Deep learning-based planar crack damage evaluation using convolutional neural networks

被引：32

作者：

Long, X. Y. ^{[1
]}

Zhao, S. K. ^{[1
]}

Jiang, C. ^{[1
]}

Li, W. P. ^{[1
]}

Liu, C. H. ^{[1
]}

机构：

[1] Hunan Univ, Coll Mech & Vehicle Engn, State Key Lab Adv Design & Mfg Vehicle Body, Changsha 410082, Peoples R China

来源：

ENGINEERING FRACTURE MECHANICS | 2021年 / 246卷

基金：

美国国家科学基金会;

关键词：

Crack damage evaluation; Deep learning; Computational vision; Deep convolutional neural network; Stress intensity factor; MODEL;

D O I：

10.1016/j.engfracmech.2021.107604

中图分类号：

O3 [力学];

学科分类号：

08 ; 0801 ;

摘要：

This article presents a novel deep learning-based damage evaluation approach by using speckled images. A deep convolutional neural network (DCNN) for predicting the stress intensity factor (SIF) at the crack tip is designed. Based on the proposed DCNN, the SIF can be automatically predicted through computational vision. The data bank consisting of a reference speckled image and lots of deformed speckled images is prepared by a camera and an MTS testing machine. Experiments were performed to verify the method, and the achieved results are quite remarkable with larger than 96% of predicted SIF values falling within 5% of true SIF values when sufficient training images are available. The results also confirm that the appropriate subset size of images within the field of view is 400 ? 400 pixel resolutions.

引用

页数：12

共 35 条

[31] Modeling arbitrary crack propagation in coupled shell/solid structures with X-FEM [J].