Manufacturing Technology 2026, 26(2):220-232 | DOI: 10.21062/mft.2026.017

Machine Learning-Based Predictive Modelling of EDM and EAM-V Processes for Performance Analysis

Shrihar Pandey1, Rajesh Kumar2, Abhay Kumar Singh3, Robert Cep ORCID...4, Priyanka Singh1, Mihir Kumar Pandey1, B. Swarna5,6
1 Department of Mechanical Engineering, AKS University, Satna 485001, India
2 Office of the Registrar, KK University, Nalanda, Bihar 803115, India
3 Department of Mechanical Engineering, Government Engineering College, Arwal 804409, India
4 Faculty of Mechanical Engineering, VSB-Technical University of Ostrava, 70800 Ostrava, Czech Republic
5 Department of Biosciences, Saveetha School of Engineering. Saveetha Institute of Medical and Technical Sciences, Chennai 602105, India
6 University Centre for Research & Development, Chandigarh University, Mohali 140413, India

Abstract- The promotion of Electrical Discharge Machining (EDM) and vibration aided Electric Arc Machining (EAM-V) processes is characterized in the study in terms of their capability for precision manufacture, mainly drawing any performance comparisons from a machine learning approach. The present machine learning study aims to predict some important metrics of machining utility, such as Material Removal Rate (MRR), Tool Wear Rate (TWR), and Surface Roughness (SR), against process parameters like current, pulse-on/off time, etc. Some advanced models like Gradient Boosting and Random Forest are used to analyze the efficacy and effectiveness of EDM and EAM-V, comparing the respective influences these parameters have on honing outcomes. The study describes an elaborate methodology: data collection, preprocessing, feature scaling, and application of multiple regression algorithms for machining performance forecasting. The experimental data for model training and testing were partitioned into 80% and 20%, respectively. The results revealed that Gradient Boosting (GB) performed better than Random Forest (RF) for all parameters. In GB, the R² values of MRR, TWR, and SR were higher; hence, its degree of accuracy was superior in comparison with RF. For instance, an R² value of 0.970, 0.994, and 0.999 was achieved by GB for MRR, TWR, and SR, respectively, thus proving its better predictive ability. Moreover, according to average predicted values, EAM-V performs better for MRR; EDM, comparatively, from TWR and SR, is more suitable for precision applications. The performance validation of GB through RMSE and MAE also confirms its efficacious predictions.

Keywords: EDM, EAM-V, Machine Learning, Material Removal Rate, Tool Wear Rate, Surface Roughness, Gradient Boosting, Random Forest
Grants and funding:

The authors extend their acknowledgement to the financial support of the European Union under the REFRESH-Research Excellence For REgion Sustainability and High-tech Industries project number CZ.10.03.01/00/22_003/0000048 via the Operational Programme Just Transition and has been done in connection with project Students Grant Competition SP2026/061 "Sustainable manufacturing technologies" and SP2026/060 "Research of innovative  manufacturing technologies" financed by the Ministry of Education, Youth and Sports and Faculty of Mechanical Engineering V©B-TUO

Received: June 3, 2025; Revised: March 21, 2026; Accepted: March 24, 2026; Prepublished online: April 22, 2026; Published: April 23, 2026  Show citation

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Pandey S, Kumar R, Kumar Singh A, Cep R, Singh P, Kumar Pandey M, Swarna B. Machine Learning-Based Predictive Modelling of EDM and EAM-V Processes for Performance Analysis. Manufacturing Technology. 2026;26(2):220-232. doi: 10.21062/mft.2026.017.
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