A Surrogate Artificial Neural Network Model for Estimating the Fatigue Life of Steel Components Based on Finite Element Simulations

Abstract

A surrogate artificial neural network (ANN) model trained on the data generated from a computational finite element-based (FE-based) model is developed. The developed ANN model enables the estimation of the fatigue life (number of load cycles to failure) of component-like specimens with stress concentrators. Using the developed model, the component-specific S-N curves can be generated with an accuracy comparable to that of the computational FE-based model. The investigation covered through- and surface-hardened steel components with different numbers and types of stress concentrators. The basis for data generation is the parametrized computational FE-based model, which enables the determination of the stress–strain response and the calculation of the fatigue life of examined components under cyclic loading conditions. The computational FE-based model can be adjusted to include components with different geometries and heat treatment conditions. The computational FE-based model incorporates nonlinear material behavior to provide a more accurate representation of the component’s behavior, which results in higher computational costs. In contrast, the developed ANN model provides a quicker and more efficient way to assess the fatigue life of both through- and surface-hardened components, overcoming these limitations.