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qNMR provides the most universally applicable form of direct concentration or purity determination without need for reference materials of impurities or the calculation of response factors but only exhibiting suitable NMR properties. The workshop presents basic terms of statistics and uncertainty analysis, which are the basis for qNMR spectroscopy and data analysis such as, e.g., standard deviations, linear regression, significance tests, etc. and gives typical examples of applications in qNMR spectroscopy.
Rapidly advancing technologies and progressive digitisation are posing challenges to the established quality infrastructure (QI). In response, the key stakeholders of the German QI established the initiative QI-Digital aimed at developing new solutions for modern quality assurance. One of the central use cases herein is quality assurance for additive manufacturing, in which a fully interlinked additive manufacturing process chain is established. The intention is to collect and process data from each production step, allowing for a comprehensive digital view of the physical material flow. Within this process chain, prototypes of digital QI tools like machine readable standards and digital quality certificates are being demonstrated, tested, and evolved. This is complemented by research on the process level, comprising the evaluation and refinement of methods for in-situ and ex-situ quality assurance, as well as algorithms for registration, reduction, and analysis of process data. This paper presents the status, goals, and vision for the QI-Digital use case additive manufacturing.
Als Beispiel für moderne Produktion ist die additive Fertigung (ugs. „3D-Druck“) bei der Herstellung von hochkomplexen metallischen Bauteilen, bionisch inspiriertem Leichtbau oder Prototypen nicht mehr wegzudenken. Die Qualitätssicherung (QS) von Bauteilen für sicherheitskritische Anwendungen stellt jedoch noch eine Herausforderung dar. Die Additive Fertigung (AM) ist ein vergleichsweise junges und datenintensives Fertigungsverfahren. Daher ist es ideal geeignet, die neuen Werkzeuge einer digitalen Qualitätsinfrastruktur (QI) für die moderne Produktion zu erproben und weiterzuentwickeln.