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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.
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.
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.
QI-Digital is a joined project aiming at digitalising Quality Infrastructure (QI) processes involving standardization, conformity assessment, accreditation, metrology, and market surveillance [1]. Federal institute of material research and testing (BAM) is working on the creation of a digital calibration certifi-cate (DCC) to achieve digital metrological traceability and conformity assessment. The utilisation of machine readable and executable DCCs in the XML format is demonstrated on an example of a tem-perature measurement at a hydrogen refueling station. The certificates will be retrieved and analysed automatically at a Process Control System or at a Digital Twin.
QI-Digital is a joined project aiming at digitalising Quality Infrastructure (QI) processes involving standardization, conformity assessment, accreditation, metrology, and market surveillance [1]. Federal institute of material research and testing (BAM) is working on the creation of a digital calibration certifi-cate (DCC) to achieve digital metrological traceability and conformity assessment. The utilisation of machine readable and executable DCCs in the XML format is demonstrated on an example of a tem-perature measurement at a hydrogen refueling station. The certificates will be retrieved and analysed automatically at a Process Control System or at a Digital Twin.
Chemical and pharmaceutical companies have to find new paths to survive successfully in a changing environment, while also finding more flexible ways of product and process development to bring their products to market more quickly – especially high-quality high-end products like fine chemicals or pharmaceuticals. A current approach uses flexible and modular chemical production units, which can produce different high-quality products using multi-purpose equipment with short downtimes between campaigns and reduce the time to market of new products.
NMR spectroscopy appeared as excellent online analytical tool and allowed a modular data analysis approach, which even served as reliable reference method for further Process Analytical Technology (PAT) applications. Using the available datasets, a second data analysis approach based on artificial neural networks (ANN) was evaluated. Therefore, amount of data was augmented to be sufficient for training. The results show comparable performance, while improving the calculation time tremendously. In future, such fully integrated and interconnecting “smart” systems and processes can increase the efficiency of the production of specialty chemicals and pharmaceuticals.
At the end of the article, ideas for solutions are discussed in order to speed up the implementation of new special products from the point of view of process analytics and to network the existing process chains more closely.
European tribological research organisations unify their capabilities for the European Tribology Centre (https://www.i-tribomat.eu/). At its core is a database, which data are made comparable and trusted via harmonised procedures for their generation. This contribution will focus on wear volume data.
European tribological research organisations unify their capabilities for the European Tribology Centre (https://www.i-tribomat.eu/). At its core is a database, which data are made comparable and trusted via harmonised procedures for their generation. This contribution will focus on wear volume data.
Die Optimierung und Validierung des digitalen Wasserstofftankstellenmanagements mit Sensortechnologien ist das übergeordnete Ziel des Teilprojekts Digitale Qualitätsinfrastruktur mit Sensortechnologien (QIST) am Use Case H2, im Rahmen der digitalen Qualitätsinfrastruktur (QI digital[1]). Hierfür sollen u.a. Sensornetzwerke mit digital-gestützten Auswertungsstrategien intelligent gestaltet werden (Stichwort KI bzw. digitaler Zwilling). Verschiedene, sich ergänzende Sensoren, Systemkomponenten und KI-Methoden stehen als Bausteine intelligenter Sensorsysteme zur Verfügung, mit dem Zweck, die physikalischen und chemischen Parameter an und in Anlagen umfassend und effizient zu überwachen sowie Fehlfunktionen zuverlässig zu detektieren und zu interpretieren, Abbildung 1.
Konkrete Arbeitsschritte sind, nach dem Aufbau der Versuchsplattform „Wasserstofftankstelle“, die Instrumentierung von Sensorik und deren digitale Integration in das Tankstellenmanagementsystem sowie die Validierung im Realbetrieb. Die somit erzielten Messergebisse sowie die Messunsicherheiten, Historien und Verfahren werden in digitaler Form aufbereitet, gespeichert und fortlaufend in die KI-basierte Datenauswertung einbezogen, inkl. der Anbindung an die metrologische Rückführungskette.
Ziele sind:
• Einführung eines neuen messtechnischen Ansatzes und eines digitalen Zwillings für den Anwendungsfall "Wasserstofftankstelle" auf Basis der H2-Sensorik sowie der Zustandsgrößen Druck und Temperatur
• Qualitätssicherung durch Applikationen zuverlässiger Gassensorik, Manometer und Thermometer sowie KI-Methoden zur Prozesskontrolle und Detektion von Fehlfunktionen
• Nutzung von digitalen Kalibrierscheinen (DCCs) insbesondere für die Messgröße Temperatur zur Realisierung der metrologischen Rückführung in einer digitalen Qualitätsinfrastruktur