用神经网络预测汽车钢疲劳寿命，精度高达95%以上。
Modelling of automotive steel fatigue lifetime by machine learning method
- 采用3-75-1结构MLP神经网络建模
- 不同工况下平均误差仅0.02%至4.59%
- 适合材料疲劳寿命预测与工程可靠性分析
本研究采用多层感知机（MLP）神经网络对QSTE340TM钢的疲劳寿命进行建模，网络结构为3-75-1，可根据载荷循环次数N、应力比R和过载比Rol预测裂纹长度。该模型表现出高精度，不同R和Rol条件下平均绝对百分比误差（MAPE）在0.02%至4.59%之间。神经网络有效捕捉了输入参数与疲劳裂纹扩展间的非线性关系，在多种加载条件下均提供可靠预测。
原文摘要 · Abstract (English)
In the current study, the fatigue life of QSTE340TM steel was modelled using a machine learning method, namely, a neural network. This problem was solved by a Multi-Layer Perceptron (MLP) neural network with a 3-75-1 architecture, which allows the prediction of the crack length based on the number of load cycles N, the stress ratio R, and the overload ratio Rol. The proposed model showed high accuracy, with mean absolute percentage error (MAPE) ranging from 0.02% to 4.59% for different R and Rol. The neural network effectively reveals the nonlinear relationships between input parameters and fatigue crack growth, providing reliable predictions for different loading conditions.
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