Engineering Methods for Robust Product Design: Using Taguchi Methods in Technology and Product Development, 1st edition

  • William Y. Fowlkes
  • Clyde M. Creveling

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"I believe this book will help a great deal to clarify misconceptions about Dr. Genichi Taguchi's approach to robust design, such as why dynamic signal-to-noise ratio is used and the role of orthogonal arrays in parameter design and tolerance design. The authors understand the intent of robust design is to prevent fire instead of becoming better fire fighters!"

Ñ Shin Taguchi
President, American Supplier Institute

With practical techniques, real-life examples, and special software, this hands-on book/disk package teaches practicing engineers and students how to use Taguchi Methods and other robust design techniques that focus on engineering processes in optimizing technology and products for better performance under the imperfect conditions of the real world.

The unique WinRobust Lite software included with the book, together with a number of practice problems, enables you to conduct and analyze Taguchi experiments by simplifying the tedious process of performing the many necessary computations.

The book contains complete information on the process of engineering robust products that are insensitive to sources of variability in manufacturing and customer use. You will find detailed instructions for planning, designing, conducting, and analyzing the experiments that are used to optimize a product's performance under a variety of "stressed" conditions. An entire section focuses on designing products that achieve additivity, the property that reduces negative interactions. In addition, the book offers a systematic method for optimizing cost, quality, and cycle time. It even discusses the relationship of robust design to such other quality processes as Quality Function Deployment and Six Sigma.

Numerous case studies, taken from the authors' extensive practical experience, illustrate how robust design theories and techniques actually work in the real world of product engineering. With the techniques described in this book as well as the WinRobust Lite software, you will be better able to design robust products that are high-quality, durable, and able to perform well in the marketplace.

Table of contents



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.


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.


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 Parameter Optimization Experiment.

Introduction. The Data Analysis Procedure. An Example of the Analysis of the Parameter Optimization Experiment. Estimating the Effects of Each Factor Using ANOM. Identifying the Optimum Control Factor Set Points. The Two-Step Optimization Process. The Additive Model. The Predictive Equation. The Verification Tests. Summary: Succeeding at Parameter Design. Exercises for Chapter 12.

13. Examples of Parameter Design.

The Ice Water Experiment: Smaller-the-Better. The Gyrocopter Experiment: Dynamic Larger-the-Better. The Catapult Experiment. Conclusion. Exercises for Chapter 13.

14. Parameter Design Case Studies.

Introduction. Paper Handling - An Operating Window Example with Two Signal Factors. Improvement of a Capstan Roller Printer Registration. Enhancement of a Camera Zoom Shutter Design. Summary.


15. Modifying Orthogonal Arrays.

Introduction. Downgrading a Column. Upgrading a Column. Compound Factors. Summary of Chapter 15. Exercises for Chapter 15.

16. Working with Interactions.

The Nature of Interactions in Robust Design. Interactions Defined. How Interactions Are Measured. Degrees of Freedom for Interactions. Setting Up the Experiment When Interactions Are Included. Summary of Chapter 16. Exercises for Chapter 16.

17. Analysis of Variance (ANOVA).

Introduction. An Example of the ANOVA Process. Degrees of Freedom. Error Variance and Pooling. Error Variance and Replication. Error Variance and Utilizing Empty Columns. The F-Test. WinRobust Examples. Summary. Exercises for Chapter 17.

18. The Relationship of Robust Design to Other Quality Processes.

Quality Function Deployment (QFD) and Robust Design. Design of Experiments and Robust Design. Six Sigma Quality Process and Robust Design. Summary.

Appendix A Glossary.

Appendix B Quick Start Guide for WinRobust Lite.

Appendix C Orthogonal Arrays.

Appendix D Bibliography.

Index. 0201633671T04062001

Published by Pearson (August 30th 1995) - Copyright © 1995