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Optimizing Precision Machining of Inconel Alloy Through Hybrid Taguchi and Meta-Heuristic GA Method in Electrochemical Machining
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Author(s): Satyanarayana Tirlangi (Visakha Institute of Engineering and Technology, Visakhapatnam, India), Hari Banda (Villa College, Maldives), R. Vadivel (BNM Institute of Technology, Bangalore, India), Sudheer Kumar Battula (Lakireddy Balireddy College of Engineering (Autonomous), India), M. Sabarimuthu (Kongu Engineering College, Erode, India)and Mohammed Ali H. (SRM Institute of Science and Technology, Ramapuram, India)
Copyright: 2024
Pages: 20
Source title:
Metaheuristics Algorithm and Optimization of Engineering and Complex Systems
Source Author(s)/Editor(s): Thanigaivelan R. (AKT Memorial College of Engineering and Technology, India), Suchithra M. (SRM Institute of Science and Technology, India), Kaliappan S. (KCG College of Technology, India)and Mothilal T. (KCG College of Technology, India)
DOI: 10.4018/979-8-3693-3314-3.ch004
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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.
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