Abstract—Optimizing quality engineering problems has become a common problem when there are several correlated product quality characteristics. Moreover, in design of experiments, controlling covariate effects could reduce error and uncovered variances as well as give more insight about the process. This work identifies process variables to analyze correlated multiple responses with stochastic covariates. It also considers dispersion effects and specification limits besides location effects in an integrated framework based on desirability function. At the end, efficacies of the proposed approach are assessed by a numerical example.
—Design of experiments, multiresponse optimization, covariate effects, principal component analysis, and desirability function.
T. H. Hejazi is with the Department of Industrial Engineering and Management Systems ,Amirkabir University of Technology ,Tehran, Iran (e-mail: email@example.com).
A. Salmasnia is with the Department of Industrial Engineering, Faculty of Engineering, Tarbiat Modares University, Tehran, Iran (e-mail: firstname.lastname@example.org).
M. Bastan is with the Department of Industrial Engineering, Faculty of Engineering, Eyvanakey University, Semnan, Iran (e-mail: email@example.com).
Cite: Taha Hossein Hejazi, Ali Salmasnia, and Mahdi Bastan, "Optimization of Correlated Multiple Response Surfaces with Stochastic Covariate," International Journal of Computer Theory and Engineering
vol. 5, no. 2, pp. 341-345, 2013.