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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Autores principales:	Morteza Nazerian, Fateme Naderi, Ali Partovinia, Antonios N. Papadopoulos, Hamed Younesi-Kordkheili
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	ANN bending strength protein adhesive RSM sandwich panel Plant ecology QK900-989
Acceso en línea:	https://doaj.org/article/68f06268bfd14afa9097249292958b78
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spelling	oai:doaj.org-article:68f06268bfd14afa9097249292958b782021-11-25T17:38:09ZModeling the Bending Strength of MDF Faced, Polyurethane Foam-Cored Sandwich Panels Using Response Surface Methodology (RSM) and Artificial Neural Network (ANN)10.3390/f121115141999-4907https://doaj.org/article/68f06268bfd14afa9097249292958b782021-11-01T00:00:00Zhttps://www.mdpi.com/1999-4907/12/11/1514https://doaj.org/toc/1999-4907The 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.Morteza NazerianFateme NaderiAli PartoviniaAntonios N. PapadopoulosHamed Younesi-KordkheiliMDPI AGarticleANNbending strengthprotein adhesiveRSMsandwich panelPlant ecologyQK900-989ENForests, Vol 12, Iss 1514, p 1514 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	ANN bending strength protein adhesive RSM sandwich panel Plant ecology QK900-989
spellingShingle	ANN bending strength protein adhesive RSM sandwich panel Plant ecology QK900-989 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)
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.
format	article
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
title	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_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_sort	modeling the bending strength of mdf faced, polyurethane foam-cored sandwich panels using response surface methodology (rsm) and artificial neural network (ann)
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/68f06268bfd14afa9097249292958b78
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Modeling the Bending Strength of MDF Faced, Polyurethane Foam-Cored Sandwich Panels Using Response Surface Methodology (RSM) and Artificial Neural Network (ANN)

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