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 str...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.展开更多
文摘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.
