Prediction of Surface Roughness and Adhesion Strength of Wood by Artificial Neural Networks

ÖZŞAHİN Ş., Singer H.

JOURNAL OF POLYTECHNIC-POLITEKNIK DERGISI, cilt.22, sa.4, ss.889-900, 2019 (ESCI) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 22 Sayı: 4
  • Basım Tarihi: 2019
  • Doi Numarası: 10.2339/politeknik.481762
  • Dergi Adı: JOURNAL OF POLYTECHNIC-POLITEKNIK DERGISI
  • Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI), TR DİZİN (ULAKBİM)
  • Sayfa Sayıları: ss.889-900
  • Karadeniz Teknik Üniversitesi Adresli: Evet

Özet

Determining the surface roughness and adhesion strength of wood materials used in the manufacturing of furniture and decoration elements is very crucial in terms of evaluating the quality of the final product. In this article, firstly, the surface roughness prediction model was developed with the artificial neural network (ANN) to examine the effects of wood species, cutting direction and sandpaper type on surface roughness. Then, the effects of varnish type, wood species, cutting direction and surface roughness on adhesion strength were investigated with the adhesion strength prediction model developed with ANN. The prediction models with the best performance were determined by statistical and graphical comparisons. It has been observed that ANN models yielded very satisfactory results with acceptable deviations. As a result, the findings of this study could be employed effectively into the furniture and decoration industry to reduce time, energy and cost for empirical investigations.