Machine learning for predicting resistance spot weld quality in automotive manufacturing
Resistance Spot Welding (RSW) stands as the primary joining process in the automotive industry, renowned for its suitability for automation and integration into high-production assembly lines. Despite its advantages, accurately evaluating RSW remains challenging, resulting in additional costs and pr...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123024018139 |
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| author | Nuttapong Chuenmee Nattachai Phothi Kontorn Chamniprasart Sorada Khaengkarn Jiraphon Srisertpol |
| author_facet | Nuttapong Chuenmee Nattachai Phothi Kontorn Chamniprasart Sorada Khaengkarn Jiraphon Srisertpol |
| author_sort | Nuttapong Chuenmee |
| collection | DOAJ |
| description | Resistance Spot Welding (RSW) stands as the primary joining process in the automotive industry, renowned for its suitability for automation and integration into high-production assembly lines. Despite its advantages, accurately evaluating RSW remains challenging, resulting in additional costs and production steps. Current inspection methods, reliant on random checks after cars leave the Body-in-White (BIW), often lead to significant time losses, emphasizing the necessity for enhanced quality assessment. This study aims to transition from random checks to 100 percent inspection using data analysis and machine learning techniques. By predicting weld quality levels prior to car body completion, this approach aims to improve quality control. Five distinct algorithms—Artificial Neural Network (ANN), Convolution Neural Network (CNN), Long Short-Term Memory (LSTM), Random Forest Classifier (RFC), and Extreme Gradient Boosting (XGBoost)—were assessed. The research highlights that the proposed methodology, particularly leveraging XGBoost, achieves a notable prediction accuracy of 97.1% when applied to unseen data. |
| format | Article |
| id | doaj-art-7a8f5ed1727a4ea3a3aa6daf1c4f725d |
| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-7a8f5ed1727a4ea3a3aa6daf1c4f725d2024-12-26T08:57:28ZengElsevierResults in Engineering2590-12302025-03-0125103570Machine learning for predicting resistance spot weld quality in automotive manufacturingNuttapong Chuenmee0Nattachai Phothi1Kontorn Chamniprasart2Sorada Khaengkarn3Jiraphon Srisertpol4Mechatronics Engineering Program, School of Mechanical Engineering, Suranaree University of Technology, Nakhon Ratchasima, ThailandDepartment of Production Engineering Technology, Faculty of Industrial Technology, Loei Rajabhat University, Loei, ThailandMechatronics Engineering Program, School of Mechanical Engineering, Suranaree University of Technology, Nakhon Ratchasima, ThailandMechatronics Engineering Program, School of Mechanical Engineering, Suranaree University of Technology, Nakhon Ratchasima, ThailandMechatronics Engineering Program, School of Mechanical Engineering, Suranaree University of Technology, Nakhon Ratchasima, Thailand; Corresponding author.Resistance Spot Welding (RSW) stands as the primary joining process in the automotive industry, renowned for its suitability for automation and integration into high-production assembly lines. Despite its advantages, accurately evaluating RSW remains challenging, resulting in additional costs and production steps. Current inspection methods, reliant on random checks after cars leave the Body-in-White (BIW), often lead to significant time losses, emphasizing the necessity for enhanced quality assessment. This study aims to transition from random checks to 100 percent inspection using data analysis and machine learning techniques. By predicting weld quality levels prior to car body completion, this approach aims to improve quality control. Five distinct algorithms—Artificial Neural Network (ANN), Convolution Neural Network (CNN), Long Short-Term Memory (LSTM), Random Forest Classifier (RFC), and Extreme Gradient Boosting (XGBoost)—were assessed. The research highlights that the proposed methodology, particularly leveraging XGBoost, achieves a notable prediction accuracy of 97.1% when applied to unseen data.http://www.sciencedirect.com/science/article/pii/S2590123024018139Resistance spot welding (RSW)Weld inspectionWeld quality classificationData analysisMachine learning |
| spellingShingle | Nuttapong Chuenmee Nattachai Phothi Kontorn Chamniprasart Sorada Khaengkarn Jiraphon Srisertpol Machine learning for predicting resistance spot weld quality in automotive manufacturing Results in Engineering Resistance spot welding (RSW) Weld inspection Weld quality classification Data analysis Machine learning |
| title | Machine learning for predicting resistance spot weld quality in automotive manufacturing |
| title_full | Machine learning for predicting resistance spot weld quality in automotive manufacturing |
| title_fullStr | Machine learning for predicting resistance spot weld quality in automotive manufacturing |
| title_full_unstemmed | Machine learning for predicting resistance spot weld quality in automotive manufacturing |
| title_short | Machine learning for predicting resistance spot weld quality in automotive manufacturing |
| title_sort | machine learning for predicting resistance spot weld quality in automotive manufacturing |
| topic | Resistance spot welding (RSW) Weld inspection Weld quality classification Data analysis Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2590123024018139 |
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