Research project: Detecting errors in the production process at an early stage

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Anomalies and errors in the production process cause high costs and have a negative impact on sustainability and productivity. If such errors can be detected immediately when they occur, then cost-intensive reworking, time-consuming final inspections and delivery delays can be avoided. The German-Czech research project AIQUAMA (AI-based Quality Management for Smart Factories) is investigating the use of artificial intelligence in the quality management of production processes. The goal is error-free and reject-free production. The project was launched with a kick-off at the Forum Digitale Technologien in Berlin on November 7, 2022. In the future, it will be increasingly important to ensure quality directly at the time of process execution. Particularly in less automated processes, simple robot solutions with intelligent AI will watch employees at work and inform them in the event of deviations, outliers or irregularities. The goal is for the manufacturer to be able to put such a solution into operation even with little robotics and data science knowledge. AIQUAMA aims to achieve zero-defect production based on near real-time incremental quality monitoring during production. This is done by evaluating multi-sensor data streams using AI methods. AIQUAMA will use a combination of symbolic models and statistical machine learning based on real but also synthetic training data. In order to avoid quality-related errors in advance, an intelligent online planning component will be extended in such a way that quality-related parameters are also taken into account in the best possible way during plan generation and task assignment. Especially in manual assembly or machining processes or in work steps performed in hybrid by teams of humans and collaborative robots, errors still happen. One such error is, for example, a worker reaching into the wrong material box or to the wrong tool during a manual assembly task, or a robot giving a hand at the wrong time. However, suitable combinations of different sensor systems should now make it possible to detect errors in the production process earlier than before and therefore eliminate them more sustainably. Detected errors are explained transparently via suitable user interfaces so that they can be avoided in the future. For the technical implementation, the partners fall back on the standardized I4.0 architecture with management shells developed in the BaSys project series and use the open source middleware BaSyx as well as open source results from their ecosystem.

AIQUAMA is a German-Czech research cooperation in the field of Industry 4.0, based on common foundations, such as from RICAIP (EU H2020). The project partners are: German Research Center for Artificial Intelligence, Czech Institute of Informatics, Robotics and Cybernetics (CIIRC) at the Czech Technical University in Prague (CTU), Central European Institute of Technology at the University of Brno (CEITEC BUT), Technical University of Ostrava together with application partners Volkswagen AG and Škoda Auto, which do not receive funding. Together with Volkswagen, DFKI will implement and realistically evaluate an AIQUAMA demonstration system in the field of manual and hybrid assembly in Saxony. AIQUAMA has a project volume of approximately 1.8 million euros and is funded by the Federal Ministry of Education and Research over a period of three years.

Autor(en): mak

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