Abstract
Recently, research has been undergoing in the area of natural fibre-reinforced composite as an alternative for metals and plastics in automobile applications. It is mainly because of its strength and lighter weight. Literature shows that tribological studies are made in large numbers to evaluate these composite's strength, corrosion and wear behaviour. The machinability of these natural fibres is to be studied to understand the further scope of these materials in their respective application. In this research machinability of pineapple fibre is studied under the abrasive water jet machining process at a higher transverse speed. The material removal rate is chosen as a tool for evaluating the machinability of these composites. The Taguchi-based approach is used for planning and optimising the experiments. The optimal parameter for maximising the material removal rate is done using the signal-to-noise ratio analysis. Also, an Artificial neural network algorithm is developed to predict the responses. The results of the ANN model show that the model predicts the response with an accuracy of 94.9%. This ANN prediction model of machining study could be helpful when these composites are imposed in automobile and aerospace applications.
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