THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

Generalized Linear Models (2nd ed.)

https://homepages.fhv.at/hgb/New-Papers/CMAME07_BS07b.pdf

Robust Optimization - A Comprehensive Survey

The purpose of this article is to provide a survey of the various stages in the development of response surface methodology RSM. The coverage of these stages is organized in three parts that describe the evolution of RSM since its introduction in the early 1950s. Part I covers the period, 1951-1975, during which the so-called classical RSM was developed. This includes a review of basic experimental designs for fitting linear response surface models, in addition to a description of methods for the determination of optimum operating conditions. Part II, which covers the period, 1976-1999, discusses more recent modeling techniques in RSM, in addition to a coverage of Taguchi's robust parameter design and its response surface alternative approach. Part III provides a coverage of further extensions and research directions in modern RSM. This includes discussions concerning response surface models with random effects, generalized linear models, and graphical techniques for comparing response surface designs. Copyright © 2010 John Wiley & Sons, Inc.

Response Surface Methodology.

Response surface methodology

It is more than a decade since Genichi Taguchi's ideas on quality improvement were inrroduced in the United States. His parameter-design approach for reducing variation in products and processes has generated a great deal of interest among both quality practitioners and statisticians. The statistical techniques used by Taguchi to implement parameter design have been the subject of much debate, however, and there has been considerable research aimed at integrating the parameter-design principles with well-established statistical techniques. On the other hand, Taguchi and his colleagues feel that these research efforts by statisticians are misguided and reflect a lack of understanding of the engineering principles underlying Taguchi's methodology. This panel discussion provides a forum for a technical discussion of these diverse views. A group of practitioners and researchers discuss the role of parameter design and Taguchi's methodology for implementing it. The topics covered include the importance of vari...

Taguchi's parameter design: a panel discussion

The Webster’s dictionary defines quality control as “an aggregate of activities (as design analysis and statistical sampling with inspection for defects) designed to ensure adequate quality in manufactured products.” This is a passive definition in that it concerns only with ‘adequate quality’ and makes no mention of an effort to constantly improve the quality. In this paper by quality control we also mean an active effort to improve the quality.

Quality Engineering using Design of Experiments

A survey done by J M Juran[1] showed that the Japanese yields of LSI chips are higher than those of the American and European manufacturers by a factor of two to three Another study made by K B Clark[2] showed that the design and development cycle for a specific product was about 30 months for one American electronics company while it was only about 18 months for the Japanese manufacturer of the same product Clark defines the development cycle as the time taken from the beginning of the product design until the high volume production starts He observed that the design transfer from development organization to the manufacturing organization was smooth for the Japanese company while for the American company it took several iterations

Quality Engineering through Design Optimization

In this paper we describe the off-line quality control method and its application in optimizing the process for forming contact windows in 3.5-μm complementary metal-oxide semiconductor circuits. The offline quality control method is a systematic method of optimizing production processes and product designs. It is widely used in Japan to produce high-quality products at low cost. The key steps of off-line quality control are: (i) Identify important process factors that can be manipulated and their potential working levels; (ii) perform fractional factorial experiments on the process using orthogonal array designs; (iii) analyze the resulting data to determine the optimum operating levels of the factors (both the process mean and the process variance are considered in this analysis; (iv) conduct an additional experiment to verify that the new factor levels indeed improve the quality control.

Off-line quality control in integrated circuit fabrication using experimental design

Robust product and process design is an important technique for achieving high quality at low cost. It involves making the product's function much less sensitive to various sources of noise such as manufacturing variation, environmental variation and deterioration. This is a problem in optimization involving minimization of the mean square loss resulting from the deviation of the product's function from its target. Here we show that the optimization can be carried out in two steps: first maximize a quantity called signal-to-noise ratio (S/N) and then bring the performance on target by special adjustment parameters. The two-step procedure works for a wide variety of product functions and makes the optimization process more efficient and practical compared to the direct minimization of the quadratic loss function.

M. S. Phadke

Papers

Taguchi's parameter design: a panel discussion

Quality Engineering using Design of Experiments

Quality Engineering through Design Optimization

Off-line quality control in integrated circuit fabrication using experimental design

Optimization of product and process design for quality and cost