A BP-neural network predictor model for plastic injection molding process

A neural network model for predicting the quality or soundness of the injected plastic parts based on key process variables and material grade variations has been developed. The approach uses a backpropagation 4-2-3 net (BPN) trained based on inputs/outputs data which were taken from simulation works carried out through a CAE software. The system reduces the time required for planning and optimizing of process conditions or operating parameters.