Machinability of different Cu-Gr composites in milling: Performance parameters prediction via machine learning models

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dc.authoridSAP, SERHAT/0000-0001-5177-4952
dc.authoridAcar, Erdi/0000-0002-1451-7874
dc.authoridSener, Ramazan/0000-0001-6108-8673
dc.authoridMemis, Samet/0000-0002-0958-5872
dc.contributor.authorSap, Serhat
dc.contributor.authorAcar, Erdi
dc.contributor.authorDegirmenci, Uenal
dc.contributor.authorUsca, uesame Ali
dc.contributor.authorMemis, Samet
dc.contributor.authorSener, Ramazan
dc.date.accessioned2025-05-29T02:58:00Z
dc.date.available2025-05-29T02:58:00Z
dc.date.issued2025
dc.departmentÇanakkale Onsekiz Mart Üniversitesi
dc.description.abstractThe machinability of copper-graphite (Cu-Gr) composites has gained significant attention due to their unique thermal, electrical, and mechanical properties. This study experimentally investigates the machinability performances (such as surface roughness, flank wear, cutting temperature, and energy consumption) of Cu-Gr hybrid composite materials during milling. It predicts these parameters with machine learning models. The study aims to contribute to sustainable and optimized manufacturing processes by analyzing the effects of different cutting parameters and cooling/lubrication conditions on this performance. Furthermore, advanced artificial intelligence-based models predict machining outcomes, providing a robust framework for process enhancement and industrial implementation. Although there are comprehensive studies on the machining performances of metal matrix composites in the literature, there is limited information on Cu-Gr composites' mechanical and thermal behaviors in milling processes. To address this deficiency, a full factorial experimental plan was applied on six different Cu-Gr composites and the effects of different cutting speeds, feed rates and cooling/ lubrication environments (Dry, MQL, cryogenic LN2) on flank wear, surface roughness, cutting temperature and energy consumption were analyzed. The materials used in the study were prepared by mixing graphite and hard phases (Al2O3 and Cr3C2) in specific proportions, and these composites were compared in terms of machinability. Afterward, the output parameters of the experimental results are predicted by employing the well-known machine learning models and the experimental results. The results manifested that Gradient-Boosted Decision Tree Regression performs better than the other ten machine learning models in predicting machinability parameters. Finally, this study highlights potential areas for future research and provides a practical guide for optimizing CuGr composites in manufacturing processes and achieving sustainability goals. It has engineering value in efficiency, cost reduction, and developing environmentally friendly applications, especially for the automotive, aerospace, and energy sectors.
dc.identifier.doi10.1016/j.eswa.2025.126770
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.scopus2-s2.0-85217245336
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2025.126770
dc.identifier.urihttps://hdl.handle.net/20.500.12428/30239
dc.identifier.volume272
dc.identifier.wosWOS:001426149400001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherPergamon-Elsevier Science Ltd
dc.relation.ispartofExpert Systems With Applications
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250529
dc.subjectCu-Gr composites
dc.subjectMachinability Metrics
dc.subjectMachine Learning
dc.subjectRegression
dc.subjectPrediction
dc.titleMachinability of different Cu-Gr composites in milling: Performance parameters prediction via machine learning models
dc.typeArticle

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