Sustainable micro-texturing of Inconel 617 using waste oil based modified nano-fluid as dielectric: process–surface relationships and ANN–NSGA-II optimization

Micro-textured surfaces are increasingly engineered to improve friction–wear stability, lubricant retention and coating adhesion in components operating under severe thermal and corrosive environments. Inconel 617 (IN617), a nickel-based superalloy used in high-temperature energy and nuclear systems, is difficult to texture via conventional machining; therefore, non-contact texturing routes are desirable. Electric discharge texturing (EDT) is a viable option however, it is essentially desired to mitigate the harmful effects of EDT without compromising the productivity aspect. In EDT oil-based dielectrics are commonly engaged which contribute to environmental pollution through the emission of toxic fumes and aerosols. Therefore, this study comprehensively investigates the potential of waste cooking oil (WCO) dielectric modified with nano-powders and non-ionic surfactants in EDT of IN617 which has not been systematically studied so far. A structured experimental design evaluates seven factors (powder type/concentration, electrode type, surfactant type/concentration, discharge current and pulse ratio) against measurable outputs surface roughness (Ra), material removal rate (MRR), and specific energy consumption (SEC). Surface morphology (microscopy/SEM) is used to interpret crater geometry, melt redeposition and texture non-uniformity as a function discharge conditions. An artificial neural network (ANN) surrogate model is trained to capture nonlinear process–surface interactions and is embedded within NSGA-II to identify Pareto-optimal conditions that maximize MRR while minimizing Ra and SEC. Relative to the least favorable condition, the optimal combination increases MRR by 372.29 %, improves surface finish by 23.21 %, and reduces SEC by 13.97 %. In addition, WCO yields an average CO₂-equivalent reduction of 98.92 ± 1.01 % compared with kerosene oil.