Investigation on damage behaviors of carbon fiber‐reinforced nylon 6 thermoplastic composite laminates using acoustic emission and digital image correlation  techniques

• Published in: Polymer composites
• Main Authors: Zhu, Jixin, Hu, Kejun, Han, Wenqin, Shi, Qinghe, Wang, Yingming, Zhao, Fengling, Zhu, Fuxian
• Format: Journal Article
• Language: English
• Published: 13.09.2024
• DOI: 10.1002/pc.29063
• ISSN: 0272-8397

Abstract The objective of this paper is to analyze the mechanical properties and damage mechanisms of carbon fiber‐reinforced polyamide thermoplastic composite laminates. Four specimens with different ply orientations were designed for open‐hole tensile experiments, and interlaminar toughness experiments including double cantilever beam and end‐notched flexural were carried out. The experimental process was monitored synchronously using acoustic emission, and the strain field changes of the tensile specimens were captured using digital image correlation technology. The unsupervised clustering of the peak frequencies of the acoustic emission signals based on the K‐means++ algorithm was employed to ascertain the peak frequency ranges corresponding to the various damage modes. Typical signals from different specimens were selected, and the gray wolf algorithm was used to optimize the variational modal parameters to decompose the signals. The waveform characteristics, frequency components, and Hilbert spectra of each damage mode were given. The correlation analysis of the intrinsic mode function (IMF) components of the same damage in different specimens demonstrated that the IMF components exhibited high similarity. By analyzing the time series changes in the energy of each damage mode in different specimens, the contribution of different damage modes to the evolution of laminated plate damage was evaluated. Highlights The mechanical properties of CF/PA6 laminates were investigated based on open‐hole tensile specimens and pre‐cracked delamination specimens. Unsupervised clustering of AE peak frequencies using K‐means++ to establish the relationship between peak frequencies and damage patterns. AE counts and cumulative energy were used to assess damage evolution. By identifying a single damage signal and providing a more intuitive treatment of the damage energy evolution.