Cover image for Engineering methods for robust product design : using taguchi methods in technology and product development
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Engineering methods for robust product design : using taguchi methods in technology and product development
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Reading : Addison-Wesley, 1995
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1v + 1 computer disk
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9780201633672
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30000005033026 TS156 F68 1995 Open Access Book Book
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Summary

Summary

Aims to clarify misconceptions about Dr Genichi Taguchi's approach to robust design, while analysing as to why dynamic signal-to-noise ratio is used as well as the role of orthogonal arrays in parameter design and tolerance design.


Author Notes

William Y. Fowlkes, winner of the prestigious Taguchi Award for his work at Eastman Kodak, is experienced both in using Taguchi methods as well as teaching them. He teaches a course on robust design at the Rochester Institute of Technology, has created a video tape and tele-course on the subject that is used at General Motors, and was instrumental in creating the training materials on robust design that continue to be used at Eastman Kodak.

Clyde "Skip" Creveling is the president and founder of Product Development Systems & Solutions Inc. (PDSS) ( http://www.pdssinc.com ). Since PDSS' founding in 2002, Mr. Creveling has led Design for Six Sigma (DFSS) initiatives at Motorola, Carrier Corporation, StorageTek, Cummins Engine, BD, Mine Safety Appliances, Callaway Golf, and a major pharmaceutical company. Prior to founding PDSS, Mr. Creveling was an independent consultant, DFSS Product Manager, and DFSS Project Manager with Sigma Breakthrough Technologies Inc. (SBTI). During his tenure at SBTI he served as the DFSS Project Manager for 3M, Samsung SDI, Sequa Corp., and Universal Instruments.

Mr. Creveling was employed by Eastman Kodak for 17 years as a product development engineer within the Office Imaging Division. He also spent 18 months as a systems engineer for Heidelberg Digital as a member of the System Engineering Group. During his career at Kodak and Heidelberg he worked in R&D, Product Development/Design/System Engineering, and Manufacturing. Mr. Creveling has five U.S. patents.

He was an assistant professor at Rochester Institute of Technology for four years, developing and teaching undergraduate and graduate courses in mechanical engineering design, product and production system development, concept design, robust design, and tolerance design. Mr. Creveling is also a certified expert in Taguchi Methods.

He has lectured, conducted training, and consulted on product development process improvement, design for Six Sigma methods, technology development for Six Sigma, critical parameter management, robust design, and tolerance design theory and applications in numerous U.S, European, and Asian locations. He has been a guest lecturer at MIT, where he assisted in the development of a graduate course in robust design for the System Design and Management program.

Mr. Creveling is the author or coauthor of several books, including Six Sigma for Technical Processes , Six Sigma for Marketing Processes , Design for Six Sigma in Technology and Product Development , Tolerance Design , and Engineering Methods for Robust Product Design . He is the editorial advisor for Prentice Hall's Six Sigma for Innovation and Growth Series.

Mr. Creveling holds a B.S. in mechanical engineering technology and an M.S. from Rochester Institute of Technology.


Excerpts

Excerpts

Quality in products and product related processes is now, more than ever, a critical requirement for success in manufacturing. In fact, for many successful companies, such as Motorola, Toyota, Ford, Bausch & Lomb, Xerox, and Kodak, it is fair to say that quality is a corporate priority. These companies have realized that to obtain customer loyalty, their products have to be perceived as nearly flawless. In addition, to be competitive, their product development process must minimize waste, cycle time, and rework. The practices adopted by companies that are succeeding in the quality competition vary, but two common elements can be found. Careful attention to the customer is absolutely paramount. Products must satisfy a diverse customer base, with features accurately targeted to customer requirements. Technology must serve customer needs and wants, or the latest and greatest widget will languish on the shelf. Also, continuous improvement, applied to both products and business processes, is ubiquitous. At the Eastman Kodak Company, the authors have been participants in the ongoing effort to improve the equipment development process. The result has been a world-class process for developing products. This process features Quality Function Deployment (QFD) for capturing the voice of the customer, Robust Design (Quality Engineering) to deliver the level of quality demanded by the customer, and a disciplined engineering process for managing the business of product commercialization. Much of the Kodak Equipment Commercialization Process is described in Professor Don Clausing's book Total Quality Development (ASME Press, 1994). Physics and engineering principles are the basis for beginning a good product design or fixing problems with a design that is already in existence. Any graduate from engineering school knows these fundamental subjects as well. They have been used effectively by many generations of engineers. However, they alone are no longer enough. The current competitive situation requires a disciplined engineering process that ties together the multitude of engineering tools currently being taught and practiced. The need to further define the process for linking the principles of engineering and physics to commercialization inspired the writing of this book. The authors' experiences in applying Robust Design to mechanical and electrical systems, electrophotographic process optimization, and chemical process optimization at Kodak have demonstrated convincingly that Dr. Taguchi's design optimization techniques are extremely effective in reducing cycle time and rework. Every company that employs Robust Design does so in the context of their own internal culture. Only the books written by Dr. Taguchi follow his views in totality. This book is a reflection of how we have internalized Dr. Taguchi's insights and teachings into our culture at Kodak. In this industrial environment, we have found broad acceptance and a strong willingness to employ Taguchi methods when practiced in an engineering context. This, of course, is exactly how Dr. Taguchi and those who have listened to him over the years approach the topic - as an engineering process. The successes experienced at Kodak and at many other companies we have encountered are derived from Dr. Taguchi's advice T6: "Spend about 80% of your time in engineering analysis and planning and about 20% actually running experiments and evaluating the results." Recently, engineering process improvement has been introduced into the academic arena. Courses on Robust Design, QFD, Six Sigma, and other quality processes can now be found at an increasing number of schools. Some of the leaders in this new trend include the Massachusetts Institute of Technology, Stanford University, Georgia Institute of Technology, and Michigan Technological University. Rochester Institute of Technology (RIT), where we teach and serve on the Industrial Advisory Board, recently adopted elements of a quality engineering curriculum as mechanical engineering electives. This book is largely based on our experience in teaching the Robust Design course at RIT in an engineering department. This is unique, because much of the academic attention given to Dr. Taguchi's methods has come from the statistics community as a result of Dr. Taguchi's use of empirical statistical techniques, particularly design of experiments. This has led to a misunderstanding of robust design as being statistical in nature. This book takes an entirely fresh look at robust design as an engineering process, where the emphasis is on using engineering analysis to improve product performance. This book offers simple, yet effective, guidelines on how to practice robust design in the context of a total quality development effort. In these pages, the fundamental metrics of quality engineering are fully developed, and the rationale behind them is explained. Designing low-cost solutions is a given requirement. We discuss the impact of robust design on the cost of a design, as well as how cost and quality can be co-optimized using Dr. Taguchi's Quality Loss Function. The fundamental statistical tools (e.g., design of experiments, analysis of variance, and analysis of interactions) are explained in what we hope will be an intuitive yet mathematically precise way. A healthy balance exists between the statistical sciences and the engineering sciences. In this book, we try to introduce practical insight into the statistical side of Robust Design, while maintaining the hightest priority in basing the experimental approach on sound engineering principles. The most important element of engineering success is clear thinking, planning, analysis, and communication. For this reason, we offer this book primarily as a guide on how to invest your time efficiently in the 80% up-front engineering required, particularly as it pertains to technology development and product commercialization. The Structure of This Book Chapter 1 is organized to provide a broad introduction to Quality Engineering and to establish the fundamental concepts needed to build the reader's understanding for work presented in later chapters. The rest of the book is presented in three major parts. The first is an introduction to Quality Engineering Metrics. It consists of Chapters 2 through 6. Robust Design is a data driven process. Chapter 2 goes through Introductory Data Analysis for Robust Design and is presented to establish a context for how data will be treated throughout the rest of the book. Chapter 3 presents the theory and derivation of the various forms of the Quality Loss Function. An application of the quality loss function to tolerance design is also included. Chapter 4 presents the fundamental knowledge behind the Signal-to-Noise Ratio. The static and dynamic signal-to-noise ratios are fully discussed with numerous examples in Chapters 5 and 6, respectively. The second part delves into the details of the parameter design process with a special emphasis on achieving additivity.1 Additivity is a property of a design that reduces harmful interactions,1 thus simplifying the optimization process. Chapter 7 is a practical Introduction to Designed Experiments. Without the use of designed experiments, the process of optimizing a product becomes a time consuming endeavor laced with rework and unwanted surprises due to interactions. Chapter 8 is focused on a thorough discussion concerning the Selection of the Quality Characteristics. Few choices in the process of quality engineering are as critical as the selection of the physical responses to be measured during the designed experiments. Chapter 9 provides a sound basis for the Selection and Testing of Noise Factors to stress the design during the development of robustness. Constructing viable noise factor experiments is an indispensable step in preparing for credible and realistic optimization experiments. Chapter 10 completes the discussion on the selection of experimental parameters by giving strategies for the Selection of Control Factors. Chapter 11 shows how to lay out the Parameter Optimization Experiment and is followed in Chapter 12 with the Analysis and Verification of the Parameter Optimization Experiment. Quantifying the individual control factor effects on the overall design performance is highly prized information to the engineering team. In summary, Chapters 7-12 are designed to take you through a comprehensive process of planning, experimenting, and verifying optimized parameter performance. This book is intended to be useful for teaching and learning the principles and practices of robust design. Chapter 13 demonstrates the parameter design process by covering, in detail, three examples that are actually used as workshop problems by the authors during courses in robust design at the Rochester Institute of Technology and at Eastman Kodak Company. These simple examples are good illustrations of the techniques and can be performed by the reader to practice the method. Chapter 14 demonstrates the parameter design process by presenting three actual Kodak case studies, previously unpublished. Real design problems always take on additional complexity that is intentionally avoided in heuristic examples. These case studies show how parameter design is effective at real-life problem solving. The performance improvements are significant and lasting. The third and final part of the book is geared toward the engineering practitioner who is interested in more advanced techniques of Robust Design. Chapter 15 provides the necessary information to allow the engineering team to modify arrays to aid in the optimization of unique cases of parameter design. Working with Interactions (Chapter 16) is probably the most controversial topic among the methods of Robust Design. We have produced a balanced approach to maintaining statistical validity of experimentation while promoting the use of enineering knowledge and experience during the construction and analysis of designed experiments that may contain interactive control factors. Chapter 17 teaches the method of the Analysis of Variance, and advanced tool for analyzing designed experiments. Chapter 18 completes the book with a discussion of three special topics within the field of Robust Design. They are the relationship of Robust Design to (1) Quality Function Deployment, (2) Classical Design of Experiments, and (3) Six Sigma. Because the empirical methods of Robust Design require statistical analysis of large amounts of data, WinRobust Lite software is included with this book. Numerous examples are provided to introduce the reader to many helpful features contained in this PC-based Windows software package. This is the first book of its kind to integrate a custom software package with the text. This union with computer-aided Robust Design techniques will provide you with a comprehensive set of tools that will simplify the tedious process of computation, thus freeing your efforts to focus on the essential engineering issues behind the functional performance you seek to optimize. Acknowledgments The authors would like to acknowledge, with great respect, our sensei and teachers of Robust Design, Dr. Genichi Taguchi and his son Shin Taguchi; Dr. Madhav Phadke; Prof. Don Clausing (Massachusetts Institute of Technology); and Prof. Tom Barker (Rochester Institute of Technology). Each of these individuals has been very generous in sharing his unique insights and knowledge of quality engineering. We would like to thank the management at Kodak who supported our efforts to learn, teach, and advise others within Kodak in the proficient use of these methods. In particular we would like to recognize Tom Plutchak and Martin Berwick, who supported us in bringing Dr. Madhav Phadke to Kodak to get us started on our journey, and then encouraged us to get busy and make something valuable happen with our new-found knowledge. We have been very fortunate to have had the opportunity to create and teach one of the first undergraduate-level courses in the United States specifically on the topic of Robust Design. This book is, in large part, a product of our course notes. We greatly appreciate the support and encouragement of Bob Merrill, the current chairman, and Ron Amberger, the past chairman, of the Department of Mechanical Engineering Technology within the College of Applied Science and Technology at the Rochester Institute of Technology. Additional support came from the members of the Industrial Advisory Board of the Department of Mechanical Engineering Technology, and in particular from the advisory board chairman, John Shannon of the Bausch & Lomb Corporation. Thanks to each of them. Special recognition is due to George Walgrove and Tom Foster, engineers who through their frequent and imaginative use of parameter design have helped make Kodak world-class in the Robust Design process. We would also like to thank the individuals who contributed case studies to this book: Chuck Bennett, Marc Bermel, Atsushi Hatakeyama, Shigeomi Koshimizu, Mike Parsons, Allen Rushing, Steve Russell, Markus Weber, Reinhold Weltz, and Mark Zaretsky. We wish we could list all the other contributors who have added their experiences to ours to help make this book possible, but it is impossible to be comprehensive in such a list, so we simply thank them collectively. Finally, we would like to thank Susan Baruch for her help in preparing the manuscript, our editor Jennifer Joss, our technical reviewers, and the entire staff at Addison-Wesley for their support. 0201633671P04062001 Excerpted from Engineering Methods for Robust Product Design: Using Taguchi Methods in Technology and Product Development by William Y. Fowlkes, Clyde M. Creveling All rights reserved by the original copyright owners. Excerpts are provided for display purposes only and may not be reproduced, reprinted or distributed without the written permission of the publisher.

Table of Contents

Foreword
Preface
1 Introduction to Quality Engineering
An Overview
The Concept of Noise in Robust Design
Product Reliability and Quality Engineering
What Is Robustness? What Is Quality?
On-Target Engineering
How Is Quality Measured?
The Phases of Quality Engineering in Product Commercialization
Off-Line Quality Engineering
On-Line Quality Engineering
The Link between Sir Ronald Fisher and Dr
Genichi Taguchi
A Brief History - The Taguchi Method of Quality Engineering
Concluding Remarks
Exercises for Chapter 1
I Quality Engineering Metrics
2 Introductory Data Analysis for Robust Design
The Nature of Data
Graphical Methods of Data Analysis
Quantitative Methods of Data Analysis
An Introduction to the Two-Step Optimization Process
Summary
Exercises for Chapter 2
3 The Quality Loss Function
The Nature of Quality
Relating Performance Distributions to Quality
The Step Function: An Inadequate Description of Quality
The Customer Tolerance
The Quality Loss Function: A Better Description of Quality
The Quality Loss Coefficient
An Example of the Quality Loss Function
The Types of Quality Loss Functions
Loss Function Case Study
Summary
Exercises for Chapter 3
4 The Signal-to-Noise Ratio
Properties of the S N Ratio
Derivation of the S/N Ratio
Defining the Signal-to-Noise Ratio from the Mean Square Derivation
Identifying the Scaling Factor
Summary
Exercises for Chapter 4
5 The Static Signal-to-Noise Ratios.Introduction, Static vs. Dynamic Analysis
The Smaller-the-Better Type Signal-to-Noise Ratio
The Larger-the-Better S/N Ratio
The Operating Window: A Combination of STB and LTB
A Signal-to-Noise Ratio for Probability
The Nominal-the-Best Signal-to-Noise Ratios
Two-Step Optimization
A Comparative Analysis of Type I NTB and Type II NTB
A Note on Notation
Summary
Exercises for Chapter 5
6 The Dynamic Signal-to-Noise Methods and Metrics
Introduction
The Zero-Point Proportional Case
The Reference-Point Proportional Case
Nonlinear Dynamic Problems
The Double-Dynamic Signal-to-Noise Ratio
Summary
Exercises for Chapter 6
II Parameter Design
7 Introduction to Designed Experiments.Experimental Approaches
The Analysis of Means (ANOM)
Degrees of Freedom
Full Factorial Arrays
Fractional Factorial Orthogonal Arrays
Summary of Chapter 7
Exercises for Chapter 7
8 Selection of the Quality Characteristics
Introduction
Engineering Analysis in the Planning Stage
The Ideal Function of the Design
Guidelines for Choosing the Quality Characteristic
Summary: The P-diagram
Exercises for Chapter 8
9 The Selection and Testing of Noise Factors
Introduction
The Role of Noise Factor - Control Factor Interactions
Experimental Error and Induced Noise
Noise Factors
Choosing the Noise Factors
The Noise Factor Experiment
Analysis of Means for Noise Experiments
Examples
Other Approaches to Studying Noise Factors
Case Study: Noise Experiment on a Film Feeding Device
Summary of Chapter 9
Exercises for Chapter 9
10 The Selection of Control Factors
Introduction
Selecting Control Factors to Improve Tunability and Robustness
Selecting and Grouping Engineering Parameters to Promote Additivity
Sliding Levels for Control Factors
Example: The Catapult
Example: The Paper Gyrocopter
Summary: The P-diagram
Exercises for Chapter 10
11 The Parameter Optimization Experiment
Introduction
Dr. Taguchi's Parameter Design Approach
Layout of the Static Experiment
Layout of the Dynamic Experiment
Choosing the Noise Factor Treatment
Choosing the S/N Ratio
Summary of Chapter 11
Exercises for Chapter 11
12 The Analysis and Verification of the Parame