Now showing 1 - 10 of 60
  • Publication
    Implementing human-robot collaboration in highly dynamic environments: Assessment, planning and development
    ( 2024) ;
    Jaya, T.
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    Thiele, Gregor
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    Human-robot collaboration (HRC) applications have been slowly making their path in the industry. Although the required hardware and the methods for the planning and development of collaborative robotic applications are mostly already developed, some industrial branches still struggle to implement HRC. This is the case in motorcycle production, where, unlike car production, the assembly line has been optimized for manual work. Based on the use case described above, this paper identifies new requirements of HRC for automated screwing assembly operations in flexible production environments. In order to compensate deviations in the position of the tool relative to the workpiece, a screwing strategy based on force control is proposed. Parameter sensitivity is considered and supported experimentally with a screwing task performed by a cobot, where a method for contact detection between the nutrunner and the screw head is analyzed. This paper brings a guideline for experts from the manufacturing system engineering to implement HRC in highly dynamic assembly environments.
  • Publication
    A Practical Approach to Realize a Closed Loop Energy Demand Optimization of Milling Machine Tools in Series Production
    ( 2023)
    Can, Alperen
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    Schulz, Hendrik
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    El-Rahhal, Ali
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    Thiele, Gregor
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    Energy efficiency is becoming increasingly important for industry. Many approaches for energy efficiency improvements lead to the purchase of new hardware, which could neglect the sustainability. Therefore, optimizing the energy demand of existing machine tools (MT) is a promising approach. Nowadays energy demand optimization of MT in series production is mainly done manually by the operators, based on implicit knowledge gained by experience. This involves manual checks to ensure that production targets like product quality or cycle time are met. With data analytics it is possible to check these production targets autonomously, which allows optimizing production systems data driven. This paper presents the approach and evaluation of a closed loop energy demand optimization of auxiliary units for milling MT during series production. The approach includes, inter alia, a concept for machine connectivity using edge devices and a concept for validating production targets
  • Publication
    Signal conditioning of a novel ultrasonic transducer with integrated temperature and amplitude sensors
    ( 2023)
    Karbouj, Bsher
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    Vibration amplitude of ultrasonic transducer has an impact on the overall process quality, process speed and ultrasonic transducer lifetime in industrial applications. A new ultrasonic transducer design has been developed with integrated sensor disks that have different electrical and mechanical properties. The combination of sensor has been designed for amplitude measurements and is also able to measure the transducer temperature in the real time. This paper deals with the analog signal processing that combines the different "raw" signals from the sensor disks to extract the information such as amplitude and temperature. For this goal, a chain of different signal filters and adjustment elements was used. Reliable amplitude and temperature measurements during real-time operation were obtained by the applied signal processing. The obtained results were validated with an external temperature sensor and a laser vibrometer.
  • Publication
    Green incremental learning - Energy efficient ramp-up for AI-enhanced part recognition in reverse logistics
    ( 2023) ;
    Schimanek, Robert
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    Koch, Paul
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    Chavan, Vivek Prabhakar
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    Bilge, Pinar
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    Dietrich, Franz
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    Artificial Intelligence (AI) has made significant progress in supporting circular economy and reverse logistics by learning from diverse data to predict, e.g., routes or to assist workers in sorting. However, it remains an open question how AI can be integrated and trained into such operational processes, where little to no data has been collected previously. Traditionally, AI models would only be rated by their accuracy. This paper aims to introduce the concept of green incremental learning, i.e. rating AI models not only for their accuracy but to evaluate energy efficiency as well. A ramp-up of a data-driven AI system for part recognition is explored under consideration of energy efficiency. Therefore, we combine online and incremental learning, working with growing data sets to simulate a ramp-up phase. We present experiments of incremental learning on business and image data, partially supported by regular joint training steps. We start local CPU-based machine learning and prediction on business data from the first sample. Finally, we compare incremental learning to traditional batch learning and show energy-saving potential of up to 62 % without a significant drop in accuracy.
  • Publication
    Learning Demonstrator for Anomaly Detection in Distributed Energy Generation
    ( 2022-04-07)
    Pelchen, Timo
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    Thiele, Gregor
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    Radke, Marcel
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    Schade, David
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    Machine learning based anomaly detection methods on process data can be used to secure critical infrastructure. The design and installation of these methods require detailed understanding of both the facilities and the machine learning methods. Therefore, they are mostly incomprehensible for non-experts and thus acting as a barrier hindering the fast spread of such technologies. This article presents the systematic development of a demonstrator which enables presentations of anomaly detection on the example of a simulated wind farm. The specially designed user-interface allows a comprehensive experience. This article documents the use of the demonstrator for experts experienced in energy systems which are interested in the application of machine learning algorithms.
  • Publication
    Industrielle kraftgeregelte Schraubprozesse
    Manuelle Schraubmontagen profitieren von menschlichen feinmotorischen Fähigkeiten für die flexible Positionierung der Werkzeuge und Bauteile. Solche Prozesse lassen sich mithilfe eines kooperativen Robotersystems automatisieren, welches flexibel in einer dynamischen Umgebung agiert und insbesondere die Fähigkeit mitbringt, hohe Prozesskräfte aufzunehmen. In diesem Beitrag wird eine Methode zur Automatisierung von kraftgeregelten Schraubvorgängen beschrieben und die sich dabei ergebenden Herausforderungen erläutert.
  • Publication
  • Publication
    Decomposition of a Cooling Plant for Energy Efficiency Optimization Using OptTopo
    ( 2022)
    Thiele, Gregor
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    Johanni, Theresa
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    Sommer, David
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    The operation of industrial supply technology is a broad field for optimization. Industrial cooling plants are often (a) composed of several components, (b) linked using network technology, (c) physically interconnected, and (d) complex regarding the effect of set-points and operating points in every entity. This leads to the possibility of overall optimization. An example containing a cooling tower, water circulations, and chillers entails a non-linear optimization problem with five dimensions. The decomposition of such a system allows the modeling of separate subsystems which can be structured according to the physical topology. An established method for energy performance indicators (EnPI) helps to formulate an optimization problem in a coherent way. The novel optimization algorithm OptTopo strives for efficient set-points by traversing a graph representation of the overall system. The advantages are (a) the ability to combine models of several types (e.g., neural networks and polynomials) and (b) an constant runtime independent from the number of operation points requested because new optimization needs just to be performed in case of plant model changes. An experimental implementation of the algorithm is validated using a simscape simulation. For a batch of five requests, OptTopo needs 61 (Formula presented.) while the solvers Cobyla, SDPEN, and COUENNE need 0.3 min, 1.4 min, and 3.1 min, respectively. OptTopo achieves an efficiency improvement similar to that of established solvers. This paper demonstrates the general feasibility of the concept and fortifies further improvements to reduce computing time.
  • Publication
    Steigerung der Energieeffizienz mittels Energiekennzahlen am Beispiel der Metallverarbeitung
    ( 2022)
    Sigg, Stefan
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    Kühn, Armin
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    Roder, Sven
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    Thiele, Gregor
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    Die Energiewende und die einhergehende Forderung nach Effizienzsteigerungen stellen produzierende Unternehmen vor betriebliche und technische Herausforderungen. Methoden und Technologien des Energiemanagements gewinnen damit auch im Mittelstand an Relevanz. Dabei sind Kennzahlen ein Schlüssel, um die aktuelle Energieeffizienz einzuschätzen und Potenziale für Einsparungen zu identifizieren. Der Artikel dokumentiert Erfahrungen der Anwender mit Bezug auf Konzepte aus der Wissenschaft, um Interessierten aus der Industrie den Einstieg in das Thema zu erleichtern.
  • Publication
    Contact Information Flow and Design of Compliance
    ( 2022) ;
    Radke, Marcel
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    Hartisch, Richard
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    Identifying changes in contact during contact-rich manipulation can detect task state or errors, enabling improved robustness and autonomy. The ability to detect contact is affected by the mechatronic design of the robot, especially its physical compliance. Established methods can design physical compliance for many aspects of contact performance (e.g. peak contact force, motion/force control bandwidth), but are based on time-invariant dynamic models. A change in contact mode is a discrete change in coupled robot-environment dynamics, not easily considered in existing design methods.Towards designing robots which can robustly detect changes in contact mode online, this paper investigates how mechatronic design can improve contact estimation, with a focus on the impact of the location and degree of compliance. A design metric of information gain is proposed which measures how much position/force measurements reduce uncertainty in the contact mode estimate. This information gain is developed for fully- and partially-observed systems, as partial observability can arise from joint flexibility in the robot or environmental inertia. Hardware experiments with various compliant setups validate that information gain predicts the speed and certainty with which contact is detected in (i) monitoring of contact-rich assembly and (ii) collision detection.