ESTIMATION THE PROPERTIES OF PARTICLEBOARDS MANUFACTURED FROM VINE PRUNINGS STALKS USING ARTIFICIAL NEURAL NETWORKS

Year 2015, Volume: 1 Issue: 1, 24 - 33, 07.04.2015

Hüseyin Gürüler , Serkan Ballı , Mehmet Yeniocak , Osman Göktaş

https://doi.org/10.22531/muglajsci.209996

Cited By: 5

Abstract

In this study, pruned vine particles and wood particles in five various proportions were used as the raw material for three-layer particleboards. Primarily, small size sample panels (56x56x2 cm) were manufactured. The physical (thickness swelling (TS), water absorption (WA)), and mechanical (modulus of rupture (MOR), modulus of elasticity (MOE), internal bond (IB)) screw holding (SH) properties of particleboards were determined. Although direct measurement is the most reliable method, it is very complex and time consuming. Also every proportion is not applicable. So that, soft computing methods which are the powerful tools for input-output mapping were preferred. Artificial neural networks (ANNs) were used to estimation. The results show that ANN system capable to predict properties of particleboards in a time and cost effective way.

Keywords

Neural networks, Agricultural residue, Particleboard, Prediction of properties

YAPAY SİNİR AĞLARI KULLANILARAK BAĞ BUDAMA ARTIKLARINDAN ÜRETİLEN YONGA LEVHALARIN ÖZELLİKLERİNİN TAHMİN EDİLMESİ

Abstract

Bu çalışmada, yonga levha hammaddesi olarak beş farklı oranda, bağ budama yongaları ve odun yongaları kullanılmıştır. Öncelikle küçük boyutlu numune paneller üç katmanlı olarak (56x56x2 cm) üretilmiştir. Bu levhaların; fiziksel (kalınlığına şişme, su alma) ve mekaniksel (eğilme direnci, elastikiyet modülü, iç yapışma ve vida tutma) özellikleri belirlenmiştir. Direk ölçüm alma metodu en güvenilir metot olmasına rağmen çok karmaşık ve zaman alıcıdır. Üstelik her oran uygulanabilir değildir. Bu nedenle girdi-çıktı haritalamada en güçlü araçlar olan esnek (yazılımsal) hesaplama yöntemleri tercih edilmiştir. Bu aşamada, yapay sinir ağları (YSA) tahmin amaçlı kullanıldı. Sonuçlar yapay sinir ağları sisteminin zamandan ve maliyetten kazanç sağlayarak yonga levhaların özelliklerinin belirlenmesinde yetkin olduğunu göstermektedir

Keywords

Sinir ağları, Tarımsal atıklar, Yonga levha, Özelliklerin tahmini

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