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

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外文摘要：This research presents an innovative approach to accurately predict the nugget diameter in resistance spot welding(RSW)by leveraging machine learning and trans-fer learning methods.Initially,low-fidelity(LF)data were obtained through finite element numerical simulation and design of experiments(DOEs)to train the LF machine learn-ing model.Subsequently,high-fidelity(HF)data were col-lected 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 thor-oughly 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 criti-cal process parameters in RSW.

外文关键词：

Resistance spot welding(RSW)Nugget diameter predictionMulti-fidelity neural networksTransfer learning

作者：

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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作者单位：

Key Laboratory for Light-weight Materials,Nanjing Tech University,Nanjing 210009,People's Republic of China

School of Advanced Technology,Xi'an Jiaotong-Liverpool University,Suzhou 215123,Jiangsu,People's Republic of China

Material Academy,JITRI,Suzhou 215100,Jiangsu,People's Republic of China

School of Mechanical Engineering,Shanghai Jiao Tong University,Shanghai 200240,People's Republic of China

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出版年：

2024

DOI：

10.1007/s40436-024-00503-2

先进制造进展(英文版)

ISSN：

年,卷(期)：2024.12(3)