Partition experimental designs for sequential processes: Part I-first-order models

The output quality or performance characteristics of a product often depend not only the effect of the factors in the current process but on the effect of factors from preceding processes. Statistically-designed experiments provide a systematic approach to study the effects of multiple factors on process performance by offering a structured set of analyses of data collected through a design matrix. One important limitation of experimental design methods is that they have not often been applied to multiple sequential processes. The objective is to create a first-order experimental design for multiple sequential processes that possess several factors and multiple responses. The first-order design expands the current experimental designs to incorporate two processes into one partitioned design. The designs are evaluated on the complexity of the alias structure and their orthogonality characteristics. The advantages include a decrease in the number of experimental design runs, a reduction in experiment execution time, and a better understanding of the overall process variables and their influence on each of the responses.