scholarly journals Intelligent damage recognition of composite materials based on deep learning and ultrasonic testing

AIP Advances ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 125227
Author(s):  
Caizhi Li ◽  
Weifeng He ◽  
Xiangfan Nie ◽  
Xiaolong Wei ◽  
Hanyi Guo ◽  
...  
Keyword(s):  
Deep Learning ◽  
Composite Materials ◽  
Ultrasonic Testing ◽  
Damage Recognition

Infrared and ultrasonic intelligent damage recognition of composite materials based on deep learning

2021 ◽  
Vol 60 (28) ◽  
pp. 8624
Author(s):  
Caizhi Li ◽  
Xiangfan Nie ◽  
Zhihao Chang ◽  
Xiaolong Wei ◽  
Weifeng He ◽  
...  
Keyword(s):  
Deep Learning ◽  
Composite Materials ◽  
Damage Recognition

Ultrasonic Testing Methods for Composite Materials

1979 ◽  
Vol 1 (2) ◽  
pp. 8 ◽  
Author(s):  
TT Chiao ◽  
KL Reifsnider ◽  
EG Henneke ◽  
KL Reifsnider
Keyword(s):  
Composite Materials ◽  
Ultrasonic Testing ◽  
Testing Methods

scholarly journals Research on Vehicle Appearance Damage Recognition Based on Deep Learning

2021 ◽  
Vol 1880 (1) ◽  
pp. 012024
Author(s):  
Qianqian Zhu ◽  
Wei Hu ◽  
Yingnan Liu ◽  
Zihao Zhao
Keyword(s):  
Deep Learning ◽  
Damage Recognition

scholarly journals Online Damage Monitoring of SiCf-SiCm Composite Materials Using Acoustic Emission and Deep Learning

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 140534-140541 ◽  
Author(s):  
Alireza Nasiri ◽  
Jingjing Bao ◽  
Donald Mccleeary ◽  
Steph-Yves M. Louis ◽  
Xinyu Huang ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Deep Learning ◽  
Composite Materials ◽  
Damage Monitoring

scholarly journals Deep Learning-Based Ultrasonic Testing to Evaluate the Porosity of Additively Manufactured Parts with Rough Surfaces

Metals ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 290
Author(s):  
Seong-Hyun Park ◽  
Jung-Yean Hong ◽  
Taeho Ha ◽  
Sungho Choi ◽  
Kyung-Young Jhang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Ultrasonic Testing ◽  
Signal To Noise Ratio ◽  
Rough Surfaces ◽  
Acoustic Microscopy ◽  
Processing Conditions ◽  
Multi Layer Perceptron ◽  
Ultrasonic Signals ◽  
Low Snr

Ultrasonic testing (UT) has been actively studied to evaluate the porosity of additively manufactured parts. Currently, ultrasonic measurements of as-deposited parts with a rough surface remain problematic because the surface lowers the signal-to-noise ratio (SNR) of ultrasonic signals, which degrades the UT performance. In this study, various deep learning (DL) techniques that can effectively extract the features of defects, even from signals with a low SNR, were applied to UT, and their performance in terms of the porosity evaluation of additively manufactured parts with rough surfaces was investigated. Experimentally, the effects of the processing conditions of additive manufacturing on the resulting porosity were first analyzed using both optical and scanning acoustic microscopy. Second, convolutional neural network (CNN), deep neural network, and multi-layer perceptron models were trained using time-domain ultrasonic signals obtained from additively manufactured specimens with various levels of porosity and surface roughness. The experimental results showed that all the models could evaluate porosity accurately, even that of the as-deposited specimens. In particular, the CNN delivered the best performance at 94.5%. However, conventional UT could not be applied because of the low SNR. The generalization performance when using newly manufactured as-deposited specimens was high at 90%.

Sign in / Sign up

Export Citation Format

Share Document