Modelling of automotive steel fatigue lifetime by machine learning method

• URL: http://arxiv.org/abs/2501.11154v1
• Date: Sun, 19 Jan 2025 19:46:48 GMT
• Title: Modelling of automotive steel fatigue lifetime by machine learning method
• Authors: Oleh Yasniy, Dmytro Tymoshchuk, Iryna Didych, Nataliya Zagorodna, Olha Malyshevska,
• Abstract summary: This problem was solved by a Multi-Layer Perceptron (MLP) neural network with a 3-75-1 architecture. The proposed model showed high accuracy, with mean absolute percentage error (MAPE) ranging from 0.02% to 4.59%.
• Score: 0.0
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• Abstract: 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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