Journals / Erzincan Üniversitesi Fen Bilimleri Enstitüsü Dergisi / 2019 / Cilt: 12 - Sayı: 1

Use of Radial Basis Function Neural Network in Estimating Wood Composite Materials According to Mechanical and Physical Properties

Ahşap Kompozit Malzemelerin Mekanik ve Fiziksel Özelliklerine göre Tahmininde Radyal Temelli Fonksiyon Sinir Ağının Kullanımı

Pages
116–123
Publication Date
2019-03-24
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Moreover, the present study will fill this gap in literature.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Knowing the mechanical and physical properties of a material is the most important criteria for engineers and designers interested in determining the intended use of the material.
📊
Key Findings & Data:The prediction of wood composite materials based on their mechanical and physical properties plays an important role in their future application.
💡
Conclusion & Impact:Moreover, the present study will fill this gap in literature.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Physico-mechanical properties · Plywood · Fiberboard

Abstract

Knowing the mechanical and physical properties of a material is the most important criteria for engineers and designers interested in determining the intended use of the material. The prediction of wood composite materials based on their mechanical and physical properties plays an important role in their future application. In this study, radial basis function network approach was employed for prediction according to mechanical and physical properties of wood composite materials such as particleboard, fiberboard, oriented strand board and plywood, which have widespread use in the furniture industry and construction sector. Four physical and mechanical properties were used as the board density, bending strength, bending elastic modulus and tensile strength in the prediction of the wood composite materials. This study will assist wood composite users in the selection of wood composite materials that will provide the mechanical and physical properties determined in advance for any construction. Moreover, the present study will fill this gap in literature.

Özet

Mühendisler ve tasarımcılar açısından bir malzemenin mekanik ve fizikselözelliklerinin bilinmesi malzemenin kullanım amacının belirlenmesinde en önemli kriterlerdendir. Ahşap kompozit malzemelerin mekanik ve fiziksel özelliklere göre tahmini, gelecekteki ahşap kompozit malzeme uygulamalarında önemli bir rol oynayacaktır. Bu çalışmada mobilya endüstrisinde ve inşaat sektöründe yaygın kullanıma sahip olan yonga levha, lif levha, yönlendirilmiş yonga levha ve kontrplak gibi ahşap kompozit malzemelerin mekanik özelliklerine göre tahmin işlemi radyal temelli fonksiyon ağı ile gerçekleştirilmiştir. Ahşap kompozit malzemelerin tahmininde levha yoğunluğu, eğilme direnci, eğilme elastikiyet direnci ve çekme direnci olarak dört fiziksel ve mekanik özellik kullanılmıştır. Bu çalışma, ahşap kompozit malzeme kullanıcılarının herhangi bir konstrüksiyon için önceden belirledikleri mekanik ve fiziksel özellikleri sağlayacak ahşap kompozit malzemenin seçiminde yardımcı olacaktır. Ayrıca, bu çalışma literatürdeki bu boşluğu dolduracaktır.

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Cite This Article

KAYA, A. İ., İLKUÇAR, M., ÇİFCİ, A. (2019). Use of Radial Basis Function Neural Network in Estimating Wood Composite Materials According to Mechanical and Physical Properties. Erzincan Üniversitesi Fen Bilimleri Enstitüsü Dergisi, Cilt: 12 - Sayı: 1, 116–123. https://ajindex.com/articles/234072

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