Hybrid composite sensors for improved visual inspection of impact damage

Tabatabaeian, Ali (2024) Hybrid composite sensors for improved visual inspection of impact damage. PhD thesis, University of Glasgow.

Abstract

Advanced visual inspection techniques are essential for ensuring the structural integrity and reliability of laminated fibre reinforced polymer (FRP) composite structures. Given the ever increasing applications of FRP composites in various industries such as aerospace, wind turbine, and automotive, the demand for accurate, efficient, and non-destructive methods to monitor their health becomes paramount. Traditional inspection methods often fall short in detecting defects or damage in composite structures, which can compromise their performance and safety over time. A prime example of this is barely visible impact damage (BVID) caused by out-of-plane loadings such as indentation and low-velocity impact that can considerably reduce the residual strength. Therefore, developing advanced visual inspection techniques is essential for early detection, localisation, and characterisation of defects, thereby enabling proactive maintenance and extending the lifespan of composite structures.

The work in this thesis explores the viability of using hybrid composite sensors for detecting BVID in laminated FRP composite structures. Drawing inspiration from the colour-changing mechanisms found in nature, hybrid composite sensors composed of thin-ply glass and carbon layers are designed and attached to the surface of the laminated FRP composites exposed to out-of-pane loadings. A comprehensive experimental characterisation, including quasi-static indentation and low-velocity impact tests alongside non-destructive evaluations such as ultrasonic C-scan and visual inspection, is conducted to assess the sensor’s efficacy in detecting BVID. After this, a complementary numerical and theoretical study is conducted to optimise the sensor’s design through a parametric study, enabling the evaluation of key design parameters to tailor the sensor for specific applications. This numerical model can serve as a cost-effective and reliable tool for fast parametric design studies. The effectiveness of the sensor is further examined, particularly in automated visual inspection, when employing deep learning-assisted techniques. These deep learning models use images captured from the surfaces of both damaged and intact composites, enabling assessment of the sensors’ potential in enhancing damage pattern recognition and classification. Ultimately, the proposed sensing technology is implemented on curved FRP composite panels, serving as a real-life case study representative of composite gas cylinders. The findings of this research offer insights into the design, characterisation, and application of bio-inspired hybrid composite sensors, thereby enhancing visual inspection capabilities for detecting BVID in composite structures.

• Item Type: Thesis (PhD)
• Qualification Level: Doctoral
• Subjects: T Technology > TA Engineering (General). Civil engineering (General)
• Colleges/Schools: College of Science and Engineering > School of Engineering
• Supervisor's Name: Harrison, Dr. Philip and Fotouhi, Dr. Mohammad
• Date of Award: 2024
• Depositing User: Theses Team
• Unique ID: glathesis:2024-84556
• Copyright: Copyright of this thesis is held by the author.
• Date Deposited: 16 Sep 2024 09:52
• Last Modified: 16 Sep 2024 09:55
• Thesis DOI: 10.5525/gla.thesis.84556
• URI: https://theses.gla.ac.uk/id/eprint/84556

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