Modeling of Mechanical Properties of Wood-Polymer Composites with Artificial Neural Networks

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dc.contributor.authorEroglu, Mustafa Altay
dc.contributor.authorAltun, Suat
dc.contributor.authorCiritcioglu, Hasan Huseyin
dc.date.accessioned2024-09-29T16:06:19Z
dc.date.available2024-09-29T16:06:19Z
dc.date.issued2024
dc.departmentKarabük Üniversitesien_US
dc.description.abstractMechanical properties (tensile strength (TS), modulus of elasticity in tensile (MET), flexural strength (FS), modulus of elasticity (MOE)) of the material to be obtained depending on the production parameters in the production of high-density polyethylene (HDPE) wood-polymer composites with Scots pine wood flour additive were predicted using Artificial Neural Networks (ANN) model and without destructive testing. In the first stage of the study, an ANN model was developed using data from 56 different studies in the literature on the mechanical properties of wood polymer composites. In the second stage, in order to determine the reliability of the model, output values were estimated using input parameters that had not been used in training and testing of the model. Based on the same input parameters, test specimens were produced and mechanical tests were performed. The results obtained from the experiments and ANN model were compared by considering the mean absolute percentage error (MAPE) value. The coefficient of determination (R 2 ) values obtained in the training and testing phase of the ANN models were all higher than 0.90. In this way, the mechanical properties of the wood polymer composite were successfully predicted by the ANN model. Because most of the MAPE values obtained from the mechanical tests were below 10%, the model was considered a reliable model.en_US
dc.description.sponsorshipKarabuk University Scientific Research Project Coordination Office [KBUBAP-22-DS-133]en_US
dc.description.sponsorshipThe authors are grateful to Professor Dr. Ugur Guvenc and Associate Professor Dr. Serhat Duman for help in development of the ANN model. The authors are grateful for the support of the Karabuk University Scientific Research Project Coordination Office Under Grant No: KBUBAP-22-DS-133.en_US
dc.identifier.doi10.15376/biores.19.3.4468-4485
dc.identifier.endpage4485en_US
dc.identifier.issn1930-2126
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-85194899588en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage4468en_US
dc.identifier.urihttps://doi.org/10.15376/biores.19.3.4468-4485
dc.identifier.urihttps://hdl.handle.net/20.500.14619/6765
dc.identifier.volume19en_US
dc.identifier.wosWOS:001259919000002en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
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.subjectTensile strengthen_US
dc.subjectFlexural strengthen_US
dc.subjectModulus of elasticityen_US
dc.subjectHDPEen_US
dc.subjectMAPEen_US
dc.titleModeling of Mechanical Properties of Wood-Polymer Composites with Artificial Neural Networksen_US
dc.typeArticleen_US

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