Enhancing EDM performance characteristics of Inconel 625 superalloy using response surface methodology and ANFIS integrated approach

Abstract

The objective of the present work is to find out the optimal level as well as the influence of the electrical discharge machining (EDM) process on material removal rate (MRR), surface roughness (SR), and tool wear rate (TWR). The machining is carried out on Inconel 625 superalloy by using a copper tool electrode in a kerosene submerged medium. The optimum level of input parameters such as current, pulse on time (Ton), pulse off time (Toff), and gap voltage are calculated to achieve maximum MRR and minimum SR and TWR. L27 orthogonal design is used to perform the experiments and optimum levels of MRR, SR and TWR have been obtained by the response surface methodology (RSM) and adaptive network-based fuzzy inference system (ANFIS) approach. Analysis of variance (ANOVA) is used to identify the significant and non-significant parameters. For implementing, RSM methodology, the Design Expert 13 software was used for the optimization and modelling of the experimental data. MATLAB R20b software was utilized for developing the ANFIS model. It is found that the pulse on time (Ton) and pulse off time (Toff) are the significant parameters for MRR and SR and more wire wear increases as the value of pulse on time (Ton) increases. The obtained quadratic RSM model is found significant for MRR, SR, and TWR. Similarly, ANFIS proved to be accurate for the optimization of output responses with model accuracy percentages noted as 95.55% for MRR, 90.35% for TWR, and 97.82% for SR. The obtained results show that the predicted values and experimental values are very well suited for the proposed objective. The obtained data can be utilized for the EDM industry during the machining of Inconel alloy.