Artificial neural networks in variable process control Application in particleboard manufacture
Artificial neural networks are an efficient tool for modelling production control processes using data from the actual production as well as simulated or design of experiments data. In this study two artificial neural networks were combined with the control process charts and it was checked whether the data obtained by the networks were valid for variable process control in particleboard manufacture. The networks made it possible to obtain the mean and standard deviation of the internal bond strength of the particleboard within acceptable margins using known data of thickness, density, moisture content, swelling and absorption. The networks obtained met the acceptance criteria for test values from non-standard test methods, as well as the criteria for using these values in statistical process control.
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Format: | artículo biblioteca |
Language: | English |
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CSIC - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA)
2009
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Subjects: | Artificial neural networks (ANN), Statistical process control (SPC), Internal bond strength, Wood based panels, |
Online Access: | http://hdl.handle.net/20.500.12792/1555 http://hdl.handle.net/10261/292741 |
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dig-inia-es-10261-2927412023-02-20T07:32:03Z Artificial neural networks in variable process control Application in particleboard manufacture García Esteban, Lidia García Fernández, F. de Palacios, P. Conde, M. Artificial neural networks (ANN) Statistical process control (SPC) Internal bond strength Wood based panels Artificial neural networks are an efficient tool for modelling production control processes using data from the actual production as well as simulated or design of experiments data. In this study two artificial neural networks were combined with the control process charts and it was checked whether the data obtained by the networks were valid for variable process control in particleboard manufacture. The networks made it possible to obtain the mean and standard deviation of the internal bond strength of the particleboard within acceptable margins using known data of thickness, density, moisture content, swelling and absorption. The networks obtained met the acceptance criteria for test values from non-standard test methods, as well as the criteria for using these values in statistical process control. 2023-02-20T07:32:03Z 2023-02-20T07:32:03Z 2009 artículo Investigacion Agraria Sistemas y Recursos Forestales 18(1): 92-100 (2009) 1131-7965 http://hdl.handle.net/20.500.12792/1555 http://hdl.handle.net/10261/292741 10.5424/fs/2009181-01053 2340-3578 en open CSIC - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA) |
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Artificial neural networks (ANN) Statistical process control (SPC) Internal bond strength Wood based panels Artificial neural networks (ANN) Statistical process control (SPC) Internal bond strength Wood based panels |
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Artificial neural networks (ANN) Statistical process control (SPC) Internal bond strength Wood based panels Artificial neural networks (ANN) Statistical process control (SPC) Internal bond strength Wood based panels García Esteban, Lidia García Fernández, F. de Palacios, P. Conde, M. Artificial neural networks in variable process control Application in particleboard manufacture |
description |
Artificial neural networks are an efficient tool for modelling production control processes using data from the actual production as well as simulated or design of experiments data. In this study two artificial neural networks were combined with the control process charts and it was checked whether the data obtained by the networks were valid for variable process control in particleboard manufacture. The networks made it possible to obtain the mean and standard deviation of the internal bond strength of the particleboard within acceptable margins using known data of thickness, density, moisture content, swelling and absorption. The networks obtained met the acceptance criteria for test values from non-standard test methods, as well as the criteria for using these values in statistical process control. |
format |
artículo |
topic_facet |
Artificial neural networks (ANN) Statistical process control (SPC) Internal bond strength Wood based panels |
author |
García Esteban, Lidia García Fernández, F. de Palacios, P. Conde, M. |
author_facet |
García Esteban, Lidia García Fernández, F. de Palacios, P. Conde, M. |
author_sort |
García Esteban, Lidia |
title |
Artificial neural networks in variable process control Application in particleboard manufacture |
title_short |
Artificial neural networks in variable process control Application in particleboard manufacture |
title_full |
Artificial neural networks in variable process control Application in particleboard manufacture |
title_fullStr |
Artificial neural networks in variable process control Application in particleboard manufacture |
title_full_unstemmed |
Artificial neural networks in variable process control Application in particleboard manufacture |
title_sort |
artificial neural networks in variable process control application in particleboard manufacture |
publisher |
CSIC - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA) |
publishDate |
2009 |
url |
http://hdl.handle.net/20.500.12792/1555 http://hdl.handle.net/10261/292741 |
work_keys_str_mv |
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1767603386827407360 |