改进 RSW 金块直径预测方法:释放多保真度神经网络和迁移学习的力量

IF 4.2 2区 工程技术 Q2 ENGINEERING, MANUFACTURING Advances in Manufacturing Pub Date : 2024-06-18 DOI:10.1007/s40436-024-00503-2
Zhong-Jie Yue, Qiu-Ren Chen, Zu-Guo Bao, Li Huang, Guo-Bi Tan, Ze-Hong Hou, Mu-Shi Li, Shi-Yao Huang, Hai-Long Zhao, Jing-Yu Kong, Jia Wang, Qing Liu
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引用次数: 0

摘要

本研究提出了一种创新方法,利用机器学习和迁移学习方法准确预测电阻点焊(RSW)中的焊块直径。首先,通过有限元数值模拟和实验设计(DOE)获得低保真(LF)数据,以训练 LF 机器学习模型。随后,通过 RSW 工艺实验收集高保真(HF)数据,并利用迁移学习技术对低保真模型进行微调。模型的准确性和泛化性能得到了全面验证。结果表明,结合不同保真度的数据集并采用迁移学习可以显著提高预测精度,同时最大限度地降低与实验相关的成本,为预测 RSW 的关键工艺参数提供了一种有效且有价值的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Improving RSW nugget diameter prediction method: unleashing the power of multi-fidelity neural networks and transfer learning

This research presents an innovative approach to accurately predict the nugget diameter in resistance spot welding (RSW) by leveraging machine learning and transfer learning methods. Initially, low-fidelity (LF) data were obtained through finite element numerical simulation and design of experiments (DOEs) to train the LF machine learning model. Subsequently, high-fidelity (HF) data were collected from RSW process experiments and used to fine-tune the LF model by transfer learning techniques. The accuracy and generalization performance of the models were thoroughly validated. The results demonstrated that combining different fidelity datasets and employing transfer learning could significantly improve the prediction accuracy while minimize the costs associated with experimental trials, and provide an effective and valuable method for predicting critical process parameters in RSW.

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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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