USE OF ARTIFICIAL NEURAL NETWORKS IN PREDICTING PARTICLEBOARD QUALITY PARAMETERS

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dc.creatorRafael Rodolfo de Melo
dc.creatorEder Pereira Miguel
dc.date2016
dc.date.accessioned2026-07-07T03:17:24Z
dc.descriptionThis study aims to assess Artificial Neural Networks (ANN) in predicting particleboard quality based on its physical and mechanical properties. Particleboards were manufactured using eucalyptus (Eucalyptus grandis) and bonded with urea-formaldehyde and phenol-formaldehyde resins. To characterize quality, physical (density and water absorption and thickness swelling after 24-hour immersion) and mechanical (static bending strength and internal bond) properties were assessed. For predictions, adhesive type and particleboard density were adopted as ANN input variables. Networks of multilayer Perceptron (MLP) were adopted, training 100 networks for each assessed parameter. The results pointed out ANN as effective in predicting quality parameters of particleboards. With this technique, all the assessed properties presented models with adjustments higher than 0.90.
dc.formatapplication/pdf
dc.identifier0100-6762
dc.identifierhttps://www.redalyc.org/articulo.oa?id=48848756019
dc.identifier.urihttp://hdl.handle.net/123456789/429260
dc.languageen
dc.publisherUniversidade Federal de Viçosa
dc.relationhttp://www.redalyc.org/revista.oa?id=488
dc.rightsRevista Árvore
dc.sourceRevista Árvore (Brasil) Num.5 Vol.40
dc.subjectAgrociencias
dc.titleUSE OF ARTIFICIAL NEURAL NETWORKS IN PREDICTING PARTICLEBOARD QUALITY PARAMETERS
dc.typeartículo científico

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