Multi-response Optimization of Electro-Discharge Machining Parameters by Using Jaya Algorithm for Thin Wire-Arc Additive Manufactured IN625 Surface

Wire-arc additive manufacturing (WAAM) often exhibits lower shape accuracy compared to other metal additive manufacturing techniques. Layer deposition results in a distinct surface profile that requires additional machining to achieve the desired final part. Conventional machining systems face challenges on finishing the thin features or harder alloys produced through WAAM. In this study, a 5.5 mm thin nonuniform surface of Inconel 625 superalloy prepared by the WAAM process is machined using electro-discharge machining (EDM) as an alternate post-processing technique. The study investigates the effects of process parameter’s and its optimization for individual responses by using Taguchi analysis and a population-based meta-heuristic technique of Jaya algorithm. The varying parameters selected were pulse-on time (Ton), pulse-off time (Toff), peak current (Ip), and gap voltage (Vg) for the responses of material removal rate (MRR) and surface roughness (SR). Further, the objective to achieve the multi-response optimization considering the MRR and SR as equally important factors is also performed by using the Jaya algorithm. Optimum EDM parameters for normalized multi-response function obtained by Jaya algorithm were Ton = 5 µs, Toff = 8 µs, Ip = 8 A, Vg = 25 V. The experimental MRR and SR for optimized parameters were calculated as 6.861 mm3/min and 3.79 Ra with the prediction error of 10.78% and 5.67%, respectively. The developed models were experimentally validated and demonstrated satisfactory performance, despite considerable prediction errors. This investigation provides essential insights into controlling the EDM process for post-processing the WAAM surfaces.