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

L. G. Esteban, F. García Fernández, P. de Palacios, M. Conde

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.

Keywords


PARTICLE BOARDS; PROCESS CONTROL; WOOD PROPERTIES; NEURAL NETWORKS

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DOI: 10.5424/fs/2009181-01053

Webpage: www.inia.es/Forestsystems