Physics-Informed Neural Networks for Multiaxial Fatigue Life Prediction of Aluminum Alloy

Abstract

The ability to predict multiaxial fatigue life of Al-Alloy 7075-T6 under complex loading conditions is critical to assessing its durability under complex loading conditions, particularly in aerospace, automotive, and structural applications. This paper presents a physical-informed neural network (PINN) model to predict the fatigue life of Al-Alloy 7075-T6 over a variety of multiaxial stresses. The model integrates the principles of the Geometric Multiaxial Fatigue Life (GMFL) approach, which is a novel fatigue life prediction approach to estimating fatigue life by combining multiple fatigue criteria. The proposed model aims to estimate fatigue damage accumulation by the GMFL method. The proposed GMFL-PINN combines this physics-based approach with data-driven neural networks. Experimental validation demonstrates that GMFL-PINN outperforms FS, Smith–Watson–Topper (SWT) and Li–Zhang (LZH) fatigue life prediction methods which provides a reliable and scalable solution for structural health assessment and fatigue analysis.