Modelling of automotive steel fatigue lifetime by machine learning method
This work addresses fatigue prediction for automotive steel, which is incremental as it applies an existing machine learning method to a specific material and dataset.
The study tackled the problem of predicting fatigue life for QSTE340TM steel by using a Multi-Layer Perceptron neural network, achieving high accuracy with mean absolute percentage error ranging from 0.02% to 4.59% across different stress and overload ratios.
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.