Design of experiments integrated with neural networks for optimization and predictive modelling of electrode wear of novel Ti-6Al-4V-SiCp composites during die sinking electric discharge machining

Die Sink Electric Discharge Machining is a widely used manufacturing process for shaping hard and electrically conductive materials. This study investigates the effects of various electrode materials such as, Ti-6Al-4V-SiCp, Brass and Copper on the machining performance of AISI 316 l Stainless Steel workpieces using EDM. The methodology involved optimizing parameters such as Electrode Material, Discharge Current, Gap Voltage, Spark Gap, Pulse-on Time, and Pulse-off Time. From the extensive experimantation it was observed that the combination of Ti-6Al-4V-SiCp electrode material, 8Amp Discharge Current, 90 V Gap Voltage, 75 μ m Spark Gap, 100 μ s Pulse-on Time and 15 μ s Pulse-off Time has resulted in lowest electrode eear rate, higher machining time, and low electrode surface roughness ratio. Ti-6Al-4V-SiCp electrodes possess higher hardness and electrical conductivity compared to Brass and Copper Electrodes leading to higher wear resistance against repeated thermal shocks during electric discharge machining operation. Feed Forward Artificial Neural Network is successfully applied to predict the output characteristics of the experimentation with high accuracy of 98.3% (Electrode Wear Rate), 94.6% (Machining Time) and 93.8% (Electrode Surface Roughness Ratio). Further, microstructure analysis concludes that lowest wear is observed in Ti-6Al-4V-SiCp electrodes compared to Brass and Copper electrodes.