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
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Zusammenfassung: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 this study. The fatigue performance and failure mechanism of the 2024-T3 Al alloy are analyzed. Furthermore, the fatigue life prediction physical model of the 2024-T3 Al alloy is established by using the energy method of fracture mechanics. The physical model incorporates critical physical parameters. Meanwhile, the PSO algorithm optimizes the hyperparameters of the XGBoost model based on fatigue data of the 2024-T3 Al alloy. Eventually, the optimized XGBoost model is used to solve the parameters of the physical model. Furthermore, the analytical equation of the fatigue life prediction model is obtained. This paper provides a new method for solving the parameters of the fatigue life prediction model, which reduces the error and cost of obtaining the model parameters in the experiment and shortens the time required.
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ISSN:1996-1944
1996-1944
DOI:10.3390/ma17215332