Artificial Neural Networks based prediction and Multi Response Optimization on EDM of Aluminium/Fly ash composites

Abstract: Aluminium Metal matrix composites reinforced with fly ash particles of three different particle size ranges ((53–75) μm, (75–103) μm and (103–125) μm) were fabricated using stir casting technique. Electrical discharge machining (EDM) was employed to machine the composite materials with copper electrode. The influence of EDM process parameters namely peak current, pulse-on-time, pulse-off-time, particle size and the percentage fly ash on Material Removal Rate (MRR), Tool Wear Rate (TWR) and Surface Roughness(SR) were investigated. Artificial Neural Network (ANN) model was employed to predict the material removal rate, tool wear rate and surface roughness of the composites. The experimental values coincide with the predicted values of the proposed networks. The process parameters are then optimized using desirability based multi response optimization technique to maximize the MRR and minimize both TWR and SR. Increase in peak current and pulse-on time increased the MRR while increase in pulse-off time, percentage fly ash and fly ash particle size decreased the MRR.