This file was created by the TYPO3 extension bib --- Timezone: CEST Creation date: 2024-04-26 Creation time: 01-21-26 --- Number of references 3 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 inproceedings 2020-mann-ur-weldseamstudy 2020 6 https://www.comsys.rwth-aachen.de/fileadmin/papers/2020/2020-mann-weld-seam-geometry-control.pdf Proceedings of the 2020 Internal Conference on Ubiquitous Robots Internal Conference on Ubiquitous Robots June 22-26, 2020 10.1109/UR49135.2020.9144839 1 SamuelMann RenéGlebke IkeKunze DominikScheurenberg RahulSharma UweReisgen KlausWehrle DirkAbel article 2020_mann_welding_layers Advanced Structured Materials 2020 4 1 125 101-118 A connected, digitalized welding production unlocks vast and dynamic potentials: from improving state of the art welding to new business models in production. For this reason, offering frameworks, which are capable of addressing multiple layers of applications on the one hand and providing means of data security and privacy for ubiquitous dataflows on the other hand, is an important step to enable the envisioned advances. In this context, welding production has been introduced from the perspective of interlaced process layers connecting information sources across various entities. Each layer has its own distinct challenges from both a process view and a data perspective. Besides, investigating each layer promises to reveal insight into (currently unknown) process interconnections. This approach has been substantiated by methods for data security and privacy to draw a line between secure handling of data and the need of trustworthy dealing with sensitive data among different parties and therefore partners. In conclusion, the welding production has to develop itself from an accumulation of local and isolated data sources towards a secure industrial collaboration in an Internet of Production. Proceedings of the 1st International Conference on Advanced Joining Processes (AJP '19) Welding Production; Industrie 4.0; Internet of Production; Data Security; Data Privacy Internet-of-Production https://www.comsys.rwth-aachen.de/fileadmin/papers/2020/2020-mann-welding-layers.pdf Springer Ponta Delgada, Azores, Portugal October 24-25, 2019 978-981-15-2956-6 1869-8433 10.1007/978-981-15-2957-3_8 1 SamuelMann JanPennekamp TobiasBrockhoff AnahitaFarhang MahsaPourbafrani LukasOster Merih SeranUysal RahulSharma UweReisgen KlausWehrle Wilvan der Aalst