Structural Health Monitoring of Polymer Composites Using Piezo-resistivity

Abstract

The utilization of Structural Health Monitoring (SHM) applications was in the developmental phase, intended for implementation in sectors like aerospace, automotive, and wind energy, where safety was paramount. Non-destructive evaluation (NDE) techniques, increasingly employed since World War II, remained widely used, notably within the aerospace industry. Conversely, unlike Non-destructive Testing (NDT) methods, the focus of SHM was primarily on the early identification of damage. This involved collecting, assessing, and analysing data extracted from materials during operational periods at specific intervals. The goal was to subsequently pinpoint the damage location, quantify its extent, and ascertain the remaining material strength, all contributing to enhanced safety and maintenance. This approach led to cost reductions by optimizing timing and aiding subsequent design endeavours through the acquired data. Advanced composite materials, due to their distinctive mechanical properties, gained prominence across various sectors—most notably aerospace, automotive, and wind energy—where safety was important. This increased adoption drew attention to SHM investigations within the realm of composite materials. A key driver for this was the intricate damage structures inherent to composites, especially the criticality of detecting Barely Visible Impact Damage (BVID), a significant issue. This thesis presented a comprehensive exploration of SHM applications for Polymer Matrix Composites (PMC) commonly employed in aviation. The investigation employed the piezoresistance properties of PMC. In the first step, the piezoresistive characteristics of Multi-Walled Carbon Nanotube (MWCNT), Carbon Black (CB), and Graphene Nanoplatelet (GNP)/Epoxy systems were evaluated via tensile and compression tests. Results revealed distinct behaviors of all three nanomaterials under both loading conditions. Notably, GNP demonstrated a gauge factor around 70 times higher than that of commercially available strain gauges under tensile loading. In the next step, Glass Fibre Reinforced Polymers (GFRP) electrical properties enhanced with MWCNT and CB and non-woven carbon veil were investigated through tensile and four-point bending tests. These tests indicated that the nanomaterials conferred self-strain and damage-sensing capabilities to GFRP. While the non-woven carbon veil also exhibited self-sensing capacity against tensile strain, it failed to do so under compression strain. In addressing the detection of BVID, samples comprising MWCNT/GFRP and non-woven carbon-veiled GFRP were prepared and tested using the resistance change method during an impact test in the third step. This approach successfully identified BVID damage and made assumptions about the residual strength of materials based on resistance change data obtained during the tests. Additionally, impact 20 damage detection was conducted using THz spectroscopy and compared to ultrasound testing, a traditional NDT method. The conductive nature of the MWCNT and carbon veil showed that the THz method could be used for MWCNT and carbon veil embedded GFRP after impact damage. Finally, the study delved into SHM of scarf step bonded joints, a repair method for composite materials. With a carbon veil placed across the GFRP scarf step, the innovative method developed enabled initial damage detection and ongoing monitoring of bonded joint health by tracking damage progression. Conclusively, this research conducted a comprehensive analysis of self-sensing and damage-sensing capabilities in PMC, prominent in aerospace, by integrating carbon-based nanomaterials and non-woven carbon veil to achieve piezoresistive characteristics. Through subjecting these materials to diverse tests, promising outcomes were demonstrated for their applicability in SHM, illuminating a potential pathway for future research endeavours.

Date of Award	27 Jun 2024
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Ian Kinloch (Main Supervisor), Cristina Valles (Co Supervisor) & Mark Bissett (Co Supervisor)