Quality Control, Robust Design, and the Taguchi Method

1. Front Matter

   Pages i-xxiv

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2. Overview

   1. Front Matter

      Pages 1-1

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   2. Taguchi’s Quality Philosophy: Analysis and Commentary

      • Raghu N. Kackar

      Pages 3-21

   3. Macro-Quality with Micro-Money

      • Lewis E. Katz, Madhav S. Phadke

      Pages 23-30

   4. Quality Engineering using Design of Experiments

      • M. S. Phadke

      Pages 31-50

   5. Off-Line Quality Control, Parameter Design, and the Taguchi Method

      • Raghu N. Kackar

      Pages 51-76

   6. Quality Engineering through Design Optimization

      • G. Taguchi, M. S. Phadke

      Pages 77-96

3. Case Studies

   1. Front Matter

      Pages 97-97

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   2. Off-Line Quality Control in Integrated Circuit Fabrication using Experimental Design

      • M. S. Phadke, R. N. Kackar, D. V. Speeney, M. J. Grieco

      Pages 99-141

   3. Optimizing the Wave Soldering Process

      • K. M. Lin, R. N. Kackar

      Pages 143-157

   4. Robust Design: A Cost-Effective method for Improving Manufacturing Processes

      • Raghu N. Kackar, Anne C. Shoemaker

      Pages 159-174

   5. Tuning Computer Systems for Maximum Performance: A Statistical Approach

      • William A. Nazaret, William Klingler

      Pages 175-185

   6. Design Optimization Case Studies

      • Madhav S. Phadke

      Pages 187-212

4. Methodology

   1. Front Matter

      Pages 213-213

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   2. Testing in Industrial Experiments with Ordered Categorical Data

      • Vijayan N. Nair

      Pages 215-235

   3. Performance Measures Independent of Adjustment: an Explanation and Extension of Taguchi’s Signal-To-Noise Ratios

      • Ramon V. Leon, Anne C. Shoemaker, Raghu N. Kackar

      Pages 237-268

   4. A Geometric Interpretation of Taguchi’s Signal to Noise Ratio

      • Khosrow Dehnad

      Pages 269-287

   5. A Data Analysis Strategy for Quality Engineering Experiments

      • Vijay Nair, Daryl Pregibon

      Pages 289-309

In 1980, I received a grant from Aoyama-gakuin university to come to the United States to assist American Industry improve the quality of their products. In a small way this was to repay the help the US had given Japan after the war. In the summer of 1980, I visited the AT&T Bell Laboratories Quality Assurance Center, the organization that founded modern quality control. The result of my first summer at AT&T was an experiment with an orthogonal array design of size 18 (OA18) for optimization of an LSI fabrication process. As a measure of quality, the quantity "signal-ta-noise" ratio was to be optimized. Since then, this experi­ mental approach has been named "robust design" and has attracted the attention of both engineers and statisticians. My colleagues at Bell Laboratories have written several expository articles and a few theoretical papers on robust design from the viewpoint of statistics. Because so many people have asked for copies of these papers, it has been decided to publish them in a book form. This anthology is the result of these efforts. Despite the fact that quality engineering borrows some technical words from traditional design of experiments, the goals of quality engineering are different from those of statistics. For example, suppose there are two vendors. One vendor supplies products whose quality characteristic has a normal distribution with the mean on target (the desired value) and a certain standard deviation.

• control
• design
• optimization
• quality
• quality control
• quality control, reliability, safety and risk

• AT&T Bell Laboratories, USA

  Khosrow Dehnad

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