Even though machine parameters can nowadays be controlled with a high level of reproducibility, there are often quality problems. Self-optimising systems are a viable solution.
"Self-optimising systems are characterised by their ability to endogenously modify their objectives in response to changing conditions, and autonomously adapt their parameters and structure and as a result their behaviour to fulfil their objectives." (Definition according to Gausemeier 2004)
In the Cluster of Excellence "Integrative Production Technology for High-Wage Countries", research is being carried out into the implementation of self-optimising systems based on various production processes. In this connection, IKV is investigating the self-optimising injection moulding process.
The aim of the research project is to develop an injection moulding process that adjusts to the present production conditions and thus enables consistently high product quality. It takes into account the shrinkage and warpage behaviour as well as the rheological properties of the material, and derives new process parameters for consistent part quality. These process parameters are realised through model-predictive and iteratively learning control loops for the injection speed and the cavity pressure on the machine. This makes it possible to balance out problems such as batch fluctuations, differences in residual moisture contents or differing thermal boundary conditions and, despite these influences, produce injection-moulded parts of consistent quality.