Fatigue Safety Factor Prediction Model of Diesel
Engine Body Based on Physics-Based Neural Network

doi:10.3969/j.issn.1001-2222.2025.05.008

Vehicle Engine ›› 2025, Vol. 0 ›› Issue (5) : 52. DOI: 10.3969/j.issn.1001-2222.2025.05.008

Fatigue Safety Factor Prediction Model of Diesel Engine Body Based on Physics-Based Neural Network

PU Bowen^1,2,LIAO Huiya¹,SUN Xingyue³,WANG Genquan²,DIAO Zhanying²,CHEN Xu¹

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Abstract

To address the efficient prediction of fatigue safety factors (SF) for the block structure of diesel engine, several typical positions of block main bearing were selected and a gradient-constrained physical information neural network (GC-PINN) was proposed as a surrogate model for SF prediction by combining modern sampling methods with stochastic finite element simulation calculations. Through incorporating the physical gradient relationship between bolt preload and SF into machine learning models, the training process of surrogate model was guided and constrained to improve its generalization ability and prediction accuracy. Prediction accuracy comparisons between the GC-PINN surrogate model and models based on artificial neural networks, random forests, and support vector regression showed that the surrogate model had better prediction performance under small sample conditions, exhibiting the lowest median mean square root error value (RMSE) of only 0.294.

Key words

engine body / physics-based neural network / high-cycle fatigue / safety factor / prediction / surrogate model

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PU Bowen, LIAO Huiya, SUN Xingyue, WANG Genquan, DIAO Zhanying, CHEN Xu. Fatigue Safety Factor Prediction Model of Diesel Engine Body Based on Physics-Based Neural Network[J]. Vehicle Engine. 2025, 0(5): 52 https://doi.org/10.3969/j.issn.1001-2222.2025.05.008