USE OF ARTIFICIAL NEURAL NETWORKS IN PREDICTING PARTICLEBOARD QUALITY PARAMETERS
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| Format: | Artículo científico |
| Langue: | en |
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Universidade Federal de Viçosa
2016
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| _version_ | 1876470224674357248 |
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| author | Rafael Rodolfo de Melo |
| author_facet | Rafael Rodolfo de Melo |
| contents | USE OF ARTIFICIAL NEURAL NETWORKS IN PREDICTING PARTICLEBOARD QUALITY PARAMETERS Rafael Rodolfo de Melo Eder Pereira Miguel Agrociencias Physico Particleboards mechanical properties Artificial intelligence This 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. 2016 artículo científico 0100-6762 https://www.redalyc.org/articulo.oa?id=48848756019 en http://www.redalyc.org/revista.oa?id=488 Revista Árvore application/pdf Universidade Federal de Viçosa Revista Árvore (Brasil) Num.5 Vol.40 |
| format | Artículo científico |
| id | redalyc_48848756019 |
| institution | Redalyc |
| language | en |
| publishDate | 2016 |
| publisher | Universidade Federal de Viçosa |
| spellingShingle | USE OF ARTIFICIAL NEURAL NETWORKS IN PREDICTING PARTICLEBOARD QUALITY PARAMETERS Rafael Rodolfo de Melo Agrociencias Physico Particleboards mechanical properties Artificial intelligence USE OF ARTIFICIAL NEURAL NETWORKS IN PREDICTING PARTICLEBOARD QUALITY PARAMETERS Rafael Rodolfo de Melo Eder Pereira Miguel Agrociencias Physico Particleboards mechanical properties Artificial intelligence This 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. 2016 artículo científico 0100-6762 https://www.redalyc.org/articulo.oa?id=48848756019 en http://www.redalyc.org/revista.oa?id=488 Revista Árvore application/pdf Universidade Federal de Viçosa Revista Árvore (Brasil) Num.5 Vol.40 |
| title | USE OF ARTIFICIAL NEURAL NETWORKS IN PREDICTING PARTICLEBOARD QUALITY PARAMETERS |
| topic | Agrociencias Physico Particleboards mechanical properties Artificial intelligence |
| url | https://www.redalyc.org/articulo.oa?id=48848756019 |