07 Fakultät Konstruktions-, Produktions- und Fahrzeugtechnik

Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/8

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Institute: search.filters.UBSInstitut.Institut für Steuerungstechnik der Werkzeugmaschinen und FertigungseinrichtungenAuthor: search.filters.author.Riedel, Oliver

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Now showing 1 - 6 of 6
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    A process-planning framework for sustainable manufacturing
    (2021) Reiff, Colin; Buser, Matthias; Betten, Thomas; Onuseit, Volkher; Hoßfeld, Max; Wehner, Daniel; Riedel, Oliver
    Process planning in manufacturing today focuses on optimizing the conflicting targets of cost, quality, and time. Due to increasing social awareness and subsequent governmental regulation, environmental impact becomes a fourth major aspect. Eventually, sustainability in manufacturing ensures future competitiveness. In this paper, a framework for the planning of sustainable manufacturing is proposed. It is based on the abstraction and generalization of manufacturing resources and part descriptions, which are matched and ranked using a multi-criteria decision analysis method. Manufacturing resources provide values for cost, quality, time and environmental impacts, which multiply with their usage within a manufacturing task for a specific part. The framework is validated with a detailed modeling of a laser machine as a resource revealing benefits and optimization potential of the underlying data model. Finally, the framework is applied to a use case of a flange part with two different manufacturing strategies, i.e., laser metal-wire deposition and conventional milling. The most influential parameters regarding the environmental impacts are the raw material input, the manufacturing energy consumption and the machine production itself. In general, the framework enabled the identification of non-predetermined manufacturing possibilities and the comprehensive comparison of production resources.
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    Reinforcement learning methods based on GPU accelerated industrial control hardware
    (2021) Schmidt, Alexander; Schellroth, Florian; Fischer, Marc; Allimant, Lukas; Riedel, Oliver
    Reinforcement learning is a promising approach for manufacturing processes. Process knowledge can be gained automatically, and autonomous tuning of control is possible. However, the use of reinforcement learning in a production environment imposes specific requirements that must be met for a successful application. This article defines those requirements and evaluates three reinforcement learning methods to explore their applicability. The results show that convolutional neural networks are computationally heavy and violate the real-time execution requirements. A new architecture is presented and validated that allows using GPU-based hardware acceleration while meeting the real-time execution requirements.
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    Fehlertolerante Sicherheitssteuerung aus der Cloud : Softwarebasierte Sicherheitssteuerungen
    (2023) Fischer, Marc; Walker, Moritz; Lechler, Armin; Riedel, Oliver; Verl, Alexander
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    Updating the Linux TAPRIO scheduler in deterministic time
    (2022) Arnim, Christian von; Gessner, Gernot; Jarwitz, Michael; Lechler, Armin; Riedel, Oliver
    In flexible production systems with distributed control systems which communicate with each other via a real time network, changes in the requirements for real time communication during operation must not lead to a temporary failure of the deterministic communication. Rather, the changes of the network configuration must become active at exactly predictable times in order to guarantee the functionality of the deterministic communication all the time. This paper shows a realization of how the configuration of a real time network schedule under Linux can be adjusted at predetermined times without interrupting the communication. For this purpose, the real time scheduler TAPRIO is integrated into the library libnl, and the performance of this extension is evaluated in a test case. It is shown that the modification of the network configuration is reliably possible in several successive communication cycles.