Multi-functional Inconel 625 micro-machining and process optimization using ANFIS

Abstract

The primary objective of this study is to develop an adaptive neuro-fuzzy inference-based system (ANFIS) that assesses the ideal parameters and impact of the micro-electro-discharge machining (Micro-EDM) technique on surface roughness (SR). In this work multi-functional Inconel alloy specimens were machined using micro-elector-discharge machining (Micro-EDM) with zinc-coated copper electrodes in a graphite nano abrasive submersion medium. An ANFIS and Taguchi design of experiments (DOE) was employed to evaluate the Micro-EDM method. The study utilized Taguchi's L9 mixed orthogonal arrays (OA) to consider three process variables, namely open voltage (V), capacitance (C), and powder concentration (CP). ANOVA analysis was conducted to identify important process variables and evaluate their influence on surface properties. The experimental results showed that all approaches were effective in representing the process. Based on the results of the response surface methodology (RSM) and ANFIS predictions, the correlation coefficients (R2) of the experimental roughness were 0.995649 and the root mean square error (RMSE) value was 1.01226e-06, respectively. The results obtained indicate a strong alignment between the predicted and experimental values, effectively fulfilling the proposed objective. These findings hold significant potential for practical applications in the Micro-EDM industry, particularly in the machining of Inconel alloy.