Artificial neural network modeling for predicting elastic strain of white birch disks during drying

Fu, Zhihao; Avramidis, Stavros; Zhao, Jun; Cai, Yingchun

doi:10.1007/s00107-017-1183-x

Cited by 17 publications

(14 citation statements)

References 26 publications

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“…It is not only strong at processing nonlinearity, self-organizing adjustment, adaptive learning, and fault-tolerant anti-noise [8][9][10] but also can effectively deal with nonlinear and complex fuzzy processes. An effective network prediction model can be established without any assumption or theoretical relationship analysis, based on the historical data and powerful self-organization integration capabilities [11,12].…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Modeling for Predicting Wood Moisture Content in High Frequency Vacuum Drying Process

Chai

Chen

Cai

et al. 2018

Forests

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The moisture content (MC) control is vital in the wood drying process. The study was based on BP (Back Propagation) neural network algorithm to predict the change of wood MC during the drying process of a high frequency vacuum. The data of real-time online measurement were used to construct the model, the drying time, position of measuring point, and internal temperature and pressure of wood as inputs of BP neural network model. The model structure was 4-6-1 and the decision coefficient R2 and Mean squared error (Mse) of the training sample were 0.974 and 0.07355, respectively, indicating that the neural network model had superb generalization ability. Compared with the experimental measurements, the predicted values conformed to the variation law and size of experimental values, and the error was about 2% and the MC prediction error of measurement points along thickness direction was within 2%. Hence, the BP neural network model could successfully simulate and predict the change of wood MC during the high frequency drying process.

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Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Modeling for Predicting Wood Moisture Content in High Frequency Vacuum Drying Process

Chai

Chen

Cai

et al. 2018

Forests

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“…Tiryaki et al (2014) proposed an ANN model to predict surface roughness of wood in machining process. Fu et al (2017) predicted elastic strain of white birch disks during drying using ANN. In addition, ANN was used to detect the structural damage in medium density fi berboard (Long and Rice, 2008), to predict bending strength and modulus of elasticity of structural plywood board (Fernández et al, 2012), to model the moisture absorption and thickness swelling of oriented strand board (Özşahin, 2012).…”

Section: Sažetak • U Istraživanju Je Modelirana Upojnost Vode I Debljmentioning

confidence: 99%

“…A total of 128 data obtained from experimental study were used to set an ANN model. The number of different hidden neurons (5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20) and two different training algorithms (LM and SCG) were used to achieve the optimal network with the best performance. As a result, the optimal network was obtained in 16 hidden neurons and the LM algorithm.…”

Section: Zaključakmentioning

confidence: 99%

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

2019

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In this study, water absorption and thickness swelling values of medium density fiberboard (MDF) were modelled by artificial neural networks (ANN). MDF panels were produced with different rates of paraffin (0.0-control, 0.5, 1 and 1.5 %) at different press temperatures (170 and 190 °C). After conditioning of MDF, water absorption (WA) and thickness swelling (TS) of samples were carried out at specific intervals within 24 hours. Then, the data obtained from these experiment were modelled using ANN. Paraffin addition rate, press temperature and immersion time in water were used as the input parameters, while WA and TS values of MDF were used as the output parameters. After training of ANN, it was found that correlation coefficients (R) were close to 1 for training, validation, test and all data set. Mean absolute percentage error (MAPE) and mean square error (MSE) were determined as 2.94 % and 0.57, respectively, for all data sets. As a result of this study, the use of proposed ANN model may be recommended to predict the water absorption and thickness swelling of panels instead of complex and time-consuming studies such as empirical formulas.

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“…These methods test the drying stress, but unless the precision of the results is improved, such tests cannot achieve detection online. Simulation has mainly used a BP artificial neural network to predict the drying stress that is generated at a specific moment where external parameters of drying stress are input (Fu et al 2017), but this method cannot be used to predict the development and change in drying stress in the next moment. The generation and development of drying stress are dynamic parameters that change with time (drying process) and are affected by the temperature and humidity of the external environment, the wood self-shape, and the moisture content .…”

Section: Introductionmentioning

confidence: 99%

Prediction of wood elastic strain development trend in conventional drying process based on GM-BP model

Wan-hui

Zhao

Zhu

et al. 2020

BioRes

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The elastic strain of wood reflects the nature (stretching or compression) and the magnitude of the drying stress at that time during the conventional drying process. The accurate prediction of strain is important to optimize the drying process and to improve drying speed and quality. In this work, the elastic strain was measured in real time, and moisture content was measured by periodic weighing during the drying process. Using these data, the GM (1,1) grey prediction model was used to predict moisture content in adjacent periods in the future. Based on the moisture content predicted by GM (1,1), a BP neural network was constructed to predict the development trend of elastic strain in the surface layer and core layer. The prediction results of the GM-BP combination model showed that the fitting error range of the prediction of the surface layer elastic strain was [-5×10-3~5×10-3], with a mean square error (MSE) of 2.31×10-7. The elastic strain of the core layer was [-2×10-3~2×10-3], and the MSE was 3.86×10-8. Thus, the GM-BP model achieved high accuracy for predicting the development trend of elastic strain. It can provide a new method and innovative thinking for the optimization and control of wood drying process.

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Artificial neural network modeling for predicting elastic strain of white birch disks during drying

Cited by 17 publications

References 26 publications

Artificial Neural Network Modeling for Predicting Wood Moisture Content in High Frequency Vacuum Drying Process

Artificial Neural Network Modeling for Predicting Wood Moisture Content in High Frequency Vacuum Drying Process

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

Prediction of wood elastic strain development trend in conventional drying process based on GM-BP model

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