Many industries have adopted the use of statistically designed experiments for achieving a robust process, i.e., a process that is insensitive to changes and perturbations in the uncontrollable process variables. In Japan, and then within the United States and other countries, Dr. Genichi Taguchi popularized the use of experimental design for achieving robustness. The Taguchi designs are generated by crossing two orthogonal designs called the inner and outer array designs. As an improvement to the Taguchi system of designs, a number of authors have suggested alternative designs which are based on a single factor array. This dissertation will describe and evaluate a new class of response surface designs, designs of "mixed resolution," which are based on a single factor array and can be adopted for achieving a robust process.

In the evaluation of mixed resolution designs, the problem of finding globally optimum designs for achieving a robustness process is addressed and solved. D- and G-optimal designs for the mixed resolution model are defined and their optimality properties are studied. The D- and G-efficiencies of the mixed resolution designs are then calculated.

A discussion of model building considerations is presented as the different experimental design approaches allow for the estimation of different sets of model terms. The Taguchi system of experimental design in contrast with mixed resolution designs will be the primary focus in this comparative discussion. This will include an experimental run size comparison between single array and crossed orthogonal arrays.

The final chapter will include comments on related research problems with emphasis on computer generated designs and their application to the mixed resolution model and the use of signal-to-noise ratios as a response surface analysis tool for achieving robustness using mixed resolution designs.