A Study of the Impacts of Measurement errors on Robust Parameter Design for Multi-response

Abstract

Robust parameter design is a principle in quality improvement methodologies that is directed towards reducing the effects of errors which are either poised by the noise factors or the control factors. Response surface methodology is an effective approach to robust parameter design. Previous studies discussed Robust parameter design based on the response surface model by considering measurement errors in control variables for a single response variable. However, in process design, determining optimal levels of control variables is an important issue in some problems with different outputs. This study therefore investigates the impacts of measurement errors in the levels of control variables on processes with multiple quality characteristics (responses). Different variances of error were tested on the levels of control variables and the analysis of response surface modeling and optimization was performed. The result showed that as measurement errors in the levels of control variables increase, the coefficient of determinations for the multi-response and the expected quality loss deviates from what is obtainable in the initial state. It can be concluded based on the result however, that measurement errors in the levels of control variables exert impacts on robust parameter design for multi-response.