Automatic defect depth estimation for ultrasonic testing in carbon fiber reinforced composites using deep learning

Ultrasonic testing (UT) is commonly used to inspect the geometric shape of internal damage in composite materials and the test results need to be interpreted by trained experts. In this work, an automatic signal classification method based on deep learning is proposed for depth estimation of the detects introduced by low-velocity impact (LVI) in carbon fiber reinforced plastics (CFRPs). Three kinds of neural networks, LSTM, CNN, and CNNLSTM are used to analyze the attributes with different depths. Then, trained models are applied to identify the depth information of impact damage. The results show that the CNN-LSTM model is a more accurate in-depth classification for LVI defects in CFRP based on A-scan signals than the other two structures.

Automated summary

This work proposes an automatic signal classification method based on deep learning for depth estimation of low-velocity impact (LVI) damage in carbon fiber reinforced plastics (CFRPs). Three types of neural networks, LSTM, CNN, and CNNLSTM, are used to analyze the attributes at different depths and identify the depth information of impact damage. The results show that the CNN-LSTM model is more accurate in-depth classification for LVI defects in CFRP based on A-scan signals compared to the other two structures.