Prediction of corrosion fatigue crack growth rate in 7050 aluminum alloy based on machine learning

Jiachuan Xie

doi:10.1117/12.3051761

18 November 2024 Prediction of corrosion fatigue crack growth rate in 7050 aluminum alloy based on machine learning

Jiachuan Xie

Proceedings Volume 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) ; 134032Y (2024) https://doi.org/10.1117/12.3051761
Event: International Conference on Algorithms, High Performance Computing, and Artificial Intelligence, 2024, Zhengzhou, China

Abstract

This study aims to predict the corrosion fatigue crack propagation rate in 7050 aluminum alloy using machine learning methods, specifically models based on the XGBoost algorithm, BP neural network, and random forest. Due to the widespread application of 7050 aluminum alloy in aviation, automotive, and other industries, accurate prediction of its corrosion fatigue performance is crucial for enhancing the reliability of engineering structures. By thoroughly analyzing the research background of 7050 aluminum alloy, trends in fatigue life prediction, and machine learning feature selection algorithms, significant features are identified for the XGBoost algorithm, BP neural network, and random forest. The performance of these three algorithms is compared, and the XGBoost algorithm is ultimately selected for its excellent predictive performance, achieving a mean squared error of 0.94 and a coefficient of determination of 0.98. The results provide valuable insights into corrosion fatigue life prediction for aerospace-grade 7050 aluminum alloy and establish a theoretical foundation for subsequent model development.

Citation Download Citation

Jiachuan Xie "Prediction of corrosion fatigue crack growth rate in 7050 aluminum alloy based on machine learning", Proc. SPIE 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) , 134032Y (18 November 2024); https://doi.org/10.1117/12.3051761

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