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

Year 2019, Volume: 22 Issue: 4, 889 - 900, 01.12.2019

Şükrü Özşahin , Hilal Singer

https://doi.org/10.2339/politeknik.481762

Cited By: 5

Abstract

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.

Keywords

Artificial neural networks, wood, surface roughness, adhesion strength, prediction

Odunun Yüzey Pürüzlülüğünün ve Adezyon Direncinin Yapay Sinir Ağları ile Tahmini

Abstract

Mobilya ve dekorasyon elemanlarının
üretiminde kullanılan ağaç malzemelerin yüzey pürüzlülüğünün ve adezyon
direncinin belirlenmesi, nihai ürünün kalitesinin değerlendirilmesi bakımından
çok önemlidir. Bu makalede ilk olarak, odun türü, kesme yönü ve zımpara kağıdı
türünün yüzey pürüzlülüğü üzerine etkilerini incelemek için yapay sinir ağı
(YSA) ile yüzey pürüzlülüğü tahmin modeli geliştirilmiştir. Daha sonra, vernik
türü, odun türü, kesme yönü ve yüzey pürüzlülüğünün adezyon direnci üzerine
etkileri YSA ile geliştirilen adezyon direnci tahmin modeliyle araştırılmıştır.
En iyi performansa sahip tahmin modelleri istatistiksel ve grafiksel
karşılaştırmalar yoluyla belirlenmiştir. YSA modellerinin kabul edilebilir
sapmalarla oldukça tatmin edici neticeler elde ettiği görülmüştür. Sonuç olarak
bu çalışmanın bulguları, deneysel araştırmalar için zaman, enerji ve maliyeti
azaltmak amacıyla mobilya ve dekorasyon endüstrisinde etkili bir şekilde
uygulanabilir.  

Keywords

Yapay sinir ağları, odun, yüzey pürüzlülüğü, adezyon direnci, tahmin

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