A NUMERICAL AND EXPERIMENTAL INVESTIGATION OF NEURAL-NETWORK-BASED INTELLIGENT CONTROL OF MOLDING PROCESSES

The current investigation focused on the development of intelligent in jection molding processes by utilizing a neural network based control unit. In this study, the emphasis was on the control of flow front pro gression during injection molding processes. The progression of a flow front into a mold cavity is crucial since it dictates the locations o f possible air voids and weld lines. It is desired that the flow front progresses towards the vent locations and that weld lines coincide wi th locations where their quality decreasing influence has a minimum im pact on the overall part performance. The intelligent control scheme d eveloped is based on a neural network that was trained with data obtai ned from a first-principles based process model rather than actual mol ding experimentation. The control strategy was developed such that one can specify a desired flow progression scheme and the controller will take corrective actions during the molding process to realize this sc heme. This is done by controlling the inlet flow rate at various inlet gate locations. Experiments were conducted with a 2-D, complex shaped mold cavity to test the performance of the control unit during actual injection molding processes. The mold had two inlet gates and three d ifferent desired flow progression schemes were considered. In all case s, the first principles model/neural network based control unit was ab le to steer the flow front along the corresponding desired flow progre ssion path.