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
{"title":"A machine learning-based calibration method for strength simulation of self-piercing riveted joints","authors":"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","doi":"10.1007/s40436-024-00502-3","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":7342,"journal":{"name":"Advances in Manufacturing","volume":null,"pages":null},"PeriodicalIF":4.2000,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Manufacturing","FirstCategoryId":"5","ListUrlMain":"https://link.springer.com/article/10.1007/s40436-024-00502-3","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, MANUFACTURING","Score":null,"Total":0}
引用次数: 0
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.
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.