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Fully Convolutional Neural Network With GRU for 3D Braided Composite Material Flaw Detection
- Source :
- IEEE Access, Vol 7, Pp 151180-151188 (2019)
- Publication Year :
- 2019
- Publisher :
- IEEE, 2019.
-
Abstract
- Automated ultrasonic signal classification systems are often utilized for the recognition of a large number of ultrasonic signals in engineering materials. Existing defect classification methods are mainly image-based and serve to extract features for various defects. In this paper, we propose a novel detection baseline model based on a fully convolution network (FCN) and gated recurrent unit (GRU) to classify ultrasonic signals from flawed 3D braided composite specimens with debonding defects. In the proposed algorithm, the proposed Gated Recurrent Unit Fully Convolutional Network (GRU-FCN) is used to extract temporal characteristics of ultrasonic signals; the GRU module is key to enhancing the performance of FCNs. Experimental results on an in-house dataset indicated that the proposed model performs very well against all baselines. We also developed a scheme to interpret the relationship between A-scan and C-scan images and a 3D depth model representation to visualize the location information of defects.
- Subjects :
- General Computer Science
3D braided composite specimens
Computer science
Braided composite
business.industry
Model representation
General Engineering
Baseline model
Pattern recognition
fully convolution networks
Convolutional neural network
Convolution
Image (mathematics)
gated recurrent unit
Key (cryptography)
General Materials Science
Ultrasonic sensor
Artificial intelligence
lcsh:Electrical engineering. Electronics. Nuclear engineering
time series
business
Ultrasonic signal classification
lcsh:TK1-9971
C-scan images
Subjects
Details
- Language :
- English
- ISSN :
- 21693536
- Volume :
- 7
- Database :
- OpenAIRE
- Journal :
- IEEE Access
- Accession number :
- edsair.doi.dedup.....1333a87962a4d4fa50afafd32741c7c8