Quality prediction method for automotive body resistance spot welding based on digital twin technology
Abstract In the resistance spot welding (RSW) process of automotive bodies, accurately predicting the welding quality is of vital importance for ensuring the safety and reliability of vehicles. However, traditional prediction methods are limited by the constraints of on-site data collection, which p...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-09959-z |
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| author | Ruiping Luo Shengwen Zhou Liangyi Nie Bowen Dong |
| author_facet | Ruiping Luo Shengwen Zhou Liangyi Nie Bowen Dong |
| author_sort | Ruiping Luo |
| collection | DOAJ |
| description | Abstract In the resistance spot welding (RSW) process of automotive bodies, accurately predicting the welding quality is of vital importance for ensuring the safety and reliability of vehicles. However, traditional prediction methods are limited by the constraints of on-site data collection, which poses challenges to the accuracy of predictions. Therefore, a new RSW quality prediction method based on digital twin (DT) is proposed. Firstly, virtual data of dynamic resistance is generated through the DT model. Then, the generative adversarial network (GAN) method is employed to expand the virtual data and physical data. Finally, the prediction model of RSW quality is constructed through a back propagation (BP) neural network and Bayesian optimization method. Finally, a case study is conducted to verify the effect of virtual data on the performance of the prediction model and demonstrate the effectiveness of the proposed method. In addition, the SHAP (Shapley additive explanations) method is used to investigate the impact of various input variables on RSW quality, thereby establishing a foundation for optimizing the RSW process. |
| format | Article |
| id | doaj-art-77eacc1da993427fab9a666a10447128 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-77eacc1da993427fab9a666a104471282025-08-20T04:02:45ZengNature PortfolioScientific Reports2045-23222025-07-0115111610.1038/s41598-025-09959-zQuality prediction method for automotive body resistance spot welding based on digital twin technologyRuiping Luo0Shengwen Zhou1Liangyi Nie2Bowen Dong3School of Mechanical and Electrical Engineering, Hubei Polytechnic UniversitySchool of Mechanical and Electrical Engineering, Hubei Polytechnic UniversitySchool of Mechanical and Electrical Engineering, Hubei Polytechnic UniversitySchool of Mechanical and Electrical Engineering, Hubei Polytechnic UniversityAbstract In the resistance spot welding (RSW) process of automotive bodies, accurately predicting the welding quality is of vital importance for ensuring the safety and reliability of vehicles. However, traditional prediction methods are limited by the constraints of on-site data collection, which poses challenges to the accuracy of predictions. Therefore, a new RSW quality prediction method based on digital twin (DT) is proposed. Firstly, virtual data of dynamic resistance is generated through the DT model. Then, the generative adversarial network (GAN) method is employed to expand the virtual data and physical data. Finally, the prediction model of RSW quality is constructed through a back propagation (BP) neural network and Bayesian optimization method. Finally, a case study is conducted to verify the effect of virtual data on the performance of the prediction model and demonstrate the effectiveness of the proposed method. In addition, the SHAP (Shapley additive explanations) method is used to investigate the impact of various input variables on RSW quality, thereby establishing a foundation for optimizing the RSW process.https://doi.org/10.1038/s41598-025-09959-zDigital twinResistance spot weldingQuality predictionBayesian optimizationSHAP |
| spellingShingle | Ruiping Luo Shengwen Zhou Liangyi Nie Bowen Dong Quality prediction method for automotive body resistance spot welding based on digital twin technology Scientific Reports Digital twin Resistance spot welding Quality prediction Bayesian optimization SHAP |
| title | Quality prediction method for automotive body resistance spot welding based on digital twin technology |
| title_full | Quality prediction method for automotive body resistance spot welding based on digital twin technology |
| title_fullStr | Quality prediction method for automotive body resistance spot welding based on digital twin technology |
| title_full_unstemmed | Quality prediction method for automotive body resistance spot welding based on digital twin technology |
| title_short | Quality prediction method for automotive body resistance spot welding based on digital twin technology |
| title_sort | quality prediction method for automotive body resistance spot welding based on digital twin technology |
| topic | Digital twin Resistance spot welding Quality prediction Bayesian optimization SHAP |
| url | https://doi.org/10.1038/s41598-025-09959-z |
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