Odunun Yüzey Pürüzlülüğünün ve Adezyon Direncinin Yapay Sinir Ağları ile Tahmini

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.

Anahtar Kelimeler:

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

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

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,

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