Real-time damage analysis of 2D C/SiC composite based on spectral characters of acoustic emission signals using pattern recognition

Xianglong Zeng, Hongyan Shao, Rong Pan, Bo Wang, Qiong Deng, Chengyu Zhang, Tao Suo

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

In this study, unsupervised and supervised pattern recognition were implemented in combination to achieve real-time health monitoring. Unsupervised recognition (k-means++) was used to label the spectral characteristics of acoustic emission (AE) signals after completing the tensile tests at ambient temperature. Using in-plane tensile at 800 and 1000°C as implementing examples, supervised recognition (K-nearest neighbor (KNN)) was used to identify damage mode in real time. According to the damage identification results, four main tensile damage modes of 2D C/SiC composites were identified: matrix cracking (122.6–201 kHz), interfacial debonding (201–294.4 kHz), interfacial sliding (20.6–122.6 kHz) and fiber breaking (294.4–1000 kHz). Additionally, the damage evolution mechanisms for the 2D C/SiC composites were analyzed based on the characteristics of AE energy accumulation curve during the in-plane tensile loading at ambient and elevated temperature with oxidation. Meanwhile, the energy of various damage modes was accurately calculated by harmonic wavelet packet and the damage degree of modes could be analyzed. The identification results show that compared with previous studies, using the AE analysis method, the method has higher sensitivity and accuracy. [Figure not available: see fulltext.].

Translated title of the contribution基于声发射信号频谱特征的2D C/SiC复合材料损伤模式实时分析
Original languageEnglish
Article number422177
JournalActa Mechanica Sinica/Lixue Xuebao
Volume38
Issue number10
DOIs
StatePublished - Oct 2022

Keywords

  • 2D C/SiC composites
  • Acoustic emission
  • Pattern recognition
  • Real-time health monitoring

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