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    Item type:Research Data, Access status: with files ,
    Supplementary Dataset for: Identifying process windows of aluminum alloys in laser powder bed fusion using machine learning
    (2026-08-05)
    Kuehne, Maxim
    ;
    Bossen, Bastian
    ;
    Kristanovski, Oleg
    ;
    Jäger, Sebastian
    ;
    Kelbassa, Ingomar
    The corresponding paper investigates a novel machine learning (ML) based approach for the identification of process windows for aluminum based alloys. The metal additive manufacturing (AM) sector is limited by the scarcity of certified materials. Developing new materials is costly and time-consuming due to the complexity of AM processes where numerous parameters impact the part's quality. The goal of this study is using simulation and empirical data of AlSi10Mg and an alloy from the Al5000 series (AlMgty80) to train ML models to predict part density in laser powder bed fusion of metals. The models are used to predict process windows for a new alloy from the Al7000 series (AlZnty) to identify the potential of recognising process windows prior to empirical testing. By integrating simulated data, material influences are indirectly accounted for in the dataset. Various ML algorithms were trained and evaluated using root mean squared error (RMSE), R², and qualitative process map assessments. Results show that ML models based on AlSi10Mg and AlMgty80 data accurately predict the density for the respective materials, achieving a RMSE around 1% and R² close to 0.9. Predictions for AlZnty showed larger errors (RMSE 3 – 7 %, R² near zero), yet the qualitative evaluation of process maps show that high-density regions align with experimental data due to the model systematically underestimating the density. Overall, the methodology demonstrates the ability to identify process windows for novel alloys using simulations and ML models based on similar alloys, despite limitations in density prediction accuracy. Future work will involve training models including AlZnty data to iteratively enhance accuracy. Furthermore, limited data availability continues to challenge ML in AM, highlighting the need for extensive databases.
    Data Type: Dataset
      12  12
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    Item type:Research Data, Access status: with files ,
    Data supplement for publication: Comprehensive study of 3D liquid flow fields in additively manufactured structures for SMART reactors using large-scale vertical magnetic resonance imaging and computational fluid dynamics
    (2026-05-07)
    Merbach, Timo Alexander
    ;
    Acikgöz, Serhan
    ;
    Wigger, Christoph
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    Iraqi Houssaini, Selma
    ;
    Adrian, Muhammad
    The TUHH Open Research (TORE) repository contains raw and processed data associated with the publication "Comprehensive study of 3D liquid flow fields in additively manufactured structures for SMART reactors using large-scale vertical magnetic resonance imaging and computational fluid dynamics". The dataset comprises experimental magnetic resonance imaging velocimetry data, numerical results from OpenFOAM simulations, and corresponding design files. The publication investigates the flow behaviour in additively manufactured TPMS structures using a large-bore vertical 3 T MRI system. Cross-validation between experimental measurements and numerical simulations is performed. Three different TPMS geometries are analysed within the Darcy-Forchheimer regime. The folder is accompanied by a ReadMe file and a separate folder structure file, which provide detailed information on file organisation, naming conventions, and their relation to the reported results.
    Data Type: Experimental Data ; Data Type: Simulation Data
      67  28
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    Item type:Research Data, Access status: with files ,
    Supplementary material to publication with title: Design Guidelines for Laser Powder Bed Fusion of Triply Periodic Minimal Surface Structures for Applications in Smart Reactors. - Project: SFB1615 - Subproject: C01-Integration of components into adaptive geometries
    (2026-01-22)
    Acikgöz, Serhan
    ;
    Merbach, Timo Alexander
    ;
    Wigger, Christoph
    This study investigates the design and manufacturability of TPMS structures using 316L stainless steel via PBF-LB/M, focusing on the interaction of key parameters: porosity, unit cell size, and sheet thickness, of which two are independent variables while the third is a dependent variable. Through numerical simulations, experimental validation, and process optimization, practical design guidelines are developed. The design parameters of Gyroid-TPSf and Schwarz-Diamond-TPSf samples include porosities ranging from 70 % to 90 % and unit cell sizes from 2 mm to 20 mm. The results indicate that specifically, at large unit cell sizes (e.g., 20 mm), the decreased curvature radius reduces self-supporting effects, leading to insufficient mechanical stability during printing and resulting in local deformation. Conversely, at small unit cell sizes combined with high porosity levels (e.g., 2 mm and 90 %), the sheet thickness becomes critically thin, often below the printable resolution, resulting in incomplete or fragile structures.
    Data Type: Dataset
      108  33
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    Item type:Research Data, Access status: with files ,
    Supplementary material to publication with title: Design guidelines for multi-material parts manufactured in the material extrusion of metals process
    (2025-10-02)
    Hünting, Jan
    Multi-material additive manufacturing (MMAM) enables the integration of different materials within a single component, allowing for tailored functionality and the reduction of process steps. Among available metal additive manufacturing (AM) techniques, material extrusion of metals (MEX/M) offers a cost effective and feasible approach for the production of multi-material parts using a commercial feedstock. This study focuses on the fabrication of green parts composed of pure copper (Cu) and Inconel 718 (IN718), selected for their highly complementary properties: Cu offers excellent thermal conductivity, while IN718 provides outstanding mechanical strength, as well as high corrosion and oxidation resistance. These characteristics make the Cu-IN718 combination particularly attractive for demanding aerospace applications, such as heat exchangers and combustion chambers. A Design of Experiments (DoE) approach using Response Surface Methodology (RSM) was employed to evaluate the influence of key printing parameters, such as nozzle temperature, printing speed, extrusion multiplier, and layer thickness on the density and dimensional accuracy of green part test specimens. Optimal process parameter sets for each individual material were identified and a corresponding parameter set for the multi material combination was subsequently derived. Different geometric features, various material topologies and two interlocking geometries were analyzed to assess the print quality, and geometric accuracy of multi-material prints. The results were used to derive a set of design guidelines aimed at the manufacturability, reliability, and quality of Cu-IN718 green parts using MEX/M. These guidelines contribute to future standardization efforts in multi material extrusion, thus supporting more robust and reproducible production of components via metal AM.
    Data Type: Dataset
      59  42
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    Item type:Research Data, Access status: with files ,
    Supplementary material to publication with title: SMART-Reactors : tailoring gas holdup distribution by additively manufactured lattice structures
    (2025-04-02)
    Maiwald, Maria Isabelle
    ;
    Spille, Claas
    The files are used to show the possibility of influencing the local gas holdup and bubble size distribution in a gas-liquid process using additively manufactured lattice structures (AMLS). The .stl file can be used to produce the AMLS.
    Data Type: Dataset
      28  83
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    Item type:Research Data, Access status: with files ,
    Cost Application for the Publication: Methodology for Cost Estimation Using Characteristic Factors in Additive Manufacturing
    (2024-08-27)
    Asami, Mohammad Karim
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    Herzog, Dirk
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    Deutschmann, Torben
    ;
    Röver, Tim
    ;
    Kelbassa, Ingomar
    A cost tool was created from the mathematical equations and relationships, which determines the costs for the additive manufacturing processes depending on the number and dimensions of the component. Furthermore, weight reductions are specified in percent for known conventional costs that are required to achieve cost parity to the conventional manufacturing method. Therfore, this application allow an easy and fast estimation of suitable additive manufacturing technologies denpending on the selected material, dimension and quantity of a component. With the knowledge of the manufacturing restriction of the each technology this tool deliver a great estimation in finding the right application. It is also possible to adjust the costs and add different machines to this tool, which enables the comparison between technologies.
    Data Type: Research Software
      108  36
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    Item type:Research Data, Access status: with files ,
    Supplementary material to article with title: Development and Assessment of a Methodology for Abstraction of Topology Optimization Results to Enable the Substitution of Optimized Beams
    (2023-10-10)
    Röver, Tim
    ;
    Bader, Maximilian
    ;
    Asami, Mohammad Karim
    ;
    Emmelmann, Claus
    ;
    Kelbassa, Ingomar
    The data is supplementary material to the article with title "Development and Assessment of a Methodology for Abstraction of Topology Optimization Results to Enable the Substitution of Optimized Beams" of the International Congress on Applications of Lasers & Electro-Optics (ICALEO) 2023, 16-19 October 2023, Chicago, Illinois, United States. A more detailed background on the files is given in the article. Mph-files can be opened with the software COMSOL Multiphysics 6.0. Implemented submethodologies can be viewed using the Application Builder of the Software.
    Data Type: Simulation Data
      168  115