Loading…
Simulation of the gas-assisted injection moulding process using a viscoelastic extension to the Cross-WLF viscosity model
An approximation to the viscoelastic Maxwell model is developed and combined with a Cross-WLF shear- and temperature-dependent model as a means of introducing aspects of viscoelasticity into the Cross-WLF model at a low computational cost. The main objective of the model is to simulate the gas-assis...
Saved in:
Published in: | Proceedings of the Institution of Mechanical Engineers. Part E, Journal of process mechanical engineering Journal of process mechanical engineering, 2011-11, Vol.225 (4), p.239-254 |
---|---|
Main Authors: | , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Summary: | An approximation to the viscoelastic Maxwell model is developed and combined with a Cross-WLF shear- and temperature-dependent model as a means of introducing aspects of viscoelasticity into the Cross-WLF model at a low computational cost. The main objective of the model is to simulate the gas-assisted injection moulding (GAIM) process with the aspect of a material's strain history included. It is shown that the model gives a transient and steady response comparable to the Doi–Edwards viscoelastic model in constant rate shear and uniaxial deformations, and follows WLF temperature dependence. The model is implemented in a three-dimensional finite element code using the ‘pseudo-concentration’ method to model the polymer and gas phases. The ordinary Cross-WLF model had demonstrated a consistent under-prediction of residual wall thickness (RWT) measurements in comparison to experimental results. It is shown that the viscoelastic extension to the Cross-WLF model gives a marked increase in RWT and exhibits aspects of stress relaxation and history dependence. The model is tested against variations of other process control parameters. It is shown that the simulation gives the correct qualitative response for all control parameters assessed, with quantitative prediction within a factor of 2. |
---|---|
ISSN: | 0954-4089 2041-3009 |
DOI: | 10.1177/0954408911409134 |