Assessment of the effect of the process-induced porosity defects on the fatigue properties of wire arc additive manufactured Al–Si–Mg alloy
Process-induced porosity significantly affects the fatigue resistance of additive-manufactured components under cyclic loads. In this study, the influence of process-induced porosity defect characteristics on the fatigue properties of wire arc additive manufacturing (WAAM) Al–Si–Mg parts was quantit...
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Elsevier
2025-03-01
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| Series: | Journal of Materials Research and Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2238785425000833 |
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| author | Teng Zhan Ke Xu Zhipeng Fan Hanlin Xiang Congchang Xu Tianjiao Mei Yuanyuan Wei Wentao Chen Luoxing Li |
| author_facet | Teng Zhan Ke Xu Zhipeng Fan Hanlin Xiang Congchang Xu Tianjiao Mei Yuanyuan Wei Wentao Chen Luoxing Li |
| author_sort | Teng Zhan |
| collection | DOAJ |
| description | Process-induced porosity significantly affects the fatigue resistance of additive-manufactured components under cyclic loads. In this study, the influence of process-induced porosity defect characteristics on the fatigue properties of wire arc additive manufacturing (WAAM) Al–Si–Mg parts was quantitatively analyzed using the Kitagawa-Takahashi diagram and machine learning methods. Two specimens with different porosity and distributions were fabricated using two welding wires with different surface states. The cross-section porosity of the two groups was 0.42% and 2.06%, respectively. According to the test results, the static strength of the two groups was equivalent, but the elongation of the porosity group specimen was reduced by 58.7%, and the fatigue strength (139.8 MPa) was lower than that of the control group (160.2 MPa). Combining high-cycle fatigue post-mortem inspection and scanning electron microscopy analysis, the geometric features of the critical defects were obtained. The fatigue performance was assessed by combining extreme value statistics and the Kitagawa-Takahashi diagram according to critical defects, which showed good consistency but was still not conservative as some failure data were within the safe-life range. Therefore, four parameters of applied stress and the projected area, location, and morphology of the critical defects were trained using an extreme gradient boosting model (XGBoost) and random forest (RF). The XGBoost model is 95.7 % more accurate than the RF model in predicting fatigue life. The importance of four parameters in limiting fatigue life is ranked in the above order. |
| format | Article |
| id | doaj-art-9b699fc33d7e420491c372bd608481c5 |
| institution | Kabale University |
| issn | 2238-7854 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
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| series | Journal of Materials Research and Technology |
| spelling | doaj-art-9b699fc33d7e420491c372bd608481c52025-01-16T04:28:50ZengElsevierJournal of Materials Research and Technology2238-78542025-03-0135777791Assessment of the effect of the process-induced porosity defects on the fatigue properties of wire arc additive manufactured Al–Si–Mg alloyTeng Zhan0Ke Xu1Zhipeng Fan2Hanlin Xiang3Congchang Xu4Tianjiao Mei5 Yuanyuan Wei6Wentao Chen7Luoxing Li8College of Mechanical and Vehicle Engineering, Hunan University, Changsha, 410082, ChinaChongqing Changan Automobile Co., Ltd, Chongqing, 400023, ChinaChongqing Changan Automobile Co., Ltd, Chongqing, 400023, ChinaCollege of Mechanical and Vehicle Engineering, Hunan University, Changsha, 410082, ChinaCollege of Mechanical and Vehicle Engineering, Hunan University, Changsha, 410082, China; Corresponding author.College of Mechanical and Vehicle Engineering, Hunan University, Changsha, 410082, ChinaCollege of Mechanical and Vehicle Engineering, Hunan University, Changsha, 410082, ChinaHunan Lince Rolling Stock Equipment Co., Ltd., Zhuzhou, 412001, ChinaCollege of Mechanical and Vehicle Engineering, Hunan University, Changsha, 410082, ChinaProcess-induced porosity significantly affects the fatigue resistance of additive-manufactured components under cyclic loads. In this study, the influence of process-induced porosity defect characteristics on the fatigue properties of wire arc additive manufacturing (WAAM) Al–Si–Mg parts was quantitatively analyzed using the Kitagawa-Takahashi diagram and machine learning methods. Two specimens with different porosity and distributions were fabricated using two welding wires with different surface states. The cross-section porosity of the two groups was 0.42% and 2.06%, respectively. According to the test results, the static strength of the two groups was equivalent, but the elongation of the porosity group specimen was reduced by 58.7%, and the fatigue strength (139.8 MPa) was lower than that of the control group (160.2 MPa). Combining high-cycle fatigue post-mortem inspection and scanning electron microscopy analysis, the geometric features of the critical defects were obtained. The fatigue performance was assessed by combining extreme value statistics and the Kitagawa-Takahashi diagram according to critical defects, which showed good consistency but was still not conservative as some failure data were within the safe-life range. Therefore, four parameters of applied stress and the projected area, location, and morphology of the critical defects were trained using an extreme gradient boosting model (XGBoost) and random forest (RF). The XGBoost model is 95.7 % more accurate than the RF model in predicting fatigue life. The importance of four parameters in limiting fatigue life is ranked in the above order.http://www.sciencedirect.com/science/article/pii/S2238785425000833Porosity defectsWire arc additive manufacturingAl–Si7–Mg0.6 alloyMachine learning modelFatigue assessment |
| spellingShingle | Teng Zhan Ke Xu Zhipeng Fan Hanlin Xiang Congchang Xu Tianjiao Mei Yuanyuan Wei Wentao Chen Luoxing Li Assessment of the effect of the process-induced porosity defects on the fatigue properties of wire arc additive manufactured Al–Si–Mg alloy Journal of Materials Research and Technology Porosity defects Wire arc additive manufacturing Al–Si7–Mg0.6 alloy Machine learning model Fatigue assessment |
| title | Assessment of the effect of the process-induced porosity defects on the fatigue properties of wire arc additive manufactured Al–Si–Mg alloy |
| title_full | Assessment of the effect of the process-induced porosity defects on the fatigue properties of wire arc additive manufactured Al–Si–Mg alloy |
| title_fullStr | Assessment of the effect of the process-induced porosity defects on the fatigue properties of wire arc additive manufactured Al–Si–Mg alloy |
| title_full_unstemmed | Assessment of the effect of the process-induced porosity defects on the fatigue properties of wire arc additive manufactured Al–Si–Mg alloy |
| title_short | Assessment of the effect of the process-induced porosity defects on the fatigue properties of wire arc additive manufactured Al–Si–Mg alloy |
| title_sort | assessment of the effect of the process induced porosity defects on the fatigue properties of wire arc additive manufactured al si mg alloy |
| topic | Porosity defects Wire arc additive manufacturing Al–Si7–Mg0.6 alloy Machine learning model Fatigue assessment |
| url | http://www.sciencedirect.com/science/article/pii/S2238785425000833 |
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