Application of Evolutionary Optimization Techniques Towards Non-Traditional Machining for Performance Enhancement

Abstract

Electro-chemical machining is a non-conventional machining method that is used for machining of very complicated shape. In this chapter an attempt has been made to carry out multi-objective optimization of the surface roughness (SR) and material removal rate (MRR) for the ECM process of EN 19 on a CNC ECM machine using copper electrode through evolutionary optimization techniques like teaching-learning-based optimization (TLBO) technique and biogeography-based optimization (BBO) technique. The input parameters considered are electrolyte concentration, voltage, feed rate, inter-electrode gap. TLBO and BBO techniques were used to obtain maximum MRR and minimum SR. In addition, obtained optimized values were validated for testing the significance of the TLBO and BBO techniques, and a very small error value of MRR and SR was found. BBO outperformed TLBO in every aspect like less percentage error and better-optimized values; however, TLBO took less computation time than the BBO.