The plastics extrusion blow nolding process consists of three stages

i. e.

parison formation parison inflation

and part cooling. In this paper

the mechanism of these three stages was studied using different methods. Neural network method was used to investigate the parison swell affected by the die temperature and extrusion flow rate. The comparison of the experimentally determined parison swell of high density polyethylene with the predicted ones using the neural network model showed very agreement between the two. The parison swell can be predicted at different processing conditions from the neural network model. A mathematical model based on the thin membrane approximation and neo-Hookean constitutive relations was used to describe the parison free inflation. The instantaneous images of parison inflation within a mold cavity were obtained by employing a video capture technique. The theoretically predicted parison growth profiles are found to be in good agreement with the experimental measurements. Based on the ANSYS finite element software

three-dimensional cooling of blow molded part was investigated. The transient temperature profiles at various locations across the thickness of part can be predicted. The influence of the major operating variables

part thickness

and the thermal properties of plastics and mold materials on the cooling of parts can also be predicted.