In the ADELeS research project, we cooperate with the XITASO GmbH, the Friedrich-Alexander Universität Nürnberg-Erlangen and the Rehau SE funded by the Bavarian Ministry of Economic Affairs, Energy and Technology starting in September 2021. In ADELeS we aim at building an AI-based assistance system to detect and resolve quality defects and other errors within a manufacturing process of extrusion-based machines. This system will strongly interact with the machine operators leading to various requirements on explainability of predictions, suggested actions and the overall usability. Long term, this system will improve overall product quality and mitigate the effects of demographic change and the resulting lack of skilled labor. Therefore, it will futureprove the profitability of the bavarian manufacturing sites.

The envisioned method combines explainable machine learning systems with various forms of expert knowledge and traditional statistical tools. The assistant will interact with operators in the form of passiv suggestions on possible actions as well as by actively adjusting certain aspects of line control directly.

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MOOCS - Metaheuristics for Optimisation of Organic Computing Systems

The application of metaheuristics in OC systems still poses difficulties, especially when trying to find the most suitable metaheuristic or metaheuristics. They should be applicable to all optimisation problems in the OC system and, in addition, self-adaptively respond to changes in the system’s environment.
This project aims improving the application of metaheuristics in OC systems. It will be examined which metaheuristics are suitable for specific problems and, thereby, advance the state of the art through analysing the components of the metaheuristic responsible for these relations. Furthermore, self-adaptive strategies for applying and exchanging these components will be developed, which will lead to a more general optimiser, suitable for many problems common to OC.

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