Solving the Multidisciplinary Robust Parameter Design Problem for Mixed Type Quality Characteristics under Asymmetric Conditions

Quality practitioners often identify robust parameter design (RPD) as one of the most important and effective methods for process and quality improvement. Within this framework, identifying the optimal factor settings that achieve desired process targets with minimum variance is critical and can translate to significant reductions in product waste and processing costs. In solving this problem, most traditional RPD models consider only a single quality characteristic of interest. However, products are often judged by multiple quality characteristics, which often have conflicting objectives. Conventional RPD models that address the multi‐response problem typically only examine like‐type cases, and those that consider mixed types of quality characteristics often overlook any asymmetry that is likely to exist in certain types. In contrast, this article proposes a multidisciplinary RPD methodology that provides an enhanced approach for modeling multiple, mixed type quality characteristics; uses the skew normal distribution to allow for a fuller and more accurate representation of asymmetric system properties and to facilitate simultaneous modeling of both symmetric and asymmetric conditions; and implements a priority‐based optimization scheme that affords engineers' and decision makers' flexibility in establishing and modifying optimization priorities. A numerical example is used to demonstrate the proposed methodology, and the results are compared traditional approaches to illustrate potential improvements.