Optimization of EDM parameters for enhanced machining of X37CrMoV5-1 tool steel using taguchi method and regression analysis.

Kapil Surani, Hiteshkumar Patel, Chandrakant Sonawane, Choon Kit Chan, Saurav Dixit, Subhav Singh, Mohd Aamir Mumtaz

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Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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7 authors.

Kapil SuraniMechanical Engineering Department, Gujarat Technological University-Institute of Technology & Research, Mehsana, Gujarat, 384460, India.

Hiteshkumar PatelMechanical Engineering Department, Gujarat Technological University-Institute of Technology & Research, Mehsana, Gujarat, 384460, India.

Chandrakant SonawaneSymbiosis Institute of Technology Pune, Symbiosis International Deemed University, Pune, Maharashtra, India. crsonawane@gmail.com.

Choon Kit ChanFaculty of Engineering and Quantity Surveying, INTI International University, 71800, Nilai, Negeri Sembilan, Malaysia.

Saurav DixitCentre of Research Impact and Outcome, Chitkara University, Rajpura, Punjab, 140417, India.

Subhav SinghDivision of research and development, Lovely Professional University, Phagwara, Punjab, 146001, India.

Mohd Aamir MumtazCivil Engineering Department, College of engineering, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia.

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6 · The paper itself

Abstract

Electrical Discharge Machining (EDM) is a vital non-traditional process for shaping hard-to-machine alloys used in precision tooling applications. This study experimentally investigates the machining performance of X37CrMoV5-1 tool steel under varying EDM parameters: peak current (Ip), pulse-on time (Ton), duty cycle (DC), and gap voltage (Vg). Experiments were conducted using the Taguchi L9 orthogonal array, and regression models were developed to predict Material Removal Rate (MRR) and Tool Wear Rate (TWR). The results revealed that peak current had the most dominant influence on both responses, contributing approximately 64.3% to MRR and 57.8% to TWR variation. The optimal parameters for maximizing MRR were found to be I

Indexed as

EDMProcess InnovationRegression analysisTaguchi (DOE)X37CrMoV5-1 steel

Identifiers

PMID41276598

DOI10.1038/s41598-025-28664-5

PMCPMC12700910

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