’ needs are met, depending on the superordinate corporate objectives and the competitive and environmental conditions, with a large degree of ruggedness and accuracy in the internal processes. The quality planning tasks can be derived from this objective.

Existing product Existing process

Customer‘s needs (quantified)

Identify the customer Develop the product List of customers

Product characteristics

Identify the customer‘s needs

Optimize the product definitions

Customer‘s needs (in customer‘s language)

Quality objectives (as related to production)

Translate the customer‘s needs

Develop the process

Customer‘s needs (in our language)

Process definition

Establish units of measure for quality assessment Quality units of measure Introduce suitable measurement methods Customer‘s needs (quantified)

Optimize the process and Ensure process capability Optimized process Ready to hand over Hand over the process to the Responsible line departments Process ready for production © Hagen Consulting & Training GmbH

Fig.3.1:

8

"Roadmap" for Quality Planning According to Juran

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The following statements apply to each of these steps: 1)

The linear sequence - the result of one step is always the basis for the next step

2)

The three-way role for each step - as customer, processor and supplier

3)

The agreement on generally applicable units of measure for assessing the quality

4)

The installation of a measurement system (sensors) for the quality assessment using the units of measure agreed upon.

3.2

Communication Basis

Only when the areas responsible for planning and executing work together will it be possible to manufacture a product economically that takes both the customer’s requirements and the manufacturing costs into consideration. An integrated view of the customers' requirements and the tolerances called for by the design is the basis for creating an economical process. In the context of "simultaneous engineering", the organizational unit responsible for the planning and the one responsible for production must start communicating with one another at an early stage. Today this is frequently implemented by modern project management. A very good communication platform is formed by regularly occurring coordination meetings held with the design and production departments, in which the economical implementation of the requirements is discussed. In such a discussion, both partners must have the same rights when it comes to the optimal product design. At the same time, it is necessary to find a compromise between the customer requirements and economical implementation.

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3.3

Tolerance Specifications and Process Dispersion

The tolerance specifications are based on the requirements for the quality characteristics, and are derived from the customer's requirements for the functionality. Often a product characteristic's production dispersion is the determining quantity for defining the tolerance. Every concession that entails the target value is not being met exactly and that tolerates deviations from this value conflicts with complete fulfillment of the functions. This correlation is shown in Taguchi's loss function. 0,5 0,45 0,4 0,35 0,3 0,25 0,2 0,15 0,1 0,05 0 -6

-4

-2 Process

Fig.3.2:

0 Loss function

2

4

6

Loss

Loss Function According to Taguchi

Consideration of the process dispersions when designing the tolerances is necessary for an economical manufacturing process. As a rule, the scale of the process variation is not yet known in the development phase, and therefore can only be estimated on the basis of experience with existing processes that are similar.

10

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Approaches for defining tolerances in line with the process, such as − − − −

Statistical tolerancing Stepped limit values and grades Function-oriented tolerancing Tolerance design taking into account the effect when values are exceeded (quality characteristic curve)

are described in VDA Volume 4, "Tolerancing Methods". 3.4

Converting Product Characteristics

Requirements

into

Process

Comprehensive understanding of the correlation between product characteristics and process characteristics is a prerequisite for economical process control. Process or input-

Maßnahmen am Prozess related actions oder bei den Eingaben

Q-Information

Process Prozessmerkmale characteristics

Fig. 3.3:

e Mitw elt

Result (Product) Ergebnis (Produkt)

ho d

Mate

characteristics

Met

sch M as chin e

Men

5’M’

rial

Process Prozess

Inputs Eingaben

Product Produktmerkmale

Process Control Plan

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Processes are controlled and guided by changing the process characteristics. For this purpose, the product limit values must be transferred into process limit values as far as possible. For process control, the correlation between the product characteristic and the process characteristic must be known. For example, this correlation can be determined with the help of statistical experiment design (see VDA Volume 4 "Design of Experiments"). 3.5

Inspection Planning

One of the tasks of inspection planning is to define inspection methods and measuring and test equipment (measurement processes), the inspection intervals and number of parts to be inspected that are suitable for the respective process. The objective is to achieve an effective and efficient inspection strategy throughout the entire process chain. At the same time, the costs from − Production processes − Inspection processes − Reworking processes should be reduced to a minimum. Criteria for defining the inspection intervals: − Disturbance frequency determined ahead of time or known from similar processes − Control frequency (relating to time or number of pieces, for example, 5 7 samples between two controls) − New manufacturing technology − Tool life and intervals between tool exchange or readjustment − Inspection effort − Cycle time, lot size − Sorting effort − Process capability (quality capability parameter) − ...

12

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The inspection frequency should be chosen to ensure that it is still possible to access parts manufactured between two samples, in order to prevent passing on faulty parts to the assembly area or delivering them to the end customer. The inspection interval can be dynamically adjusted, based on the experience with the process (inspection dynamization). 4

Process Qualification

4.1

Measurement Equipment Capability and Measurement Uncertainty

The measurement processes and measurement equipment used during the capability examinations must be able to meet the required precision of the measurement result, which results from the respective application purpose. Terms and processes for determining precision, linearity, stability, repeating accuracy and reproducibility are described in DIN 55350, Part 13, and methods for determining the measurement uncertainty are described in VDA Volume 5. 4.2

Machine Capability

As a part of the acceptance of manufacturing equipment, a machine capability study is usually performed by the machine manufacturer. The objective of this examination is to reduce the variables producing effects to only those caused by the manufacturing equipment itself. This is accomplished by keeping the basic parameters as constant as possible, so that possible influences from man, material and environment are minimized or avoided. The result of the machine capability study is a preliminary statement on the suitability of the manufacturing equipment for fulfilling the specified requirements. As a rule, at least 50 parts are manufactured in an uninterrupted sequence. The relevant quality characteristics are measured and the measurement results are recorded and statistically evaluated according to the order in which they were manufactured, for example, with regard to stability and the distribution time model. Finally, a machine capability index Cmk is calculated, which must reach or exceed a prescribed minimum value.

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In special cases, the production or measurement of 50 parts can mean an unjustifiably high expenditure (for example, when the inspection involves the destruction of the part) and can make it necessary to manage with fewer parts. This, however, leads to a reduction in the reliability of the statement, which means that the confidence interval of the capability parameter is enlarged. To compensate for this effect, the minimum value for Cm or Cmk (see note) can be correspondingly increased. This is shown in Table 4.1 by way of example. For example, the requirement of 1.67 in connection with 50 values may increase to 1.9 if only 20 parts can be evaluated. The numerical values are calculated in such a way that the lower 95 % or 99 % confidence limit of Cm has the same value when the sample size n is smaller as it does when n = 50. Cm / Cmk Recommendation Number of pieces

PA = 95 %

PA = 99 %

50

1.67

1.67

40 30 20 15

1.72 1.78 1.91 2.03

1.74 1.84 2.03 2.23

Table 4.1:

Recommendation for Cm and Cmk for 50 and less than 50 values is derived from the confidence interval of the quality capability parameter for stable processes

Note: According to DIN ISO 21747 and DIN ISO 3534-2, the terms Cp/Cpk are used for stable processes, instead of Cm/Cmk. The German standard DIN 55319 "Qualitätsfähigkeitskenngrößen" (Quality Capability Statistics) which was still valid at the time this document went to press will be replaced by DIN ISO 21747. Due to the fact that only 50 consecutively manufactured parts do not allow the actual working spread to be registered completely, this machine capability study allows only preliminary statements to be made about process performance.

14

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Alternatively, the following procedure can be selected: withdrawal of at least 50 parts from a close-to-series production (preliminary run, for example, 3 shifts or days) in the form of random samples (for example, m = 10; n = 5). Under some circumstances, this procedure may replace the preliminary process capability study. 4.3

Preliminary Process Capability

When performing this study, at least 125 units to be examined are withdrawn from the process. The distribution of these 125 parts among the individual samples and also the intervals between withdrawn samples must be defined for the specific process and cannot be specified categorically. A sample size of 3-5 parts is customary practice. An assessment of the process stability can take place with the help of a quality control chart (original value sequence), a probability network or by analyzing the variance. In addition, it should be attempted to assign a distribution time model to the data record that best describes it (see (see Section 5.1). The preliminary process capability study should take place under series conditions, so that all unavoidable dispersion influences can have an effect. 4.4

Process Capability

Statistical evaluation of the control charts is used to assess process capability. In this connection, for example, control frequency, stability and value distribution are examined and quality capability indices (Cp/Cpk, Pp/Ppk) are calculated. The process owner defines the observation time frame necessary for this specific process (for example, depending on the cycle time). A time frame of 20 production days should normally be taken as the basis.

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5

Process Analysis

5.1

Distribution Time Models (corresponding to DIN 55319 and DIN ISO 21747)

A distribution time model is a mathematical description of real process performance using a moment distribution with parameters that are constant or changing in time. The resulting distribution should correspond to the real process performance, meaning that the image of the density function of the resulting theoretical distribution should fit very closely to a histogram acquired from the measured data. Depending on the moment distribution and its behavior in time, there are eight different types of distribution time models, which are described in DIN 55319 and DIN ISO 21747 (marked A to D, as in the figures on the following pages). Often it is possible to infer the resulting distribution time model from the nature of the process and the tolerance statements (for example, characteristic bounded on one end). The following are examples of practically unavoidable influences: − Systematic wear and tear of a part during the processing, − Use of a new tool with a different nominal size, − Fluctuations when lots are changed. Note: The assignment of a distribution time model to an examined process has nothing to do with the quality capability of this process, as this assignment does not produce any reference to the tolerance range. The figures on the following pages show a few examples for the processes addressed above, and for the associated distribution time models; the following diagrams are represented: − − − −

Schematic depiction of the distribution time model, Time history of the separate values (single-value diagram) Histogram Representation of the single values in the normal distribution probability network − x − s quality control chart − Box plot

16

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USG

OSG

A1

USG

OSG

USG

OSG

A2

_

Normal distr. Normalvert .

x = 130,0392 s = 0,032599 C p = 1,79 C pk = 1,42

Fig. 5.1:

Log. NV x = 0,50 s = 0,36074 Cp = − − − C pk = 1,93

Varied distr. Mischvert . x = − 0,0025 s = 0,88206

Pp = 1,29 Ppk = 1,27

Distribution Time Models and Examples - Part 1

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17

USG

OSG

Pp = 2,14 Ppk = 2,12

Ppk = 0,61

x = 0,0885 s = 1,55818

18

OSG

Mixed Variedvalue distr. Mischvert . x = 0,9842 s = 1,65321 Pp = 0,84

Normal distr.. Normalvert

Fig. 5.1:

USG

USG

OSG

Varied distr. Mischvert .

x = 19,9973 s = 0,06374 Pp = 1,87 Ppk = 1,69

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USG

Varied distr.

Mischvert.

x = 30,0067 s = 0,03970

Fig.5.1:

OSG

Varied distr. Mischvert . x = 20,0045

s = 0,01255

Pp = 1,58

Pp = 0,81

Ppk = 1,48

Ppk = 0,71

Distribution Time Models and Examples - Part 3

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19

5.2

Process Improvement

Processes never improve on their own. Process analysis is necessary for determining starting points for possible process improvements. Some examples of aids for process analysis are graphical representations, such as in the preceding sections, and statistical tests (see the table in the appendix, for example). Furthermore, it is helpful if there is access to documentation of the basic parameters and process characteristics, such as lot change, tool change and changes in the process environment, for example, as these often correlate directly with significant changes in the measured data. Let us take a closer look at a few figures in Section 5.1 by way of example: B: The process mean location is stable in time, but the dispersion changes over time. The latter is particularly clearly shown in the single-value diagram and in the s-line of the quality control chart. C2: In the last third of the single-value diagram and the mean chart, a jump to larger measured values can be seen. Several single values are outside the tolerance range. As a result, the histogram is asymmetrical. C3: Single-value diagram and mean chart reflect a typical process that follows a trend. The characteristic here is the inside diameter, which decreases as the tool wear increases. C4: The progressions that can be seen in the single-value diagram and mean chart are characteristic for a process with jumps when the lot is changed. D: Both the process location and the process dispersion are unstable. To determine possible causes of the observed process performance, use of a structured procedure has proven helpful; this so-called "Six Sigma" method is gaining increasing significance. With this method, the problem solution process is divided into the phases: − − − − − 20

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Based on the first letters in each of the terms, Define, Measure, Analyze, Improve, Control, this is also called the "DMAIC" phase model in this context. With this model, suitable individual methods are assigned to each phase to support this particular section. This includes methods of descriptive and inferential statistics, as well as non-statistical problem solving methods. The improvement team is not bound to any prescribed procedure in this processing, but instead chooses the method to be applied on the basis of the problem situation. A significant element in this procedure is rigorous and complete processing of each partial step. 6

Process Control

6.1

Quality Control Chart (QCC)

Quality control charts are graphical aids for collecting and representing measured values (sample results) or statistical parameters (or numerical results) and for comparing them with previously defined control limits. Forms, either filled in by hand or, increasingly, supported by software are used to implement this quality assurance tool. The control limits, which are usually marked as LCL and UCL for lower and upper control limits, should not be confused with the specification limits (LSL and USL, for upper and lower specification (or engineering) limits) defined in the drawing by the engineer. The control limits are chance dispersion limits of the statistical parameters (for example, of a mean), which are tracked with the control chart. If control limits are exceeded, it is assumed that this was caused by "nonrandom variables", and measures are taken to confirm the disturbance in the process. The control limits are calculated using the characteristic distribution that is determined as a part of the preliminary phase.

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21

27,58 27,56

Mean /mm

27,54 27,52

Mean LCL Center line UCL

27,50 27,48 27,46 27,44 27,42 5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

Time of day

Fig.6.1:

Example of a Mean Chart (Schematic)

6.1.1

Quality Control Chart for Controlling the Process Location There are chiefly three types of control charts used for controlling the process location. − Shewhart QCC or Shewhart QCC with extended limits − Pearson chart for characteristics bounded on one end − Acceptance QCC An additional chart line may serve to control the process dispersion; this line, for example, could represent the standard deviation or range of the sample (see Section 6.3). Shewhart QCC This type of control chart goes back to A. W. Shewhart, who developed the control chart method in the 1920's. The control limits are determined for the specific process, i.e., symmetrically to the process mean location, using only the natural process dispersion. They can, however, also be defined symmetrically to the corresponding characteristic target value. An approximation for the process dispersion is ˆ , which is calculated from the sample results the standard deviation σ observed in the preliminary phase. 22

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Shewhart QCC with extended limits If the process being examined has systematic changes in the mean (unstable process), the additional systematic dispersion portion µˆ add must be taken into account when calculating the control limits. There are several possibilities for determining the additional dispersion portion, for example, − Calculation with the help of the dispersion or the sample means s x :

µˆ add = 1.5 · s x The factor 1.5 is an empirically determined extension factor. − Determination as a part of an analysis of variance (ANOVA) USL

USL

Distribution of the separate values

UCL

UCL

µˆadd

permissible system variable

LCL distribution of the means

LSL Fig.6.2:

LCL

LSL

Location of the Control Limits With Normal Distribution and Extended Normal Distribution

If necessary, the control limits determined in this way can be extended in the interest of economical considerations, as long as the requirements placed on the quality capability (or performance) allow this.

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23

Acceptance chart In the case of acceptance charts, control limits are used that are defined working from the limits of the tolerance range. For example, this can be useful if the process shows unavoidable mean changes because of tool wear:

LCL = LSL + k ⋅ σˆ

UCL = USL − k ⋅ σˆ

ˆ indicates the "inner process dispersion" (the moment In this case, σ dispersion determined earlier) and k is a limiting factor (e.g., k = 3). The safety margin between UCL and USL and between LCL and LSL is adjusted with k. This safety margin increases as k increases, so that the risk of non-conforming product characteristics becomes less. At the same time, however, the difference UCL - LCL between the control limits also becomes less, and consequently the leeway available to the process decreases. As a result, when defining the limiting factor k, the process safety and economic viability aspects must always be balanced. K can be calculated on a statistical basis using the desired combination of "non-control probability” and the momentary excess portion as the basis. This means that a "permissible" excess portion must be specified. However, this is incompatible with a modern zero-error strategy and represents a serious disadvantage with this type of chart. Pearson chart In the case of the Shewhart chart, the control limits correspond to the error band of the normally distributed means. For a characteristic that is bounded on one end (or with a generally skewed distribution) and a small sample size, the sample means are no longer necessarily normally distributed. In this instance, the use of a Pearson chart may be expedient. Compared to the corresponding Shewhart chart, this type of chart has the advantage that the control limits are somewhat farther apart. The disadvantage, however, is that the control limit calculation is more complicated and is, for all intents and purposes, only possible with computer assistance.

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Quality control chart with moving average As a rule, a sample size of n = 5 is recommended when control charts are used to control the process location. Depending on the circumstances, this sample size can cause problems, for example, in case of − Inspections with a high expenditure (destructive inspection), − Processes in which the product is a homogenous medium (chemical process) − Processes with a large cycle time − Processes with small short-term dispersion In such cases, use of a moving average can be considered for the standard size. This involves summarizing the results of several separate samples (e.g., 5 pieces) For each measured value that is added in a new separate sample, the measured value of the "oldest" part of the last group of 5 is dropped, and then the new average is calculated (see example in 8.3). The control limits are calculated in the same way as for the "normal" mean chart. 6.1.2

Quality Control Charts for Controlling the Process Dispersion

With regard to the process capability, the process dispersion is just as important as the process mean location. As a rule, the standard deviation s is used as the parameter for the dispersion. We leave out a depiction of the range chart here. It is possible to depict the s chart as a second diagram in combination with the mean chart, instead of keeping it as a separate chart. This is called the combination x s chart (cf. examples in 5.1 and 8.3). Both the mean x i and the standard deviation s i of each sample are entered. The size of the current value of this standard deviation s i depends on the average process dispersion estimated from σ$ and can be randomly somewhat larger or smaller from sample to sample than a long-term average value s .

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If the standard deviation s i of a current sample lies outside the control limits UCLS and LCLS, however, this is interpreted as a sign of a significant change in the process dispersion. UCLS and LCLS are limits of the error band around s. They can be 2 calculated with the help of the χ distribution (pronounced as chi-squared distribution) (cf. formulas in 8.1). For simplicity's sake, the corresponding constants for n are given in a table. Example for n = 5:

6.2

σˆ LCLS = 0.227 · σˆ

UCLS = 1.927

Recording a QCC

After a sample's mean and standard deviation are entered in the control chart, a check is made as to whether these are within the associated control limits or not. Values above the UCL or below the LCL indicate a significant process change, which requires a reaction using suitable measures (control intervention). An intervention is naturally also necessary if separate values of the characteristic lie outside the tolerance range. As a part of this short-term evaluation, causes must be determined and corrective measures taken and documented, and a decision must be made on the treatment of the parts that were manufactured since the last sample (e.g., sorting). 6.3

Evaluating a QCC

Control charts are evaluated at defined intervals. This is done by calculating statistical parameters that are suitable for assessing the long-term process performance. A process can naturally only be improved if the appropriate measures are taken. In the broadest sense, SPC includes all measures that are suitable for correcting or stabilizing the process in the required way or for improving the knowledge about the process and the process performance itself.

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6.4

Use of QCC Before the Series Start

Even before the series start (e.g., as a part of the machine capability study), the QCC can be used as a simple graphical aid, in order to gather information on the process performance and record and assess measurement results. 6.5

Response Characteristic of a QCC

Entering a sample result in a control chart with subsequent assessment of the location of the observed parameter (e.g., the mean) with regard to the control limits, corresponds to a statistical test that only reacts to a significant process change with a certain probability. This control chart responsiveness particularly depends on the sample size; it can be shown as a function of the shifting of the process mean location. This representation is called the control characteristic or operation characteristic (see example in 8.3). 7

Process Assessment

Depending on the length of the study and the process performance, different parameters are used for the process assessment. A distinction is made here between a short-term (machine) and long-term (process) study on the one hand, and between stable/unstable process performance on the other. The parameters can be classified as follows: Term

Standard requirement

Meaning

Distinction criterion

Cm, Cmk *

≥ 1,67

Machine capability index

Short-term (machine) study

Cp, Cpk

≥ 1.33

Process capability index

Long-term (process) study, stable process

Pp, Ppk

≥ 1.33

Process performance index

Long-term (process) study, unstable process

Table 7.1:

Quality Capability Statistics

*Note: According to DIN ISO 21747 and DIN ISO 3534-2, the terms Cp/Cpk are used for stable processes, instead of Cm/Cmk. The German standard DIN 55319 "Qualitätsfähigkeitskenngrößen" (Quality Capability Statistics) which was still valid at the time this document went to press will be replaced by DIN ISO 21747. VDA-Volume 4: Economical Process Design and Process Control Dokument wurde bereitgestellt vom VDA-QMC Internetportal am 01.04.2015 um 10:54

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Processes that do not correspond to the distribution time models A1 or A2 are instable, according to the definition and classification by DIN 55319 and DIN ISO 21747. In this case, only the process performance indices Pp und Ppk may be specified. Due to the instability, it may be useful to agree upon a higher minimum value for Pp and Ppk. 7.1

Quantile Method

A distribution time model must first be assigned to the observed (empirical) characteristic distribution, as described in Section 5. Then the quantile method (percentile method) can be used to help calculate the associated quality capability parameter. This is done by determining the 0.135 % quantile (Lp) and the 99.865 % quantile (Up) of the distribution. These values can be determined with software support or with the help of a probability network. The quantiles consequently include just 99.73 % of the characteristic distribution and therefore define the process spread. In the case of normal distribution, this corresponds to the range ± 3 s. Using the quantile method, the procedure originally developed in connection with the normal distribution is analogously transferred to any distributions. The universal applicability to any distribution time model represents a significant advantage of this method. Alternative calculation methods are shown in DIN 55319 and DIN ISO 21747. The quantities Cm, Cp and Pp consequently put the spread calculated in this way in proportion to the tolerance. The location of the characteristic distribution relative to the limiting values LSL and USL is not taken into consideration in this view. LSL USG

USL OSG

Mean Mittelwert

Lp

Up

Up 0,135%

Op 99,865%

99,73%

Fig.7.1: 28

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Note: In machine capability studies, the assignment of a distribution time model is problematic, as most of the available samples are small. In particular, it is not expedient to distinguish between capability and performance. Calculation of the quality capability statistics related to the dispersion: Terms

Formula

USL − LSL Op −U p

Cm, Cp or Pp Depending on distribution time model

Special case: It is only possible to estimate the dispersion range using the dispersion parameter σ ˆ for normally distributed collectives. 7.2

Effective Parameters (Minimum Process Capability/Process Performance)

The quantities Cmk, Cpk and Ppk include the location of the quantiles relative to the limit values: USG

Up 0,135%

Fig.7.2:

Mean Mittelwert

OSG

USG

Op 99,865%

OSG

Mean Mittelwert

Up 0,135%

Op 99,865%

Diagram for Illustrating the Effective Parameters

Terms

Formula

Cmk, Cpk or Ppk Depending on distribution time model

USL − µˆ min  ;  O p − µˆ

µˆ − LSL   µˆ − U p 

µˆ is an estimated value for the mean position. This can be the total mean or the median (50 % quantile) of the distribution. VDA-Volume 4: Economical Process Design and Process Control Dokument wurde bereitgestellt vom VDA-QMC Internetportal am 01.04.2015 um 10:54

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Note: The values Up and Op and therefore also Ppk depend on the concrete statistical distribution model. For an automated distribution assignment, the evaluation program must contain a type of filter, in the form of a statistical test. The selected distribution can differ, depending on the evaluation strategy. For example, for the evaluation as found in 8.3.3, the assignment of an extended normal distribution would also be possible, so that the result would be somewhat different quantiles and therefore a different Ppk value. To avoid discrepancies in the results of independent evaluations, it is recommended that the following information be taken into account when calculating the capability parameters, and that these be agreed upon ahead of time, if possible. Example for information required for comparing the quality capability parameters (to DIN 55319) 1.5

Capability parameter Cpk Selected distribution time model

D

Calculation method according to table 5

M4

Number of values for the calculation

2000

Quantile used for M4, if different from 7.4 Other information

7.3

Process Capability und Tolerances

If the quantile method for calculating the quality capability parameters is applied to the 850 processes examined by DaimlerChrylser (see Section 2.1), the resulting distribution of these quantities approximates a lognormal distribution (cf. Fig. 7.3). Accordingly, approximately 70 % of the examined processes have a capability index that is less than 1.33, or below the common minimum value for capable processes (cf. Section 7). For approximately 50 % of the assessed processes, this value is even less than the critical value of 1.0.

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Such a process produces a certain portion of parts whose characteristic values lie outside the tolerance range. If these formally faulty parts are supplied to the assembly area by special releases, and the "tolerance violations" remain harmless for the operability of the end product, one can conclude that larger tolerances, at least in these cases, could have been selected right from the start.

Fig.7.3:

Distribution of the Capability Parameters of 850 Examined Processes

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8

Appendix

8.1

Formulas for Calculating Control Limits

x − s quality control chart (Shewhart chart; stable process) Control limits for the process location (mean

UCL = µ + u1−α / 2 ∗

σˆ

x ):

LCL = µ − u1−α / 2 ∗

n

σˆ n

Control limits for the process dispersion (standard deviation s):

UCL =

χ 2 f ;1 − α / 2 f

∗ σˆ

LCL =

χ 2 f ;α /2 f

∗ σˆ

x acceptance chart Control limits for the process location (mean

u 1 − Pa  UCL = USL −  u1− p + n 

  ∗ σˆ 

u 1 − Pa  UCL = LSL +  u1− p + n 

  ∗ σˆ 

x ):

Calculation of the control limits for the process dispersion is the same as in the Shewhart chart.

Additional formulas for calculations can be found in publications in this field.

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8.2

Formula Symbols

Cm, Cmk

Machine capability index

Cp, Cpk

Process capability index

χ2f; 1-α/2

Quantile of the chi-squared distribution with f degrees of freedom at the value 1-α/2

χ2f; α/2

Quantile of the chi-squared distribution with f degrees of freedom at the value α/2

m

Number of separate samples

µˆ

Estimated value for the mean of a population

µˆadd

Extension of the control limits (system mean change)

k

Limiting factor in an acceptance chart

LCL

Lower control limit

LLV

Lower limit value

LSL

Minimum value (lower specification limit)

n

Number of values in a separate sample

PA

Confidence factor;

Pp, Ppk

Process performance index

LP

Estimated value for the 0.135 % quantile of a characteristic distribution

UP

Estimated value for the 99.865 % quantile of a characteristic distribution

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s

Empirical standard deviation

si

Standard deviation of sample number i

s2

Average variance; mean of the squared standard deviations

sx

Standard deviation of the means

σˆ

Estimated value for the standard deviation of a population

T

Tolerance of a characteristic

u1-α/2

Quantile of the standard normal distribution at the value 1 α/2

u1-p

Quantile or the standard normal distribution at the value 1 p

UCL

Upper control limit

USL

Maximum value (upper specification limit)

ULV

Upper limit value

x

Arithmetic mean

xi

Mean of the sample number i

x

Mean of the means

~ x

Median;

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8.3

Examples

8.3.1

Quality Control Chart for Moving Averages

64.5 64.1 65.6 66.0 63.0 65.5 65.0 65.9 65.7 65.8 64.4

63.3 65.8 66.1 65.4 66.5 66.4 63.6 63.3 66.8 65.5 63.8

Table 8.1:

5.1 65.9 64.0 64.6 65.2 63.2 65.0 65.3 65.8 63.6 64.4

64.7 66.9 63.3 64.9 64.9 65.7 64.1 65.4 65.2 67.9 65.5

5.4 64.2 65.1 66.5 63.4 64.3 65.3 64.6 64.0 66.4 64.9

65.1 63.7 63.6 66.2 65.1 65.8 64.5 62.0 65.3 64.3 65.7

65.9 62.8 64.5 65.5 62.8 66.3 65.5 64.6 64.6 65.2 62.6

66.1 65.2 64.5 64.6 65.1 64.6 66.8 64.7 64.9 65.2 64.5

62.8 64.4 65.5 65.7 63.4 65.2 63.8 66.1 65.4 65.1 67.1

63.3 63.3 65.8

Case Example

By combining 3 separate values at a time, "pseudo samples" with a size n=3 result. st

x1 =

64,5 + 64,1 + 65,6 = 64,7 3

nd

x2 =

64,1 + 65,6 + 66,0 = 65,2 3

x3 =

65,6 + 66,0 + 63,0 = 64,9 3

1 sample: (64.5; 64.1; 65.6)

2 sample: (64.1; 65.6; 66.0)

rd

3 sample: (65.6; 66.0; 63.0) etc.

The following result from the data record (Table 8.1)

µˆ = x = 64.916

and

σˆ = stotal = 1.109

Note: µˆ = x is the mean of all separate values. It is not permissible to calculate a total mean x from the moving averages x i , because then each ˆ separate value would be included more than once. Correspondingly, σ may also not be calculated from the separate standard deviations s i .

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The control limits for moving averages are calculated like those for "normal" averages (cf. formulas in Section 8.1): Location chart: UCL x = µˆ + 2.578 ⋅

LCL x = µˆ − 2.578 ⋅

Dispersion chart:

σˆ 3

σˆ 3

= 64.916 + 2.578 ⋅

= 64.916 − 2.578 ⋅

1.109 3

1.109 3

= 66.567

= 63.265

UCLs = 2.302 ⋅ σˆ = 2.302 ⋅ 1.109 = 2.55 LCL s = 0.071 ⋅ σˆ = 0.071 ⋅ 1.109 = 0.08

Fig.8.1:

36

x -s Shewhart Chart With Moving Characteristic Values for the Data From Table 8.1

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The following figure shows only the first 10 samples (separate values and sample characteristic values). Because the first mean and the first standard deviation cannot be calculated until after the third separate sample, the associated curves start with a corresponding delay.

Separate values

Fig. 8.2:

Means

x -s Shewhart Chart with Moving Characteristic Values for the First 10 Separate Samples

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8.3.2

Operation Characteristic for a Shewhart Chart With Extended Limits

Fig. 8.3:

Value Progression

Fig. 8.4:

Quality Control Chart

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Note: Several samples in the s chart fall below the lower control limit (standard deviation s = 0). With a sample size of n = 5, such an event is very improbable. Possibly this is the result of incorrect measurements (e.g., faulty sensor). to LSL

to USL

Fig. 8.5:

Operation Characteristic

The operation characteristic (OC) indicates the probability that the control chart will react to changes in the mean. In Fig. 8.5, this confidence factor is applied via the faulty portion (theoretical portion exceeding the limit). In this example, the process mean location is not identical to the center value of the tolerance range and the natural control limits are asymmetric to this center value (cf. Figures 8.3 and 8.4). As a result, different OC curves arise with regard to the maximum value and the minimum value. The control probabilities for a given error portion p are read from the y-axis, and correspond to the probability with which this (one-sided) error portion is detected by this quality control chart.

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The larger the sample size n, the steeper the operation characteristic is and the more sensitively the control chart addresses process shifts. In Figure 8.5, it can be seen that with reference to the USL, for example, an error portion of 0.3 % will be detected with a probability of P = 0.5. 8.3.3

Example for Process Analysis and Evaluation of the Long-Term (Process) Capability

Characteristic: "Fat little eye“ (linear measure), LSL = 18.94 mm, USL = 19.00 mm

The representation of the separate values corresponding to the order in which the parts were produced gives an impression of the process fluctuations in time. Fig. 8.6: Separate Value Representation

With the help of statistical tests, appropriately configured computer programs can select the distribution model (from specified possibilities) that best fits the data record, and then display the histogram graphically as an overlay. In this case, this is a mixed distribution.

Fig. 8.7: Histogram

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The goodness of fit for the model can be assessed with the help of the probability network. Mixed distribution data does not reveal a linear point sequence in the normal distribution probability network. The goodness of the fit is shown in the correspondence of the measured data with the theoretical curve shape. Fig. 8.8: Probability Network The frequency of the separate values is shown in the actual value plot by means of stacked arrows. Unlike the histogram, the appearance of the value distribution consequently does not depend on the class organization. In particular, the resolution of the measurement system can be seen. In this case, it is 1 µm, or less than 2 % of the tolerance, and consequently is sufficient. Fig. 8.9: actual value plot

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An overview of significant characteristics of the data record and its statistical parameters complete the process analysis. In the above example, the quality capability statistics Pp and Ppk are also given. The corresponding requirements are met. Fig. 8.10:

Evaluation Results

The evaluation of the 2160 separate values shows that the process location and distribution are not stable. The result is a mixed distribution with: UP = 18.954 mm

42

µˆ = x = 18.969 mm

O p = 18.981 mm

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Calculation of the process performance using the quantile method:

Pp =

USL − LSL 19.00 − 18.94 = = 2.22 O p −U p 18.981 − 18.954

USL − µˆ 19.00 − 18.969 = = 2.58 O p − µˆ 18.981 − 18.969

USL − µˆ P pk = min  ;  O p − µˆ

µˆ − LSL 18.969 − 18.94 = = 1.93 µˆ − U p 18.969 − 18.954

µˆ − LSL   = 1.93 µˆ − U p 

Due to the calculation with rounded values, the results differ slightly from the results established with software. The figure to the left demonstrates the effect of an unsuitable control limit calculation. This x − s control chart (with n=5) shows numerous violations of the control limits in both chart tracks, because normal distribution was assumed for their calculation. Fig. 8.11: Inappropriate Limits

Control

If the control limits are calculated with the additional mean dispersion taken into account, the mean track reacts considerably less often. Fig. 8.12: Control Limits Correctly Calculated for the Specific Process

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In the case of unstable processes, an acceptance chart as described in Section 6.2.3 can also be used. In such a case, the control limits are more widely spaced than in Figure 8.12; there is consequently more "leeway" allowed the process. This does, however, reduce Pp and Ppk. Fig. 8.13: Acceptance Chart

To communicate the process analysis results, it is expedient to provide a clear compilation of the significant diagrams and evaluation results on one page (see the example in Fig. 8.14).

44

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Fig. 8.14:

Compilation of the Study Results

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45

Diagrams

Characteristic values

8.4

46

Operative Range for Statistical Parameters, Procedures and Aids Procedures

Operative range

Mean, median

Estimated values for the process location

Standard deviation, range

Estimated values for the process dispersion

Skewness, kurtosis, excess

Assessment of the distribution shape

Regression coefficient

Statement on the goodness of fit for a model

Capability indices, excess portion

Performance and capability of a process

Track of the original values

Detection of special features, such as: outliers, trends, periodicities, fluctuations in the mean, values outside the tolerance range

Actual value plot

Estimating the distribution shape, resolution of the mean, number of values outside the tolerance range

Histogram

Estimating the distribution shape, estimating the capability indices

Probability network (Separate values//by class)

Model distribution accurate, estimating excess portions?

Total lines

Model distribution accurate

Quality Control Chart

Assessment of the process stability with regard to location and dispersion: detection of run, trend, middle third and control limit violations

Operation characteristic

Sensitivity of the quality control chart

Probability network (means)

Detection of process disturbances with regard to the location

CHI² network

Detection of process disturbances with regard to the dispersion

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Test procedures

xy plot

Representation of position tolerances; Detection of dependencies between two characteristics

Box plot

Quick overview using statistical parameters

Randomness
Swed Eisenhart
Trend

Detection of non-random value sequences of separate values and means; Detection of special features, such as trends

Goodness-of-fit test
Shapiro – Wilk
CHI²
d’Agostino
Kolmogoroff – Smirnoff
Extended Shapiro – Wilk
Kurtosis
Asymmetry

Evaluation criteria whether there are deviations from the model distribution

Correlation matrix

Determine whether dependencies exist between characteristics and parameters or between characteristics

Autocorrelation

Determine whether there is a system influence on the following attributes of this characteristic within a measurement series.

Outliers
David, Hartley, Pearson
Grubbs

Determine whether there are outliers

Variance analysis
F-test

Determine whether there is a dispersion among the samples; estimate their magnitude

Equality of variances Bartlett

Determine changes in variances




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8.5

DIN –

Bibliography

Deutsches Institut für Normung (German Institute for Standardization):

ISO 3534-1 Statistik, Begriffe und Formelzeichen Teil 1 Wahrscheinlichkeitsverteilungen and allgemeine Statistik (Statistics - Vocabulary and Symbols Part 1: Probability and general statistical terms) Beuth-Verlag ISO 3534-2 Statistik, Begriffe und Formelzeichen Teil 2: Statistische Prozesslenkung (Statistics - Vocabulary and Symbols Part 2: Statistical quality control), Beuth-Verlag ISO 3534-3 Statistik, Begriffe und Formelzeichen Teil 3 Versuchsplanung (Statistics - Vocabulary and symbols Part 3: Design of Experiments), BeuthVerlag DIN ISO 21747: Process Performance and Capability Indices DIN 55319, Qualitätsfähigkeitskenngrößen, Teil 1 DGQ (German Association of Quality) (publisher): DGQ Volume 11-04 Managementsysteme–Begriffe, Beuth-Verlag, 2002 VDA - Verband der Automobilindustrie (publisher), VDA Volume 5 Prüfprozesseignung Dietrich/Schulze: Statistische Verfahren zur Maschinen- und Prozessqualifikation (Statistical Procedures for Machine and Process Qualification) Carl Hanser Verlag, 2003

48

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W. Geiger, Qualitätslehre, Verlag Vieweg, 1998 W. Masing: Handbuch Qualitätsmanagement, Carl Hanser Verlag Timischl: Statistische Verfahren, Carl Hanser Verlag Kjell Magnusson et alia: Implementing Six Sigma, John Wiley & Sons, INC Forrest W. Breyfogle III: Implementing Six Sigma, Carl Hanser-Verlag

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Verband der Automobilindustrie

Quality Management in the Automobile Industry

4

Quality Assurance in the Process Landscape - General, risk analyses, methods, process models -

8D Method

st

1 edition 2010

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