Issue |
Int. J. Simul. Multidisci. Des. Optim.
Volume 1, Number 1, October 2007
|
|
---|---|---|
Page(s) | 55 - 60 | |
DOI | https://doi.org/10.1051/ijsmdo:2007007 | |
Published online | 12 December 2007 |
Experimental investigation of micro injection molding and relevant numerical simulations in 3D case
1
Department of Applied Mechanics, Southwest Jiaotong University, 610031 Chengdu, P.R. China
2
FEMTO-ST Institute, ENSMM, 24 chemin de l'épitaphe, 25030 Besancon, France
Corresponding author: zqcheng@netease.com
Received:
16
July
2007
Accepted:
27
September
2007
Micro injection molding is one of the key technologies to produce small and complicated components in various materials, including plastic, metal, ceramics etc. In order to improve product quality and to reduce manufacturing cost, the specified experiments have been realized to analyze the process parameters. A specially designed mould with five channels has been manufactured at ENSMM. The polypropylene is chosen as injected material in micro mould for the sake of its good fluidity and replication ability. The micro injection experiments are accomplished with the use of a micro injection system. For the application of numerical simulation, the viscous behaviors of polypropylene have been measured. A fully vectorial explicit algorithm has been developed and implemented in the FEM software. The simulations of micro injection process are performed with different wall boundary conditions. Comparisons between the simulation results and the experimental ones show that the simulation with slippery wall boundary condition fits well the experimental results.
© ASMDO, EDP Sciences, 2007
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.