Acoustic Emission Pattern Recognition on Tensile Damage Process of C/SiC Composites Using an Improved Genetic Algorithm

Abstract

The acoustic emission data collected during room temperature tensile test of 2D-C/SiC composites were analyzed by hierarchical clustering and unsupervised pattern recognition method based on an improved genetic algorithm. Combined with the SEM observation on the fracture surface, five damage modes were identified and their typical acoustic emission characteristics were obtained. According to the analysis of energy distribution, cumulative event number and cumulative energy of different damage modes, the damage evolution process of C/SiC composites can be divided into four stages. The first stage (damage initiation stage) shows mainly matrix microcracks and interface debonding. In the second stage, matrix crack reaches saturated and then causes a considerable quantity of interlaminar delamination and fiber failure. The third stage is a gradual damage development stage and all kinds of damage keep occurring except the breakage of fiber bundles. In the last stage, a large amount of fiber bundles break and the sample eventually fails.

Compression damage identification of stitched 2D-C/SiC based on acoustic emission pattern recognition

“…It can be seen from Figure 7(a, b) that the typical waveforms of Class A and Class B are similar, which indicates that Class A and B should belong to the fracture of the same composition of C/SiC. Class A and B, exhibiting the characteristics of low frequency and energy, are associated with typical matrix cracking, which is consistent with the cracking of SiC matrix initiating at low stress due to its brittle nature [26]. Due to the larger damage aera in matrix between fiber bundles, the corresponding AE energy and frequency are higher, it can be inferred that Class A corresponds to the matrix cracking between the fiber bundles, and Class B corresponds to the matrix cracking in fiber bundle.…”

“…Researchers have tried to solve this problem with pattern recognition technology, mainly unsupervised cluster analysis [23][24][25]. This method has been used to analyze several kinds of CMCs and identifies that AE signals grouping has a strong correlation with damage mechanisms [13,14,[25][26][27].…”