Physics-Informed Neural Networks for Multiaxial Fatigue Life Prediction of Aluminum Alloy
Akbari E. Chakherlou T.N. Tabrizchi H. Mosavi A.
2025Tech Science Press
CMES - Computer Modeling in Engineering and Sciences
2025#145Issue 1305 - 325 pp.
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. Copyright
aluminum alloy , artificial intelligence , big data , critical plane analysis , data science , deep learning , fatigue , fatigue function , machine learning , Multiaxial fatigue criteria
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Faculty of Mechanical Engineering, University of Tabriz, Tabriz, 51666-16471, Iran
Department of Computer Science, Faculty of Mathematics, Statistics, and Computer Science, University of Tabriz, Tabriz, 51666-16471, Iran
Doctoral School of Applied Informatics and Applied Mathematics, John von Neumann Faculty of Informatics, Obuda University, Budapest, 1034, Hungary
Institute of the Information Society, Ludovika University of Public Service, Budapest, 1083, Hungary
Faculty of Innovative Technologies, Abylkas Saginov Karaganda Technical University, Karaganda, 100000, Kazakhstan
Faculty of Economics and Informatics, Univerzita J. Selyeho, Komarno, 945 01, Slovakia
Faculty of Mechanical Engineering
Department of Computer Science
Doctoral School of Applied Informatics and Applied Mathematics
Institute of the Information Society
Faculty of Innovative Technologies
Faculty of Economics and Informatics
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