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dc.contributor.authorAkyüz, İlker
dc.contributor.authorErsen, Nadir
dc.contributor.authorTiryaki, Sebahattin
dc.contributor.authorBayram, Bahadır Çağrı
dc.contributor.authorAkyüz, Kadri Cemil
dc.contributor.authorPeker, Hüseyin
dc.date.accessioned2021-02-24T08:27:21Z
dc.date.available2021-02-24T08:27:21Z
dc.date.issued2019en_US
dc.identifier.citationAkyüz, I., Ersen, N., Tiryaki, S., Bayram, B. Ç., Akyüz, K. C., & Peker, H. (2019). Modeling and comparison of bonding strength of impregnated wood material by using different methods: Artifıcial neural network and multiple linear regression, 64(3), 483-498.en_US
dc.identifier.urihttps://hdl.handle.net/11494/2641
dc.descriptionResearch was carried out under development project (No: 2016. F90.02.05), financed by Artvin Coruh University Scientific Research Project Coordinator.en_US
dc.description.abstractIn this study, the effects of vacuum time, diffusion time and pressing time on the bonding strength of Larix decidua wood impregnated with Immersol-Aqua and bonded with Klebit-303 were investigated. The vacuum time, diffusion time, and pressing time were predicted by using the artificial neural network (ANN) model and multiple linear regression (MLR) methods and the results of ANN and MLR methods were compared. The highest bonding strength (7.664 N.mm(-2)) was achieved when the vacuum time, the diffusion time and the pressing time were 20, 60 and 60 minutes, respectively, while the lowest value (4.62 N.mm(-2)) was achieved when the vacuum time, the diffusion time and the pressing time were 80, 120 and 20 minutes, respectively. The model results are as follows: The MAPE value for testing phase in the ANN was 7.266 and R-2 value was 0.751 whereas the MAPE value of the MLR was 9.365 and R-2 value was 0.558. The ANN model has been found to have better prediction performance than the MLR model.en_US
dc.language.isoengen_US
dc.publisherSlovak Forest Products Research Insten_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial neural networken_US
dc.subjectMLRen_US
dc.subjectBonding strengthen_US
dc.subjectImpregnationen_US
dc.titleModeling and comparison of bonding strength of impregnated wood material by using different methods: Artifıcial neural network and multiple linear regressionen_US
dc.typearticleen_US
dc.relation.journalWood Researchen_US
dc.departmentAÇÜ, Artvin Meslek Yüksekokuluen_US
dc.authorid0000-0003-3643-1390en_US
dc.authorid0000-0002-7771-6993en_US
dc.identifier.volume64en_US
dc.identifier.issue3en_US
dc.identifier.startpage483en_US
dc.identifier.endpage497en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.contributor.institutionauthor[0-Belirlenecek]en_US


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