Abstract
The advanced machining processes have attracted the attention of researchers due to their better machining performances, especially for hard-to-cut materials. Among all advanced machining processes Electro-Chemical Machining (ECM) has the capability to generate complex profile on electrically conductive difficult to machine materials. In this paper, authors have tried to investigate the process behavior of ECM on Aluminum-Silicon Carbide-Graphite (AlSiC-Gr) composite material. Further, the hybrid approach of Artificial Neural Network (ANN) and Genetic Algorithm (GA) has been utilized for modeling and optimization of machining performance during ECM of Al-SiC-Gr. The experimental results are used for generating the ANN based model. It was observed that the predicted results of responses through model appropriately fit with experimental results. The optimization results reveal that the material removal rate and surface roughness have improved by 137% and 58%, respectively as compared to average experimental values.
doi: 10.17756/nwj.2023-s1-030
Citation: Rahi DK, Dubey AK, Maurya A. 2023. Intelligent Modeling and Optimization of Performance Characteristics in Electro-chemical Machining of HMMC.NanoWorld J 9(S1): S148-S152.