Using Artificial Neural Networks (ANN) for Modeling Predicting Hardness Change of Wood during Heat Treatment

Nguyen, Thi Hai Van; Nguyen, Tat Thang; Ji, Xiaodi; Lanh, Khoa Thi; Guo, Minghui

doi:10.1088/1757-899x/394/3/032044

Cited by 11 publications

(7 citation statements)

References 7 publications

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“…Nguyen et al used temperature, treatment time and wood type as the input layer, and wood hardness as the output layer [108]. The measurement coefficient (R2) of all data sets obtained is greater than 0.99, and the prediction is more effective.…”

Section: Application Of Anns In the Field Of Wood Dryingmentioning

confidence: 99%

Drying Stress and Strain of Wood: A Review

Yin

Liu

2021

Applied Sciences

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Wood drying stress causes various drying defects, which result from the wood microstructure and the transfer of heat and mass during the drying. It is the fundamental way to solve the problem of defects to clarify the law and mechanism of wood stress and strain development during drying. In this paper, based on the defects of wood drying, the theory and experimental testing methods of drying stress and strain were summarized. Meanwhile, artificial neural networks (ANN) and their application in the wood drying field were also investigated. The traditional prong and slicing methods were used practically in the research and industry of wood drying, but the stress changes in-process cannot be trapped. The technologies of image analysis and near-infrared spectroscopy provide a new opportunity for the detection of drying stress and strain. Hence, future interest should be attached to the combination of the theory of heat and mass transfer and their coupling during drying with the theory of microscopic cell wall mechanics and macroscopic drying. A more complete image acquisition and analysis system should be developed to realize the real-time monitoring of drying strain and cracking, practically. A more feasible and reasonable prediction model of wood drying stress and strain should be established to achieve the accuracy of the prediction.

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Section: Application Of Anns In the Field Of Wood Dryingmentioning

confidence: 99%

Drying Stress and Strain of Wood: A Review

Yin

Liu

2021

Applied Sciences

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“…The data used as input variables in ANN for evaluating wood can be physical and mechanical characteristics (Nasir et al, 2018), heat treatment temperature (Van Nguyen et al, 2018, Zanuncio et al, 2017, tree age (Leite et al, 2016), wood species (Van Nguyen et al, 2018), basic density (Zanuncio et al, 2016), basal area (in m 2 / ha), annual average increment (in m³/ ha / year), total height and diameter at 1.3 m from the ground (Leite et al, 2016). This study is pioneering in using mineral elements contained in charcoals as predictive variable in ANN modeling.…”

Section: Identificating the Charcoal Originmentioning

confidence: 99%

Artificial Neural Networks To Distinguish Charcoal from Eucalyptus and Native Forests Based on Their Mineral Components

et al. 2020

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Charcoal has been used as a renewable energy source in many countries. However, the indiscriminate use of wood from native forests is detrimental to sustainability. The development of rapid and efficient methodologies for distinguishing charcoal produced from native Forest or Eucalyptus plantations is essential to curb illegal coal transport and trade. The aim of this study was to distinguish charcoal from native and Eucalyptus woods by Artificial Neural Network (ANN) based on their mineral composition. Specimens from native woods (Apuleia sp., Cedrela sp., Aspidosperma sp., Jacaranda sp., Peltogyne sp., Dipteryx sp. and Gochnatia sp.) and from Eucalyptus sp. hybrid woods from commercial forest plantations were pyrolysed at temperatures from 300 to 700°C in order to simulate the actual pyrolysis conditions and species widely used illegally in southeastern Brazil. The composition and proportion of the mineral elements of charcoal were determined by X-ray fluorescence (XRF).ANNs were trained based on the elemental composition of the charcoal specimens to classify the species and origin of the charcoals (native forest and Eucalyptus). ANNs based on mineral element content yielded high percentage of correct classification for charcoal specimens by species (72% accuracy) or origin (97% accuracy) from an independent validation sample set.

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“…In this study, a proposed ANN model was designed using the Matlab Neural Network Toolbox and using a multi-layer perception (MLP) model for prediction. The MLP architecture consists of an input layer, one or more hidden layers, and an output layer [4]. The input layer consists of two input nodes: applied load during indentation measurement and applied current density during ED process.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

“…Each neuron has inputs and generates output signals that are sent to other neurons in the network as inputs via the interconnections. ANN approach is used in many fields of chemical and material engineering such as: prediction of yield strength, tensile strength and elongation of cast alloys [3], for estimation on of the deposition rate of copper-tin during electroplating, hardness predictions of nickle-CBN composites [2], evaluating the change of wood hardness during heat treatment [4], etc.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network for Composite Hardness Modeling of Cu/Si Systems Fabricated Using Various Electrodeposition Parameters

Mladenović

Lamovec

Jović

et al. 2019

2019 IEEE 31st International Conference on Microelectronics (MIEL)

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Copper coatings are produced on silicon wafer by electrodeposition (ED) for various cathode current densities. The resulting composite systems consist of 10 m monolayered copper films electrodeposited from sulphate bath on Si wafers with sputtered layers of Cr/Au. Hardness measurements were performed to evaluate properties of the composites. The composite hardness (H c ) was characterized using Vickers microindentation test. Then, an artificial neural network (ANN) model was used to study the relationship between the parameters of metallic composite and their hardness. Two experimental values: applied load during indentation test and current density during the ED process were used as the inputs to the neural network. Finally, the results of the composite hardness (experimental and predicted) were used to estimate the film hardness (H f ) of copper for each variations of the current density. This article shows that ANN is an useful tool in modeling composite hardness change with variation of experimental parameters predicting hardness change of composite Si/Cu with average error of 6 %. Using created ANN model it is possible to predict microhardness of Cu film for current density or indentation load for which we do not have experimental data.

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Using Artificial Neural Networks (ANN) for Modeling Predicting Hardness Change of Wood during Heat Treatment

Cited by 11 publications

References 7 publications

Drying Stress and Strain of Wood: A Review

Drying Stress and Strain of Wood: A Review

Artificial Neural Networks To Distinguish Charcoal from Eucalyptus and Native Forests Based on Their Mineral Components

Artificial Neural Network for Composite Hardness Modeling of Cu/Si Systems Fabricated Using Various Electrodeposition Parameters

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