Acoustic emission study on damage mechanism of composite materials repaired with bi-adhesive and damage classification model

• Published in: Journal of adhesion science and technology Vol. 38; no. 10; pp. 1718 - 1737
• Main Authors: Liu, Ran, Zhang, Peng-fei, Shen, Gong-tian, Li, Qing, Liu, Shuo, Zhou, Wei
• Format: Journal Article
• Language: English
• Published: Utrecht Taylor & Francis 18.05.2024; Taylor & Francis Ltd
• Subjects: Acoustic emission; Acoustics; Adhesives; Algorithms; Bi-adhesive; Clustering; Composite materials; Damage assessment; damage evolution behavior; damage mode classification; Evolution; Feature extraction; Frequency domain analysis; Machine learning; Parameters; Repair; Signal classification; Waveforms; Wavelet transforms
• ISSN: 0169-4243; 1568-5616
• DOI: 10.1080/01694243.2023.2270731

The load-bearing capacity of damaged composite materials can be improved using the double adhesive repair technique. This study uses acoustic emission (AE) as the primary detection method to examine the damage evolution of modified double-bonded repair specimens and discover the most effective configuration for bi-adhesive repair. The introduction of machine learning and signal feature extraction in the analysis of huge acoustic emission parameters has significantly improved the accuracy and efficiency of damage classification. Following the preliminary division of the clustering results of the unsupervised algorithm, the corresponding relationship between various clustering types and damage types is confirmed using the energy characteristics of wavelet packet decomposition. Parameter and waveform analysis are extensively utilized in studying the AE damage of composite materials and have complementary advantages. Thus, a supervised classifier is established by combining characteristic parameters with time-frequency domain analysis of the signal waveform. Furthermore, machine learning is used to process the large signal set and analyze the debonding damage behavior of the patch. In the end, it is shown that the specimens with the modified adhesive in the middle of the repair area displayed superior repair results. Hence, when combined with machine learning, the signal classification of the damage evolution behavior of repaired specimens is realized with high accuracy.