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Abstract
Robust parameter design has been extensively studied and applied in industrial experiments over the past twenty years. The purpose of robust parameter design is to design a process or product that is robust to uncontrollable changes in the noise variables. In many applications the noise variables are continuous, for which several assumptions, often difficult to verify in practice, are necessary to estimate the response variance. In this paper, we consider the case where the noise variable is categorical in nature. We discuss the impact that the assumptions for continuous and categorical noise variables have on the robust settings and on the overall process variance estimate. A designed experiment from industry is presented to illustrate the results.
Additional information
Notes on contributors
William A. Brenneman
Dr. Brenneman is a member of the Statistical Quality Control Section within the Biometrics and Statistical Sciences Department. He is a Member of ASQ. His e-mail address is [email protected].
William R. Myers
Dr. Myers is Head of the Statistical Quality Control Section within the Biometrics and Statistical Sciences Department. He is a Member of ASQ. His e-mail address is [email protected].
