Damage detection using self-sensing composites

Malik, Shoaib Ahmad (2011). Damage detection using self-sensing composites. University of Birmingham. Ph.D.

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Abstract

The primary aim of this research programme was to enable damage detection in glass fibre reinforced composites using the reinforcing fibres as the sensing element. In other words, E-glass fibres were used as light guides to detect the fracture of individual fibres, when loaded in tension. This was achieved by monitoring the transmitted light intensity through the reinforcing glass fibres. Two types of glass fibres and matrices were evaluated. In the case of glass fibres, E-glass and custom-made small-diameter (12 µm) optical fibre (SDOF) were used. Three types of low refractive index resin systems with specified failure strains were also used. The basic technology involved illuminating one end of the fibre bundle or composite with a white light or laser source and the opposite end was imaged using a high-speed CCD camera. Acoustic emission monitoring of fibre bundles revealed that there were two types of failures occurring in a bundle, a lower amplitude of the acoustic emission signal (AES) related to the inter-fibre friction and a high amplitude of the AES to fibre fractures. This characteristic was also confirmed by a Weibull statistical analysis where it was demonstrated that a two parameter distribution was present corresponding to two different flaw distributions. In the case of self-sensing composites, it was found that the specific failure modes in the composites (matrix failure, fibre fracture, debonding) generate their characteristic amplitudes of the AES and frequencies. These failure modes were recorded and correlated to the tensile test data. It was demonstrated that the attenuation of transmitted light can be related to the fracture of fibres in the bundle or a composite test specimen. It was found that the image analysis routines were capable of identifying and tracking the survival or fracture of each fibre in the bundle or composite. The results obtained from mechanical loading, acoustic emission and images analysis were cross-correlated.

Type of Work:

Thesis (Doctorates > Ph.D.)

Award Type:

Doctorates > Ph.D.

Supervisor(s):