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.
The full text can be downloaded at https://link.springer.com/article/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
. Improving RSW nugget diameter prediction method: unleashing the power of multi-fidelity neural networks and transfer learning[J]. Advances in Manufacturing, 2024
, 12(3)
: 409
-427
.
DOI: 10.1007/s40436-024-00503-2
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