基于网格搜索优化的LSTM多轴疲劳寿命预测方法

Grid Search Optimized LSTM Method for Multi-Axial Fatigue Life Prediction

传统的多轴疲劳寿命预测模型通常局限于特定的材料和载荷条件。为了应对这种不足,本研究采用深度学习方法处理不同的多轴疲劳加载条件,使用网格搜索优化算法确定长短期记忆网络(LSTM)深度学习模型的学习率、隐藏层数和迭代次数3个超参数的最优组合,使深度学习模型达到最佳预测性能。使用该方法对纯钛、SS 316L、TC4铝合金3种材料进行寿命预测,结果均在2倍误差带以内,且1.5倍误差带内的预测结果占比较高。利用2组调整过的数据集对所提方法的外推能力进行测试,结果表明,所提方法对未知加载路径的外推能力较好。基于网格搜索的LSTM多轴疲劳寿命预测方法可用于各种材料、各种加载路径的多轴疲劳寿命预测。

Traditional models for predicting multi-axial fatigue life are typically limited to specific materials and loading conditions.To address this limitation,this study adopts a deep learning approach to handle different multi-axial fatigue loading conditions.By using a grid search optimization algorithm,the optimal combination for three hyperparameters of the LSTM deep learning modellearning rate,number of hidden layers,and number of iterations are determined to achieve the best predictive performance.The method was applied to predict the fatigue life of three materials including pure titanium,SS 316L,and TC4 aluminum alloy.The results were all within the scatter band of two times the standard deviation,with a significant portion of predictions falling within the scatter band of 1.5 times.Furthermore,the extrapolation ability of the proposed method was validated using two sets of adjusted datasets,demonstrating good extrapolation performance for unknown loading paths.Therefore,the grid search optimized LSTM method for multi-axial fatigue life prediction can be applied to various materials and loading paths for multi-axial fatigue life prediction.