From the Publisher:
Phadke was trained in robust design techniques by Genichi Taguchi, the mastermind behind Japanese quality manufacturing technologies and the father of Japanese quality control. Taguchi's approach is currently under consideration to be adopted as a student protocol with the US govrnment. The foreword is written by Taguchi. This book offers a complete blueprint for structuring projects to achieve rapid completion with high engineering productivity during the research and development phase to ensure that high quality products can be made quickly and at the lowest possible cost. Some topics covered are: orthogonol arrays, how to construct orthogonal arrays, computer-aided robutst design techniques, dynamic systems design methods, and more.

Quality Engineering Using Robust Design

FUNDAMENTAL QUALITY IMPROVEMENT AND STATISTICAL CONCEPTS. Basic Tools for Improving Quality. Basic Concepts in Statistics and Probability. CONTROL CHARTS AND PROCESS CAPABILITY. Control Charts for Measurements with Subgrouping (for One Variable). Control Charts for Measurements without Subgrouping (for One Variable). Control Charts for Attributes. Process Capability. Alternatives to Shewhart Charts. Multivariate Control Charts for Measurement Data. Miscellaneous Control Chart Topics. BEYOND CONTROL CHARTS: GRAPHICAL AND STATISTICAL METHODS. Other Graphical Methods. Linear Regression. Design of Experiments. Contributions of Genichi Taguchi and Alternative Approaches. Evolutionary Operation. Analysis of Means. Using Combinations of Quality Improvement Tools. Answers to Selected Exercises. Appendix. Indexes.

Statistical methods for quality improvement

Loss of markets to Japan has recently caused attention to return to the enormous potential that experimental design possesses for the improvement of product design, for the improvement of the manufacturing process, and hence for improvement of overall product quality. In the screening stage of industrial experimentation it is frequently true that the “Pareto Principle” applies; that is, a large proportion of process variation is associated with a small proportion of the process variables. In such circumstances of “factor sparsity,” unreplicated fractional designs and other orthogonal arrays have frequently been effective when used as a screen for isolating preponderant factors. A useful graphical analysis due to Daniel (1959) employs normal probability plotting. A more formal analysis is presented here, which may be used to supplement such plots and hence to facilitate the use of these unreplicated experimental arrangements.

An Analysis for Unreplicated Fractional Factorials

We review major developments in the design of experiments, offer our thoughts on important directions for the future, and make specific recommendations for experimenters and statisticians who are students and teachers of experimental design, practitioners of experimental design, and researchers jointly exploring new frontiers. Specific topics covered are optimal design, computer-aided design, robust design, response surface design, mixture design, factorial design, block design, and designs for nonlinear models.

Experimental Design: Review and Comment

Applications of Statistics to Industrial Experimentation: Daniel/Applications

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