Parison Dimension Prediction in Extrusion Blow Molding Using Neural Network Approach: A New Strategy

Abstract

As the plastics extrusion blow molded parts are getting more and more complex, it is necessary to optimize the parison dimension distribution. Predicting the parison dimension distribution is useful to optimize the thickness distribution and property of the final part. The dependency between parison dimensions and materials characteristics, processing conditions, and die geometry is a highly nonlinear and fully coupled one. In this work, diameter and thickness swells of the high-density polyethylene parison extruded under different flow rates were obtained by a well-designed experiment. The obtained data were then used to train and test the artificial neural network (ANN) model. Trained and tested ANN model can be used to predict the dimensions at any location on the parison within a given range.