Multi-stage online robust parameter design based on Bayesian GP model. (October 2022)

• Record Type: Journal Article
• Authors: Ma, Yan; Wang, Jianjun; Feng, Zebiao; Tu, Yiliu
• Is Part Of: Computers & industrial engineering. Volume 172:Part A(2022)
• Publication Date: 2022-10
• Subjects: Quality design -- Robust parameter design -- Gaussian process model -- Bayesian method -- Quality loss; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2022.108551
• Languages: English
• ISSNs: 0360-8352

Abstract

Abstract: Online robust parameter design (RPD) for the complex production process has recently attracted increasing attention among researchers and practitioners. However, the existing online RPD methods usually ignore the model uncertainty of initial steps, which may lead to the overestimated optimal solutions in the early stage of online RPD. This paper proposes a multi-stage robust optimization approach based on the Bayesian Gaussian process (BGP) model to improve the robustness of the optimal solutions of the online RPD process. First, the Gibbs sampling method is used to estimate the hyperparameters of the BGP model. Second, the global optimization and clustering analysis techniques are combined to determine the optimal design region of input variables. Consequently, the Bayesian posterior probability analysis technique is used to obtain the optimal robust design region for performing the online parameter optimization. Finally, an online RPD model is constructed by integrating the global optimization algorithm, parameter update strategy, and quality loss function. The proposed approach is validated through a simulation example and a laser drilling case study. The comparison results show that the proposed approach obtains more robust optimal solutions than the existing ones. Highlights: Multi-stage modeling approach is proposed to provide the robust feasible region. Integrating global optimization algorithm and parameter update strategy. Accuracy and efficiency of the optimal solution are considered simultaneously. Extending the application of robust parameter design (RPD) in engineering practice.