Multi-Objective Optimization of ECG Process Applying Soft Computing Techniques

Abstract

The present research work focuses on the selection of significant machining parameters depending on the nature-inspired algorithm while machining alumina-aluminum interpenetrating phase composites during electrochemical grinding. Control parameters like electrolyte concentration (C), voltage (V), depth of cut (D) and electrolyte flow rate (F) have been considered for experimentation. The response data are initially trained and tested by using Artificial Neural Network. The contradictory responses like higher material removal rate (MRR), lower surface roughness (Ra), lower overcut (OC) and lower cutting force (Fc) are ensured individually by employing Firefly Algorithm. A multi-response optimization for all the responses is done initially by using the Genetic algorithm. Finally, in order to obtain a single set of parametric combination for all the output simultaneously fuzzy based Grey Relational Analysis technique is adopted. These natures driven soft computing techniques corroborates well during the parametric optimization of the electrochemical grinding process.