Investigation and neural network prediction of wood bonding quality based on pressing conditions

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dc.authoridAYDIN, AYTAC/0000-0001-7460-9618
dc.authoridBARDAK, selahattin/0000-0001-9724-4762
dc.contributor.authorBardak, Selahattin
dc.contributor.authorTiryaki, Sebahattin
dc.contributor.authorNemli, Gokay
dc.contributor.authorAydin, Aytac
dc.date.accessioned2025-03-23T19:41:29Z
dc.date.available2025-03-23T19:41:29Z
dc.date.issued2016
dc.departmentSinop Üniversitesi
dc.description.abstractThis paper presents an application of artificial neural network (ANN) to predict the bonding strength of the wood joints pressed under different conditions. An experimental investigation firstly was carried out and then an ANN model was developed based on the experimental data. In the experimental investigation, Oriental beech (Fagus orientalis L) and Oriental spruce (Picea orientalis (L.) Link.) samples bonded with polyvinyl acetate (PVAc) adhesive were pressed at four different temperatures (20, 40, 60 and 80 degrees C) for four different durations (2, 8, 14 and 20 min). The experimental results showed that higher values of bonding strength were obtained when high temperatures were combined with short pressing duration. Similar findings could be also obtained with longer pressing time for lower temperatures. The first case may be recommended to increase the efficiency of the production process, allowing a greater quantity of production per unit time. The ANN results showed a good agreement with the experimental results. It was shown that prediction error was within acceptable limits. The results revealed that the developed ANN model is capable of giving adequate prediction for bonding strength with an acceptable accuracy level. The desired outputs of bonding strength can be thus obtained by conducting less number of time-consuming and costly experimental investigations using the proposed model. (C) 2016 Elsevier Ltd. All rights reserved.
dc.identifier.doi10.1016/j.ijadhadh.2016.02.010
dc.identifier.endpage123
dc.identifier.issn0143-7496
dc.identifier.issn1879-0127
dc.identifier.scopus2-s2.0-84959492489
dc.identifier.scopusqualityQ1
dc.identifier.startpage115
dc.identifier.urihttps://doi.org/10.1016/j.ijadhadh.2016.02.010
dc.identifier.urihttps://hdl.handle.net/11486/6593
dc.identifier.volume68
dc.identifier.wosWOS:000378470400014
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier Sci Ltd
dc.relation.ispartofInternational Journal of Adhesion and Adhesives
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250323
dc.subjectNeural network
dc.subjectBonding strength
dc.subjectPrediction
dc.subjectPressing conditions
dc.subjectPVAc
dc.subjectWood
dc.titleInvestigation and neural network prediction of wood bonding quality based on pressing conditions
dc.typeArticle

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