Modeling the Bending Strength of MDF Faced, Polyurethane Foam-Cored Sandwich Panels Using Response Surface Methodology (RSM) and Artificial Neural Network (ANN)
The present study evaluates and compares predictions on the performance and the approaches of the response surface methodology (RSM) and the artificial neural network (ANN) so to model the bending strength of the polyurethane foam-cored sandwich panel. The effect of the independent variables (formal...
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MDPI AG
2021-11-01
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| Series: | Forests |
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| Online Access: | https://www.mdpi.com/1999-4907/12/11/1514 |
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| author | Morteza Nazerian Fateme Naderi Ali Partovinia Antonios N. Papadopoulos Hamed Younesi-Kordkheili |
| author_facet | Morteza Nazerian Fateme Naderi Ali Partovinia Antonios N. Papadopoulos Hamed Younesi-Kordkheili |
| author_sort | Morteza Nazerian |
| collection | DOAJ |
| description | The present study evaluates and compares predictions on the performance and the approaches of the response surface methodology (RSM) and the artificial neural network (ANN) so to model the bending strength of the polyurethane foam-cored sandwich panel. The effect of the independent variables (formaldehyde to urea molar ratio (MR), sandwich panel thickness (PT) and the oxidized protein to melamine-urea-formaldehyde synthesized resin weight ratio (WR)) was examined based on the bending strength by the central composite design of the RSM and the multilayer perceptron of the ANN. The models were statistically compared based on the training and validation data sets via the determination coefficient (<i>R</i><sup>2</sup>), the root mean squares error (RMSE), the absolute average deviation (AAD) and the mean absolute percentage error (MAPE). The <i>R</i><sup>2</sup> calculated for the ANN and the RSM models was 0.9969 and 0.9960, respectively. The models offered good predictions; however, the ANN model was more precise than the RSM model, thus proving that the ANN and the RSM models are valuable instruments to model and optimize the bending properties of the sandwich panel. |
| first_indexed | 2024-03-10T05:29:32Z |
| format | Article |
| id | doaj.art-68f06268bfd14afa9097249292958b78 |
| institution | Directory Open Access Journal |
| issn | 1999-4907 |
| language | English |
| last_indexed | 2024-03-10T05:29:32Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Forests |
| spelling | doaj.art-68f06268bfd14afa9097249292958b782023-11-22T23:24:50ZengMDPI AGForests1999-49072021-11-011211151410.3390/f12111514Modeling the Bending Strength of MDF Faced, Polyurethane Foam-Cored Sandwich Panels Using Response Surface Methodology (RSM) and Artificial Neural Network (ANN)Morteza Nazerian0Fateme Naderi1Ali Partovinia2Antonios N. Papadopoulos3Hamed Younesi-Kordkheili4Department of Bio Systems, Faculty of New Technologies and Aerospace Engineering, Shahid Beheshti University, Evin 1983969411, IranDepartment of Bio Systems, Faculty of New Technologies and Aerospace Engineering, Shahid Beheshti University, Evin 1983969411, IranDepartment of Bio Refinery, Faculty of New Technologies and Aerospace Engineering, Shahid Beheshti University, Evin 1983969411, IranLaboratory of Wood Chemistry and Technology, Department of Forestry and Natural Environment, International Hellenic University, GR-661 00 Drama, GreeceDepartment of Wood and Paper Sciences, Faculty of Natural Resources, Semnan University, Semnan 1911135131, IranThe present study evaluates and compares predictions on the performance and the approaches of the response surface methodology (RSM) and the artificial neural network (ANN) so to model the bending strength of the polyurethane foam-cored sandwich panel. The effect of the independent variables (formaldehyde to urea molar ratio (MR), sandwich panel thickness (PT) and the oxidized protein to melamine-urea-formaldehyde synthesized resin weight ratio (WR)) was examined based on the bending strength by the central composite design of the RSM and the multilayer perceptron of the ANN. The models were statistically compared based on the training and validation data sets via the determination coefficient (<i>R</i><sup>2</sup>), the root mean squares error (RMSE), the absolute average deviation (AAD) and the mean absolute percentage error (MAPE). The <i>R</i><sup>2</sup> calculated for the ANN and the RSM models was 0.9969 and 0.9960, respectively. The models offered good predictions; however, the ANN model was more precise than the RSM model, thus proving that the ANN and the RSM models are valuable instruments to model and optimize the bending properties of the sandwich panel.https://www.mdpi.com/1999-4907/12/11/1514ANNbending strengthprotein adhesiveRSMsandwich panel |
| spellingShingle | Morteza Nazerian Fateme Naderi Ali Partovinia Antonios N. Papadopoulos Hamed Younesi-Kordkheili Modeling the Bending Strength of MDF Faced, Polyurethane Foam-Cored Sandwich Panels Using Response Surface Methodology (RSM) and Artificial Neural Network (ANN) Forests ANN bending strength protein adhesive RSM sandwich panel |
| title | Modeling the Bending Strength of MDF Faced, Polyurethane Foam-Cored Sandwich Panels Using Response Surface Methodology (RSM) and Artificial Neural Network (ANN) |
| title_full | Modeling the Bending Strength of MDF Faced, Polyurethane Foam-Cored Sandwich Panels Using Response Surface Methodology (RSM) and Artificial Neural Network (ANN) |
| title_fullStr | Modeling the Bending Strength of MDF Faced, Polyurethane Foam-Cored Sandwich Panels Using Response Surface Methodology (RSM) and Artificial Neural Network (ANN) |
| title_full_unstemmed | Modeling the Bending Strength of MDF Faced, Polyurethane Foam-Cored Sandwich Panels Using Response Surface Methodology (RSM) and Artificial Neural Network (ANN) |
| title_short | Modeling the Bending Strength of MDF Faced, Polyurethane Foam-Cored Sandwich Panels Using Response Surface Methodology (RSM) and Artificial Neural Network (ANN) |
| title_sort | modeling the bending strength of mdf faced polyurethane foam cored sandwich panels using response surface methodology rsm and artificial neural network ann |
| topic | ANN bending strength protein adhesive RSM sandwich panel |
| url | https://www.mdpi.com/1999-4907/12/11/1514 |
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