A modeling study to evaluate the quality of wood surface

Abstract: The goal of this study was to develop a model to predict sanding conditions of different type of materials such as Lebnon cedar (Cedrus libani) and European Black pine (Pinus nigra). Specimens were prepared using different values of grit size, cutting speed, feed rate, and sanding directio...

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Autores principales:	Hazir,Ender, Koc,Kücük Huseyin
Lenguaje:	English
Publicado:	Universidad del Bío-Bío 2018
Materias:	Artificial neural network laser measurement stylus measurement surface quality wood sanding process
Acceso en línea:	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-221X2018000400691
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spelling	oai:scielo:S0718-221X20180004006912019-02-14A modeling study to evaluate the quality of wood surfaceHazir,EnderKoc,Kücük Huseyin Artificial neural network laser measurement stylus measurement surface quality wood sanding process Abstract: The goal of this study was to develop a model to predict sanding conditions of different type of materials such as Lebnon cedar (Cedrus libani) and European Black pine (Pinus nigra). Specimens were prepared using different values of grit size, cutting speed, feed rate, and sanding direction. Surface quality values of specimens were measured employing a laser- based robotic measurement system and stylus type measurement equipment. Full factorial design based Analysis of Variance was applied to determine the effective factors. These factors were used to develop the Artificial Neural Networks models for two different measurement systems. The MATLAB Neural Network Toolbox was used to predict the Artificial Neural Networks models. According to the results, the Artificial Neural Networks models were performed using Mean Absolute Percentage Error and R-square values. Mean Absolute Percentage Error values for laser and stylus equipment were found as 2,405 % and 3,766 %, respectively. R-square values were determined as 96,2% and 92,7 % for laser and stylus measurement equipment, respectively. These results showed that the proposed models can be successfully used to predict the surface roughness values.info:eu-repo/semantics/openAccessUniversidad del Bío-BíoMaderas. Ciencia y tecnología v.20 n.4 20182018-10-01text/htmlhttp://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-221X2018000400691en10.4067/S0718-221X2018005041501
institution	Scielo Chile
collection	Scielo Chile
language	English
topic	Artificial neural network laser measurement stylus measurement surface quality wood sanding process
spellingShingle	Artificial neural network laser measurement stylus measurement surface quality wood sanding process Hazir,Ender Koc,Kücük Huseyin A modeling study to evaluate the quality of wood surface
description	Abstract: The goal of this study was to develop a model to predict sanding conditions of different type of materials such as Lebnon cedar (Cedrus libani) and European Black pine (Pinus nigra). Specimens were prepared using different values of grit size, cutting speed, feed rate, and sanding direction. Surface quality values of specimens were measured employing a laser- based robotic measurement system and stylus type measurement equipment. Full factorial design based Analysis of Variance was applied to determine the effective factors. These factors were used to develop the Artificial Neural Networks models for two different measurement systems. The MATLAB Neural Network Toolbox was used to predict the Artificial Neural Networks models. According to the results, the Artificial Neural Networks models were performed using Mean Absolute Percentage Error and R-square values. Mean Absolute Percentage Error values for laser and stylus equipment were found as 2,405 % and 3,766 %, respectively. R-square values were determined as 96,2% and 92,7 % for laser and stylus measurement equipment, respectively. These results showed that the proposed models can be successfully used to predict the surface roughness values.
author	Hazir,Ender Koc,Kücük Huseyin
author_facet	Hazir,Ender Koc,Kücük Huseyin
author_sort	Hazir,Ender
title	A modeling study to evaluate the quality of wood surface
title_short	A modeling study to evaluate the quality of wood surface
title_full	A modeling study to evaluate the quality of wood surface
title_fullStr	A modeling study to evaluate the quality of wood surface
title_full_unstemmed	A modeling study to evaluate the quality of wood surface
title_sort	modeling study to evaluate the quality of wood surface
publisher	Universidad del Bío-Bío
publishDate	2018
url	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-221X2018000400691
work_keys_str_mv	AT hazirender amodelingstudytoevaluatethequalityofwoodsurface AT kockucukhuseyin amodelingstudytoevaluatethequalityofwoodsurface AT hazirender modelingstudytoevaluatethequalityofwoodsurface AT kockucukhuseyin modelingstudytoevaluatethequalityofwoodsurface
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A modeling study to evaluate the quality of wood surface

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