IMECH-IR  > 流固耦合系统力学重点实验室
Using Deep Learning Techniques for Sandwich Panels with Truss Core Damage Detection
Wang YB(王亚博)1,2; Lu LL(路玲玲)1,2; Song HW(宋宏伟)1,2
Source PublicationIOP Conference Series: Materials Science and Engineering
2019-07-01
Conference Name2019 International Conference on Advanced Electronic Materials, Computers and Materials Engineering (AEMCME 2019)
Conference Date19–21 April 2019
Conference PlaceChangsha, China
Abstract

Abstract In the previous study, the accuracy of damage detection for the sandwich structures with truss core (SPTC) was affected by the selected damage index, Other than this, human subjective judgment could also not directly determine the degree and the location of damage for SPTC. In this paper, the deep learning method is applied to identify the damage for SPTC, and the dataset of the training deep learning model is obtained based on the dynamic method. This paper adopts to the Caffe, which is a deep learning open source framework, object detection model Faster R-CNN is utilized to study the lattice sandwich plate. The damage data set, the optimal hyperparameters for training the deep learning model, and the optimal ratios of the test set and training set for damage dataset are also studied. It is difficult to detect the damage of SPTC applying to the deep learning algorithms, so the good results cannot be gotten. In this paper, the method of Faster R-CNN has used extracts the deep features of the defective target by ZF that is a kind of Convolutional Neural Network (CNN), the method effectively solves the problem that the traditional algorithm cannot effectively detect the damage. As to the damage of SPTC that the traditional algorithms could also identify, the deep learning algorithm is excelled, the experimental mean average precision(mAP) can be raised to 90%. At the same time, the deep learning method can effectively identify locations and size of the damage in SPTC, the method is proven that the accuracy is higher and the speed is faster for damage detection. In the future, a real-time damage monitoring system is possible, and the theory is worth exploring further.

Keyworddeep learning sandwich panels with truss core damage dection
WOS IDIOP:1757-8981-563-4-042028
Indexed ByCPCI-S
Language英语
Citation statistics
Document Type会议论文
Identifierhttp://dspace.imech.ac.cn/handle/311007/79710
Collection流固耦合系统力学重点实验室
Affiliation1.Key Laboratory for Mechanics in Fluid Solid Coupling Systems, Institute of Mechanics, Chinese Academy of Sciences, Beijing, China
2.School of Engineering Science, University of Chinese Academy of Sciences, Beijing, China
Recommended Citation
GB/T 7714
Wang YB,Lu LL,Song HW. Using Deep Learning Techniques for Sandwich Panels with Truss Core Damage Detection[C],2019.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Wang YB(王亚博)]'s Articles
[Lu LL(路玲玲)]'s Articles
[Song HW(宋宏伟)]'s Articles
Baidu academic
Similar articles in Baidu academic
[Wang YB(王亚博)]'s Articles
[Lu LL(路玲玲)]'s Articles
[Song HW(宋宏伟)]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Wang YB(王亚博)]'s Articles
[Lu LL(路玲玲)]'s Articles
[Song HW(宋宏伟)]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.