Prediction of parison swell in plastics extrusion blow molding using a neural network method

A neural network-based model approach is presented in which the effects of the die temperature and flow rate on the diameter and thickness swells of the parison in the continuous extrusion blow molding of high-density polyethylene (HDPE) are investigated. Comparison of the neural network model predictions with experimental data yields very good agreement and demonstrates that the neural network model can predict the parison swells at different processing parameters with a high degree of precision (within 0.001).