Neural-fuzzy modeling of plastic injection molding machine for intelligent control

Neural network and fuzzy logic reasoning can complement each other to form an integrated model which capitalizes on the merits and at the same time offsets the pitfalls of the involved computational intelligence technologies. This article presents a neural-fuzzy model which consists of a neural network for suggesting the change of process parameters, together with a fuzzy reasoning mechanism for acquiring modified parameter values based on the induced parameter values from the neural network. This model is particularly useful in parameter-based control situations where there may be multiple inputs and multiple outputs involved. This model, which serves to learn from sample data and allows to extract rules which are then fuzzified prior to fuzzy inference, is implemented for the dimensional control of injection molding parts, the dimensions of which are primarily determined by the molding process parameters such as injection time and cooling temperature. (C) 1999 Elsevier Science Ltd. All rights reserved.