Prediction of optimum CNC cutting conditions using artificial neural network models for the best wood surface quality, low energy consumption, and time savings

Basit Öğe Kaydı
dc.authorid0000-0001-5303-8967en_US
dc.contributor.authorÇakıroğlu, Evren Osman
dc.contributor.authorDemir, Aydın
dc.contributor.authorAydın, İsmail
dc.contributor.authorBüyüksarı, Ümit
dc.date.accessioned2022-06-17T07:42:07Z
dc.date.available2022-06-17T07:42:07Z
dc.date.issued2022
dc.departmentAÇÜ, Artvin Meslek Yüksekokulu, İç Mekan Tasarımı Bölümüen_US
dc.description.abstractThis study aimed to predict the CNC cutting conditions for the best wood surface quality, energy, and time savings using artificial neural network (ANN) models. In the CNC process, walnut, and ash wood were used as materials, while three different cutting tool diameters (3 mm, 6 mm, and 8 mm), spindle speed (12000 rpm, 15000 rpm, and 18000 rpm), and feed rate (3 m/min, 6 m/min, and 9 m/min) were determined as cutting conditions. After the cutting processes were completed with the CNC machine, energy consumption and processing time were determined for all groups. Surface roughness and wettability tests were performed on the processed wood samples, and their surface qualities were determined. The experimentally obtained data were analysed in ANN, and the models with the best performance were obtained. By using these prediction models, optimum cutting conditions were determined. Using the findings of the study, the optimum cutting condition values can be determined for walnut and ash wood with the smoothest and best wettable surface. Furthermore, in CNC processes using such materials, minimum energy consumption and shorter processing time can be obtained with optimum cutting conditions.
dc.identifier.citationÇakıroğlu, E. O., Demir, A., Aydın, İ., & Büyüksarı, Ü. (2022). Prediction of Optimum CNC Cutting Conditions Using Artificial Neural Network Models for the Best Wood Surface Quality, Low Energy Consumption, and Time Savings. BioResources, 17(2), 2501-2524.en_US
dc.identifier.doi10.15376/biores.17.2.2501-2524
dc.identifier.endpage2524en_US
dc.identifier.issue2en_US
dc.identifier.scopusqualityQ3
dc.identifier.startpage2501en_US
dc.identifier.urihttps://hdl.handle.net/11494/3871
dc.identifier.volume17en_US
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorÇakıroğlu, Evren Osman
dc.language.isoenen_US
dc.publisherNorth Carolina State Univ Dept Wood & Paper Scien_US
dc.relation.ispartofBioResources
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectCutting conditionsen_US
dc.subjectArtificial neural networken_US
dc.subjectCNC machineen_US
dc.subjectSurface qualityen_US
dc.subjectEnergy consumptionen_US
dc.subjectProcessing timeen_US
dc.titlePrediction of optimum CNC cutting conditions using artificial neural network models for the best wood surface quality, low energy consumption, and time savingsen_US
dc.typeArticle

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