Probabilistic Design for Optimization and Robustness for Engineers E-Book

Bryan Dodson

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 346

Veröffentlichungsjahr: 2014

Beschreibung

Probabilistic Design for Optimization and Robustness:

• Presents the theory of modeling with variation using physical models and methods for practical applications on designs more insensitive to variation.
• Provides a comprehensive guide to optimization and robustness for probabilistic design.
• Features examples, case studies and exercises throughout.

The methods presented can be applied to a wide range of disciplines such as mechanics, electrics, chemistry, aerospace, industry and engineering. This text is supported by an accompanying website featuring videos, interactive animations to aid the readers understanding.

Leseprobe

Probabilistic Design for Optimization and Robustness for Engineers

Bryan Dodson

Executive Engineer, SKF, USA

Patrick C. Hammett

Lead Faculty Six Sigma Program, Integrative Systems & Design, College of Engineering, University of Michigan, Ann Arbor, USA

René Klerx

Principal Statistician, SKF, The Netherlands

This edition first published 2014 © 2014 John Wiley & Sons, Ltd

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Library of Congress Cataloging-in-Publication Data

Dodson, Bryan, 1962–     Probabilistic design for optimization and robustness for engineers / Bryan Dodson, Patrick C.     Hammett, René Klerx.        pages cm     Includes bibliographical references and index.     ISBN 978-1-118-79619-1 (cloth)   1. Industrial design–Statistical methods. 2. Reliability (Engineering) 3. Robust statistics. I. Hammett, Patrick C. II. Klerx, René. III. Title.     TS171.9.D63 2014     620′.00452–dc23

2014013950

A catalogue record for this book is available from the British Library.

ISBN: 978-1-118-79619-1

Contents

Preface

Acknowledgments

1 New product development process

1.1 Introduction

1.2 Phases of new product development

1.3 Patterns of new product development

1.4 New product development and Design for Six Sigma

1.5 Summary

Exercises

2 Statistical background for engineering design

2.1 Expectation

2.2 Statistical distributions

2.3 Probability plotting

2.4 Summary

Exercises

Notes

3 Introduction to variation in engineering design

3.1 Variation in engineering design

3.2 Propagation of error

3.3 Protecting designs against variation

3.4 Estimates of means and variances of functions of several variables

3.5 Statistical bias

3.6 Robustness

3.7 Summary

Exercises

Notes

4 Monte Carlo simulation

4.1 Determining variation of the inputs

4.2 Random number generators

4.3 Validation

4.4 Stratified sampling

4.5 Summary

Exercises

Notes

5 Modeling variation of complex systems

5.1 Approximating the mean, bias, and variance

5.2 Estimating the parameters of non-normal distributions

5.3 Limitations of first-order Taylor series approximation for variance

5.4 Effect of non-normal input distributions

5.5 Nonconstant input standard deviation

5.6 Summary

Exercises

Notes

6 Desirability

6.1 Introduction

6.2 Requirements and scorecards

6.3 Desirability—single requirement

6.4 Desirability—multiple requirements

6.5 Desirability—accounting for variation

6.6 Summary

Exercises

Notes

7 Optimization and sensitivity

7.1 Optimization procedure

7.2 Statistical outliers

7.3 Process capability

7.4 Sensitivity and cost reduction

7.5 Summary

Exercises

Notes

8 Modeling system cost and multiple outputs

8.1 Optimizing for total system cost

8.2 Multiple outputs

8.3 Large-scale systems

8.4 Summary

Exercises

Notes

9 Tolerance analysis

9.1 Introduction

9.2 Tolerance analysis methods

9.3 Tolerance allocation

9.4 Drift, shift, and sorting

9.5 Non-normal inputs

9.6 Summary

Exercises

Notes

10 Empirical model development

10.1 Screening

10.2 Response surface

10.3 Taguchi

10.4 Summary

Exercises

Notes

11 Binary logistic regression

11.1 Introduction

11.2 Binary logistic regression

11.3 Logistic regression and customer loss functions

11.4 Loss function with maximum (or minimum) response

11.5 Summary

Exercises

Notes

12 Verification and validation

12.1 Introduction

12.2 Engineering model V&V

12.3 Design verification methods and tools

12.4 Process validation procedure

12.5 Summary

Notes

References

Bibliography

Answers to selected exercises

Index

End User License Agreement

List of Tables

Chapter 2

Table 2.1

Table 2.2

Table 2.3

Table 2.4

Table 2.5

Table 2.6

Table 2.7

Chapter 3

Table 3.1

Table 3.2

Table 3.3

Chapter 4

Table 4.1

Table 4.2

Table 4.3

Chapter 5

Table 5.1

Table 5.2

Table 5.3

Table 5.4

Table 5.5

Chapter 6

Table 6.1

Table 6.2

Table 6.3

Table 6.4

Table 6.5

Chapter 7

Table 7.1

Table 7.2

Table 7.3

Table 7.4

Table 7.5

Chapter 8

Table 8.1

Table 8.2

Chapter 9

Table 9.1

Table 9.2

Table 9.3

Chapter 10

Table 10.1

Table 10.2

Table 10.3

Table 10.4

Table 10.5

Table 10.6

Table 10.7

Table 10.8

Table 10.9

Table 10.10

Table 10.11

Table 10.12

Table 10.13

Table 10.14

Table 10.15

Table 10.16

Chapter 11

Table 11.1

Table 11.2

Table 11.3

Table 11.4

Table 11.5

Chapter 12

Table 12.1

Table 12.2

Guide

Cover

Table of Contents

Preface

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Preface

Engineers spend years learning mathematical models to describe the behavior of systems. However, only a small portion of the engineering curriculum is dedicated to accounting for variation faced by product and process designers. Even here, the focus is usually limited to controlling manufacturing variation through tolerance analysis. Today, many engineering curricula offer elective courses in experimental design or robust design, but these courses focus more on system optimization and reducing variation in design through experimentation. This book presents the theory of modeling variation using physical models and presents methods for practical applications including making designs less sensitive to variation. This approach helps create designs that are easy to manufacture, with less design and manufacturing costs, and utilize more realistic tolerances. Methods are presented for determining nominal parameter settings that minimize output variation, determining the output variation caused by each input parameter, and minimizing total system costs, which includes the cost of non-conformance.

A challenge for this book is the lack of in-depth statistical training for many engineers. Many engineering curricula require a single course on probability or have no requirement at all. Stochastic modeling and optimization require some advanced statistical methods. Introductory chapters provide a logical roadmap to allow a complete understanding of the material without overwhelming the reader with excessive statistical rigor. Worked examples in the text are available on the Wiley website (www.wiley.com/go/robustness_for_engineers) along with animation software and computer-based exercises to aid understanding.

Acknowledgments

Paolo ReGroup Business ExcellenceSKF

Rajeev SundarrajGraduate Student, Class of 2013Industrial and Operations EngineeringUniversity of Michigan, Ann Arbor

Silvio VasconiRegional Manager Engineering Consultancy ServicesSKF

The authors wish to acknowledge the following individuals for their contribution in providing valuable input and industry examples.

Steven GeddesManufacturing Validation Solutions

Donald LynchSKF

Patrick WalshManufacturing Validation Solutions

1New product development process

1.1 Introduction

The development of new products is a major competitive issue as consumers continuously demand new and improved products. One outcome of this competitive landscape is the need for shorter product life cycles while still achieving ever increasing expectations for product quality and performance measures. This has required companies to significantly enhance their capabilities to better identify true customer wants, translate them into quantifiable product functional requirements, quickly develop, evaluate, and integrate new design concepts to meet them, and then effectively bring these concepts to market through new product offerings.

Several companies (e.g., Apple, General Electric (GE), Samsung, Toyota, General Motors (GM), Ford) have made great strides improving the effectiveness of new product development. For example, many companies have created processes to quickly gather voice of the customer information via surveys, customer clinics, or other sources. Samsung, for instance, has a well-designed system of scorecards and tool application checklists to manage risk and cycle time from the voice of the customer through the launch of products that meet customer and business process demands (Creveling et al., 2003). In addition, advances in computer simulation and modeling techniques permit manufacturers to evaluate many design concept alternatives, thereby resolving many potential problems at minimal costs. This also allows one to minimize assumptions and simplifications that reduce the accuracy of the answer (Tennant, 2002). Finally, even when there is a need to construct physical prototypes, the cost has been lowered through rapid prototyping processes.