Parametric Appraisal of Electrochemical Machining of AISI 4140 Chromoly steel using Hybrid Taguchi - WASPAS - Sunflower optimization algorithm

¹ Department of Advanced Materials Technology, CSIR-Institute of Minerals and Materials Technology, Bhubaneswar, Odisha, India
² Environment & Sustainability Department, CSIR-Institute of Minerals & Materials Technology, Bhubaneswar, Odisha, India
³ Hydro & Electrometallurgy Department, CSIR-Institute of Minerals and Materials Technology, Bhubaneswar 751013, India
⁴ Academy of Scientific and Innovative Research, CSIR-HRD Centre Campus, Ghaziabad, Uttar Pradesh, India
⁵ Department of Mechanical Engineering, Parala Maharaja Engineering College, Berhampur, Odisha, India
⁶ School of Mechanical Engineering, Lovely Professional University, Phagwara, Punjab, India
⁷ Department of Materials Engineering, Indian Institute of Science, Bengaluru, Karnataka, India
⁸ Materials Chemistry Department, CSIR-Institute of Minerals & Materials Technology, Bhubaneswar, Odisha, India

^* Corresponding author: swastik.rock002@gmail.com

Abstract

Electrochemical machining (ECM) is a significant technique for getting rid of metal that employs anodic dissolution to get complex contours and deep, precise holes, mostly in the components used in automotive or aerospace sectors. To achieve such high surface characteristics, the selection of factors is important. This work deals with the ECM of AISI 4140 Chromoly steel to investigate the surface roughness and material removal rate (MRR) on the machined specimen using a copper tool electrode. Factors like voltage, signal, and feed rate were optimized by hybrid optimization techniques. To acquire optimal factor configurations, the Taguchi-based WASPAS approach was utilised, accompanied by the Sunflower optimisation methodology. ANOVA was then used to determine the component that was the most impactful factor. A confirmation test is used to signify the outcomes of electrochemical machining. It was revealed that feed rate was among the most significantly relevant factors in affecting surface roughness and MRR. Also, all the optimization approaches provided similar predictions and agreed with the results fetched by the previous research.