Hybrid Machine Learning Method for Remaining Fatigue Life Prediction of the Metallic Materials under Two-step Loading

doi:10.3901/JME.2025.12.073

Abstract

Abstract: Remaining fatigue life prediction is crucial for the safety and reliability of engineering structures. When the structure is subjected to multi-step loading, it becomes challenging to predict the remaining fatigue life due to the complexity of fatigue damage accumulation and inherent noise and uncertainty in the data. To address this problem, a hybrid machine learning method, the GA-BP-GPR method, is established, which uses the GA-BP method (genetic algorithm-backpropagation neural network) to map the complex relationship between input features and remaining fatigue life and combines with GPR (Gaussian process regression) to obtain the corresponding uncertainty, for estimating the remaining fatigue life of metal materials under two-step loading. The GA-BP-GPR method is comprehensively evaluated on a dataset of 328 samples from 12 metal materials. The predicted results of the test set using the proposed method fall within the ±3 scatter band. In comparison with the five machine learning methods and two traditional methods, the results indicate that the proposed method can achieve higher accuracy and reliability in predicting remaining fatigue life under two-step loading. In addition, the predicted results of the proposed method for Al-2024-T42 fall within the ±2 scatter band, indicating the proposed method has generalization ability for remaining fatigue life prediction under two-step loading for new materials.

Key words: remaining fatigue life, fatigue life prediction, genetic algorithm, Gaussian process regression, machine learning

CLC Number:

O346

XU Zili, GAO Jingjing, QIN Manqing, HE Mengfu. Hybrid Machine Learning Method for Remaining Fatigue Life Prediction of the Metallic Materials under Two-step Loading[J]. Journal of Mechanical Engineering, 2025, 61(12): 73-82.

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