{"id":15228,"date":"2022-11-09T09:01:04","date_gmt":"2022-11-09T08:01:04","guid":{"rendered":"https:\/\/saarland-informatics-campus.de\/?post_type=sic_news&p=15228"},"modified":"2022-11-15T09:08:54","modified_gmt":"2022-11-15T08:08:54","slug":"detecting-anomalies-and-errors-in-the-production-process-at-an-early-stage-aiquama-research-project-launched","status":"publish","type":"sic_news","link":"https:\/\/saarland-informatics-campus.de\/en\/piece-of-news\/detecting-anomalies-and-errors-in-the-production-process-at-an-early-stage-aiquama-research-project-launched\/","title":{"rendered":"Detecting anomalies and errors in the production process at an early stage – AIQUAMA research project launched."},"content":{"rendered":"

Anomalies and defects in the production process cause high costs and have a negative impact on sustainability and productivity. If it is possible to detect such defects 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) investigates the use of artificial intelligence in the quality management of production processes. The goal is error-free and reject-free production. AIQUAMA was launched with a kick-off at the \u201cForum Digitale Technologien\u201d in Berlin on November 7, 2022.<\/strong><\/p>\n

In the future, it will be increasingly important to ensure quality directly during process execution. Particularly in less automated processes, such as assembly, simple robotic 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.<\/p>\n

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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.<\/p>\n

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 by hybrid teams of humans and collaborative robots, errors still happen. One such error is, for example, a worker reaching into the wrong material box or the wrong tool during a manual assembly task or a robot giving a hand at the wrong time.<\/p>\n

However, suitable combinations of different sensor systems should now enable errors in the production process to be detected earlier than before and therefore eliminated more sustainably. Detected errors are explained transparently via suitable user interfaces so they can be avoided in the future.<\/p>\n

For the technical implementation, the partners fall back on the standardized I4.0 architecture with asset administration shells developed in the\u00a0BaSys<\/a>\u00a0project series and use the open-source middleware BaSyx as well as open-source results from its ecosystem.<\/p>\n

AIQUAMA is a German-Czech research cooperation in the field of Industry 4.0, based on common foundations, such as from RICAIP (EU H2020).<\/p>\n

The project partners are:<\/strong><\/p>\n