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

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<sub>P</sub> = 9 A, T<sub>ON</sub> = 155 µs, DC = 28%, and Vg = 50 V., while the optimal conditions for minimizing TWR were I<sub>P</sub> = 3 A, T<sub>ON</sub> = 210 µs, DC = 19%, and Vg = 50 V. The machining conditions achieved an MRR of 5.8538 mm<sup>3</sup>/min and a TWR of 0.0535 mm<sup>3</sup>/min, representing a 27.5% improvement in MRR and 21.3% reduction in TWR compared to previously reported studies on similar tool steels. The regression models showed high accuracy with R<sup>2</sup> values exceeding 0.987, confirming their suitability for performance prediction. Overall, the study demonstrates that controlled adjustment of Ip and Ton significantly enhances EDM efficiency for X37CrMoV5-1 tool steel.

Authors

Institutions

Publication Details

Journal
Scientific Reports
Published
2025-11-24
DOI
https://doi.org/10.1038/s41598-025-28664-5
Primary Topic
Advanced Machining and Optimization Techniques
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Choon Kit Chan, Subhav Singh, Mohd Aamir Mumtaz, Saurav Dixit et al.
Scientific Reports
Advanced Machining and Optimization Techniques
article

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

Choon Kit Chan, Subhav Singh, Mohd Aamir Mumtaz, Saurav Dixit, H. Patel, Chandrakant Sonawane, Kapil Surani
article en

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<sub>P</sub> = 9 A, T<sub>ON</sub> = 155 µs, DC = 28%, and Vg = 50 V., while the optimal conditions for minimizing TWR were I<sub>P</sub> = 3 A, T<sub>ON</sub> = 210 µs, DC = 19%, and Vg = 50 V. The machining conditions achieved an MRR of 5.8538 mm<sup>3</sup>/min and a TWR of 0.0535 mm<sup>3</sup>/min, representing a 27.5% improvement in MRR and 21.3% reduction in TWR compared to previously reported studies on similar tool steels. The regression models showed high accuracy with R<sup>2</sup> values exceeding 0.987, confirming their suitability for performance prediction. Overall, the study demonstrates that controlled adjustment of Ip and Ton significantly enhances EDM efficiency for X37CrMoV5-1 tool steel.

Scientific ReportsVol. 15(1)
Lovely Professional University (IN), INTI International University (MY), Peter the Great St. Petersburg Polytechnic University (RU), Imam Mohammad ibn Saud Islamic University (SA), Symbiosis International University (IN), Gujarat Technological University (IN), Graphic Era University (IN), Chitkara University (IN)
Openalex Percentile: Top 64%
Advanced Machining and Optimization Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.