Monitoring and Modeling the Injection Molding Process

In injection molding, the two most critical parameters are flow at the high shear rates that occur, and scorch time. Most reported work has concentrated on these and ignored other less significant factors.
The approach taken by many operators of injection molding machines is to determine the effect of machine controls on mix temperature at each stage, and to determine how to achieve high injection temperatures and short injection times without scorching or underfilling. Equally, this approach allows adjustments for batch-to-batch variations in viscosity, scorch time, and cure rate. This technique forms the basis of most trouble-shooting charts published by equipment manufacturers.
At the other extreme, in terms of sophistication of approach, is to measure various properties of the compound that are relevant to flow, temperature rise, and curing of the material at various stages in the process. For example, one study assessed the

• Behavior during heating in the screw section by use of a Defometer
• The cure time using a Brabender Plasticorder
• The change of viscosity with time at different temperatures and the resistance to scorch at low shear using a Mooney viscometer
• The behavior during injection using a capillary viscometer
• The duration of induction period and the rate of cure using a Monsanto Rheometer
Such a complete evaluation would be time consuming and uneconomical for a molding shop to undertake for each compound, machine, and mold and the authors found that for most purposes the Defometer measurements were sufficient.
Another approach is to generate an ªoperating windowº for the injection molding process, using a capillary rheometer. This defines those combinations of inject time and inject temperature at a given mold temperature, which should produce completely filled unscorched parts.
There have been a number of attempts to analyze and model the injection molding process. To be able to design an efficient injection-molding system, a model must be able to predict the flow rate of the material, the pressure drop over the mold network, the temperature of the material, and the forces that develop in the mold network [10]. Theoretical models for the filling stage to the injection molding cycle, which enable prediction of the above variables, have been developed.
The aim is to develop a mathematical model of mold filling that can predict the temperature distribution of the rubber compound as it fills the mold. This temperature distribution, together with the rheometer data, could then be used to predict the onset of cross-linking and help set up optimized injection molding conditions