NEURAL-NETWORK PROCESS MODELING OF A CONTINUOUS MANUFACTURING OPERATION

Neural-network techniques for the development of models of critical parameters in continuous forest products manufacturing processes are described. Predictive models of strength parameters in particleboard manufacturing were developed utilizing both backpropagation and counterpropagation neural network techniques. The modeled strength parameters were modulus of rupture and internal bond. The backpropagation neural network model did not provide sufficient accuracy in predicting the values of the strength parameters. Counterpropagation was successful at predicting modulus of rupture within +/- 10% and internal bond within +/- 15%. The trained counterpropagation network can be used to improve process control and reduce the amount of substandard and scrap board produced. Efforts are underway to refine the counterpropagation network and further improve its predictive capability, as well as to evaluate alternative neural network paradigms.