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@Incollection { 2023_rueppel_crd-b2.ii,
   title = {Model-Based Controlling Approaches for Manufacturing Processes},
   year = {2023},
   month = {2},
   day = {8},
   pages = {221-246},
   abstract = {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).},
   keywords = {Process control; Model-based control; Data aggregation; Model identification; Model optimization},
   tags = {internet-of-production},
   url = {https://www.comsys.rwth-aachen.de/fileadmin/papers/2023/2023-rueppel-iop-b2.i.pdf},
   publisher = {Springer},
   series = {Interdisciplinary Excellence Accelerator Series},
   booktitle = {Internet of Production: Fundamentals, Applications and Proceedings},
   ISBN = {978-3-031-44496-8},
   DOI = {10.1007/978-3-031-44497-5_7},
   reviewed = {1},
   author = {R{\"u}ppel, Adrian Karl and Ay, Muzaffer and Biernat, Benedikt and Kunze, Ike and Landwehr, Markus and Mann, Samuel and Pennekamp, Jan and Rabe, Pascal and Sanders, Mark P. and Scheurenberg, Dominik and Schiller, Sven and Xi, Tiandong and Abel, Dirk and Bergs, Thomas and Brecher, Christian and Reisgen, Uwe and Schmitt, Robert H. and Wehrle, Klaus}
}

@Incollection { 2023_klugewilkes_crd-b2.iv,
   title = {Modular Control and Services to Operate Line-less Mobile Assembly Systems},
   year = {2023},
   month = {2},
   day = {8},
   pages = {303-328},
   abstract = {The increasing product variability and lack of skilled workers demand for autonomous, flexible production. Since assembly is considered a main cost driver and accounts for a major part of production time, research focuses on new technologies in assembly. The paradigm of Line-less Mobile Assembly Systems (LMAS) provides a solution for the future of assembly by mobilizing all resources. Thus, dynamic product routes through spatiotemporally configured assembly stations on a shop floor free of fixed obstacles are enabled. In this chapter, we present research focal points on different levels of LMAS, starting with the macroscopic level of formation planning, followed by the mesoscopic level of mobile robot control and multipurpose input devices and the microscopic level of services, such as interpreting autonomous decisions and in-network computing. We provide cross-level data and knowledge transfer through a novel ontology-based knowledge management. Overall, our work contributes to future safe and predictable human-robot collaboration in dynamic LMAS stations based on accurate online formation and motion planning of mobile robots, novel human-machine interfaces and networking technologies, as well as trustworthy AI-based decisions.},
   keywords = {Lineless mobile assembly systems (LMAS); Formation planning; Online motion planning; In-network computing; Interpretable AI; Human-machine collaboration; Ontology-based knowledge management},
   tags = {internet-of-production},
   url = {https://www.comsys.rwth-aachen.de/fileadmin/papers/2023/2023-klugewilkes-iop-b2.iv.pdf},
   publisher = {Springer},
   series = {Interdisciplinary Excellence Accelerator Series},
   booktitle = {Internet of Production: Fundamentals, Applications and Proceedings},
   ISBN = {978-3-031-44496-8},
   DOI = {10.1007/978-3-031-44497-5_13},
   reviewed = {1},
   author = {Kluge-Wilkes, Aline and Baier, Ralph and Gossen, Daniel and Kunze, Ike and M{\"u}ller, Aleksandra and Shahidi, Amir and Wolfschl{\"a}ger, Dominik and Brecher, Christian and Corves, Burkhard and H{\"u}sing, Mathias and Nitsch, Verena and Schmitt, Robert H. and Wehrle, Klaus}
}