Evaluation of AE waveform characteristics for various damage models in CFRP

The goal of this study is to classify Acoustic Emission (AE) signals based on their characteristics into several fracture types. AE method allows us to classify the damage process of CFRP with machine learning. However, unsupervised learning was often applied for classification which required manually matching the resulting clusters to specific damage types. In contrast, supervised learning to AE signals can automatically determine the damage types. A supervised learning technique uses many AE signals corresponding to each damage mode (supervised signals) in advance. In this study, supervised signals for Mode I and Mode II delamination were extracted from AE signals generated during Double Cantilever Beam (DCB) and Short Beam (SB) tests, respectively. While these delamination modes are dominant in each test, AE signals from other damage types, such as matrix cracking and fiber breakage, are also present. To account for this, additional AE data were obtained from tensile and three-point bending tests, where such damage is dominant. Finally, principal component analysis (PCA) was applied to visualize the distribution of AE signals. The results showed that AE signals from DCB and SB formed distinct clusters, while some overlapped with clusters from tensile and three-point bending signals. This suggests that by excluding overlapping clusters, it is possible to extract features that are more specific to Mode I and Mode II delamination.