Energy Consumption Prediction of Additive Manufactured Tensile Strength Parts Using Artificial Intelligence

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dc.authoridUlkir, Osman/0000-0002-1095-0160
dc.contributor.authorUlkir, Osman
dc.contributor.authorBayraklilar, Mehmet Said
dc.contributor.authorKuncan, Melih
dc.date.accessioned2024-12-24T19:28:29Z
dc.date.available2024-12-24T19:28:29Z
dc.date.issued2024
dc.departmentSiirt Üniversitesi
dc.description.abstractThe manufacturing sector's interest in additive manufacturing (AM) methods is increasing daily. The increase in energy consumption requires optimization of energy consumption in rapid prototyping technology. This study aims to minimize energy consumption with determined production parameters. Four machine learning algorithms are preferred to model the energy consumption of the fused deposition modeling-based 3D printer. The real-time measured test sample data were trained, and the prediction model between the parameters of 3D fabrication and the energy consumption was created. The predicted model was evaluated using five performance criteria. These are mean square error (MSE), mean absolute error (MAE), root mean squared error (RMSE), R-squared (R2), and explained variance score (EVS). It has been seen that the Gaussian Process Regression model predicts energy consumption in the AM with high accuracy: R2 = 0.99, EVS = 0.99, MAE = 0.016, RMSE = 0.022, and MSE = 0.00049.
dc.identifier.doi10.1089/3dp.2023.0189
dc.identifier.endpagee1920
dc.identifier.issn2329-7662
dc.identifier.issn2329-7670
dc.identifier.issue5
dc.identifier.scopus2-s2.0-85180282994
dc.identifier.scopusqualityQ1
dc.identifier.startpagee1909
dc.identifier.urihttps://doi.org/10.1089/3dp.2023.0189
dc.identifier.urihttps://hdl.handle.net/20.500.12604/7085
dc.identifier.volume11
dc.identifier.wosWOS:001138544300001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMary Ann Liebert, Inc
dc.relation.ispartof3d Printing and Additive Manufacturing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241222
dc.subjectenergy consumption
dc.subjectadditive manufacturing
dc.subjectmachine learning
dc.subjectfused deposition modeling
dc.subject3D printer
dc.titleEnergy Consumption Prediction of Additive Manufactured Tensile Strength Parts Using Artificial Intelligence
dc.typeArticle

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