基于混合机器学习模型的两级加载下金属材料的剩余疲劳寿命预测方法

doi:10.3901/JME.2025.12.073

摘要/Abstract

摘要： 剩余疲劳寿命预测对于保障工程结构的安全可靠至关重要。特别是，当结构物遭受变幅值载荷时，由于疲劳损伤累积的复杂性性以及数据中固有的噪声和不确定性，使得剩余疲劳寿命预测具有挑战性。为了解决这个问题，建立混合机器学习模型GA-BP-GPR方法，该方法使用GA-BP方法(遗传算法-反向传播神经网络)映射输入和剩余寿命之间的复杂关系结合GPR (高斯过程回归)获取相应的不确定性，用于估计两级加载下金属材料的剩余疲劳寿命。在包含12种金属材料、总共328个样本的数据集上对GA-BP-GPR方法进行全面评估。GA-BP-GPR方法对测试集所有预测结果都落在3倍分散带内；与5种机器学习方法和2种传统疲劳预测方法对比结果表明所提方法对两步载荷下的剩余寿命预测具有较高的准确性和可靠性。此外，所提方法对没有参与训练的Al-2024-T42的预测结果都在2倍分散带内，表明所提方法对新材料剩余疲劳寿命预测的泛化能力。

关键词: 剩余疲劳寿命, 疲劳寿命预测, 遗传算法, 高斯过程回归, 机器学习

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

中图分类号:

O346

徐自力, 高京京, 覃曼青, 何孟夫. 基于混合机器学习模型的两级加载下金属材料的剩余疲劳寿命预测方法[J]. 机械工程学报, 2025, 61(12): 73-82.

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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链接本文: http://www.cjmenet.com.cn/CN/10.3901/JME.2025.12.073

http://www.cjmenet.com.cn/CN/Y2025/V61/I12/73

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