Design of a hybrid model of finite element method and machine learning to predict mode I-II crack expansions

Abstract

Compound mode I-II crack expansion is a common fracture source for the failures of mechanical components in real-world running structures. Therefore, prediction of crack extensions of mode I-II loading is a long-term research hotspot. Software FRANC3D is widely used to simulate the growth of fatigue cracks with high precision for engineering applications. However, the high computational cost for the usage of FRANC3D are obviously. Data-driven machine learning model is another strategy to predict crack expansion with low accuracy for the lack of training samples in real-world running structures. In order to fast and accuracy predict compound mode I-II crack expansion, a hybrid model of Finite Element Method (FEM) and Machine Learning (ML) is developed by interchangeably using FEM and ML. Two cases are given to validate the performance of the present hybrid model by using FEM and Support Vector Regression (SVR) and Generalized Regression Neural Network (GRNN), respectively to predict compound mode I-II crack expansions in a stress plate. Finally, to verify the high precision and efficiency of the hybrid model compared with the results of simulation and other models.