Optimizing Precision Machining of Inconel Alloy Through Hybrid Taguchi and Meta-Heuristic GA Method in Electrochemical Machining

Abstract

This study focuses on optimizing the electrochemical machining (ECM) technique for Inconel alloy, recognized for its problematic machinability. Employing a methodical approach, the Taguchi technique with a L9 array architecture was originally applied for testing. Subsequently, the research used the genetic algorithm (GA) as a metaheuristic optimization technique to improve and optimize the experimental findings. The improved parameters acquired using GA were shown to give a greater material removal rate (MRR) compared to the original Taguchi technique, highlighting the efficiency of the hybrid methodology. Specifically, the GA optimization produced a lowered voltage of 14.8V, an electrolyte concentration of 185.3 g/L, and an enhanced flow rate of 1.7 L/min, resulting in a better MRR of 0.876 g/min. This hybrid technique offers a thorough strategy for gaining greater efficiency in ECM for Inconel alloy machining, integrating the methodical planning of trials with the exploration capabilities of the genetic algorithm.