Modeling and validation of the effects of processing parameters on the dimensional stability of an injection-molded polypropylene plate

There has recently been a growing amount of interest in developing process control methods to maintain consistent part quality during injection molding. Some commercially available control methods are believed to offer improved machine and process capability in order to achieve this goal. Regardless of these advantages, however, these methods typically require considerable experimentation before an optimal process setting can be obtained for a particular case. Therefore, a more systematic scheme that does not require extensive experimentation, which in turn costs time and money, is needed. Computer-aided engineering (CAE) is considered to be a good tool for predicting the flow behavior in the cavity during the filling and post-filling stages. If CAE software can accurately predict a part quality indicator, such as one concerned with the dimensional stability, under a given process condition, time-consuming experimentation would no longer be necessary to determine the relationship between the process conditions and part quality. However, the relationship between the process conditions and part quality indicators, such as the dimensional stability, is highly nonlinear and takes no explicit form. The goal of this study is to use CAE tools to establish such a relationship and generate part quality data based on predictions. A second-order regression model is established in order to correlate the part quality with four key process variables using an optimization technique. Finally, we obtain the reference values under these process conditions. A case study was conducted using both a simulation and experimentation for a plaque-shaped piece of polypropylene. The results indicate reasonable agreement between the simulation and experimentation.