A machine learning-based calibration method for strength simulation of self-piercing riveted joints

IF 4.2 2区 工程技术 Q2 ENGINEERING, MANUFACTURING Advances in Manufacturing Pub Date : 2024-06-25 DOI:10.1007/s40436-024-00502-3
Yu-Xiang Ji, Li Huang, Qiu-Ren Chen, Charles K. S. Moy, Jing-Yi Zhang, Xiao-Ya Hu, Jian Wang, Guo-Bi Tan, Qing Liu
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Abstract

This paper presents a new machine learning-based calibration framework for strength simulation models of self-piercing riveted (SPR) joints. Strength simulations were conducted through the integrated modeling of SPR joints from process to performance, while physical quasi-static tensile tests were performed on combinations of DP600 high-strength steel and 5754 aluminum alloy sheets under lap-shear loading conditions. A sensitivity study of the critical simulation parameters (e.g., friction coefficient and scaling factor) was conducted using the controlled variables method and Sobol sensitivity analysis for feature selection. Subsequently, machine-learning-based surrogate models were used to train and accurately represent the mapping between the detailed joint profile and its load-displacement curve. Calibration of the simulation model is defined as a dual-objective optimization task to minimize errors in key load displacement features between simulations and experiments. A multi-objective genetic algorithm (MOGA) was chosen for optimization. The three combinations of SPR joints illustrated the effectiveness of the proposed framework, and good agreement was achieved between the calibrated models and experiments.

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基于机器学习的自冲铆接强度模拟校准方法
本文介绍了一种新的基于机器学习的自冲铆接(SPR)强度模拟模型校准框架。通过对 SPR 接头从工艺到性能的综合建模进行了强度模拟,同时在搭接剪切加载条件下对 DP600 高强度钢和 5754 铝合金板组合进行了物理准静态拉伸试验。利用控制变量法和用于特征选择的 Sobol 敏感性分析,对关键模拟参数(如摩擦系数和比例因子)进行了敏感性研究。随后,使用基于机器学习的代用模型进行训练,以准确表示详细关节轮廓与其载荷-位移曲线之间的映射关系。模拟模型的校准被定义为一项双目标优化任务,目的是最大限度地减少模拟和实验之间关键载荷位移特征的误差。优化选择了多目标遗传算法(MOGA)。SPR 接头的三种组合说明了所提议框架的有效性,校准模型与实验之间取得了良好的一致性。
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来源期刊
Advances in Manufacturing
Advances in Manufacturing Materials Science-Polymers and Plastics
CiteScore
9.10
自引率
3.80%
发文量
274
期刊介绍: As an innovative, fundamental and scientific journal, Advances in Manufacturing aims to describe the latest regional and global research results and forefront developments in advanced manufacturing field. As such, it serves as an international platform for academic exchange between experts, scholars and researchers in this field. All articles in Advances in Manufacturing are peer reviewed. Respected scholars from the fields of advanced manufacturing fields will be invited to write some comments. We also encourage and give priority to research papers that have made major breakthroughs or innovations in the fundamental theory. The targeted fields include: manufacturing automation, mechatronics and robotics, precision manufacturing and control, micro-nano-manufacturing, green manufacturing, design in manufacturing, metallic and nonmetallic materials in manufacturing, metallurgical process, etc. The forms of articles include (but not limited to): academic articles, research reports, and general reviews.
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