Application of artificial neural network to predict the effect of paraffin addition on water absorption and thickness swelling of MDF

Gürgen, Ayşenur; Ustaömer, Derya; Yıldız, Sibel

doi:10.5552/drvind.2019.1839

Cited by 3 publications

(1 citation statement)

References 15 publications

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“…There are a few studies related to the wood-based panel properties based on ANNs such as modeling formaldehyde emission (Akyuz et al, 2017), predicting effect of adding paraffi n on physical properties of medium density fi berboard (Gurgen et al, 2019), predicting the internal bond strength of the particleboard under outdoor exposure (Watanabe et al, 2015;Korai and Watanabe, 2016), optimizing the process parameters in wood-based panel production (Cook et al, 2000;Ozsahin, 2013), obtaining the values of the internal bond of the particleboard using the manufacturing parameters (Cook and Chiu, 1997), predicting the particleboard mechanical properties (Fernandez et al, 2008), so on.…”

Section: Introduction 1 Uvodmentioning

confidence: 99%

Performance of ANN in Predicting Internal Bonding of Cement Particleboard Manufactured from Giant Reed and Bagasse

et al. 2021

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The present article investigates the microstructure of the cement matrices and the products of cement hydration by means of scanning electron microscopy, Fourier transform infrared spectroscopy and X-Ray diffraction. Then, the internal bonding strength (IB) is measured for the mixtures containing various amounts of nanosilica (NS), reed and bagasse particles. Finally, an Artificial Neural Network (ANN) is trained to reproduce these experimental results. The results show that the hardened cement paste including NS features the highest level of C-S-H. However, it has a lower level of C-S-H polymerization if reed or bagasse particles are applied. A relatively new dense microstructural degree is considered in the cement pastes containing NS, and a lower agglomeration is observed in the samples including reed or bagasse particles with NS. According to the microstructural analysis, the addition of NS to the samples containing reed or bagasse particles increases the unhydrated amount of C2S and C3S in the cement paste due to the decrease in the water needed for fully hydrated cement grains through portlandite (Ca(OH)2), C-S-H and ettringite increase. Besides, it is shown that the ANN prediction model is a useful, reliable and quite effective tool for modeling IB of cement-bonded particleboard (CBPB). It is indicated that the mean absolute percentage errors (MAPE) are 1.98 % and 1.45 % in the prediction of the IB values for the training and testing datasets, respectively. The determination coeffi cients (R2) of the training and testing data sets are 0.972 and 0.997 in the prediction of the bonding strength by ANN, respectively.

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Section: Introduction 1 Uvodmentioning

confidence: 99%

Performance of ANN in Predicting Internal Bonding of Cement Particleboard Manufactured from Giant Reed and Bagasse

et al. 2021

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Preparation and Properties of Wood Plastic Composites with Desirable Features Using Poplar and Five Recyclable Plastic Wastes

Wang

et al. 2021

Applied Sciences

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This study evaluated the mechanical and hydrophobic properties of wood plastic composites (WPC) prepared by the hot molding approach incorporating five different recycled plastics and poplar flour. The WPC showed excellent tensile strength (36.9 MPa) and flexural strength (44.7 MPa) associated with good hydrophobicity, and the excellent properties of WPC could be due to the application of hot pressing which improved the amount of hydroxyl groups and reduced the crystallinity of WPC. The WPC also revealed a strong and hydrostable structure and negligible emission of formaldehyde during the preparation process. Overall, the WPC could be used to substitute traditional wood-based panels as potential furniture material, hence achieving sustainable utilization of plastic wastes.

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Optimal Modified Starch Content in UF Resin for Glulam Based on Bonding Strength Using Artificial Neural Network

et al. 2022

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The purpose of this study was to present an application of the artificial neural network (ANN) that predicts the bonding strength of glulam manufactured from plane tree (Platanus orientalis L.) wood layers adhered with a combination of modified starch adhesive and UF resin. Bonding strength was measured at different weight ratios containing different values of nano-zinc oxide as an additive under different conditions of press temperature and press time. As a part of the research, an experimental design was determined. According to that, the glulam specimens were fabricated, the bonding strength of specimens was measured, and the results were statistically analyzed. Then, a model was developed to predict bonding strength using the artificial neural network (ANN) technique. To describe the results, FTIR and TGA tests were also conducted. The experimental results show that the maximum bonding strength values were obtained when the WR was at the middle level (50%), nano-zinc oxide content was at a maximum (4%), and press temperature and press time were fixed at 200 °C and 22 min, respectively. The ANN results agreed well with the experimental results. It became clear that the prediction errors were in an acceptable range. The results indicate that the developed ANN model could predict the bonding strength well with an acceptable error.

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Application of artificial neural network to predict the effect of paraffin addition on water absorption and thickness swelling of MDF

Cited by 3 publications

References 15 publications

Performance of ANN in Predicting Internal Bonding of Cement Particleboard Manufactured from Giant Reed and Bagasse

Performance of ANN in Predicting Internal Bonding of Cement Particleboard Manufactured from Giant Reed and Bagasse

Preparation and Properties of Wood Plastic Composites with Desirable Features Using Poplar and Five Recyclable Plastic Wastes

Optimal Modified Starch Content in UF Resin for Glulam Based on Bonding Strength Using Artificial Neural Network

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