New Strategies for Predicting Parison Dimensions in Extrusion Blow Molding

Abstract

In this work, two new strategies were proposed for predicting the parison thickness and diameter distributions in extrusion blow molding. The first one was a finite-element-based numerical simulation for the parison extruded from a varying die gap. The comparison of simulated and experimental parison thickness distributions indicates that the new method has certain accuracy in predicting the parison thickness from a varying die gap. The second one was an artificial neural network (ANN) approach, the characteristics of which are in sufficient patterns that can be obtained without doing too many experiments. The diameter and thickness swells of the parisons extruded under different flow rates were obtained by a well-designed experiment. The obtained data were then used to train and test the ANN model. The dimension of one location on the parison can provide one pattern to train the ANN model. Trained and tested ANN model can be used to predict the dimensions at any location on the parison within a given range. The proposed two strategies can help search the processing conditions to obtain optimal parison thickness distributions.