Surrogate model–based optimal feed-forward control for dimensional-variation reduction in composite parts' assembly processes. Issue 3 (3rd July 2018)

• Record Type: Journal Article
• Authors: Yue, Xiaowei; Shi, Jianjun
• Is Part Of: Journal of quality technology. Volume 50:Issue 3(2018)
• Page Start: 279
• Page End: 289
• Publication Date: 2018-07-03
• Subjects: composites assembly processes -- feed-forward control -- Latin hypercube sampling -- surrogate model -- variation reduction; Quality control -- Periodicals; Qualité -- Contrôle -- Périodiques; Quality control; Quality control; Periodicals; 620.0045
• DOI: 10.1080/00224065.2018.1474688
• Languages: English
• ISSNs: 0022-4065

Abstract

ABSTRACT: Dimension control and variation reduction are vital for composite parts' assembly processes. Due to the nonlinear properties of composites, physics-based models cannot accurately and efficiently approximate the assembly processes. In addition, conventional robust parameter design (RPD) and statistical process control (SPC) cannot actively compensate for dimensional errors or prevent defects. This article proposes a surrogate model–based optimal feed-forward control strategy for dimensional-variation reduction and defect prevention in the assembly of composite parts. The objective is accomplished by (i) developing a grouped Latin hypercube sampling approach tailored to the problem; (ii) adopting a universal Kriging model for dimensional prediction and then embedding the model into an optimal feed-forward control algorithm; and (iii) conducting a multiobjective optimization to determine the control actions. A case study reveals that the developed methodology can effectively reduce the mean and standard deviation of dimensional deviations for the assembly of composite parts.