Engineering Methods for Robust Product Design: Using Taguchi Methods in Technology and Product Development (paperback) Book

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 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.

III. ADVANCED TOPICS.

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