Simplifying the Development of Industrial Mixed Reality Applications Using the MR DataHub

  • Usage of mixed reality (MR) applications in the chemical process industry can offer safer and more efficient process design, plant maintenance, and operational workflows, as well as easier operator training. Despite this potential, the development of MR applications is still slow and often impeded by high costs – especially for small and medium-sized companies. To this end, this contribution presents the MR DataHub, a framework that simplifies MR application development by providing a pre-configured backend environment for data integration and by automating frontend code generation. The MR DataHub consists of a containerized backend server that enables connections to external data sinks and sources through standard interfaces such as REST API, MQTT, and OPC UA. Hence, it can link automation systems, enterprise resource management, and / or engineering data to the MR frontend environment. It enables the exchange of live process data and control commands, providing the MR applicationUsage of mixed reality (MR) applications in the chemical process industry can offer safer and more efficient process design, plant maintenance, and operational workflows, as well as easier operator training. Despite this potential, the development of MR applications is still slow and often impeded by high costs – especially for small and medium-sized companies. To this end, this contribution presents the MR DataHub, a framework that simplifies MR application development by providing a pre-configured backend environment for data integration and by automating frontend code generation. The MR DataHub consists of a containerized backend server that enables connections to external data sinks and sources through standard interfaces such as REST API, MQTT, and OPC UA. Hence, it can link automation systems, enterprise resource management, and / or engineering data to the MR frontend environment. It enables the exchange of live process data and control commands, providing the MR application with essential operational information. Based on the linked sinks and sources, the MR DataHub automatically generates type and class definitions for a frontend Unity engine. Furthermore, MR DataHub offers predefined, extensible GraphQL schemas for various industry domain standards, including DEXPI P&ID and DEXPI Process, enabling faster integration of standardized data into industrial MR applications. The benefits of the MR DataHub are demonstrated in the MR4SafeOperations project (https://mr4b.de/wir-buendnis-mr4b-projekte/mr4safeoperations/), where an MR application was developed using the MR DataHub to guide plant operators in liquid sampling procedures by providing step-by-step visual guidance integrated with live process and engineering data – enhancing operational safety. Preliminary evaluations indicate significant reductions in development time of the MR application by relying on MR DataHub’s pre-configured backend environment, predefined interfaces, and extensible GraphQL schemas.zeige mehrzeige weniger

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Autor*innen:Shady KhellaORCiD
Koautor*innen:Erik EscheORCiD, Rico Schady, Philip Parker, Hernán Darío Muñoz GilORCiD, Jens-Uwe Repke
Dokumenttyp:Posterpräsentation
Veröffentlichungsform:Präsentation
Sprache:Englisch
Jahr der Erstveröffentlichung:2025
Organisationseinheit der BAM:2 Prozess- und Anlagensicherheit
2 Prozess- und Anlagensicherheit / 2.2 Prozesssimulation
DDC-Klassifikation:Technik, Medizin, angewandte Wissenschaften / Chemische Verfahrenstechnik / Chemische Verfahrenstechnik
Freie Schlagwörter:Data Integration; Digitilization; Mixed Reality; Pseudonymisation; Software Development
Themenfelder/Aktivitätsfelder der BAM:Chemie und Prozesstechnik
Chemie und Prozesstechnik / Anlagensicherheit und Prozesssimulation
Veranstaltung:PEMT 2025 - Annual Meeting of Process Engineering and Materials Technology
Veranstaltungsort:Frankfurt am Main, Germany
Beginndatum der Veranstaltung:10.11.2025
Enddatum der Veranstaltung:11.11.2025
Verfügbarkeit des Dokuments:Datei im Netzwerk der BAM verfügbar ("Closed Access")
Datum der Freischaltung:13.01.2026
Referierte Publikation:Nein
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