Dr. Orkun Furat

Semester	Veranstaltung	Rolle
WS 2024/25	Seminar: Generatives maschinelles Lernen: Anwendungen neuronaler Netze bei räumlicher stochastischer Modellierung	Dozent
WS 2023/24	Punktprozesse	Dozent
SS 2023	Seminar: Multivariate stochastic modeling: Classical approaches and methods of machine learning	Dozent
WS 2022/23	Advanced Statistics	Dozent
SS 2021	Seminar: Spatial stochastic modeling, supported by methods of machine learning	Begleitender Dozent
WS 2020/21	Markov Ketten	Übungsleiter
SS 2019	Punktprozesse	Übungsleiter
SS 2018	Punktprozesse	Übungsleiter

• O. Furat, T. Leißner, R. Ditscherlein, O. Sedivy, M. Weber, K. Bachmann, J. Gutzmer, U. Peuker and V. Schmidt, Description of ore particles from XMT images, supported by SEM-based image analysis. Microscopy and Microanalysis 24 (2018), 461-470.
• M. Neumann, O. Furat, D. Hlushkou, U. Tallarek, L. Holzer and V. Schmidt, On microstructure-property relationships derived by virtual materials testing with an emphasis on effective conductivity. In: M. Baum, G. Brenner, J. Grabowski, T. Hanschke, S. Hartmann, and A. Schöbel (eds.), Simulation Science: Proceedings of the Clausthal-Göttingen International Workshop on Simulation Science, Göttingen 2017. Communications in Computer and Information Science (CIS), Springer 2018, pp. 145-158.
• B. Prifling, O. Furat, D. Schmidt, H. Markötter, I. Manke, T. Bernthaler, V. Knoblauch and V. Schmidt, 3D-Mikrostrukturanalyse von Elektroden in Lithium-Ionen-Batterien. DGM-Jahresmagazin Materialographie 2018, pp. 88-91.
• C. Baldermann, A. Baldermann, O. Furat, M. Krüger, M. Nachtnebel, H. Schroettner, J. Juhart, V. Schmidt and J. Tritthart, Mineralogical and microstructural response of hydrated cement blends to leaching. Construction & Building Materials 229 (2019), 116902.
• O. Furat, T. Leißner, K. Bachmann, J. Gutzmer, U. Peuker and V. Schmidt, Stochastic modeling of multidimensional particle characteristics using parametric copulas. Microscopy and Microanalysis 25 (2019), 720-734.
• O. Furat, B. Prifling, D. Westhoff, M. Weber and V. Schmidt, Statistische Analyse und Modellierung von komplexen Partikelsystemen in 3D mittels tomographischer Bilddaten. Practical Metallography 56 (2019), 787-796.
• O. Furat, M.Y. Wang, M. Neumann, L. Petrich, M. Weber, C.E. Krill III and V. Schmidt, Machine learning techniques for the segmentation of tomographic image data of functional materials. Frontiers in Materials 6 (2019), 145.
• O. Furat, M. Masuhr, F.E. Kruis and V. Schmidt, Stochastic modeling of classifying aerodynamic lenses for separation of airborne particles by material and size. Advanced Powder Technology 31 (2020), 2215-2226.
• R. Ditscherlein, O. Furat, M. de Langlard, J. Martins de Souza e Silva, J. Sygusch, M. Rudolph, T. Leißner, V. Schmidt and U.A. Peuker, Multi-scale tomographic analysis for micron-sized particulate samples. Microscopy and Microanalysis 26 (2020), 676-688.
• J. Schreier, O. Furat, M. Cankaya, V. Schmidt and U. Bröckel, Automated evaluation of contact angles in a three-phase system of selective agglomeration in liquids. Image Analysis & Stereology 20 (2020), 179-188.
• O. Furat, D. Finegan, D. Diercks, F. Usseglio-Viretta, K. Smith and V. Schmidt, Mapping the architecture of single electrode particles in 3D, using electron backscatter diffraction and machine learning segmentation. Journal of Power Sources 483 (2021), 229148.
• O. Furat, L. Petrich, D. Finegan, D. Diercks, F. Usseglio-Viretta, K. Smith and V. Schmidt, Artificial generation of representative single Li-ion electrode particle architectures from microscopy data. npj Computational Materials 7 (2021), 105.
• O. Furat, A. Baldermann, C. Baldermann, M. Dietzel and V. Schmidt, Quantitative assessment of microstructural changes of hydrated cement blends due to leaching and carbonation, based on statistical analysis of image data. Construction and Building Materials 302 (2021), 124370.
• O. Furat, U. Frank, M. Weber, S. Wawra, W. Peukert and V. Schmidt, Estimation of bivariate probability distributions of nanoparticle characteristics, based on univariate measurements. Inverse Problems in Science and Engineering 29 (2021), 1343-1368.
• J. Allen, P.J. Weddle, A. Verma, A. Mallarapu, F. Usseglio-Viretta, D.P. Finegan, A. Colclasure, W. Mai, V. Schmidt, O. Furat, D. Diercks, T. Tanim and K. Smith, Quantifying the influence of charge rate and cathode-particle architectures on degradation of Li-ion cells through 3D continuum-level damage models. Journal of Power Sources 512 (2021), 230415.
• L. Petrich, O. Furat, M.Y. Wang, C.E. Krill III and V. Schmidt, Efficient fitting of 3D tessellations to curved polycrystalline grain boundaries. Frontiers in Materials 8 (2021), 507.
• R. Ditscherlein, O. Furat, E. Löwer, R. Mehnert, R. Trunk, T. Leißner, M.J. Krause, V. Schmidt and U.A. Peuker, PARROT - A pilot study on the open access provision of particle discrete tomographic datasets. Microscopy and Microanalysis 28 (2022), 350-360.
• O. Furat, D.P. Finegan, Z. Yang, T. Kirstein, K. Smith and V. Schmidt, Super-resolving microscopy images of Li-ion electrodes for fine-feature quantification using generative adversarial networks. npj Computational Materials 8 (2022), 68.
• O. Furat, D.P. Finegan, Z. Yang, T. Tanim, K. Smith and V. Schmidt, Quantifying the Impact of Charge Rate and Number of Cycles on Structural Degeneration of Li-Ion Battery Electrodes. Journal of The Electrochemical Society 169 (2022), 100541.
• S. Englisch, R. Ditscherlein, T. Kirstein, L. Hansen, O. Furat, D. Drobek, T. Leißner, B. Apeleo Zubiri, A.P. Weber, V. Schmidt, U.A. Peuker and E. Spiecker, 3D analysis of equally X–ray attenuating mineralogical phases utilizing a correlative tomographic workflow across multiple length scales. Powder Technology 419 (2023), 118343.
• T. Wilhelm, J. Sandbrink, O. Furat, K. Bachmann, M. Rudolph and V. Schmidt, Parametric stochastic modeling of particle descriptor vectors for studying the influence of ultrafine particle wettability and morphology on flotation-based separation behavior. Powders 2 (2023), 353-371.
• O. Furat, D.P. Finegan, Z. Yang, M. Neumann, S. Kim, T.R. Tanim, P. Weddle, K. Smith and V. Schmidt, Quantifying the impact of operating temperature on cracking in battery electrodes, using super-resolution of microscopy images and stereology. Energy Storage Materials 64 (2024), 103036.
• O. Furat, T. Kirstein, T. Leißner, K. Bachmann, J. Gutzmer, U.A. Peuker und V. Schmidt, Multidimensional characterization of particle morphology and mineralogical composition using CT data and R-vine copulas. Minerals Engineering 206 (2024), 108520.
• P. Gräfensteiner, J. Friebel, L. Ditscherlein, O. Furat, U. A. Peuker and V. Schmidt, An AFM-based approach for quantification of guest particle deformation during mechano-fusion. Powder Technology 434 (2024), 119293.
• W.-X. Chen, J. M. Allen, S. Rezaei, O. Furat, V. Schmidt, A. Singh, P. J. Weddle, K. Smith and B.-X. Xu, Cohesive phase-field chemo-mechanical simulations of inter- and trans-granular fractures in polycrystalline NMC cathodes via image-based 3D reconstruction. Journal of Power Sources 596 (2024), 234054.
• L. Fuchs, T. Kirstein, C. Mahr, O. Furat, V. Baric, A. Rosenauer, L. Mädler and V. Schmidt, Using convolutional neural networks for stereological characterization of 3D hetero-aggregates based on synthetic STEM data. Machine Learning: Science and Technology 5 (2024), 025007.
• T. Kirstein, S. Aßmann, O. Furat, S. Will and V. Schmidt, Determination of droplet size from wide-angle light scattering image data using convolutional neural networks. Machine Learning: Science and Technology 5 (2024), 015049.
• T. Wilhelm, T. Vo, O. Furat, U.A. Peuker and V. Schmidt, Virtual reassembling of 3D fragments for the data-driven analysis of fracture mechanisms in multi-component materials. Computational Materials Science 242 (2024), 113065.
• E. Schach, T. Buchwald, O. Furat, F. Tischer, A. Kaas, L. Kuger, M. Masuhr, J. Sygusch, T. Wilhelm and U. A. Peuker, Progress in the Application of Multidimensional Particle Property Distributions: The Separation Function. KONA Powder and Particle Journal (2024) 2025011.
• J. Sygusch, T. Wilhelm, O. Furat, K. Bachmann, V. Schmidt and M. Rudolph, Application of multivariate Tromp functions for evaluating the joint impact of particle size, shape and wettability on the separation of ultrafine particles via flotation. Powders 3 (2024), 338-366.
• F. Strube, T. Wilhelm, J. Sygusch, B.M. Guy, O. Furat, V. Schmidt and M. Rudolph, Investigating the influence of particle size and shape on froth-flotation-based beneficiation of lithium-rich minerals in slags. Proceedings of the International Mineral Processing Congress IMPC2024, National Harbor, Washington, DC (2024), 3167-3175.
• J. Sygusch, T. Wilhelm, O. Furat, V. Schmidt and M. Rudolph, Investigating the multidimensional separation behavior of particles in a cyclosizer setting - A case study on calcite, fluorite and magnesite. Proceedings of the International Mineral Processing Congress IMPC2024, National Harbor, Washington, DC (2024), 2073-2083.
• P. Gräfensteiner, E. Löwer, O. Furat, U.A. Peuker and V. Schmidt, Artificial filter cake generation: Digital twins via stochastic 3D modeling based on micro-CT image data. Powder Technology 452 (2025), 120550.
• L. Fuchs, O. Furat, D.P. Finegan, J. Allen, F.L.E. Usseglio-Viretta, B. Ozdogru, P.J. Weddle, K. Smith and V. Schmidt, Generating multi-scale Li-ion battery cathode particles with radial grain architectures using stereological generative adversial networks. Communications Materials (in print).
• W.-X. Chen, X.-L. Peng, J.-Y. Wu, O. Furat, V. Schmidt and B.-X. Xu, A length-scale insensitive cohesive phase-field interface model: Application to concurrent bulk and interface fracture simulation in Lithium-ion battery materials. Journal of the Mechanics and Physics of Solids (in print).

• P. Rieder, O. Furat, F. Usseglio-Viretta, J. Allen, P. Weddle, D.P. Finegan, K. Smith and V. Schmidt, Stochastic 3D reconstruction of cracked polycrystalline NMC particles using 2D SEM data. arXiv preprint arXiv:2410.12020 (2024).
• N. Eiermann, O. Furat, J. Nicklas, U.A. Peuker and V. Schmidt, Quantitative characterization of hydrophobic agglomeration at different mixing intensities using a copula-based probabilistic modeling approach. Preprint (submitted)
• I.V. Curato, O. Furat, L. Proietti and B. Stroeh. Mixed moving average field guided learning for spatio-temporal data. arXiv preprint arXiv:2301.00736v3 (2023).
• S Weber, O. Furat, T. Kirstein, T. Leißner, U.A. Peuker and V. Schmidt, Computational workflow for the characterization of size, shape and composition of particles and their separation behavior during processing. Preprint (submitted)