Soft computing technique based modelling of ceramics mix electric discharge machining on LM-25/SiC composites

Silicon carbide mixed electrical discharge machining (SCM-EDM), an unconventional machining process that utilizes an electrolyte mix additive, combines the advantage of various energies (abrasive, heat). The aim of these articles is to investigate the impact of control variables on the machining performance of SCM-EDM using LM-25/SiC MMC. The major variable of SCM-EDM is identified as current intensity, pulse duration. The experimental run has conducted as per Box-Behnken design which produces statistical data solely based on the experiment run and estimates the measurement error. Artificial neural network (ANN) technique implemented to estimate experimental inputs and modelling of measure response such as material deletion rate (MRR), tool wear (TW), and surface roughness (SR). It is observed that predicted results from ANN model are compared with experimental result are quite satisfactory. The chemical composition and analysis of variance show the percentage contribution of each parameter on machining performance. Copyright (c) 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 2nd International Conference on Functional Material, Manufacturing and Performances