Fatigue Life Prediction of 2024-T3 Al Alloy by Integrating Particle Swarm Optimization—Extreme Gradient Boosting and Physical Model

The multi-parameter characteristics of the physical model pose a challenge to the fatigue life prediction of 2024-T3 aluminum (Al) alloy. In response to this issue, a parameter-solving method that integrates particle swarm optimization (PSO) with extreme gradient boosting (XGBoost) is proposed in th...

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Veröffentlicht in:	Materials Jg. 17; H. 21; S. 5332
Hauptverfasser:	Li, Zhaoji, Yue, Haitao, Zhang, Ce, Dai, Weibing, Guo, Chenguang, Li, Qiang, Zhang, Jianzhuo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Switzerland MDPI AG 01.11.2024
Schlagworte:	Accuracy Algorithms Alloys Aluminum alloys Aluminum base alloys Analysis Crack initiation Crack propagation Energy methods Error analysis Failure mechanisms Fatigue Fatigue failure Fatigue life Fatigue testing machines Fracture mechanics Hydraulics Life prediction Machine learning Materials Mathematical optimization Metal fatigue Parameters Particle swarm optimization Physical properties Prediction models Research methodology Specialty metals industry Stress concentration extreme gradient boosting particle swarm optimization Al alloy fatigue life multi-parameter physical model
ISSN:	1996-1944, 1996-1944
Online-Zugang:	Volltext
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