Modeling and multi-response optimization of Inconel 718 on machining of CNC WEDM process

This paper describes the development of artificial neural network (ANN) models and multi-response optimization technique to predict and select the best cutting parameters of wire electro-discharge machining (WEDM) process. To predict the performance characteristics namely material removal rate and surface roughness, artificial neural network models were developed using back-propagation algorithms. Inconel 718 was selected as work material to conduct experiments. A brass wire of 0.25 mm diameter was applied as tool electrode to cut the specimen. Experiments were planned as per Taguchi's L9 orthogonal array. Experiments were performed under different cutting conditions of pulse on time, delay time, wire feed speed, and ignition current. The responses were optimized concurrently using multi-response signal-to-noise (MRSN) ratio in addition to Taguchi's parametric design approach. Analysis of variance (ANOVA) was employed to identify the level of importance of the machining parameters on the multiple performance characteristics. Finally, experimental confirmations were carried out to identify the effectiveness of this proposed method. A good improvement was obtained.