Combining a neural network with a genetic algorithm for process parameter optimization

A neural-network model has been developed to predict the value of a critica l strength parameter (internal bond) in a particleboard manufacturing proce ss, based on process operating parameters and conditions. A genetic algorit hm was then applied to the trained neural network model to determine the pr ocess parameter values that would result in desired levels of the strength parameter for given operating conditions. The integrated NN-GA system was s uccessful in determining the process parameter values needed under differen t conditions, and at various stages in the process, to provide the desired level of internal bond. The NN-GA tool allows a manufacturer to quickly det ermine the values of critical process parameters needed to achieve acceptab le levels of board strength, based on current operating conditions and the stage of manufacturing. (C) 2000 Elsevier Science Ltd. All rights reserved.