Design and development of artificial neural network model for liquid pressurized system based on glass fibre reinforced epoxy (GRE) composite pipe - Ang, Jia Yi (School of Mechatronic Engineering, 2018)
The scope of this research is confined to the design and the development of a model-based identifier for the determination of the performance of the composite pipes. Therefore, the discussion in this thesis is focused on the modelling of the GRE composite pipe where its performance is identified by means of ANN modelling. It is to be highlighted that the test protocol and the classifications of the model are conducted on GRE for a limited range configuration composite pipe. Thus, the FPF determination reports only works for glass fibre/epoxy which has the configurations of; volume fraction 0.5-0.66, winding angles of 45-63, the diameter of 100-200 mm, and thickness of 3-6 mm where the parameter chosen based on the standardize configuration of the composite pipe which generally found in the field. The model predicts the FPF within the five stress ratios range from pure axial (0H:1A) to pure hoop (1H:0A). Thus, it is paramount to state that the developed model suits only for a specific reinforcement material and parameter configurations. Different materials and configurations, on the other hand, requires the signal features extraction and classification protocols to be repeated to redevelop the neural network model.