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

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

A Japanese ceramic tile manufacturer knew in 1953 that is more costly to control causes of manufacturing variations than to make a process insensitive to these variations. The Ina Tile Company knew that an uneven temperature distribution in the kiln caused variation in the size of the tiles. Since uneven temperature distribution was an assignable cause of variation, a process quality control approach would have increased manufacturing cost. The company wanted to reduce the size variation without increasing cost. Therefore, instead of controlling temperature distribution they tried to find a tile formulation that reduced the effect of uneven temperature distribution on the uniformity of tiles. Through a designed experiment, the Ina Tile Company found a cost-effective method for reducing tile size variation caused by uneven temperature distribution in the kiln. The company found that increasing the content of lime in the tile formulation from 1% to 5% reduced the tile size variation by a factor of ten. This discovery was a breakthrough for the ceramic tile industry.

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

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.

Cuthbert Daniel

Papers

Applications of Statistics to Industrial Experimentation: Daniel/Applications