Kanwal, Saumya, Sharma, Usha, Chauhan, Saurabh, Sharma, Anuj Kumar, Katiyar, Jitendra Kumar, Singh, Rabesh Kumar and Mohanty, Shalini ORCID: https://orcid.org/0000-0002-0424-792X (2025) Optimizing EDM of gunmetal with Al2O3-enhanced dielectric: experimental insights and machine learning models. Materials, 18 (19):4578. ISSN 1996-1944 (doi:10.3390/ma18194578)

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Abstract

This study investigates the optimization of electric discharge machining (EDM) param-eters for gunmetal using copper electrodes in two different dielectric environments, which are conventional EDM oil and EDM oil infused with Al2O3 nanoparticles. A Taguchi L27 orthogonal array design was used to evaluate the effects of current, voltage, and pulse-on time on Material Removal Rate (MRR), Electrode Wear Rate (EWR), and surface roughness (Ra, Rq, and Rz). Analysis of Variance (ANOVA) was used to statis-tically evaluate the influence of each parameter on machining performance. In addition, machine learning models including Linear Regression, Ridge Regression, Support Vector Regression, Random Forest, Gradient Boosting, and Neural Networks were imple-mented to predict performance outcomes. The originality of this research is not only rooted in the introduction of new models; rather, it is also found in the comparative analysis of various machine learning methodologies applied to the performance of electrical discharge machining (EDM) utilizing Al2O3-enhanced dielectrics. This inves-tigation focuses specifically on gunmetal, a material that has not been extensively stud-ied within this framework. The nanoparticle-enhanced dielectric demonstrated im-proved machining performance, achieving approximately 15% higher MRR, 20% lower EWR, and 10% improved surface finish compared to conventional EDM oil. Neural Networks consistently outperformed other models in predictive accuracy. Results in-dicate that the use of nanoparticle-infused dielectrics in EDM, coupled with data-driven optimization techniques, enhances productivity, tool life, and surface quality.

Item Type:	Article
Additional Information:	This article belongs to the Special Issue Non-conventional Machining: Materials and Processes.
Uncontrolled Keywords:	EDM, gunmetal, Al2O3 nanoparticles, machine learning, Taguchi design, surface roughness, tool wear rate, ANOVA
Subjects:	Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General)
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science > School of Engineering (ENG) Faculty of Engineering & Science
Last Modified:	08 Jan 2026 15:13
URI:	https://gala.gre.ac.uk/id/eprint/52056

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