{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T11:45:04Z","timestamp":1783511104391,"version":"3.55.0"},"reference-count":45,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2025,10,2]],"date-time":"2025-10-02T00:00:00Z","timestamp":1759363200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Materials"],"abstract":"<jats:p>This study investigates the optimization of electric discharge machining (EDM) parameters 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 statistically 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 implemented 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 investigation focuses specifically on gunmetal, a material that has not been extensively studied within this framework. The nanoparticle-enhanced dielectric demonstrated improved 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 indicate that the use of nanoparticle-infused dielectrics in EDM, coupled with data-driven optimization techniques, enhances productivity, tool life, and surface quality.<\/jats:p>","DOI":"10.3390\/ma18194578","type":"journal-article","created":{"date-parts":[[2025,10,2]],"date-time":"2025-10-02T09:38:08Z","timestamp":1759397888000},"page":"4578","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Optimizing EDM of Gunmetal with Al2O3-Enhanced Dielectric: Experimental Insights and Machine Learning Models"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-0316-174X","authenticated-orcid":false,"given":"Saumya","family":"Kanwal","sequence":"first","affiliation":[{"name":"Centre for Advanced Studies, Lucknow 226031, Uttar Pradesh, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Usha","family":"Sharma","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Babu Banarasi Das Institute of Technology and Management, Lucknow 226028, Uttar Pradesh, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saurabh","family":"Chauhan","sequence":"additional","affiliation":[{"name":"Applied Science and Humanities Department, Rajkiya Engineering College, Kannauj 209732, Uttar Pradesh, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2680-4720","authenticated-orcid":false,"given":"Anuj Kumar","family":"Sharma","sequence":"additional","affiliation":[{"name":"Centre for Advanced Studies, Lucknow 226031, Uttar Pradesh, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4189-9484","authenticated-orcid":false,"given":"Jitendra Kumar","family":"Katiyar","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8029-8780","authenticated-orcid":false,"given":"Rabesh Kumar","family":"Singh","sequence":"additional","affiliation":[{"name":"Mechanical Engineering Department, Madan Mohan Malaviya University of Technology, Gorakhpur 273010, Uttar Pradesh, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0424-792X","authenticated-orcid":false,"given":"Shalini","family":"Mohanty","sequence":"additional","affiliation":[{"name":"Faculty of Engineering and Sciences, University of Greenwich, Chatham Maritime ME4 4TB, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"986","DOI":"10.1080\/10426914.2017.1376081","article-title":"Performance evaluation of Al2O3 nano powder mixed dielectric for electric discharge machining of Inconel 825","volume":"33","author":"Kumar","year":"2018","journal-title":"Mater. 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