9312698 Tsui Robust Design is an important method for improving product or manufacturing process design by making the output response insensitive (robust) to hard-to-control variations (noise). In analyzing robust design experiments, Taguchi (1986) proposed a two- step procedure to identify the "optimal" factor settings that minimize the average quadratic loss. His two-step procedure becomes very effective in practice since it reduces the dimension of the design optimization problem and it does not require re- optimization for future changes of the target value. This research will develop improved statistical methods for analyzing robust design experiments. The researchers develop a multi-step analysis procedure for a variety of robust design problems. Their new procedure substantially reduces the dimension of the design optimization problem and does not require reoptimizaton for future changes of design requirements. This produces savings in the development time of product and manufacturing process design and allows for flexible manufacturing. The procedure also helps engineers better understand the physical mechanism of the product and process and allows the designer to make tradeoffs among the off-target bias and the individual process variances contributed by different noise factors. The research also identifies the appropriate robust design objectives and establishes their relationships with the quality loss functions for various robust design problems. They will strategies for implementing the multi-step procedure for smaller- the-better and larger-the-better problems as well as for the problems with several responses.
