Abstract
The selection of the optimal process target is critically important as it directly affects the defect rate, material cost, scrap and rework costs, and the loss to customers. Within the context of the optimal process target problem, a new model is proposed in this paper and two distinct contributions to the related topic are offered. First, while most research work assumes a given process distribution with a known variance, this paper integrates response surface designs into solving the optimal process target problem, thus removing the need to make assumptions regarding process parameters. Second, typical response surface designs consider second-order fitted functions; however, this paper considers a procedure to include higher-order polynomial terms that will result in higher prediction capabilities, thereby giving a more accurate representation of the true process. A constrained nonlinear optimization scheme is used to facilitate the development of this methodology and a numerical example is provided for illustration.
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