Insua Pinmanee, Nakkiew Wasawat, Wisittipanich Warisa
Graduate Program in Industrial Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, Thailand.
Department of Industrial Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, Thailand.
Materials (Basel). 2023 Mar 3;16(5):2081. doi: 10.3390/ma16052081.
Post weld heat treatment, or PWHT, is often used to improve the mechanical properties of materials that have been welded. Several publications have investigated the effects of the PWHT process using experimental designs. However, the modeling and optimization using the integration of machine learning (ML) and metaheuristics have yet to be reported, which are fundamental steps toward intelligent manufacturing applications. This research proposes a novel approach using ML techniques and metaheuristics to optimize PWHT process parameters. The goal is to determine the optimal PWHT parameters for both single and multiple objective perspectives. In this research, support vector regression (SVR), K-nearest neighbors (KNN), decision tree (DT), and random forest (RF) were ML techniques employed to obtain a relationship model between PWHT parameters and mechanical properties: ultimate tensile strength (UTS) and elongation percentage (EL). The results show that the SVR demonstrated superior performance among ML techniques for both UTS and EL models. Then, SVR is used with metaheuristics such as differential evolution (DE), particle swarm optimization (PSO), and genetic algorithms (GA). SVR-PSO shows the fastest convergence among other combinations. The final solutions of single-objective and Pareto solutions were also suggested in this research.
焊后热处理（PWHT）常用于改善焊接材料的机械性能。一些出版物使用实验设计研究了PWHT工艺的效果。然而，尚未有关于将机器学习（ML）与元启发式算法相结合进行建模和优化的报道，而这是迈向智能制造应用的基本步骤。本研究提出了一种使用ML技术和元启发式算法来优化PWHT工艺参数的新方法。目标是从单目标和多目标角度确定最佳的PWHT参数。在本研究中，支持向量回归（SVR）、K近邻（KNN）、决策树（DT）和随机森林（RF）是用于获得PWHT参数与机械性能（极限抗拉强度（UTS）和伸长率（EL））之间关系模型的ML技术。结果表明，在UTS和EL模型的ML技术中，SVR表现出卓越的性能。然后，将SVR与差分进化（DE）、粒子群优化（PSO）和遗传算法（GA）等元启发式算法结合使用。SVR-PSO在其他组合中收敛速度最快。本研究还给出了单目标的最终解和帕累托解。
