Integrated parameter and tolerance design with computer experiments

ABSTRACT Robust parameter and tolerance design are effective methods to improve process quality. It is reported in the literature that the traditional two-stage approach that performs parameter design followed by tolerance design to reduce the sensitivity to variations of input characteristics is suboptimal. To mitigate the problem, an integrated parameter and tolerance design (IPTD) methodology that is suitable for linear models is suggested. In this article, a computer-aided IPTD approach for computer experiments is proposed, in which the means and tolerances of input characteristics are simultaneously optimized to minimize the total cost. A Gaussian process metamodel is used to emulate the response function to reduce the number of simulations. A closed-form expression for the posterior expected quality loss is derived to facilitate optimization in computer-aided IPTD. As there is often uncertainty about the true quality and tolerance costs, multiobjective optimization with quality loss and tolerance cost as objective functions is proposed to find robust optimal solutions.