Damage detection and the classification of carbon fiber-reinforced composites using non-destructive testing (NDT) techniques are of great importance. This paper applies an acoustic emission (AE) technique to obtain AE data from three tensile damage tests determining fiber breakage, matrix cracking, and delamination. This article proposes a deep learning approach that combines a state-of-the-art deep learning technique for time series classification: the InceptionTime model with acoustic emission data for damage classification in composite materials. Raw AE time series and frequency-domain sequence data are used as the input for the InceptionTime network, and both obtain very high classification performances, achieving high accuracy scores of about 99%. The InceptionTime network produces better training, validation, and test accuracy with the raw AE time series data than it does with the frequency-domain sequence data. Simultaneously, the InceptionTime model network shows its potential in dealing with data imbalances.

This study investigated the mechanism of delamination damage in the double cantilever beam (DCB) standard test by the use of the strain energy release rate. The curve of the strain energy release rate was verified by the Rise Angle (RA) method. For this purpose, 24-layer carbon fiber/epoxy multidirectional laminates with interface orientations of 0°, 30°, 45°, and 60° were fabricated according to the standard ASTM D5528(13). In the course of this test, acoustic emission (AE) was used for real-time monitoring, and combined with micro visualization, the damage mechanism of composite multidirectional laminates was studied at multiple scales. Combining the AE detection results with micro visualization, it is found that the AE parameters and the damage to multidirectional laminates could realize a one-to-one correspondence. Through the study of the variation of the RA value, load, and strain energy release rate with the crack length, it is proved that the AE parameters can effectively characterize the initiation of delamination damage.

This study investigates delamination damage mechanisms during the double cantilever beam standard test using the strain energy release rate. The acoustic emission parameter is used to replace the original calculation method of measuring crack length to predict delamination. For this purpose, 24-layer glass/epoxy multidirectional specimens with different layups, and interface orientations of 0°, 30°, 45°, and 60°, were fabricated based on ASTM D5528 (2013). Acoustic emission testing (AE) is used to detect the damage mechanism of composite multidirectional laminates (combined with microscopic real-time observation), and it is verified that the strain energy release rate can be used as a criterion for predicting delamination damage in composite materials. By comparing the AE results with the delamination expansion images observed by microvisualization in real time, it is found that the acoustic emission parameters can predict the damage of laminates earlier. Based on the data inversion of the acoustic emission parameters of the strain energy release rate, it is found that the strain energy release rate of the specimens with different fiber interface orientations is consistent with the original calculated results.

This study investigates delamination damage mechanisms during the double cantilever beam standard test using the strain energy release rate. The acoustic emission parameter is used to replace the original calculation method of measuring crack length to predict delamination. For this purpose, 24-layer glass/epoxy multidirectional specimens with different layups, and interface orientations of 0°, 30°, 45°, and 60°, were fabricated based on ASTM D5528 (2013). Acoustic emission testing (AE) is used to detect the damage mechanism of composite multidirectional laminates (combined with microscopic real-time observation), and it is verified that the strain energy release rate can be used as a criterion for predicting delamination damage in composite materials. By comparing the AE results with the delamination expansion images observed by microvisualization in real time, it is found that the acoustic emission parameters can predict the damage of laminates earlier. Based on the data inversion of the acoustic emission parameters of the strain energy release rate, it is found that the strain energy release rate of the specimens with different fiber interface orientations is consistent with the original calculated results.

In this paper, the tensile damage mechanism of carbon fiber composites at high temperatures is analyzed. The acoustic emission technique was employed to monitor the tensile process of specimens. The acoustic emission signals at high and room temperatures were classified based on k-means and the wavelet packet energy spectrum. The results show that the damage mechanisms at high temperatures and room temperature differ. At high temperatures, there is more stress release, the material instability appears earlier, and redistribution occurs in the specimen. The damage mechanisms include matrix cracking, fiber/matrix debonding, and fiber breakage. For damage mechanism identification, the acoustic emission characteristics were used under room temperature and high-temperature conditions in the fully connected neural network, with an accuracy rate of 97.5%. The results indicate that the network is suited for both high temperatures and room temperature and can better identify various damage mechanisms.