This file was created by the TYPO3 extension bib --- Timezone: CEST Creation date: 2024-07-04 Creation time: 03-25-13 --- Number of references 2 incollection 2023_pennekamp_crd-a.i 2023 2 8 35-60 The Internet of Production (IoP) leverages concepts such as digital shadows, data lakes, and a World Wide Lab (WWL) to advance today’s production. Consequently, it requires a technical infrastructure that can support the agile deployment of these concepts and corresponding high-level applications, which, e.g., demand the processing of massive data in motion and at rest. As such, key research aspects are the support for low-latency control loops, concepts on scalable data stream processing, deployable information security, and semantically rich and efficient long-term storage. In particular, such an infrastructure cannot continue to be limited to machines and sensors, but additionally needs to encompass networked environments: production cells, edge computing, and location-independent cloud infrastructures. Finally, in light of the envisioned WWL, i.e., the interconnection of production sites, the technical infrastructure must be advanced to support secure and privacy-preserving industrial collaboration. To evolve today’s production sites and lay the infrastructural foundation for the IoP, we identify five broad streams of research: (1) adapting data and stream processing to heterogeneous data from distributed sources, (2) ensuring data interoperability between systems and production sites, (3) exchanging and sharing data with different stakeholders, (4) network security approaches addressing the risks of increasing interconnectivity, and (5) security architectures to enable secure and privacy-preserving industrial collaboration. With our research, we evolve the underlying infrastructure from isolated, sparsely networked production sites toward an architecture that supports high-level applications and sophisticated digital shadows while facilitating the transition toward a WWL. Cyber-physical production systems; Data streams; Industrial data processing; Industrial network security; Industrial data security; Secure industrial collaboration internet-of-production https://www.comsys.rwth-aachen.de/fileadmin/papers/2023/2023-pennekamp-iop-a.i.pdf Springer Interdisciplinary Excellence Accelerator Series Internet of Production: Fundamentals, Applications and Proceedings 978-3-031-44496-8 10.1007/978-3-031-44497-5_2 1 JanPennekamp AnastasiiaBelova ThomasBergs MatthiasBodenbenner AndreasBührig-Polaczek MarkusDahlmanns IkeKunze MoritzKröger SandraGeisler MartinHenze DanielLütticke BenjaminMontavon PhilippNiemietz LuciaOrtjohann MaximilianRudack Robert H.Schmitt UweVroomen KlausWehrle MichaelZeng incollection 2023_rueppel_crd-b2.ii 2023 2 8 221-246 The main objectives in production technology are quality assurance, cost reduction, and guaranteed process safety and stability. Digital shadows enable a more comprehensive understanding and monitoring of processes on shop floor level. Thus, process information becomes available between decision levels, and the aforementioned criteria regarding quality, cost, or safety can be included in control decisions for production processes. The contextual data for digital shadows typically arises from heterogeneous sources. At shop floor level, the proximity to the process requires usage of available data as well as domain knowledge. Data sources need to be selected, synchronized, and processed. Especially high-frequency data requires algorithms for intelligent distribution and efficient filtering of the main information using real-time devices and in-network computing. Real-time data is enriched by simulations, metadata from product planning, and information across the whole process chain. Well-established analytical and empirical models serve as the base for new hybrid, gray box approaches. These models are then applied to optimize production process control by maximizing the productivity under given quality and safety constraints. To store and reuse the developed models, ontologies are developed and a data lake infrastructure is utilized and constantly enlarged laying the basis for a World Wide Lab (WWL). Finally, closing the control loop requires efficient quality assessment, immediately after the process and directly on the machine. This chapter addresses works in a connected job shop to acquire data, identify and optimize models, and automate systems and their deployment in the Internet of Production (IoP). Process control; Model-based control; Data aggregation; Model identification; Model optimization internet-of-production https://www.comsys.rwth-aachen.de/fileadmin/papers/2023/2023-rueppel-iop-b2.i.pdf Springer Interdisciplinary Excellence Accelerator Series Internet of Production: Fundamentals, Applications and Proceedings 978-3-031-44496-8 10.1007/978-3-031-44497-5_7 1 Adrian KarlRüppel MuzafferAy BenediktBiernat IkeKunze MarkusLandwehr SamuelMann JanPennekamp PascalRabe Mark P.Sanders DominikScheurenberg SvenSchiller TiandongXi DirkAbel ThomasBergs ChristianBrecher UweReisgen Robert H.Schmitt KlausWehrle