A self-optimising injection moulding process with model-based control system parameterisation

• Published in: International journal of computer integrated manufacturing Vol. 29; no. 11; pp. 1190 - 1199
• Main Authors: Hopmann, Christian, Ressmann, Axel, Reiter, Matthias, Stemmler, Sebastian, Abel, Dirk
• Format: Journal Article
• Language: English
• Published: Taylor & Francis 01.11.2016
• Subjects: cavity pressure control; injection moulding; model predictive control; system identification
• ISSN: 0951-192X; 1362-3052
• DOI: 10.1080/0951192X.2015.1066035

The consideration of the pvT-behaviour (pressure, specific volume and temperature) of the plastic material in combination with closed-loop cavity pressure control allows for compensation of variable boundary conditions in the injection moulding process. By suitably implementing cavity pressure control, repeatability and product quality in injection moulding processes can be improved. However, there are still obstacles for industrial application. As the process behaviour is greatly dependent on the mould - which is interchangeable and typically designed and manufactured independently of the machine's control system - challenges arise in designing a cavity pressure controller that yields high performance and at the same time is robust enough to be suitable for universal use. The use of a model predictive controller (MPC) for cavity pressure control is being researched and found to be helpful to overcome these issues. Unlike controllers such as proportional-integral-derivative controllers, the control output is not determined using a well-tuned, but mathematically relatively simple algorithm. Instead, it performs an online optimisation based on a process model in order to obtain the control outputs. In order to operate as intended, the model used by the MPC has to be adjusted with every significant change of the system, in particular the machine, material and mould. Therefore, a process model as well as a suitable strategy for in-process identification of the necessary parameters is developed and presented. For automated parameterisation, a strategy based on two experiments is suggested and first experimental results are presented.