Self-optimizing injection molding based on iterative learning cavity pressure control

摘要

Modern injection molding machines can reproduce machine values, such as the position and speed of the plasticizing screw, with a high precision. To achieve a further improvement of the part quality, adaption and self-optimization strategies are required, which is realized by the implementation of a model-based self-optimization to an injection molding machine. Within this concept, a pvT-optimization allows an online control of the holding pressure that is tailored to the plastics material, considering the relationship between pressure, specific volume and temperature. A control strategy is required that controls the cavity pressure with respect to the reference generated by the pvT-optimization. However, cavity pressure control, in contrast to pressure control in the plasticizing unit, is hitherto not possible without a time-consuming system parametrization. Due to the repetitive character of the injection molding process, the iterative learning control (ILC) is a suitable approach. The ILC uses information gained within the previous cycle and a model to generate the optimal controller outputs for the following cycle. Based on this iterative learning, the reference tracking of the cavity pressure can be improved over several cycles. Additionally, repetitive disturbances can be compensated automatically. To improve the convergence speed of the ILC, a process model can be used explicitly. Based on this premise, an ILC for cavity pressure control is developed and researched in injection molding experiments. It is shown that the flexibility of the control strategy can be improved without compromising performance.