Fraunhofer-Institut für Produktionsanlagen und Konstruktionstechnik IPK

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  • Publication
    With synthetic data towards part recognition generalized beyond the training instances
    ( 2024)
    Koch, Paul
    ;
    Schlüter, Marian
    ;
    Krüger, Jörg
    In this work we investigate the effect of using synthetic data, generated in a simulation, in order to pre-train an AI-based image classification for industrial components. After pre-training we use real camera-captured training images to fine-tune the AI with the aim to close the Sim2Real domain gap. We compare our approach to purely using real training images of a single candidate object instance. In an exemplary case study for screw recognition, we found that a given AI classification algorithm dropped its recognition rate from 99.8% to 88.5% when testing the algorithm with known and unknown screw instances of the learned object classes, respectively. Employing our pre-training method on the basis of synthetic data, the drop in recognition rate is decreased from 99% to 96.95%. Thus, our proposed method has only a relative drop of 2.05% when shifting towards a generalized domain (including unknown part instances), while a compared approach on the basis of real camera-captured data showed a drop of 11.3%.
  • Publication
    Implementing human-robot collaboration in highly dynamic environments: Assessment, planning and development
    ( 2024)
    Bastidas Cruz, Arturo
    ;
    Jaya, T.
    ;
    Thiele, Gregor
    ;
    Krüger, Jörg
    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
    Signal conditioning of a novel ultrasonic transducer with integrated temperature and amplitude sensors
    ( 2023)
    Karbouj, Bsher
    ;
    Krüger, Jörg
    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
    A Practical Approach to Realize a Closed Loop Energy Demand Optimization of Milling Machine Tools in Series Production
    ( 2023)
    Can, Alperen
    ;
    Schulz, Hendrik
    ;
    El-Rahhal, Ali
    ;
    Thiele, Gregor
    ;
    Krüger, Jörg
    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
    Green incremental learning - Energy efficient ramp-up for AI-enhanced part recognition in reverse logistics
    ( 2023)
    Schlüter, Marian
    ;
    Schimanek, Robert
    ;
    Koch, Paul
    ;
    Briese, Clemens
    ;
    Chavan, Vivek Prabhakar
    ;
    Bilge, Pinar
    ;
    Dietrich, Franz
    ;
    Krüger, Jörg
    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
    Redundancy Concepts for Real-Time Cloud- and Edge-based Control of Autonomous Mobile Robots
    ( 2022)
    Nouruzi-Pur, Jan
    ;
    Lambrecht, Jens
    ;
    Nguyen, The Duy
    ;
    Vick, Axel
    ;
    Krüger, Jörg
    Deploying navigation algorithms on an edge or cloud server according to the Software-as-a-Service paradigm has many advantages for autonomous mobile robots in indus-trial environments, e.g. cooperative planning and less onboard energy consumption. However, outsourcing corresponding real-time critical control functions requires a high level of reliability, which cannot be guaranteed either by modern wireless networks nor by the outsourced computing infrastructure. This work introduces redundancy concepts, which enable real-time capability within these uncertain infrastructures by providing redundant computation nodes, as well as robot-controlled switching between them. Redundancies can vary regarding their physical location, robot behavior during the switchover process and degree of activeness while quality of service concerning the primary controller is sufficient. In the case that fallback redun-dancies are not continuously active, when a disturbance occurs an initial state estimation of the robot pose has to be provided and an activation time has to be anticipated. To gain some insights on expected behavior, redundant computation nodes are deployed locally on the robot and on an outsourced computation node and consequently evaluated empirically. Quantitative and qualitative results in simulation and a real environment show that redun-dancies help to significantly improve the robot-trajectory within an unreliable network. Moreover, resource-saving redundancies, which are not continuously active, can robustly take over control by using an estimated state.
  • Publication
    Towards High-Payload Admittance Control for Manual Guidance with Environmental Contact
    ( 2022)
    Haninger, Kevin
    ;
    Radke, Marcel
    ;
    Vick, Axel
    ;
    Krüger, Jörg
    Force control enables hands-on teaching and physical collaboration, with the potential to improve ergonomics and flexibility of automation. Established methods for the design of compliance, impedance control, and collision response can achieve free-space stability and acceptable peak contact force on lightweight, lower payload robots. Scaling collaboration to higher payloads can allow new applications, but introduces challenges due to the more significant payload dynamics and the use of higher-payload industrial robots. To achieve high-payload manual guidance with contact, this paper proposes and validates new mechatronic design methods: standard admittance control is extended with damping feedback, compliant structures are integrated to the environment, and a contact response method which allows continuous admittance control is proposed. These methods are compared with respect to free-space stability, contact stability, and peak contact force. The resulting methods are then applied to realize two contact-rich tasks on a 16 kg payload (peg in hole and slot assembly) and free-space co-manipulation of a 50 kg payload.
  • Publication
  • Publication
    Industrielle kraftgeregelte Schraubprozesse
    ( 2022)
    Bastidas Cruz, Arturo
    ;
    Jaya, Tanureza
    ;
    Haninger, Kevin
    ;
    Krüger, Jörg
    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
    Decomposition of a Cooling Plant for Energy Efficiency Optimization Using OptTopo
    ( 2022)
    Thiele, Gregor
    ;
    Johanni, Theresa
    ;
    Sommer, David
    ;
    Krüger, Jörg
    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.