Analysis of Progressive Tensile Damage of Multi-walled Carbon Nanotube Reinforced Carbon Fiber Composites by Using Acoustic Emission and Micro-CT

• Published in: Journal of nondestructive evaluation Vol. 40; no. 2
• Main Authors: Pei, Ning, Shang, Junjun, Bond, Leonard J.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.06.2021; Springer Nature B.V
• Subjects: Acoustic emission; Algorithms; Bearing capacity; Breakage; Carbon fibers; Characterization and Evaluation of Materials; Classical Mechanics; Cluster analysis; Computed tomography; Control; Cracking (fracturing); Damage detection; Dynamical Systems; Emission analysis; Engineering; Evolution; Fiber composites; Multi wall carbon nanotubes; Peak frequency; Signal processing; Solid Mechanics; Structural health monitoring; Vibration; Micro-CT; MWCNTs; Progressive damage; Off-axis angles; Acoustic emission (AE)
• ISSN: 0195-9298; 1573-4862
• DOI: 10.1007/s10921-021-00780-y

The potential to provide improved performance for advanced composites through the addition of multi-walled carbon nanotubes (MWCNTs) to carbon fiber composites is of interest in several applications. To investigate performance four types of composite specimens with different off-axis angles were subjected to progressive tensile loading. The results show that MWCNTs can improve the bearing capacity of the composite and the off-axis orientation angle can enhance the toughness of the composite. During loading acoustic emission (AE) signals were collected and they were post-processed using cluster analysis based on a Fuzzy C-Means algorithm. The analysis of the AE signals shows that data can be divided into categories which correlate with three damage modes: matrix cracking, fiber debonding and fiber breakage. The AE peak frequency characteristics of each damage mode were identified. Additional characterization was provided by using micro-computed tomography (Micro-CT) during the progressive tensile loading process. The CT images visualize damage location and evolution in the composites and data exhibit good correlations with the AE data for defects predication. The combination of AE and micro-CT technology were shown to effectively characterize damage evolution of the composites, and such data can potentially serve as a reference for the structural health monitoring of these composites when used in structures.