Now showing 1 - 10 of 25
  • Publication
    Production environment of tomorrow (ProMo)
    Small defects in the grain or major damage to a moulded part or tool can bring production to a standstill. SMEs in particular have neither the personnel nor the equipment to repair such damage on their own, so they send it to specialised contractors. The repair process is carried out manually, depending on the accuracy requirements, and is usually completed by a finishing process. This work requires qualified personnel and, at the same time, requires a lot of time in case of larger damages. In this paper we present a way to map the Maintenance, Repair and Operations (MRO) process chain in a partially automated manner. The symbiosis of individual technologies results in a significantly increased efficiency of the MRO process chain, which continues to focus on people and their process knowledge. While Directed Energy Deposition (DED) for the MRO of moulded parts is used widely, usually a high manual effort in measuring the component geometries and teaching of the machine tool paths is necessary. However, there are clear advantages compared to the manufacture of new parts or manual laser welding repair. At the same time, the resource and energy requirements can often be significantly reduced compared to new part production. ProMo focuses on automating the time-consuming machine programming by reducing the number of necessary work steps in CAD/CAM-based program creation. Based on a subsequent robot-guided scan, a digital actual 3D model is generated. Due to intelligent path planning algorithms, no manual programming of the robot is necessary and at the same time it is possible to detect components of different sizes, shapes and covers in this system with a minimum of effort. In addition, the operator passes on elementary information, such as the approach path of the milling head, to the subsequent processes by means of finger gestures and can thus significantly reduce tedious CAM programming steps. Now, the scanned component is transferred to a 3D-CAD model and a target/actual comparison is created for the damaged areas. Those are milled out in a defined manner and then restored using DED.
  • Publication
    Analysis and recycling of bronze grinding waste to produce maritime components using directed energy deposition
    ( 2021) ;
    Marko, Angelina
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    Kruse, Tobias
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    ;
    Additive manufacturing promises a high potential for the maritime sector. Directed Energy Deposition (DED) in particular offers the opportunity to produce large-volume maritime components like propeller hubs or blades without the need of a costly casting process. The post processing of such components usually generates a large amount of aluminum bronze grinding waste. The aim of the presented project is to develop a sustainable circular AM process chain for maritime components by recycling aluminum bronze grinding waste to be used as raw material to manufacture ship propellers with a laser-powder DED process. In the present paper, grinding waste is investigated using a dynamic image analysis system and compared to commercial DED powder. To be able to compare the material quality and to verify DED process parameters, semi-academic sample geometries are manufactured.
  • Publication
    Inertial Measurement Unit based Human Action Recognition for Soft-Robotic Exoskeleton
    ( 2021) ;
    Burgdorff, Moritz
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    Filaretov, Hristo
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    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. Exoskeletons can present a solution. Typically, such systems struggle with stiffness and discomfort and primarily a lack of battery lifetime. Soft-robotic exoskeletons offer a possibility to overcome these problems by increasing the system flexibility, not limiting the supported DoF and being actuator and joint together. Since soft-robotic exoskeletons can be designed only using power when supporting the wearer, it is possible to increase the battery lifetime by only acting on those actions for which the wearer needs support. Dealing with controls for soft-robotic exoskeleton one major difficulty is to find a compromise between saving energy and supporting the wearer. Having an action-depending control can reduce the supported actions to cover only relevant ones and increase the lifetime of the battery. The system conditions are to detect the user actions in real-time and distinguish between actions which require support and those which do not. We contribute an analysis and modification of human action recognition(HAR) benchmark algorithms from activities of the daily living, transferred them onto industrial use cases containing short and mid-term action and reduce the models to be compatible using embedded computers for real-time recognition on soft exoskeletons. We identified the most common challenges for inertial measurement units based HAR and compare the best-performing algorithms using a newly recorded data set overhead car assembly for industrial relevance. As a benchmark data set we focused on the "Opportunity" data set. By introducing orientation estimation, we were able to increase the F1 scores by up to 0.04. With an overall F1 score without a Null-class of up to 0.883, we were able to lay the foundation to use HAR for action dependent force support.
  • Publication
    Model-Based Systems Engineering (MBSE) as computer-supported approach for cooperative systems development
    ( 2020)
    Schmidt, Marvin M.
    ;
    Stark, Rainer
    With rising globalization and a trend towards Cyberphysical systems (CPSs) as well as smart products the demand for cross-company and interdisciplinary collaboration increases. To handle the complexity of these systems and products Model-Based Systems Engineering (MBSE), as an enhanced form of Systems Engineering (SE), has emerged in engineering and is adopted by many companies. While this approach tries to cope with the current complexity trends, it does address the collaborative aspect of product creation only in a small scope. This paper shall address the combination of MBSE and collaboration in engineering to form a computer-supported approach for collaborative systems development.
  • Publication
    Automated tool-path generation for rapid manufacturing and numerical simulation of additive manufacturing LMD geometries
    ( 2019) ;
    Wang, Jiahan
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    Graf, Benjamin
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    In additive manufacturing (AM) Laser Metal Deposition (LMD), parts are built by welding layers of powder feedstock onto a substrate. Applications for steel powders include forging tools and structural components for various industries. For large parts, the choice of tool-paths influences the build-rate, the part performance and the distortions in a highly geometry-dependent manner. With weld-path lengths in the range of hundreds of meters, a reliable, automated tool path generation is essential for the usability of LMD processes. In this contribution, automated tool-path generation approaches are shown and their results are discussed for arbitrary geometries. The investigated path strategies are the classical approaches: ""Zig-zag-"" and ""contour-parallel-strategies"". After generation, the tool-paths are automatically formatted into g-code for experimental build-up and ASCII for a numerical simulation model. Finally, the tool paths are discussed in regards to volume-fill, microstructure and porosity for the experimental samples. This work presents a part of the IGF project 18737N ""Welding distortion simulation"" (FOSTA P1140)
  • Publication
    A Process Model for Enhancing Digital Assistance in Knowledge-Based Maintenance
    ( 2019)
    Kovacs, Klaudia
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    Ansari, Fazel
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    Uhlmann, E.
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    Glawar, Robert
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    Sihn, Wilfried
    Digital transformation and evolution of integrated computational and visualisation technologies lead to new opportunities for reinforcing knowledge-based maintenance through collection, processing and provision of actionable information and recommendations for maintenance operators. Providing actionable information regarding both corrective and preventive maintenance activities at the right time may lead to reduce human failure and improve overall efficiency within maintenance processes. Selecting appropriate digital assistance systems (DAS), however, highly depends on hardware and IT infrastructure, software and interfaces as well as information provision methods such as visualization. The selection procedures can be challenging due to the wide range of services and products available on the market. In particular, underlying machine learning algorithms deployed by each product could provide certain level of intelligence and ultimately could transform diagnostic maintenance capabilities into predictive and prescriptive maintenance. This paper proposes a process-based model to facilitate the selection of suitable DAS for supporting maintenance operations in manufacturing industries. This solution is employed for a structured requirement elicitation from various application domains and ultimately mapping the requirements to existing digital assistance solutions. Using the proposed approach, a (combination of) digital assistance system is selected and linked to maintenance activities. For this purpose, we gain benefit from an in-house process modeling tool utilized for identifying and relating sequence of maintenance activities. Finally, we collect feedback through employing the selected digital assistance system to improve the quality of recommendations and to identify the strengths and weaknesses of each system in association to practical use-cases from TU Wien Pilot-Factory Industry 4.0.
  • Publication
    Identification of Human Dynamics in User-Led Physical Human Robot Environment Interaction
    ( 2018) ;
    Surdilovic, Dragoljub
    Human dynamic models are useful in design of physical human-robot and human-robot-environment interaction: informing choice of robot impedance, motivating relaxations to passivity-based safety constraints, and allowing online inference to user intent. Designing for performance objectives such as stable well-damped contact transitions also requires nominal models, but the use of human models in controller design is limited. Established approaches to identify human dynamics apply position or force perturbation and measure the corresponding response, mostly to validate neuromuscular hypotheses on motor control, which raises questions about their transferability to human-led collaboration. Here, human dynamics are identified in a task which closely resembles the final application, where the human leads the robot into contact with a (virtual) wall. This paper investigates the impact of human dynamics on coupled system behavior, and establishes a general framework for identification in human-led scenarios, making consideration of unmeasured human input. Experiments with different stiffness environments allow inference to human dynamics, and characterize the range of human dynamics which can be modulated by the user.
  • Publication
    Quantification and compensation of systematic errors in pressure measurements applied to oil pipelines
    ( 2018)
    Thiele, Gregor
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    Liu, Martin
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    Chemnitz, Moritz
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    The monitoring of pipeline operation is an important research topic, especially for the detection and localization of leaks as well as for an efficient control. For these purposes, physical quantities in pipelines are calculated from measurement data on the basis of a mathematical model. In contrast to static models, adaptive models vary their parameters or even their structure to reach the most probable solution. But in most cases, even the best fit will hold residuals caused by discrepancies between the real system and its model. These residuals allow an estimation of travel-time delays of pressure waves and offsets in pressure values. The basic idea of our approach is to interpret these systematic, time-invariant errors of pressure measurements in pipelines either as sensor displacements or as technical defects. The proposed procedure leads to a hypothesis for a model update, regarding the sensor positions. This displacement compensation as well as a variance analysis was successfully applied to real data from a crude oil pipeline in Europe. A cross validation proves the general capability of the developed method to reduce the uncertainties.