TY - JOUR
T1 - A multi-stage physics-informed neural network for fretting fatigue life prediction of aluminum alloy
AU - Li, Xin
AU - Guo, Haiqing
AU - Du, Xinyue
N1 - Publisher Copyright:
© IMechE 2026
PY - 2026/7
Y1 - 2026/7
N2 - As a complex fatigue-wear coupling phenomenon, fretting fatigue is affected by many factors. Proposing an accurate fatigue life prediction model presents a significant challenge. This paper proposes a physics-informed neural network framework (FFLP-PINN) to predict the fretting fatigue life of aluminum alloys. A multi-stage neural network construction strategy was developed to enhance model accuracy and generalization by progressively incorporating factors related to multiaxial fatigue and fretting damage into the training process. To evaluate the performance of FFLP-PINN, several critical plane models and purely data-driven neural networks model were compared. Meanwhile, the matching of different physical models (critical plane models) with the artificial neural network was carefully investigated. The results indicate that FFLP-PINNs outperform both critical plane models and traditional neural networks. The two-stage framework can further improve model performance. However, not all the embeddings of critical plane models provide satisfactory prediction results. The matching of physical models with artificial neural networks also has an important influence on prediction outcomes. This study provides a new methodology for predicting multi-parameter hard-modeling fatigue cases.
AB - As a complex fatigue-wear coupling phenomenon, fretting fatigue is affected by many factors. Proposing an accurate fatigue life prediction model presents a significant challenge. This paper proposes a physics-informed neural network framework (FFLP-PINN) to predict the fretting fatigue life of aluminum alloys. A multi-stage neural network construction strategy was developed to enhance model accuracy and generalization by progressively incorporating factors related to multiaxial fatigue and fretting damage into the training process. To evaluate the performance of FFLP-PINN, several critical plane models and purely data-driven neural networks model were compared. Meanwhile, the matching of different physical models (critical plane models) with the artificial neural network was carefully investigated. The results indicate that FFLP-PINNs outperform both critical plane models and traditional neural networks. The two-stage framework can further improve model performance. However, not all the embeddings of critical plane models provide satisfactory prediction results. The matching of physical models with artificial neural networks also has an important influence on prediction outcomes. This study provides a new methodology for predicting multi-parameter hard-modeling fatigue cases.
KW - aluminum alloy
KW - critical plane model
KW - fatigue life prediction
KW - fretting fatigue
KW - physics-informed neural network
UR - https://www.scopus.com/pages/publications/105034809406
U2 - 10.1177/09544062261432721
DO - 10.1177/09544062261432721
M3 - Article
AN - SCOPUS:105034809406
SN - 0954-4062
VL - 240
SP - 4862
EP - 4877
JO - Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science
JF - Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science
IS - 13
ER -