{"title":"利用基于元模型的多目标优化技术提高激光焊接异种材料电池-母线接头的接头质量","authors":"","doi":"10.1016/j.jajp.2024.100261","DOIUrl":null,"url":null,"abstract":"<div><div>In the battery pack assembly, it is essential to ensure that the cell-to-busbar joints can be produced with high quality and with minimal impact on the individual battery cells. This study examines the influence of process parameters on the joint quality for nickel-plated copper and steel plates, laser welded in an overlap configuration. Artificial neural network-based meta models, trained on numerical results from computational fluid dynamics simulations of the laser welding process, are used to predict and evaluate the joint quality. A set of optimized process parameters is identified, in order to simultaneously maximize the interface width for the joints, and minimize the formation of undercuts and in-process temperatures. In an meta model-based multi-objective optimization approach, the non-dominated sorting genetic algorithm II (NSGA-II) is used to efficiently search for trade-off solutions and the meta models are used for objective approximation. As a result, the objective evaluation time is decreased from around 9 h, when evaluated directly from numerical simulations, to only tenths of a second. From the Pareto-optimal front of trade-off solutions, three optimal solutions are selected for validation. The selected solutions are validated through laser welding experiments and numerical simulations, resulting in joints with large interface widths and low in-process temperatures without a full penetration.</div></div>","PeriodicalId":34313,"journal":{"name":"Journal of Advanced Joining Processes","volume":null,"pages":null},"PeriodicalIF":3.8000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Enhancement of joint quality for laser welded dissimilar material cell-to-busbar joints using meta model-based multi-objective optimization\",\"authors\":\"\",\"doi\":\"10.1016/j.jajp.2024.100261\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>In the battery pack assembly, it is essential to ensure that the cell-to-busbar joints can be produced with high quality and with minimal impact on the individual battery cells. This study examines the influence of process parameters on the joint quality for nickel-plated copper and steel plates, laser welded in an overlap configuration. Artificial neural network-based meta models, trained on numerical results from computational fluid dynamics simulations of the laser welding process, are used to predict and evaluate the joint quality. A set of optimized process parameters is identified, in order to simultaneously maximize the interface width for the joints, and minimize the formation of undercuts and in-process temperatures. In an meta model-based multi-objective optimization approach, the non-dominated sorting genetic algorithm II (NSGA-II) is used to efficiently search for trade-off solutions and the meta models are used for objective approximation. As a result, the objective evaluation time is decreased from around 9 h, when evaluated directly from numerical simulations, to only tenths of a second. From the Pareto-optimal front of trade-off solutions, three optimal solutions are selected for validation. The selected solutions are validated through laser welding experiments and numerical simulations, resulting in joints with large interface widths and low in-process temperatures without a full penetration.</div></div>\",\"PeriodicalId\":34313,\"journal\":{\"name\":\"Journal of Advanced Joining Processes\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.8000,\"publicationDate\":\"2024-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Advanced Joining Processes\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2666330924000773\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MATERIALS SCIENCE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Advanced Joining Processes","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666330924000773","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
摘要
在电池组组装过程中,必须确保电池单元与母线的连接质量高,并且对单个电池单元的影响最小。本研究探讨了工艺参数对重叠配置激光焊接镀镍铜板和钢板接头质量的影响。基于人工神经网络的元模型是在激光焊接过程的计算流体动力学模拟数值结果的基础上训练而成的,用于预测和评估接头质量。确定了一组优化的工艺参数,以便同时最大限度地增加接头的界面宽度,并最大限度地减少缺口的形成和降低加工过程中的温度。在基于元模型的多目标优化方法中,非支配排序遗传算法 II (NSGA-II) 被用来高效地搜索折衷解决方案,元模型被用来进行目标近似。因此,目标评估时间从直接从数值模拟评估的约 9 小时缩短到仅十分之一秒。从权衡解决方案的帕累托最优前沿中,选出三个最优解决方案进行验证。所选方案通过激光焊接实验和数值模拟进行了验证,结果是接头界面宽度大,加工过程温度低,且没有完全熔透。
Enhancement of joint quality for laser welded dissimilar material cell-to-busbar joints using meta model-based multi-objective optimization
In the battery pack assembly, it is essential to ensure that the cell-to-busbar joints can be produced with high quality and with minimal impact on the individual battery cells. This study examines the influence of process parameters on the joint quality for nickel-plated copper and steel plates, laser welded in an overlap configuration. Artificial neural network-based meta models, trained on numerical results from computational fluid dynamics simulations of the laser welding process, are used to predict and evaluate the joint quality. A set of optimized process parameters is identified, in order to simultaneously maximize the interface width for the joints, and minimize the formation of undercuts and in-process temperatures. In an meta model-based multi-objective optimization approach, the non-dominated sorting genetic algorithm II (NSGA-II) is used to efficiently search for trade-off solutions and the meta models are used for objective approximation. As a result, the objective evaluation time is decreased from around 9 h, when evaluated directly from numerical simulations, to only tenths of a second. From the Pareto-optimal front of trade-off solutions, three optimal solutions are selected for validation. The selected solutions are validated through laser welding experiments and numerical simulations, resulting in joints with large interface widths and low in-process temperatures without a full penetration.