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

Jianchun Yao; Xiaoqi Li; Jiawei Xiang

doi:10.1117/12.2685330

21 July 2023 Design of a hybrid model of finite element method and machine learning to predict mode I-II crack expansions

Jianchun Yao, Xiaoqi Li, Jiawei Xiang

Author Affiliations +

Proceedings Volume 12717, 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023); 127170Y (2023) https://doi.org/10.1117/12.2685330
Event: 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023), 2023, Wuhan, China

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.

Citation Download Citation

Jianchun Yao, Xiaoqi Li, and Jiawei Xiang "Design of a hybrid model of finite element method and machine learning to predict mode I-II crack expansions", Proc. SPIE 12717, 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023), 127170Y (21 July 2023); https://doi.org/10.1117/12.2685330

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available