Prediction of Surface Roughness Model for Nano Copper Suspended Electrochemically Machined Inconel 718 using ANFIS

Abstract

Surface roughness Ra is considered as one of the primary decision making variables in assessing the performance of any machining processes. Electro Chemcial machining (ECM) is widely used advanced manufacturing process to machine the high hardened and poor machinable materials such as Super alloys, Inconel 718, Stainless steel, Die steel, High strength temperature resistant alloys. These materials, if machined by conventional machining processes, resulting in the poor surface finish due to the presence of high cutting forces and the generation of heat. Hence, an attempt is made in this research paper to predict the surface roughness of nano copper suspended electrochemically machined Inconel 718 using Adaptive Neuro Fuzzy Infernce System (ANFIS). Inconel 718 is one of the Super alloys with Nickel-Chromium combination is highly used in aerospace industries, high temperature fasteners, turbocharger rotors and Nuclear reactors. The major influencing parameters of applied voltage (V), tool feed rate (mm/min) and electrolyte discharge rate (lpm) with three levels each is selected as predictor variables (major influencing paramters) to identify its roles on the dependent variable of Surface roughness Ra in micron of machined surface. The predicted surface roughness values are validated using confirmation experiments, the maximum deviation is 8.51%. The developed model is verified with testing data in ANFIS generalized bell membership function (gbellmf) with and the average percentage error is 3.19%. These results prove that ANFIS model with gbellmf is accurate and can be used to predict the surface roughness of the nano copper suspended electrochemically machined Inconel 718.