Machine Learning-Based Prediction of Fatigue Fracture Locations in 7075-T651 Aluminum Alloy Friction Stir Welded Joints
Friction stir welding (FSW) is a solid-state joining technique widely used for aluminum alloys in aerospace, automotive, and shipbuilding applications, yet the prediction of fatigue fracture locations within FSW joints remains challenging for structural-life assessment. In this study, we investigate...
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MDPI AG
2025-05-01
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| Series: | Metals |
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| Online Access: | https://www.mdpi.com/2075-4701/15/5/569 |
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| author | Guangming Mi Guoqin Sun Shuai Yang Xiaodong Liu Shujun Chen Wei Kang |
| author_facet | Guangming Mi Guoqin Sun Shuai Yang Xiaodong Liu Shujun Chen Wei Kang |
| author_sort | Guangming Mi |
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| description | Friction stir welding (FSW) is a solid-state joining technique widely used for aluminum alloys in aerospace, automotive, and shipbuilding applications, yet the prediction of fatigue fracture locations within FSW joints remains challenging for structural-life assessment. In this study, we investigate fatigue fracture location prediction in 7075-T651 aluminum alloy FSW joints by applying four machine learning methods—decision tree, logistic regression, a three-layer back-propagation artificial neural network (BP ANN), and a novel Quadratic Classification Neural Network (QCNN)—using maximum stress, stress amplitude, and stress ratio as input features. Evaluated on an experimental test set of eight loading conditions, the QCNN achieved the highest accuracy of 87.5%, outperforming BP ANN (75%), logistic regression (50%), and decision tree (37.5%). Building on QCNN outputs and incorporating relevant material property parameters, we derive a Regional Fracture Prediction Formula (RFPF) based on a Fourier-series quadratic expansion, enabling the rapid estimation of fracture zones under varying loads. These results demonstrate the QCNN’s superior predictive capability and the practical utility of the RFPF framework for the fatigue failure analysis and service-life assessment of FSW structures. |
| format | Article |
| id | doaj-art-4c981490e9424c6ea3df6a5528ab18f2 |
| institution | Kabale University |
| issn | 2075-4701 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Metals |
| spelling | doaj-art-4c981490e9424c6ea3df6a5528ab18f22025-08-20T03:47:58ZengMDPI AGMetals2075-47012025-05-0115556910.3390/met15050569Machine Learning-Based Prediction of Fatigue Fracture Locations in 7075-T651 Aluminum Alloy Friction Stir Welded JointsGuangming Mi0Guoqin Sun1Shuai Yang2Xiaodong Liu3Shujun Chen4Wei Kang5College of Mechanical and Energy Engineering, Beijing University of Technology, Beijing 100124, ChinaCollege of Mechanical and Energy Engineering, Beijing University of Technology, Beijing 100124, ChinaCollege of Mechanical and Energy Engineering, Beijing University of Technology, Beijing 100124, ChinaCollege of Mechanical and Energy Engineering, Beijing University of Technology, Beijing 100124, ChinaCollege of Mechanical and Energy Engineering, Beijing University of Technology, Beijing 100124, ChinaFundamental Frontier Research Center, Huairou Laboratory, Beijing 102209, ChinaFriction stir welding (FSW) is a solid-state joining technique widely used for aluminum alloys in aerospace, automotive, and shipbuilding applications, yet the prediction of fatigue fracture locations within FSW joints remains challenging for structural-life assessment. In this study, we investigate fatigue fracture location prediction in 7075-T651 aluminum alloy FSW joints by applying four machine learning methods—decision tree, logistic regression, a three-layer back-propagation artificial neural network (BP ANN), and a novel Quadratic Classification Neural Network (QCNN)—using maximum stress, stress amplitude, and stress ratio as input features. Evaluated on an experimental test set of eight loading conditions, the QCNN achieved the highest accuracy of 87.5%, outperforming BP ANN (75%), logistic regression (50%), and decision tree (37.5%). Building on QCNN outputs and incorporating relevant material property parameters, we derive a Regional Fracture Prediction Formula (RFPF) based on a Fourier-series quadratic expansion, enabling the rapid estimation of fracture zones under varying loads. These results demonstrate the QCNN’s superior predictive capability and the practical utility of the RFPF framework for the fatigue failure analysis and service-life assessment of FSW structures.https://www.mdpi.com/2075-4701/15/5/569fatigue fracturemachine learningfriction stir weldingartificial neural network |
| spellingShingle | Guangming Mi Guoqin Sun Shuai Yang Xiaodong Liu Shujun Chen Wei Kang Machine Learning-Based Prediction of Fatigue Fracture Locations in 7075-T651 Aluminum Alloy Friction Stir Welded Joints Metals fatigue fracture machine learning friction stir welding artificial neural network |
| title | Machine Learning-Based Prediction of Fatigue Fracture Locations in 7075-T651 Aluminum Alloy Friction Stir Welded Joints |
| title_full | Machine Learning-Based Prediction of Fatigue Fracture Locations in 7075-T651 Aluminum Alloy Friction Stir Welded Joints |
| title_fullStr | Machine Learning-Based Prediction of Fatigue Fracture Locations in 7075-T651 Aluminum Alloy Friction Stir Welded Joints |
| title_full_unstemmed | Machine Learning-Based Prediction of Fatigue Fracture Locations in 7075-T651 Aluminum Alloy Friction Stir Welded Joints |
| title_short | Machine Learning-Based Prediction of Fatigue Fracture Locations in 7075-T651 Aluminum Alloy Friction Stir Welded Joints |
| title_sort | machine learning based prediction of fatigue fracture locations in 7075 t651 aluminum alloy friction stir welded joints |
| topic | fatigue fracture machine learning friction stir welding artificial neural network |
| url | https://www.mdpi.com/2075-4701/15/5/569 |
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