Study of the influence of the physical properties of particleboard type P2 on the internal bond of panels using artificial neural networks

Palacios, Paloma de; Fernández, Francisco García; García-Iruela, Alberto; González-Rodrigo, Beatriz; Esteban, Luis García

doi:10.1016/j.compag.2018.10.012

Cited by 12 publications

(9 citation statements)

References 18 publications

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“…For instance, neuro-scientist use it on their biological data to give more explanation while cognitive researchers or practitioners and computer scientist may apply it to formalize various cognitive processes and to study a sub-domain of machine intelligence respectively [57]. Chen, Hegde et al [58,59] applied it to agricultural economics while Statisticians employ them as nonlinear regressive models used for classification, in non-parametric models [60][61][62][63]. Neural Networks are especially appropriate to learn patterns and remember complex relationships in large datasets.…”

Section: Neural Network Modelmentioning

confidence: 99%

Estimating China’s Trade with Its Partner Countries within the Belt and Road Initiative Using Neural Network Analysis

Dumor

Yao

2019

Sustainability

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The Belt and Road Initiative (BRI) under the auspices of the Chinese government was created as a regional integration and development model between China and her trade partners. Arguments have been raised as to whether this initiative will be beneficial to participating countries in the long run. We set to examine how to estimate this trade initiative by comparing the relative estimation powers of the traditional gravity model with the neural network analysis using detailed bilateral trade exports data from 1990 to 2017. The results show that neural networks are better than the gravity model approach in learning and clarifying international trade estimation. The neural networks with fixed country effects showed a more accurate estimation compared to a baseline model with country-year fixed effects, as in the OLS estimator and Poisson pseudo-maximum likelihood. On the other hand, the analysis indicated that more than 50% of the 6 participating East African countries in the BRI were able to attain their predicted targets. Kenya achieved an 80% (4 of 5) target. Drawing from the lessons of the BRI and the use of neural network model, it will serve as an important reference point by which other international trade interventions could be measured and compared.

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Section: Neural Network Modelmentioning

confidence: 99%

Estimating China’s Trade with Its Partner Countries within the Belt and Road Initiative Using Neural Network Analysis

Dumor

Yao

2019

Sustainability

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“…Sedangkan nilai terkecil dari papan dengan perekat PF. Nilai keteguhan rekat internal yang baik dapat dijadikan indikasi untuk kesesuaian penggunaan papan untuk berbagai penggunaan terutama aplikasi papan partikel yang melibatkan proses dengan mesin (Palacios, Fernandes, Garcia-Iurela, Gonzales-Rodrigo, & Esteban, 2018).…”

Section: Keteguhan Rekat Internalunclassified

Sifat fisik, mekanik dan akustik papan partikel berbahan dasar batang jagung (Zea mays L.) [Physical, mechanical and acoustical characteristics of particleboard made from corn stalk (Zea mays L.)]

Astari¹,

Syamani²,

Prasetiyo³

2019

J. Ris. Ind. Has. Hutan

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“…In Fig. 1, the adhesive flow rate recorded by the multipump dosing system (1); the pressure value of atomization spray head (3) is shown by the pressure gauge on the air pressure pump (2); weighing and calculating particle feeding speed by the belt scale (5); the micro-computer controller (6) real-time displays the current in the blender (4).…”

Section: Main Working Process Of Gluing Equipmentmentioning

confidence: 99%

“…Three-layer particleboard is the most common PB, in which three-layer particles with different sizes are mixed with adhesive and hot-pressed at a certain temperature [2]. In the production process, the mechanical properties of PB are the key evaluation indexes of its quality [3], and the internal bond (IB) strength is one of the most important mechanical characteristics [4].…”

Section: Introductionmentioning

confidence: 99%

Optimization of production parameters of particle gluing on internal bonding strength of particleboards using machine learning technology

et al. 2022

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The particleboard (PB) production is an extremely complex process, many operating parameters affecting panel quality. It is a big challenge to optimize the PB production parameters. The production parameters of particle gluing have an important influence on the internal bond (IB) strength of PB. In this study, using grey relation analysis (GRA) and support vector regression (SVR) algorithm, a prediction model was developed to accurately predict IB of PB through particle gluing processing parameters in a PB production line. GRA was used to analyze the grey relational grade between the particle gluing processing parameters and IB of PB, and the variables were screened. The SVR algorithm was used to train 724 groups of particle gluing sample data between six particle gluing processing parameters and IB. The SVR model was tested with 181 sets of experimental data. The SVR model was verified by 181 sets of experimental data, and the values of mean absolute error (MAE), mean relative error (MRE), root mean square error (RMSE), and Theil’s inequality coefficient (TIC) of the model were 0.008, 0.017, 0.013, and 0.014, respectively. The results showed that the prediction performance of the nonlinear regression prediction model based on GRA–SVR is superior, and the GRA–SVR prediction model can be used to real-time predict the IB in the PB production line.

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Study of the influence of the physical properties of particleboard type P2 on the internal bond of panels using artificial neural networks

Cited by 12 publications

References 18 publications

Estimating China’s Trade with Its Partner Countries within the Belt and Road Initiative Using Neural Network Analysis

Estimating China’s Trade with Its Partner Countries within the Belt and Road Initiative Using Neural Network Analysis

Sifat fisik, mekanik dan akustik papan partikel berbahan dasar batang jagung (Zea mays L.) [Physical, mechanical and acoustical characteristics of particleboard made from corn stalk (Zea mays L.)]

Optimization of production parameters of particle gluing on internal bonding strength of particleboards using machine learning technology

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