Model-Based Controlling Approaches for Manufacturing Processes
Interdisciplinary excellence accelerator series, pp. 1–26
Abstract
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).
Authors 18
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Affiliation as printed
RWTH Aachen University, Aachen, Germany
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Muzaffer Ay Aachen
Affiliation as printed
RWTH Aachen University, Aachen, Germany
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Benedikt Biernat Aachen
Affiliation as printed
RWTH Aachen University, Aachen, Germany
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Ike Kunze Aachen
Affiliation as printed
RWTH Aachen University, Aachen, Germany
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Fraunhofer Institute for Production Technology IPT
Affiliation as printed
Fraunhofer IPT, Aachen, Germany
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Samuel Mann Aachen
Affiliation as printed
RWTH Aachen University, Aachen, Germany
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Jan Pennekamp Aachen
Affiliation as printed
RWTH Aachen University, Aachen, Germany
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Pascal Rabe Aachen
Affiliation as printed
RWTH Aachen University, Aachen, Germany
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Mark P. Sanders Aachen
Affiliation as printed
RWTH Aachen University, Aachen, Germany
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Dominik Scheurenberg Aachen
Affiliation as printed
RWTH Aachen University, Aachen, Germany
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Fraunhofer Institute for Production Technology IPT
Affiliation as printed
Fraunhofer IPT, Aachen, Germany
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Tiandong Xi Aachen
Affiliation as printed
RWTH Aachen University, Aachen, Germany
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Dirk Abel Aachen
Affiliation as printed
RWTH Aachen University, Aachen, Germany
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RWTH Aachen University · Fraunhofer Institute for Production Technology IPT
Affiliation as printed
Fraunhofer IPT, Aachen, Germany
RWTH Aachen University, Aachen, Germany
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Affiliation as printed
RWTH Aachen University , Aachen , Germany
Cluster of Excellence Internet of Prod, RWTH Aachen University, Aachen, Nordrhein-Westfalen, Germany
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Uwe Reisgen Aachen
Affiliation as printed
RWTH Aachen University, Aachen, Germany
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RWTH Aachen University · Fraunhofer Institute for Production Technology IPT
Affiliation as printed
Fraunhofer IPT, Aachen, Germany
RWTH Aachen University, Aachen, Germany
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Klaus Wehrle Aachen
Affiliation as printed
RWTH Aachen University, Aachen, Germany
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