Now showing 1 - 5 of 5
  • Publication
    Taxonomy for Biological Transformation Principles in the Manufacturing Industry
    ( 2023)
    Berkhahn, Magda
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    Kremer, Gerald
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    ; ;
    Stark, Rainer
    Industry and research are seeking answers to current demands in industrial value creation, like resilience of production, sufficient product quality and sustainability of products and processes. A novel line of thought, seeking the accomplishment of those is the Biological Transformation (BT). BT describes the interweaving of biological modes of action, materials and organisms with engineering and information sciences. The conflation of disciplines from natural, technical and social sciences yields in a heterogeneous field of activities with ambiguous technical terms. An ascertainment of principles of BT is required to classify yet undifferentiated patterns in nature-based production, facilitating their systematic implementation in aiming for sustained solutions on current challenges. With increasing research in biomimetic, attempts arise to capture nature‑based activities in manufacturing through schematic classifications. Yet, basic semantics representing the effective principles of BT in the manufacturing industry is lacking. The goal of this publication is to introduce a taxonomy of Biological Transformation in manufacturing based on its core principles Bio Inspiration, Bio Integration and Bio Interaction. Within the research project BioFusion 4.0, the taxonomy was developed and applied to classify technology innovations. The paper presents the taxonomy, its development and application in use cases.
  • Publication
    Analyse und Nutzung von Aluminium-Bronze-Schleifstaub für das Laser-Pulver-Auftragsschweißen
    ( 2022-12) ;
    Marko, Angelina
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    Kruse, Tobias
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    ;
    Rethmeier, Michael
    Die additive Fertigung verspricht ein großes Potenzial für den maritimen Sektor. Insbesondere Directed Energy Deposition (DED) Verfahren bieten die Möglichkeit, großvolumige maritime Bauteile wie Propellernaben oder -schaufeln herzustellen. Bei der Nachbearbeitung solcher Bauteile fällt in der Regel eine große Menge an Schleifabfällen an. Ziel des vorgestellten Projekts ist die Entwicklung einer nachhaltigen zirkulären AM-Prozesskette für maritime Komponenten auf Basis von Aluminiumbronze-Schleifresten. Dazu soll das Material wiederaufbereitet und anschließend als Rohmaterial für die Herstellung von Schiffspropellern im Laser-Pulver DED-Verfahren verwendet werden. In der vorliegenden Arbeit werden Schleifabfälle mittels dynamischer Bildanalyse untersucht und mit kommerziellem DED-Pulver verglichen. Anschließend werden Probengeometrien aus Schleifstaub gefertigt und durch metallographische Schliffe und REM/EDX analysiert.
  • Publication
    Transferability of ANN-generated parameter sets from welding tracks to 3D-geometries in Directed Energy Deposition
    ( 2022-11-04)
    Marko, Angelina
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    Bähring, Stefan
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    Raute, Maximilian Julius
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    ;
    Directed energy deposition (DED) has been in industrial use as a coating process for many years. Modern applications include the repair of existing components and additive manufacturing. The main advantages of DED are high deposition rates and low energy input. However, the process is influenced by a variety of parameters affecting the component quality. Artificial neural networks (ANNs) offer the possibility of mapping complex processes such as DED. They can serve as a tool for predicting optimal process parameters and quality characteristics. Previous research only refers to weld beads: a transferability to additively manufactured three-dimensional components has not been investigated. In the context of this work, an ANN is generated based on 86 weld beads. Quality categories (poor, medium, and good) are chosen as target variables to combine several quality features. The applicability of this categorization compared to conventional characteristics is discussed in detail. The ANN predicts the quality category of weld beads with an average accuracy of 81.5%. Two randomly generated parameter sets predicted as “good” by the network are then used to build tracks, coatings, walls, and cubes. It is shown that ANN trained with weld beads are suitable for complex parameter predictions in a limited way.
  • Publication
    Gear Wheel Finishing with Abrasive Brushing Tools to Improve the Surface Quality of Tooth Flanks for the Industrial Application
    ( 2022)
    Gülzow, Bernhard
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    A high surface quality of tooth flanks can improve the service life and the performance of gears, as well as reduce acoustic emissions. However, high demands on the gear geometry pose a challenge for the finishing of tooth flank surfaces because the dimensional accuracy that can be achieved with modern grinding processes must not be impaired by the finishing process. A preceding study has shown fundamentally that profiled abrasive brushing tools can be used to improve the quality of individual tooth flank surfaces. Due to the integration into the grinding machine, it represents a promising alternative to common finishing applications. Before the process can be used in an industrial environment, process reliability and tool life must be examined. For this purpose, complete reference gearwheels (39 × 10) were finished with the brushing tools. It could be shown that the surface roughness can be reliably reduced by ΔRa ≈ 0.2 µm by using a single brush for an entire gearwheel without changing the gear geometry. In addition to the influence of the tool specifications on the work result, the influence of the initial roughness after grinding was considered in particular. It was found that the achievable surface roughness depends significantly on the depth of the grinding grooves, as these are retained as desired, while the roughness peaks are fully smoothed. Furthermore, a device for the machine-integrated profiling and dressing of brushing tools was successfully designed, implemented, and tested.
  • Publication
    Prediction of the Roughness Reduction in Centrifugal Disc Finishing of Additive Manufactured Parts Based on Discrete Element Method
    ( 2022)
    Kopp, Marco
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    One major drawback of additive manufacturing is the poor surface quality of parts, which negatively affects mechanical and tribological properties. Therefore, a surface finishing is necessary in most cases. Due to a high material removal rate, centrifugal disc finishing is a promising mass finishing operation for an effective surface finishing of additive manufactured parts. However, due to machining the workpieces in a freely movable manner, the process is hardly controllable, and the process design is often based on time-consuming and cost-intensive trial-and-error approaches. Especially when it comes to the machining of complex-shaped workpieces, finishing results are barely predictable. Therefore, the aim of this study is to set up a numerical simulation of the centrifugal disc finishing based on the Discrete Element Method (DEM) to predict finishing results. A procedure to determine the required DEM input parameters is presented and the simulation was validated using a freely movable force sensor. The results of the finishing experiments with additive manufactured workpieces made of Ti-6Al-4V were correlated with the simulated results. The derived correlation was used to predict local differences in the roughness reduction, which occurred when finishing workpieces with a limited accessibility to the surface. As a result, it is concluded that the complex relationship between the type of media, the accessibility to the surface, and the achievable finishing results can be modeled using the DEM.