Robust parameter design of mixed multiple responses based on a latent variable Gaussian process model

Abstract

Traditional robust parameter design methods mainly focus on the optimization of quantitative quality characteristics. However, computer experiments involving qualitative and quantitative mixed input and output occur frequently in the manufacturing industry, which prompted the authors to develop an effective meta-modeling and optimization technique for such experiments. This article combines the latent variable Gaussian process (LVGP) model and fuzzy set theory to create a mixed multi-response LVGP (MMR-LVGP) model involving qualitative and quantitative mixed input and output. Then, the optimization scheme is established by comprehensively weighing the location and dispersion effects of each quality characteristic to find the joint optimal solution of qualitative and quantitative factors. Numerical and industrial cases are used to illustrate the validity of the proposed method in the modeling and optimization of experimental data with qualitative and quantitative mixed input and output or spatio-temporal structure. The comparison results indicate that the proposed method is preferred over existing methods.

Keywords:

• Robust parameter design
• latent variable Gaussian process
• qualitative and quantitative
• fuzzy set theory
• quality loss

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The data that support the findings of this study are available from the online supplemental data, which can be accessed at https://doi.org/10.1080/0305215X.2022.2124982.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [grant numbers 72171118, 71931006, 71771121, 11901299] .