Composite materials impact damage detection using neural networks

This thesis considers two basic aspects of impact damage in composite materials, namely damage severity discrimination and impact damage location by using Acoustic Emissions (AE) and Artificial Neural Networks (ANNs). The experimental work embodies a study of such factors as the application of AE as Non-destructive Damage Testing (NDT), and the evaluation of ANNs modelling. ANNs, however, played an important role in modelling implementation. In the first aspect of the study, different impact energies were used to produce different level of damage in two composite materials (T300/914 and T800/5245). The impacts were detected by their acoustic emissions (AE). The AE waveform signals were analysed and modelled using a Back Propagation (BP) neural network model. The Mean Square Error (MSE) from the output was then used as a damage indicator in the damage severity discrimination study. To evaluate the ANN model, a comparison was made of the correlation coefficients of different parameters, such as MSE, AE energy, AE counts, etc. MSE produced an outstanding result based on the best performance of correlation. In the second aspect, a new artificial neural network model was developed to provide impact damage location on a quasi-isotropic composite panel. It was successfully trained to locate impact sites by correlating the relationship between arriving time differences of AE signals at transducers located on the panel and the impact site coordinates. The performance of the ANN model, which was evaluated by calculating the distance deviation between model output and real location coordinates, supports the application of ANN as an impact damage location identifier. In the study, the accuracy of location prediction decreased when approaching the central area of the panel. Further investigation indicated that this is due to the small arrival time differences, which defect the performance of ANN prediction. This research suggested increasing the number of processing neurons in the ANNs as a practical solution.

Synthesis and characterisation of polyacrylonitrile and its derived carbon materials

Carbon is a versatile material which is composed of different allotropes, and also come in with different structures. Carbon nanofibres (CNFs) is one dimensional carbon nanomaterials, which have exhibited superior mechanical properties, great specific area, good electrical conductivity, good biocompatibility, and ease of modification. In addition to the lower cost associated to compare with carbon nanotubes (CNTs), CNFs have been attracted in numerous applications, such as reinforcement materials, filtrations, Li-ion battery, supercapacitor as well as tissue engineering, just to list a few. Therefore, it is a great deal to understand the relationship between the fabrication conditions and the characteristics of the resulted CNFs. In this project, electrospun PAN NFs were used as precursor material to fabricate carbon nanofibres. In order to produce CNFs with good morphology, the processing parameters of PAN nanofibres by electrospinning was optimized toward to the morphology at solution concentration of 12 wt%. The optimized processing parameters at given concentration were 16 kV, 14 cm and 1.5 mL/h, which led to the formation of PAN NFs with average fibre diameter of approximately 260 nm. Along with the effect of processing parameter study, the effect of concentration on the morphology was also carried out at optimized processing parameters. It was found that by increasing concentration of PAN solution from 2 to 16%, the resulted PAN transformed from beads only, to beaded fibres and finally to smooth fibres. With further increasing concentration the morphology of smooth fibres remain with increase in the fibre diameter. Electrospun PAN NFs with average fibre of 306 nm was selected to be converted into CNFs by using standard heating procedures, stabilisation in air at 280 °C and carbonization in N2. The effect of carbonization temperature ranging from 500 to 1000 °C was investigated, by using SEM, FTIR, Raman, and Impedance spectroscopy. With increasing carbonization temperature from 500 to 1000 °C, the diameter of NFs was decreased from 260 to 187, associated with loss of almost all functional groups of NFs. It was indicated by Raman results, that the graphitic crystallite size was increased from 2.62 to 5.24 nm, and the activation energy obtained for this growth was 7570 J/mol. Furthermore, impedance results (i.e. Cole-Cole plot) revealed that the electrical characteristic of CNFs transitioned from being insulating to electrically conducting in nature, suggested by the different electrical circuits extracted from Cole-Cole plots with carbonization temperature from 500 to 800 °C. The carbonization on PAN NFs with diameter of ~431nm was carried out by using novel route, microwave plasma enhance chemical vapour deposition (MPECVD) process. To compare with carbonized PAN NFs by using conventional route, MPECVD was not only able to facilitate carbonization process, but more interestingly can form carbon nanowalls (CNWs) grown on the surfaces of carbonized PAN NFs. Suggested by the unique morphology, the potential applications for the resulted carbon fibrous hybrid materials are supercapacitor electrode material, filtrations, and etc., The method developed in this project required one step less, compared with other literature. Therefore, using MPECVD on stabilised PAN NFs is proposed as economical, and straightforward approach towards mass production of carbon fibrous hybrid materials containing CNWs.