Statistical Robust Design E-Book

Magnus Arner

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

Seitenzahl: 286

Veröffentlichungsjahr: 2014

Beschreibung

A UNIQUELY PRACTICAL APPROACH TO ROBUST DESIGN FROM A STATISTICAL AND ENGINEERING PERSPECTIVE

Variation in environment, usage conditions, and the manufacturing process has long presented a challenge in product engineering, and reducing variation is universally recognized as a key to improving reliability and productivity. One key and cost-effective way to achieve this is by robust design – making the product as insensitive as possible to variation.

With Design for Six Sigma training programs primarily in mind, the author of this book offers practical examples that will help to guide product engineers through every stage of experimental design: formulating problems, planning experiments, and analysing data. He discusses both physical and virtual techniques, and includes numerous exercises and solutions that make the book an ideal resource for teaching or self-study.

• Presents a practical approach to robust design through design of experiments.
• Offers a balance between statistical and industrial aspects of robust design.
• Includes practical exercises, making the book useful for teaching.
• Covers both physical and virtual approaches to robust design.
• Supported by an accompanying website www.wiley/com/go/robust featuring MATLAB scripts and solutions to exercises.
• Written by an experienced industrial design practitioner.

This book's state of the art perspective will be of benefit to practitioners of robust design in industry, consultants providing training in Design for Six Sigma, and quality engineers. It will also be a valuable resource for specialized university courses in statistics or quality engineering.

Leseprobe

Contents

Cover

Title Page

Copyright

Preface

Chapter 1: What is robust design?

1.1 The importance of small variation

1.2 Variance reduction

1.3 Variation propagation

1.4 Discussion

Exercises

Chapter 2: DOE for robust design, part 1

2.1 Introduction

2.2 Combined arrays: An example from the packaging industry

2.3 Dispersion effects

Exercises

Reference

Chapter 3: Noise and control factors

3.1 Introduction to noise factors

3.2 Finding the important noise factors

3.3 How to include noise in a designed experiment

3.4 Control factors

Exercises

References

Chapter 4: Response, signal, and P diagrams

4.1 The idea of signal and response

4.2 Ideal functions and P diagrams

4.3 The signal

Exercises

Chapter 5: DOE for robust design, part 2

5.1 Combined and crossed arrays

5.2 Including a signal in a designed experiment

5.3 Crossed arrays versus combined arrays

5.4 Crossed arrays and split-plot designs

Exercises

References

Chapter 6: Smaller-the-better and larger-the-better

6.1 Different types of responses

6.2 Failure modes and smaller-the-better

6.3 Larger-the-better

6.4 Operating window

Exercises

References

Chapter 7: Regression for robust design

7.1 Graphical techniques

7.2 Analytical minimization of (g′(z))2

7.3 Regression and crossed arrays

Exercises

Chapter 8: Mathematics of robust design

8.1 Notational system

8.2 The objective function

8.3 ANOVA for robust design

Exercises

References

Chapter 9: Design and analysis of computer experiments

9.1 Overview of computer experiments

9.2 Experimental arrays for computer experiments

9.3 Response surfaces

9.4 Optimization

Exercises

References

Chapter 10: Monte Carlo methods for robust design

10.1 Geometry variation

10.2 Geometry variation in two dimensions

10.3 Crossed arrays

Chapter 11: Taguchi and his ideas on robust design

11.1 History and origin

11.2 The experimental arrays

11.3 Signal-to-noise ratios

11.4 Some other ideas

Exercises

References

Appendix A: Loss functions

A.1 Why Americans do not buy American television sets

A.2 Taguchi’s view on loss function

A.3 The average loss and its elements

A.4 Loss functions in robust design

Exercises

References

Appendix B: Data for chapter 2

Appendix C: Data for chapter 5

References

Appendix D: Data for chapter 6

Reference

Index

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

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Library of Congress Cataloging-in-Publication Data

Arnér, Magnus, author.  Statistical robust design: an industrial perspective/ Magnus Arnér.   pages cm  Includes bibliographical references and index.  ISBN 978-1-118-62503-3 (cloth)  1. Industrial design–Statistical methods. 2. Robust statistics. I. Title.  TS171.9.A76 2014  745.2–dc23 2013046030

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

ISBN: 978-1-118-62503-3

Preface

For several years I have been waiting for a book to be published about robust design that I really like. The problem is not that books about robust design are nonexistent. On the contrary, there are many books on the topic, and some of them are really good. But I did not feel that anyone really was speaking directly to me. Slowly, really slowly, an insight was growing within me that the number of people who have a good statistical background and have been working as industrial practitioners with robust design is fairly small. Since it is within this small group that an author for the type of book I am missing is most likely to be found, there are not an enormous amount of potential writers. In addition, industrial practitioners are not as keen on writing books as university teachers. I came to ask myself if I was the one to write such a book. The result is the product you are holding in your hand.

There are many ways of viewing robust design. It is, for example, a methodology in product development. Thus, robust design can be included as part of a book about product development. Robust design is also quality engineering (if that is seen as something separate from product development). In the same way robust design can be part of a book about product development, it can be part of a quality engineering book. A third view takes off from the way in which knowledge that can be used to accomplish robustness is gathered, namely design of experiments. This book is not intended to be a book about product development or quality engineering in general, a book about design of experiments. It is intended to be a book about robust design. Neither product development in general nor the basics of design of experiments with two-level factorial designs are covered in the book. A potential reader may then be an engineer with some experience of product development and some basic knowledge in design of experiments. I have personally used the first six chapters of the book as a part of the Design for Six Sigma training for exactly this type of audience. However, even if this may be the primary target group, the book can hopefully be useful to a much wider audience, including engineering and statistics students.

Robust design may have been used for a very long time but without the terminology and structure it has today. In its present form it emerged in Japan after the Second World War and reached the United States around 1980. Thus it has some history. During this time there has been a constant stream of research papers. Several among them were written decades ago. However, from the point of view of industrial practice it is first and foremost during the last 15 years that robust design has become a natural element in product and process development. Earlier on, some companies were taking robust design seriously, but only a few. Nowadays it is hard to find any major company not working with robust design, or at least not claiming they do. There are a number of reasons for this increased industrial interest. One is Design for Six Sigma (DFSS), in which robust design is a major element. With an ever increasing number of engineers educated in DFSS the companies are growing their competencies and capabilities in the robust design field. The usage of design of experiments has also increased substantially during the last decade, and since this is such a central element in robust design it has increased the ability to design for robustness. Thus, the experience and knowledge among engineers has reached a critical level. Another reason is the availability of dedicated software, especially the software that can be integrated in the software architecture already in place in the industry, such as robust design modules in Computer Aided Design (CAD) software and even more importantly finite element solvers.

I do not know when I first came in touch with robust design. I remember that I wrote a review of a book on the topic in 1992, so it must have been before that. However, it was not until 2002 when I was working for an automotive company that I really had the opportunity to apply it at work. During that time I had the opportunity to learn from Shin Taguchi (the son of Genichi Taguchi who had founded modern robust design half a century earlier) whose company was engaged in some work at the automotive company during that time. Since then robust design has been a major part of my work, with applications in both the automotive and packaging industries.

Example 2.1 is based on work by Mats Martinsson, Example 3.6 and Example 5.4 on work by Markus Florentzson, and Exercise 5.2 on work by Ulrika Linné, Hossein Sohrabi, and Andreas Åberg. Besides them, there are several people who have been helpful and encouraged me in the work on this book. I would like to mention Bo Bergman, Johan Olsson, and Pietro Tarantino.

This book contains an accompanying website. Please visit www.wiley.com/go/robust

Magnus ArnérLund, Sweden

1

What is robust design?

1.1 The importance of small variation

When mass production started in the dawn of the industrial revolution, variation came in the focal point of interest. An early example that illustrates this concerns mass production of guns. The French gunsmith Honoré le Blanc realized the importance for guns to have interchangeable parts. His solution was the invention of a system for making gun parts in a standardized way. The problem that challenged le Blanc is the same as in any modern day manufacturing, as, for example, in the production of bolts and nuts. It shall be possible to pick a bolt and a nut at random that fit together. This requires that the variation in diameter, in roundness, and in thread pitch is small from bolt to bolt and from nut to nut. Unless this is the case, there will be a substantial amount of scrapping, or even worse bolts that crack or fall off while they are in use.

Before the industrial revolution, this problem was handled by good craftsmen. In the industrial era, this was not an option anymore. The importance of managing the variation became obvious. Several approaches emerged. Specifying the tolerance limits was one of them and even if the gunsmith le Blanc did not get many immediate followers in France, some Americans saw the potential of his ideas and implemented them at the armoury in Springfield. This is sometimes considered as the birth of tolerance limits (which is not quite true as tolerance limits are much older than this).

To quote Edward Deming, a forefront figure in quality engineering, ‘Variation is the enemy of quality.’ The bolt and the nut is one example. Another one is thickness variation of the plastic film on the inside of a milk package–a plastic film preventing the beverage from coming in contact with the aluminium foil that is present in most milk (and juice) packages. If this thickness varies too much, it may occasionally happen that there is a point with direct contact between the beverage and the aluminium foil. However, it is not the fact that there is a contact point that should be the centre of interest. The focus should rather be the size of the film thickness variation. The contact point is just a symptom of this problem.