ARTIFICIAL NEURAL NETWORK MODELING FOR FATIGUE LIFE ASSESSMENT OF ENGINEERING MATERIALS

Abstract

Fatigue failure is one of the primary causes of structural damage in engineering components subjected to cyclic loading. Accurate prediction of fatigue life is critical for ensuring reliability, safety, and cost-effective design of mechanical systems. Conventional fatigue life prediction methods rely on empirical models and S–N curves, which often fail to capture complex nonlinear material behavior. This paper presents a neural network–based approach for predicting fatigue life of engineering materials. The proposed model learns nonlinear relationships between stress parameters and fatigue life from experimental data. Comparative analysis with traditional regression models demonstrates superior prediction accuracy and reduced error. The results confirm that neural networks offer a reliable and efficient alternative for fatigue life estimation in mechanical engineering applications.