Ehsan Akbari1, Tajbakhsh Navid Chakherlou1, Hamed Tabrizchi2,3,*, Amir Mosavi3,4,5,6
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 305-325, 2025, DOI:10.32604/cmes.2025.068581
- 30 October 2025
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 More >
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