Multivariable signal processing for characterization of failure modes in thin-ply hybrid laminates using acoustic emission sensors

Fotouhi, Sakineh; Assaad, Maher; Nasor, Mohamed; Imran, Ahmed; Ashames, Akram; Fotouhi, Mohammad

Abstract

The aim of this study was to find the correlation between failure modes and acoustic emission (AE) events in a comprehensive range of thin-ply pseudo-ductile hybrid composite laminates when loaded under uniaxial tension. The investigated hybrid laminates were Unidirectional (UD), Quasi-Isotropic (QI) and open-hole QI configurations composed of S-glass and several thin carbon prepregs. The laminates exhibited stress-strain responses that follow the elastic-yielding-hardening pattern commonly observed in ductile metals. The laminates experienced different sizes of gradual failure modes of carbon ply fragmentation and dispersed delamination. To analyze the correlation between these failure modes and AE signals, a multivariable clustering method was employed using Gaussian mixture model. The clustering results and visual observations were used to determine two AE clusters, corresponding to fragmentation and delamination modes, with high amplitude, energy, and duration signals linked to fragmentation. In contrast to the common belief, there was no correlation between the high frequency signals and the carbon fibre fragmentation. The multivariable AE analysis was able to identify fibre fracture and delamination and their sequence. However, the quantitative assessment of these failure modes was influenced by the nature of failure that depends on various factors, such as stacking sequence, material properties, energy release rate, and geometry.

Citation

Fotouhi, S., Assaad, M., Nasor, M., Imran, A., Ashames, A., & Fotouhi, M. (2023). Multivariable signal processing for characterization of failure modes in thin-ply hybrid laminates using acoustic emission sensors. Sensors, 23(11), Article 5244. https://doi.org/10.3390/s23115244

• Journal Article Type: Article
• Acceptance Date: May 30, 2023
• Online Publication Date: May 31, 2023
• Publication Date: May 31, 2023
• Deposit Date: Jan 8, 2024
• Publicly Available Date: Jan 9, 2024
• Journal: Sensors
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 23
• Issue: 11
• Article Number: 5244
• DOI: https://doi.org/10.3390/s23115244
• Public URL: https://uwe-repository.worktribe.com/output/11597466