有限元法
人工神经网络
支持向量机
模式(计算机接口)
计算机科学
结构工程
回归分析
人工智能
机器学习
工程类
操作系统
作者
Jianchun Yao,Xiaoqi Li,Jiawei Xiang
摘要
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.
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