Set Response Surface Methodology and its Application in Solving the Wrinkle and Crack Problem in the Auto Industry

被引:0
|
作者
He, Yingdong [1 ,2 ]
Ma, Xinyan [1 ,2 ]
Tian, Yu [1 ,2 ]
He, Zhen [1 ,2 ]
Zhang, Xiaoyi [1 ,2 ]
Kim, Kwang-Jae [3 ]
Bae, Young-Mok [3 ]
机构
[1] Tianjin Univ, Coll Management & Econ, Tianjin 300072, Peoples R China
[2] Tianjin Univ, Lab Computat & Analyt Complex Management Syst CACM, Tianjin 300072, Peoples R China
[3] POSTECH, Dept Ind & Management Engn, Kyungbuk 790784, South Korea
基金
中国国家自然科学基金;
关键词
Input variables; Response surface methodology; Optimization; Metals; Optimization models; Industries; Analytical models; Barrier and engineering requirement analysis (BERA); condition relaxation strategy (CRS); multiple central tendencies (MCTs); set response surface methodology (SRSM); wrinkle and crack problem; DESIRABILITY FUNCTION-METHOD; SIMULTANEOUS-OPTIMIZATION; DESIGN;
D O I
10.1109/TR.2024.3421552
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
This study considers a response surface methodology (RSM) variation in which a response has multiple central tendencies (MCTs) that can have multiple influences on a sheet metal part. This is formulated as a set response surface (SRS) problem, which, in industrial practice, is studied using the thinning ratio example. The set RSM (SRSM), consisting of three phases, is proposed to solve the SRS problem. The first phase is the problem definition and regional division phase, where based on the analysis of MCTs and response influence, a sheet metal part that needs quality improvement is divided into q regions. The second phase is the experiment and data regression phase. The third phase is the optimization and interactive decision phase, where the condition relaxation strategy (CRS) is proposed, and relaxation is obtained based on the barrier and engineering requirement analysis. The optimization models are constructed for the (q+1)-level optimal solutions using the CRS. The proposed SRSM is verified by tests on the wrinkle and crack problem of the inner plate of the back door. A possible optimization model combination and trend analysis strategy is proposed to solve the CRS challenge for higher dimensions in the tendencies.
引用
收藏
页数:16
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