Artificial neural networks in variable process control: application in particleboard manufacture

  • L. G. Esteban Universidad Politécnica de Madrid
  • F. García Fernández Universidad Politécnica de Madrid
  • P. de Palacios Universidad Politécnica de Madrid
  • M. Conde Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria
Keywords: PARTICLE BOARDS, PROCESS CONTROL, WOOD PROPERTIES, NEURAL NETWORKS

Abstract

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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Published
2009-04-01
How to Cite
EstebanL. G., García FernándezF., PalaciosP. de, & CondeM. (2009). Artificial neural networks in variable process control: application in particleboard manufacture. Forest Systems, 18(1), 92-100. https://doi.org/10.5424/fs/2009181-01053
Section
Research Articles