Çam Kozlaklari (Pinus Pinea L.) Kullanilarak Üreti̇len Yongalevhalarin Yangin Performans, Çürüklük Dayanim Ve Yüzey Düzgünlük Özelli̇kleri̇

Aras, Uğur; Kalaycıoğlu, Hülya; Yel, Hüsnü; Durmaz, Sefa

doi:10.22531/muglajsci.283626

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…Wood is one of the most predominant biomass composites, which has received a lot of attention from the research community due to its renewable nature and innovative applications [1,2]. In wood products, the surface quality of wood has a significant effect on gluing and painting [3], and the basic criterion for surface quality is the surface roughness of wood, and the quality of wood products depends to a large extent on the surface roughness [4]. Studies have shown that the bonding properties and painting of wood products are significantly improved when the surface quality of the wood is higher, i.e., when the surface roughness is smaller [5,6].…”

Section: Introductionmentioning

confidence: 99%

Experimental Study of Surface Roughness of Pine Wood by High-Speed Milling

Chao

Liu

et al. 2023

Forests

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The surface roughness of wood has a great influence on its performance and is a very important indicator in processing and manufacturing. In this paper, we use the central composite design experiment (CCD experiment) and artificial neural network (ANN) model to study the changing pattern of surface roughness during the high-speed milling process of pine wood. In the CCD experiments, the spindle speed, feed speed, and depth of cut are used as the influencing factors, and the surface roughness is used as the index to analyze the variation law and fit the surface roughness parameter equation. By measuring the chip size in each group in the CCD experiment, the ANN model is used to predict the surface roughness under this machining parameter by measuring the chip size in each test group. The experimental results showed that the mean error of the surface roughness prediction values in the CCD experiment (12.2%) was larger than that of the ANN model (7.8%), and the mean squared error (MSE) of the ANN model was 0.025, the mean absolute percentage error(MAPE) was 0.01, and the coefficient of determination R2 was 0.95. Compared with the CCD experiment, the ANN model had a higher prediction accuracy. The results of this paper can provide some guidance for the prediction of surface roughness during wood processing.

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Section: Introductionmentioning

confidence: 99%

Experimental Study of Surface Roughness of Pine Wood by High-Speed Milling

Chao

Liu

et al. 2023

Forests

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Surface roughness estimation of wood boards using speckle interferometry

Baradit

Gatica

Yáñez

et al. 2020

Optics and Lasers in Engineering

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Production of Non-flammable, Water-repellent Polymeric Composite Particleboard from Peanut Shell

Karabulut¹

2022

Politeknik Dergisi

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 Üre formaldehitin fenol formaldehit ile modifiyesi / Modification of urea formaldehyde with phenol formaldehyde  LOI 30 değerine kadar yükseltilebilmesi / LOI can be increased up to 30.  Su tutma kapasitesinin % 37'den %21'e düşürülebilmesi / Reducing water holding capacity from 37% to 21%  Düşen mekanik dayanımın matriks yapı çapraz bağlanmasının artırılmasıyla geri kazanılması / Recovery of the decreased mechanical strength by increasing the matrix structure crosslinking

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Çam Kozlaklari (Pinus Pinea L.) Kullanilarak Üreti̇len Yongalevhalarin Yangin Performans, Çürüklük Dayanim Ve Yüzey Düzgünlük Özelli̇kleri̇

Abstract: This work explores some technological properties of three-layer particleboard produced from pine (Pinus Pinea L.)

Cited by 3 publications

References 17 publications

Experimental Study of Surface Roughness of Pine Wood by High-Speed Milling

Experimental Study of Surface Roughness of Pine Wood by High-Speed Milling

Surface roughness estimation of wood boards using speckle interferometry

Production of Non-flammable, Water-repellent Polymeric Composite Particleboard from Peanut Shell

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