@article{59102, author = {{Decker, J. and Schraa, L. and Stommel, M. and Gevers, Karina and Schöppner, Volker and Töws, P. and Uhlig, K.}}, journal = {{Welding in the World}}, title = {{{Effects of different heating strategies on the joint properties during infrared welding of glass fiber reinforced polyamide 6}}}, year = {{2024}}, } @inproceedings{59140, author = {{Schöppner, Volker and Kleinschmidt, Dennis}}, booktitle = {{AZuR-Kolloquium}}, title = {{{Modifikation von Pyrolyseruß mittels Plasmaaktivierung}}}, year = {{2024}}, } @article{59133, author = {{Austermeier, Laura and Schöppner, Volker}}, journal = {{Book of Abstracts of the 5th Aspherix(R) & CFDEM(R) Conference}}, keywords = {{Compoundieren, Doppelschneckenextruder, Drehmoment, Energieeintrag}}, publisher = {{ DCS Computing GmbH, TU Graz Institut für Prozess- und Partikeltechnik}}, title = {{{The Potential of DEM Simulation for the Prediction of Torque Differences in the Melting Zone of Co-rotating Twin-Screw Extruders}}}, doi = {{10.3217/978-3-99161-020-5}}, year = {{2024}}, } @inproceedings{59123, author = {{Brüning, Florian and Landgräber, Jan}}, booktitle = {{Annual Technical Conference of the Society of Plastics Engineers (ANTEC 2024)}}, keywords = {{extrusion, Feststoffförderung, Reibwerte, Spritzgießen}}, title = {{{Material and process effects on the tribological behavior of polymer bulk materials}}}, year = {{2024}}, } @inproceedings{59128, author = {{Brüning, Florian and Schmidt, Leon}}, booktitle = {{20. Kautschuk-Herbstkolloquium (KHK)}}, editor = {{Institut für Kautschuktechnologie e.V., Deutsches}}, keywords = {{extrusion, Kautschuk, Simulation}}, title = {{{Investigation of alternative screw concepts for rubber extrusion}}}, year = {{2024}}, } @article{59104, author = {{Gevers, Karina and Schöppner, Volker and Albrecht, M. and Gehde, Michael and Seefried, A.}}, journal = {{Joining Plastics}}, pages = {{108--114}}, title = {{{Einfluss der konvektiven Erwärmung auf die Materialschädigung von Kunststoffen beim Warmgasserienschweißen}}}, year = {{2024}}, } @inproceedings{59118, author = {{Brüning, Florian and Kleinschmidt, Dennis}}, booktitle = {{15. Kautschuk Herbst Kolloquium}}, keywords = {{Rheologie, Viskosität, Wandgleiten}}, title = {{{Influence of pre-shearing on the rheological properties of filled rubber compounds}}}, year = {{2024}}, } @inproceedings{59127, author = {{Schmidt, Leon and Brüning, Florian}}, booktitle = {{Deutsche Kautschuk Tagung 2024 (DKT 2024)}}, keywords = {{extrusion, Kautschuk, Simulation}}, title = {{{Investigation of alternative pinless screw concepts for rubber extrusion}}}, year = {{2024}}, } @article{59117, author = {{Hanselle, Felix Paul and Kleinschmidt, Dennis and Brüning, Florian}}, journal = {{The Nordic Rheology Society}}, keywords = {{Druckabhängigkeit, Rheologie, Temperaturabhängigkeit, Viskosität}}, pages = {{129–140}}, title = {{{A Cost-effective Determination of Pressure - and Temperature-Dependent Viscosity of Polymers by Linking Conventional Viscosity Data to PVT Data}}}, doi = {{10.31265/atnrs.775}}, volume = {{VOL. 32}}, year = {{2024}}, } @inproceedings{59122, author = {{Brüning, Florian and Kleinschmidt, Dennis and Petzke, Jonas Dirk Rudolf Helmut}}, booktitle = {{39th International Conference of the Polymer Processing Society}}, keywords = {{Rheologie, Viskosität, Wandgleiten}}, title = {{{Improvement of an alternative method for the correction of wall slip effects in rheological studies of filled rubber compounds}}}, year = {{2024}}, } @inproceedings{59126, author = {{Petzke, Jonas Dirk Rudolf Helmut and Brüning, Florian and Kleinschmidt, Dennis}}, booktitle = {{39th International Conference of the Polymer Processing Society}}, keywords = {{Kautschuk, Mikrowelle, Simulation, Vulkanisation}}, title = {{{Simulative Approach for Predicting the Heating Behavior of Elastomers in the Solid-State Microwave Heating Process}}}, year = {{2024}}, } @inproceedings{59125, author = {{Petzke, Jonas Dirk Rudolf Helmut and Brüning, Florian and Kleinschmidt, Dennis}}, booktitle = {{Annual Technical Conference of the Society of Plastics Engineers (ANTEC 2024)}}, keywords = {{Kautschuk, Mikrowelle, Simulation, Vulkanisation}}, title = {{{SIMULATION OF MICROWAVE HEATING IN THE VULCANIZATION PROCESS OF RUBBER EXTRUDATES}}}, year = {{2024}}, } @inproceedings{59121, author = {{Brüning, Florian and Kleinschmidt, Dennis and Petzke, Jonas Dirk Rudolf Helmut}}, booktitle = {{Deutsche Kautschuk Tagung 2024 (DKT 2024)}}, keywords = {{Rheologie, Viskosität, Wandgleiten}}, title = {{{Applicability of empirical rheological transfer functions on filled rubber compounds}}}, year = {{2024}}, } @article{58388, author = {{Austermeier, Laura and Brüning, Florian and Schöppner, Volker and Schlüter, Alexander}}, journal = {{Kunststoffland NRW Report}}, number = {{03/2024}}, pages = {{24--25}}, title = {{{Kunststoffrecycling im Lehrangebot der Universität Paderborn }}}, year = {{2024}}, } @article{59243, abstract = {{Most single-screw extruders used in the plastics processing industry are plasticizing extruders, designed to melt solid pellets or powders within the screw channel during processing. In many cases, the efficiency of the melting process acts as the primary throughput-limiting factor. If the material melts too late in the process, it may not be sufficiently mixed, resulting in substandard product quality. Accurate prediction of the melting process is therefore essential for efficient and cost-effective machine design. A practical method for engineers is the modeling of the melting process using mathematical–physical models that can be solved without complex numerical methods. These models enable rapid calculations while still providing sufficient predictive accuracy. This study revisits the modified Tadmor model by Potente, which describes the melting process and predicts the delay-zone length, extending from the hopper front edge to the point of melt pool formation. Based on extensive experimental investigations, this model is adapted by redefining the flow temperatures at the phase boundary and accounting for surface porosity at the beginning of the melting zone. Additionally, the effect of variable solid bed dynamics on model accuracy is examined. Significant model improvements were achieved by accounting for reduced heat flow into the solid bed due to the porous surface structure in the solid conveying zone, along with a new assumption for the flow temperature at the phase boundary between the solid bed and melt film.}}, author = {{Schöppner, Volker and Brüning, Florian and Knaup, Felix}}, journal = {{Polymers}}, keywords = {{delay zone, extrusion, melting modeling}}, number = {{22}}, pages = {{3130}}, title = {{{Improvement in an Analytical Approach for Modeling the Melting Process in Single-Screw Extruders}}}, doi = {{10.3390/polym16223130}}, volume = {{16}}, year = {{2024}}, } @article{62236, abstract = {{AbstractDue to its excellent biocompatibility, pure iron is a very promising implant material, but often features corrosion rates that are too low. Using additive manufacturing and modified powders the microstructure and, thus, the material properties, e.g., the corrosion properties, can be tailored for specific applications. Within the scope of this study, pure iron powder was modified with different amounts of CeO2 or Fe2O3 nanoparticles and subsequently processed by Electron Beam Powder Bed Fusion (PBF-EB/M). The corrosion-fatigue behavior of CeO2 and Fe2O3 modified iron was investigated using rotation bending tests under the influence of simulated body fluid (m-SBF). While the modification using Fe2O3 showed reduced fatigue and corrosion-fatigue strengths, it could be demonstrated that the modification with CeO2 is characterized by improved fatigue properties. The superior fatigue properties in air are attributed to the positive impact of dispersion strengthening. Additionally, an increased degradation rate compared to pure iron could be observed, eventually promoting an earlier failure of the specimens in the corrosion fatigue tests.}}, author = {{Wackenrohr, Steffen and Torrent, Christof Johannes Jaime and Herbst, Sebastian and Nürnberger, Florian and Krooss, Philipp and Frenck, Johanna-Maria and Ebbert, Christoph and Voigt, Markus and Grundmeier, Guido and Niendorf, Thomas and Maier, Hans Jürgen}}, issn = {{2397-2106}}, journal = {{npj Materials Degradation}}, number = {{1}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{Corrosion fatigue behavior of nanoparticle modified iron processed by electron powder bed fusion}}}, doi = {{10.1038/s41529-024-00470-w}}, volume = {{8}}, year = {{2024}}, } @article{62767, abstract = {{Abstract In this study, we develop a novel multi-fidelity deep learning approach that transforms low-fidelity solution maps into high-fidelity ones by incorporating parametric space information into an autoencoder architecture. This method’s integration of parametric space information significantly reduces the amount of training data needed to effectively predict high-fidelity solutions from low-fidelity ones. In this study, we examine a two-dimensional steady-state heat transfer analysis within a heterogeneous materials microstructure. The heat conductivity coefficients for two different materials are condensed from a 101  $$\times $$ ×  101 grid to smaller grids. We then solve the boundary value problem on the coarsest grid using a pre-trained physics-informed neural operator network known as Finite Operator Learning (FOL). The resulting low-fidelity solution is subsequently upscaled back to a 101  $$\times $$ ×  101 grid using a newly designed enhanced autoencoder. The novelty of the developed enhanced autoencoder lies in the concatenation of heat conductivity maps of different resolutions to the decoder segment in distinct steps. Hence the developed algorithm is named microstructure-embedded autoencoder (MEA). We compare the MEA outcomes with those from finite element methods, the standard U-Net, and an interpolation approach as an upscaling technique. Our analysis shows that MEA outperforms these methods in terms of computational efficiency and error on representative test cases. As a result, the MEA serves as a potential supplement to neural operator networks, effectively upscaling low-fidelity solutions to high-fidelity while preserving critical details often lost in traditional upscaling methods, such as sharp interfaces features lost in the context of interpolation approaches.}}, author = {{Najafi Koopas, Rasoul and Rezaei, Shahed and Rauter, Natalie and Ostwald, Richard and Lammering, Rolf}}, issn = {{0178-7675}}, journal = {{Computational Mechanics}}, number = {{4}}, pages = {{1377--1406}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{Introducing a microstructure-embedded autoencoder approach for reconstructing high-resolution solution field data from a reduced parametric space}}}, doi = {{10.1007/s00466-024-02568-z}}, volume = {{75}}, year = {{2024}}, } @article{62770, abstract = {{AbstractThe open-source parameter identification tool ADAPT (A diversely applicable parameter identification Tool) is integrated with a machine learning-based approach for start value prediction in order to calibrate a Gurson–Tvergaard–Needleman (GTN) and a Lemaitre damage model. As representative example case-hardened steel 16MnCrS5 is elaborated. An artificial neural network (ANN) is initially trained by using load–displacement curves derived from simulations of a boundary value problem—instead of using data generated for homogeneous states of deformation at material point or one-element level—with varying material parameter combinations. The ANN is then employed so as to predict sets of material parameters that already provide close solutions to the experiment. These predicted parameter sets serve as starting values for a subsequent multi-objective parameter identification by using ADAPT. ADAPT allows for the consideration of input data from multiple scales, including integral data such as load–displacement curves, full-field data such as displacement and strain fields, and high-resolution experimental void data at the micro-scale. The influence of each data set on prediction quality is analyzed. Using various types of input data introduces additional information, enhancing prediction accuracy. The validation is carried out with respect to experimental void measurements of forward rod extruded parts. The results demonstrate, by incorporating void measurements in the optimization process, that it is possible to improve the quantitative prediction of ductile damage in the sense of void area fractions by factor 28 in forward rod extrusion.}}, author = {{Gerlach, Jan and Schulte, Robin and Schowtjak, Alexander and Clausmeyer, Till and Ostwald, Richard and Tekkaya, A. Erman and Menzel, Andreas}}, issn = {{0939-1533}}, journal = {{Archive of Applied Mechanics}}, number = {{8}}, pages = {{2217--2242}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{Enhancing damage prediction in bulk metal forming through machine learning-assisted parameter identification}}}, doi = {{10.1007/s00419-024-02634-1}}, volume = {{94}}, year = {{2024}}, } @article{62768, author = {{Najafi Koopas, Rasoul and Rezaei, Shahed and Rauter, Natalie and Ostwald, Richard and Lammering, Rolf}}, issn = {{0013-7944}}, journal = {{Engineering Fracture Mechanics}}, publisher = {{Elsevier BV}}, title = {{{A spatiotemporal deep learning framework for prediction of crack dynamics in heterogeneous solids: Efficient mapping of concrete microstructures to its fracture properties}}}, doi = {{10.1016/j.engfracmech.2024.110675}}, volume = {{314}}, year = {{2024}}, } @unpublished{56289, author = {{Seeger, Karl and Genovese, Matteo and Schlüter, Alexander and Kockel, Christina and Corigliano, Orlando and Díaz Canales, Edith Benjamina and Fragiacomo, Petronilla and Praktiknjo, Aaron}}, booktitle = {{United States Association for Energy Economics (USAEE) & International Association for Energy Economics (IAEE) Research Paper Series}}, publisher = {{Elsevier BV}}, title = {{{Evaluating Supply Scenarios for Hydrogen and Green Fuels from Canada, Chile, and Algeria to Germany via a Techno-Economic Assessment}}}, year = {{2024}}, }