Extending the desirability function to account for variability measures in univariate and multivariate response experiments
One technique used frequently among quality practitioners seeking solutions to multi-response optimization problems is the desirability function approach. The technique involves modeling each characteristic using response surface designs and then transforming the characteristics into a single performance measure. The traditional procedure, however, calls for estimating only the mean response: the variability among the characteristics is not considered. Furthermore, the approach typically relies on the accuracy of second-order polynomials in its estimation, which are not always suitable. This paper, in contrast. proposes a methodology that utilizes higher-order estimation techniques and incorporates the concepts of robust design to account for process variability. Several examples are provided to illustrate the effectiveness of the proposed methodology. Published by Elsevier Ltd.
desirability function multi-response optimization robust design response surface methodology