Fraunhofer-Institut für Produktionsanlagen und Konstruktionstechnik IPK

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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.

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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.

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PowerGrasp - Design and evaluation of a modular soft-robotic arm exosuit for industrial applications

2020 , Goppold, Jean-Paul , Kuschan, Jan , Thiele, Gregor , Schmidt, H. , Krüger, Jörg , Hackbart, R. , Kostelnik, J. , Liebach, J. , Wolschke, M.

Absence from work caused by overloading the musculoskeletal system lowers the life quality of the worker and gains unnecessary costs for both the employer and the health system. Classical (rigid link) body-worn exoskeletons can help to reduce critical loading but show many disadvantages, preventing exoskeletons from extensive use in industrial environment. The presented PowerGrasp system is a very robust modular softrobotic arm exosuit sting of robust fabric with embedded rubber tubes as pressure chambers and soft-electronics and who's design is capable to overcome the critical limiting factors of classical exoskeletons. By inflating the tubes via pressure-control valves, it is possible to vary the stiffness of the chambers, which can be effectively used to generate assisting forces and moments at human joints. By using a joint based pressure control, it is possible to decrease the physical demand of overhead working for the wearer. Although the system is designed for i ndustrial overhead assembly, it can also be used in rehabilitation, craftsmanship and construction due to its portable and stand-alone concept. For assessing the impact of the PowerGrasp system, the raise of about 50 percent was shown. Finally, an evaluation study of the overall system has been conducted, showing very high user acceptance and usability.

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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

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Automated continuous learn and improvement process of energy efficiency in manufacturing

2020 , Can, Alperen , Fisch, Jessica , Stephan, Philipp , Thiele, Gregor , Krüger, Jörg

Optimizing the energy efficiency of machine tools automatically is promising. There are several metrics to be considered when it comes to automated optimization approaches in serial production which are especially quality, technical availability, and cycle time. These are not supposed to be impaired whereas they are indicated as a central obstacle. The measurements and the machine data show the actions happening in the machine which also leads to the data-driven traceability of machine states. This article presents a method to formulate the necessary expert knowledge to optimize the energy efficiency of a machine tool and is basically done by a decision tree which leads to a set of rules which will be explained in this article. This set of rules coordinate an optimization algorithm, which technically manipulates selected variables under the given rules. The development and is a result of a research which was done at the serial production of camshafts at the MB plant in Berlin.

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System identification of a hysteresis-controlled pump system using SINDy

2020 , Thiele, Gregor , Fey, Arne , Sommer, David , Krüger, Jörg

Hysteresis-controlled devices are widely used in industrial applications. For example, cooling devices usually contain a two-point controller, resulting in a nonlinear hybrid system with two discrete states. Dynamic models of systems are essential for optimizing such industrial supply technology. However, conventional system identification approaches can hardly handle hysteresis-controlled devices. Thus, the new identification method Sparse Identification of Nonlinear Dynamics (SINDy) is extended to consider hybrid systems. SINDy composes models from basis functions out of a customized library in a data-driven manner. For modeling systems that behave dependent on their own past as in the case of natural hysteresis, Ferenc Preisach introduced the relay hysteron as an elementary mathematical description. In this new method (SINDyHybrid), tailored basis functions in form of relay hysterons are added to the library which is used by SINDy. Experiments with a hysteresis controlled water basin show that this approach correctly identifies state transitions of hybrid systems and also succeeds in modeling the dynamics of the discrete system states. A novel proximity hysteron achieves the robustness of this method. The impacts of the sampling rate and the signal noise ratio of the measurement data are examined accordingly.

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Steigerung der Energieeffizienz mittels Energiekennzahlen am Beispiel der Metallverarbeitung

2022 , Sigg, Stefan , Kühn, Armin , Schmidt, Alexander , Roder, Sven , Thiele, Gregor , Krüger, Jörg

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.

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Process data based Anomaly detection in distributed energy generation using Neural Networks

2020 , Klein, Max , Thiele, Gregor , Fono, Adalbert , Khorsandi, Niloufar , Schade, David , Krüger, Jörg

The increasing share of renewable energies in the total energy supply includes a growing number of small, decentralized energy generation which also provides control energy. These decentralized stations are usually combined to a virtual power plant which takes over the monitoring and control of the individual participants via an Internet connection. This high degree of automation and the large number of frequently changing subscribers creates new challenges in terms of detecting anomalies. Quickly adaptable, variable and reliable methods of anomaly detection are required. This paper compares two approaches using Neural Networks (NN) with respect to their ability to detect anomalous behavior in real process data of a combined heat and power plant. In order to include process dynamics, one approach includes specifically engineered features, while the other approach uses Long-Short-Term-Memory (LSTM). Both approaches are able to detect rudimentary anomalies. For more demanding anomalies, the respective strengths and weaknesses of the two approaches become apparent.

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Energy Efficiency Optimization using AutomationML modeling and an EnPI methodology

2019 , Thiele, Gregor , Khorsandi, Niloufar , Krüger, Jörg

Industrial facilities are complex and heterogeneous systems in permanent technological change. The ambitions towards smart factories heighten the requirements for the flexible interconnection of various devices. These industrial entities are controlled, observed and optimized by many services. The tuning of process parameters of several linked components in order to boost the overall energy efficiency is one example of such services. AutomationML (AML) provides a hierarchical description language for industrial systems considering both structure and properties. An extension of the established standard allows for intuitive modeling of energy optimization problems. An approved energy performance indicator (EnPI) methodology was integrated in the libraries of AML in order to simplify and shorten the modeling procedure for the optimization task. The procedure is demonstrated using the example of an industrial cooling system.

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