Prediction of Optimum CNC Cutting Conditions Using Artificial Neural Network Models for the Best Wood Surface Quality, Low Energy Consumption, and Time Savings

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dc.authorwosidDemir, Aydın/V-4841-2017
dc.authorwosidAYDIN, Ismail/V-3713-2017
dc.contributor.authorÇakıroğlu, Evren Osman
dc.contributor.authorDemir, Aydın
dc.contributor.authorAydın, İsmail
dc.contributor.authorBüyüksarı, Ümit
dc.date.accessioned2023-07-26T11:54:14Z
dc.date.available2023-07-26T11:54:14Z
dc.date.issued2022
dc.departmentDÜ, Orman Fakültesi, Orman Endüstrisi Mühendisliği 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.en_US
dc.identifier.doi10.15376/biores.17.2.2501-2524
dc.identifier.endpage2524en_US
dc.identifier.issn1930-2126
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85137267656en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage2501en_US
dc.identifier.urihttps://doi.org/10.15376/biores.17.2.2501-2524
dc.identifier.urihttps://hdl.handle.net/20.500.12684/12769
dc.identifier.volume17en_US
dc.identifier.wosWOS:000798814000037en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorBüyüksarı, Ümit
dc.language.isoenen_US
dc.publisherNorth Carolina State Univ Dept Wood & Paper Scien_US
dc.relation.ispartofBioresourcesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz$2023V1Guncelleme$en_US
dc.subjectCutting Conditions; Artificial Neural Network; Cnc Machine; Surface Quality; Energy Consumption; Processing Timeen_US
dc.subjectEdge-Glued Panels; Machining Parameters; Roughness Characteristics; Taguchi Design; Wettability; Density; Optimization; Methodology; Mdf; Adhesionen_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.typeArticleen_US

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