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Institute: search.filters.UBSInstitut.Institut für Steuerungstechnik der Werkzeugmaschinen und FertigungseinrichtungenPublication Type: search.filters.UBSTyp.Zeitschriftenartikel

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Now showing 1 - 10 of 24
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    EIPPM : the Executable Integrative Product-Production Model
    (2021) Schopper, Dominik; Kübler, Karl; Rudolph, Stephan; Riedel, Oliver
    In this paper, a combination of graph-based design and simulation-based engineering (SBE) into a new concept called Executable Integrative Product-Production Model (EIPPM) is elaborated. Today, the first collaborative process in engineering for all mechatronic disciplines is the virtual commissioning phase. The authors see a hitherto untapped potential for the earlier, integrated and iterative use of SBE for the development of production systems (PS). Seamless generation of and exchange between Model-, Software- and Hardware-in-the-Loop simulations is necessary. Feedback from simulation results will go into the design decisions after each iteration. The presented approach combines knowledge of the domain “PSs” together with the knowledge of the corresponding “product” using a so called Graph-based Design Language (GBDL). Its central data model, which represents the entire life cycle of product and PS, results of an automatic translation step in a compiler. Since the execution of the GBDL can be repeated as often as desired with modified boundary conditions (e.g., through feedback), a design of experiment is made possible, whereby unconventional solutions are also considered. The novel concept aims at the following advantages: Consistent linking of all mechatronic disciplines through a data model (graph) from the project start, automatic design cycles exploring multiple variants for optimized product-PS combinations, automatic generation of simulation models starting with the planning phase and feedback from simulation-based optimization back into the data model.
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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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    Mechatronic control system for a compliant and precise pneumatic rotary drive unit
    (2019) Stoll, Johannes T.; Schanz, Kevin; Pott, Andreas
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    Parameter identification for fault analysis of permanent magnet synchronous motors based on transient processes
    (2024) Wu, Chaoqiang; Verl, Alexander
    As the market for hybrid and electric vehicles expands, electric motor production and testing technology must be continuously improved to meet the cost and quality requirements of mass production. In order to detect faults in motors during the production process, a condition monitoring tool is used for the motor end line. During most condition monitoring, the motor operates in a static state where the speed of the motor remains constant and the voltage/current is recorded for a certain period. This process usually takes a long time and requires a loader to drag the motor to a standstill at a constant speed. In this paper, various transient process testing methods are introduced. For these processes, only transient operation of the motor, such as acceleration, loss, or a short circuit, is required. By analyzing the measurement results and simulation results of motor models, unhealthy motors can be detected more effectively.
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    Efficient combination of topology and parameter optimization
    (2014) Lin, Yusheng; Sun, Zheng; Dadalau, Alexandru; Verl, Alexander
    This paper presents a combination method of Particle Swarm Optimization (PSO) and topology optimization. With this method a better result can be achieved compared with the sequential ap-plication of the two optimization methods. It inherits the ability in finding global optimum from PSO and also suits for discretized design domain. Some special schemes are used in order to provide higher computation efficiency. This method has only been tested with a convex optimization problem. The application in case of a concave problem will be a future study.
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    Impact-based feed drive actuator for discontinuous motion profiles
    (2020) Zahn, Peter; Schulte, Alexander; Verl, Alexander
    This paper discusses an approach to enable step-wise velocity changes in machine tool feed drives while reducing the reaction force of the drive on structural machine components. The implementation is based on an additional actuator that transmits well-defined impulses on the table via mechanical impacts. Possible applications are seen in processes as beam processing or handling. The approach is introduced by means of a multi-body model and afterwards experimental results are shown. On the one hand, the reduction of the tracking error while following discontinuous velocity profiles is analyzed, on the other hand, the reduced excitation of the machine structure is shown. The experimental verification of the functional principle is performed on a single axis setup where the fundamental parameters in design, material and control are quantified. Concluding, a short outlook on remaining research topics regarding the shown approach is given.
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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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    Framework for holistic online optimization of milling machine conditions to enhance machine efficiency and sustainability
    (2024) Bott, Alexander; Anderlik, Simon; Ströbel, Robin; Fleischer, Jürgen; Worthmann, Andreas
    This study addresses the challenge of the optimization of milling in industrial production, focusing on developing and applying a novel framework for optimising manufacturing processes. Recognising a gap in current methods, the research primarily targets the underutilisation of advanced data analysis and machine learning techniques in industrial settings. The proposed framework integrates these technologies to refine machining parameters more effectively than conventional approaches. The research method involved the development of the framework for the realisation and analysis of measurement data from milling machines, focusing on six machine parts and employing a machine learning system for optimization and evaluation. The developed and realised framework in the form of a software demonstrator showed its applicability in different experiments. This research enables easy deployment of data-driven techniques for sustainable industrial practices, highlighting the potential of this framework for transforming manufacturing processes.