Han-Xiong Huang

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).

HDPE/PA6 blends: parison formation behaviour in extrusion blow molding

The dependence of the diameter swell and sag of a parison extruded from blends of high-density polyethylene (HDPE)/polyamide-6 (PA6)/compatibilizer on the blend composition and flow rate is determined by analyzing video images of the parison. Among the blends tested, blends with a PA6 concentration of 35% or below exhibit an appreciable swell. A greater degree of sag begins to appear for the blend with a PA6 content of 45%. A neural network approach is applied to the experimental data, leading to a model for predicting the diameter swell profile from new levels of input variables, including the blend composition and flow rate.

Morphology Development of Polymer Blend With Different Viscosity Ratios Along an Extruder

The properties of polymer blends are largely determined by their morphology. So it is significant to investigate the morphology development of polymer blends during processing. In this work the morphology development of polymer blend was studied during flow along a single screw extruder. The polymer blend used incorporated polypropylene (PP) as its matrix phase and a high-viscosity or low-viscosity polyamide-6 (PA6) as the disperse phase. The samples of blends were taken from different positions using specially designed sampling device along the extruder online during the processing and were then examined using scanning electron microscopy (SEM). The morphology of the dispersed phase was quantitatively analyzed using image analysis software. The morphology evolution of blends along the melt conveying zone of screw was simulated. Theoretically predicted morphology evolution is in reasonable agreement with the experimental results. The aim of this work is to provide a better insight in the morphology development of blend during processing.Copyright