Application of Neural Nets as Computing Network in Augmenting Variables of Abrasive Machining in Polymer Composites

Advanced industrial sectors such as those for cars, planes, and medical devices have been calling for a metal substitute like fiber-reinforced composites. However, when traditional machining techniques are applied, these composites are severely delaminated. Therefore, it is crucial to research the materials' machining properties when they are subjected to abrasive jet machining. In order to examine the impact of process parameters on the rate of material removal and surface roughness in carbon fibre composite, they were subjected to abrasive jet machining with a new nozzle in this research. The settings were optimised using a neural network. The composites were designed and constructed with internally threaded nozzles. Given that it increases the particle velocities and creates a whirling effect for the abrasive air mixture while cutting any material, the newly created internal threaded nozzle has been demonstrated to give greater possibilities for AJM processing. ANN was successfully used to determine the ideal machining parameters.