Analysis of Dynamic Robust Design Experiment with Explicit & Hidden Noise Variables

Abstract The response model approach proposed in Tsui [25] allows greater flexibility to investigate factor effects for analyzing dynamic robust design experiments. This article generalizes the response model approach based on a generalized linear model. We develop a generalized two-step optimization procedure to substantially reduce the process variance by dampening the effect of both explicit and hidden noise variables. The proposed method provides more reliable results through iterative modeling of the residuals from the fitted response model. The method is compared with three existing approaches (the response model, the loss model, and the response function model) in two practical examples.

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