Quality Control Robust Design And The Taguchi Method

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.

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.

Explains how to prevent quality problems in the early stages of product development and design, how to use the dynamic signal-to- noise ratio as the performance index for robustness of product functions, and how to evaluate methods of data collection. The book focuses on dynamic characteristics, foll.

Quality control is changing along with the manufacturing environment. A series of revolutionary changes will occur in management contents, methods, capabilities, and real-time effectiveness and efficiency of management. As an essential factor in intelligent manufacturing, quality control systems require real and comprehensive innovation. Focused on new trends and developments in quality control from a worldwide perspective, this book presents the latest information on novel approaches in quality control. Its thirteen chapters cover three topics: intelligent manufacturing, robust design, and control charts.

This book is written primarily for engineers and researchers who use statistical robust design for quality engineering and Six Sigma, and for statisticians who wish to know about the wide range of applications of experimental design in industry. It is a valuable guide and reference material for students, managers, quality improvement specialists and other professionals interested in Taguchi's robust design methods as well as the implementation of Six Sigma. This book can also be useful to those who would like to learn about the role of Robust Design within the Six Sigma (Improve phase) methodology and Design for Six Sigma (DFSS) (Optimize) methodology. It combines classical experimental design methods with those of Taguchi's robust designs, demonstrating their prowess in DFSS and suggesting new directions for the development of statistical design and analysis.

Robust Design is the procedure used by design engineers to reduce the effects of order to produce the highest quality products possible. This book includes real life case studies focusing on mechanical, chemical and imaging design that illustrate potential problems and their solutions and offers WinRobust Lite software and practice problems.

The book presents a systematic and efficient method to design high quality / reliability and high performance products / processes at low cost. Contains case studies from diverse engineering fields to describe Robust Design / Taguchi method.Some topics covered are: orthogonal arrays, Signal-to-Noise ratios as design quality metric, computer-aided robust design techniques, and more.

Phadke was trained in robust design techniques by Genichi Taguchi, the mastermind behind Japanese quality manufacturing technologies and the "father of Japanese quality control". Readers will gain a step-by-step example showing how to implement robust design techniques using the latest perpectives from many different industries.