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Astract: Es gibt zahlreiche Publikationen, die die digitale Transformation im Tief- und Tunnelbau propagieren. Als Lösungsansatz wird dabei immer wieder Building Information Modeling (BIM) genannt und die Umsetzung der digitalen Transformation basierend auf dieser Methodik gefordert. Der prototypische Charakter eines Tunnelbauprojekts erschwert jedoch eine standardisierte Vorgehensweise. Hinzu kommt, dass die beteiligten Fachdisziplinen für unterschiedliche Gewerke individuelle Softwaretools mit proprietären (nicht standardisierten) Datenformaten nutzen. Durch diese in sich geschlossenen Datenmodelle ist eine offene BIM Arbeitsweise (Open BIM) nur schwer zu realisieren. Das hat der Lehrstuhl für Subsurface Engineering an der Montanuniversität Leoben zum Anlass genommen und zwei anwendungsorientierte Forschungsprojekte zur digitalen Transformation im Tief- und Tunnelbau lanciert, deren Inhalte in diesem Artikel kurz vorgestellt werden. Das „Zentrum am Berg (ZaB)“ dient in beiden Projekten als zentrale Infrastruktur zu Evaluierungszwecken.

Keywords: Model-Driven Software Engineering, Model-Driven, Industry 4.0, Automated Production Planning, PDDL, ISA-95, IEC-62264, Automated Planning, Model Transformation
Astract: The still ongoing globalization has brought all kinds of manufacturing companies in a difficult position. In order to be competitive, they have to adapt to the market and customer needs as quickly as possible. This adaption also includes the ability to produce new goods as fast as possible, but in old-fashioned manufacturing companies the creation of a production plan often is laborious. Another issue to put focus on is the organization of such production environments. In every structurized production plant there is a model (not necessarily an explicit one) of all the machines, equipment and material the production uses and produces. So if this information of the environment with all dependencies and relations already exists, why not using it for the plan generation? Various publications present ways of how such a flexible factory should look like. This approach relies on the a production environment and its products represented as models. Since this approach optimally should also be implemented in real-life manufacturing companies, testing this approach and evaluating the performance and the quality of the generated plans is crucial. Therefore, in this thesis the author continues the ongoing implementation of a specific production environment and collects performance data in order to provide an overview of the variants and options that bring the best performance.

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Astract: Variability is crucial in the design of many advanced goods and it is also receiving increasing attention in production systems engineering. Since OPC Unified Architecture plays an important role when it comes to standardized information exchange in modern production systems, it can be a melting pot for information from various engineering domains, such as product design and production engineering - thus, it is an ideal place to hold variability information of products and production systems alike. Based on an initial variability information model we propose additional concepts for the persisting of configurations.

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Astract: Model-driven engineering (MDE) provides tools and methods for the manipulation of formal models. In this letter, we leverage MDE for the transformation of production system models into flat files that are understood by general purpose planning tools and that enable the computation of "plans", i.e., sequences of production steps that are required to reach certain production goals. These plans are then merged back into the production system model, thus enriching the formalized production system knowledge.

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Astract: Smart production systems need to be able to adapt to changing environments and market needs. They have to reflect changes in (i) the reconfiguration of the production systems themselves, (ii) the processes they perform or (iii) the products they produce. Manual intervention for system adaptation is costly and potentially error-prone. In this article, we propose a model-driven approach for the automatic generation and regeneration of production plans that can be triggered anytime a change in any of the three aforementioned parameters occurs.