Quality Control, Robust Design, and the Taguchi Method (Wadsworth and Books/Cole Statistics/Probability Series)

Dehnad, Khosrow

ISBN 10: 0534090486 / ISBN 13: 9780534090487

Published by Springer, 1988

About this Item

Gently read. May have name of previous ownership, or ex-library edition. Binding tight; spine straight and smooth, with no creasing; covers clean and crisp. Minimal signs of handling or shelving. 100% GUARANTEE! Shipped with delivery confirmation, if you're not satisfied with purchase please return item! Ships USPS Media Mail.

Seller Inventory # OTV.0534090486.VG

Bibliographic Details

• Title: Quality Control, Robust Design, and the Taguchi Method (Wadsworth and Books/Cole Statistics/Probability Series)
• Publisher: Springer
• Publication Date: 1988
• Language: English
• ISBN 10: 0534090486
• ISBN 13: 9780534090487
• Binding: Hardcover
• Condition: very_good

About this title

Synopsis

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.