TU Bergakademie Freiberg Data Publications

Permanent URI for this collectionhttp://opara.zih.tu-dresden.de/handle/123456789/21

Data publications from research of Freiberg University of Mining and Technology.

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Browsing TU Bergakademie Freiberg Data Publications by Subject "4::42::403::403-03"

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  • ItemOpen Access
    2D Imaging Simulation from 3D Particle Data: Extended Python Notebooks, Simulation Results
    (Technische Universität Bergakademie Freiberg, 2024-07-30) Buchwald, Thomas
    2D projection imaging techniques are simulated with 3D particle data from tomography measurements. This submission contains two distinct parts: Python notebooks to simulate 2D imaging of the particles, and the resulting simulation dataset. This dataset extends a previous one (https://doi.org/10.25532/OPARA-479) by an additional method of particle surface area determination and a larger dataset of randomly oriented projections – ten instead of the previous three. The Python notebooks were created with Jupyter Lab and Anaconda. A environment.yml file is provided that recreates the Anaconda environment. The simulation dataset that results from the provided Python notebooks is made available as CSV files or as pickled Python (pandas) DataFrames. Please refer to the included Readme for a detailed description of the files contained in the archive.
  • ItemOpen Access
    2D Imaging Simulation from 3D Particle Data: Python Notebooks, Particle Datasets, and Simulation Results
    (Technische Universität Bergakademie Freiberg, 2024-05-14) Buchwald, Thomas; Ditscherlein, Ralf
    2D projection imaging techniques are simulated with 3D particle data from tomography measurements. This submission contains three distinct parts: original particle data, Python notebooks to simulate 2D imaging of the particles, and the resulting simulation dataset. The particle data comes as STLs that have been converted from VTK particle data as provided by the PARROT database (https://parrot.tu-freiberg.de/). The STL files are *not* identical with the STLs provided by PARROT as of May 2024! The particle data folder is provided as separate archive because of its large size. The Python notebooks were created with Jupyter Lab and Anaconda. A environment.yml file is provided that recreates the Anaconda environment. The simulation dataset that results from the provided Python notebooks is made available as CSV files or as pickled Python (pandas) DataFrames. Please refer to the included Readme for a detailed description of the files contained in the archive.
  • ItemOpen Access
    Correlative X-ray micro-Computed Tomography (X-µCT) scans of Engineered Artificial Minerals (EnAM)
    (Technische Universität Bergakademie Freiberg, 2024-11-07) Siddique, Asim; Schröer, Laurenz
    Characterizing complex particulate materials like slag using X-ray microcomputed tomography (μCT) is challenging due to minimal grey-scale contrast from similar attenuation properties among phases and intricate microstructures. To address this problem, we developed a standardized multi-scale correlative methodology that combines μCT at different resolutions with scanning electron microscopy and energy-dispersive X-ray spectroscopy (SEM-EDS) and X-ray fluorescence (XRF). By scanning large samples for statistical significance and sub-samples at higher resolutions, we capture detailed microstructures. Aligning SEM-EDS data with μCT scans using inherent markers enables accurate phase segmentation. Mineral mapping from SEM-EDS can help to train segmentation models for μCT data, overcoming μCT limitations and allowing precise 3D mineralogical characterization. This approach provides a robust framework for analyzing complex slag particles. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 101005611 https://excite-network.eu.
  • ItemOpen Access
    Dynamic Image Analysis: Python Notebooks, Particle Datasets, and Simulation Results
    (Technische Universität Bergakademie Freiberg, 2024-08-20) Buchwald, Thomas
    This submission serves as a validation dataset for the simulation results of 2D imaging methods from 3D particle meshes. The submission contains dynamic image analysis data that can be used for validation of simulation results, Python notebooks for extraction of particles and calculation of particle characteristics, and the final results in as pickled Pandas DataFrames and CSV files. This dataset extends to previous submission: https://doi.org/10.25532/OPARA-479 and https://doi.org/10.25532/OPARA-587, which contain the 3D particle dataset and the simulation algorithm notebooks.
  • ItemOpen Access
    Granule size and shape data from SOPAT for publication "Impact of Feed Rate and Binder Concentration on the Morphology of Spray Dried Alumina-Polymer Nano-composites"
    (Technische Universität Bergakademie Freiberg, 2025-04-24) Mitra, Rahul
    This dataset comprises in-line experimental SOPAT data from spray drying investigations of alumina and polyvinylpyrrolidone-30 composite granules. The spray drying process outcomes are documented through inline optical images of dried granules obtained via a SOPAT in-line imaging system providing maximum and minimum Feret diameter and aspect ratio information.
  • ItemOpen Access
    Micro-CT data for SPP 2364 "Model-based control of spray synthesis of structured granules with specified properties using transfer functions derived by multivariate stochastic models and machine learning"
    (Technische Universität Bergakademie Freiberg, 2025-04-23) Mitra, Rahul
    The data set comprises the micro-CT data from the 1st funding period of the SPP 2364, project number 504580586. The data published here contains the raw, reconstructed micro-CT images of the two spray dried samples (at two different feed rates) tested. The scans were done using the Zeiss Xradia Versa 510.
  • ItemOpen Access
    Research data for: "Guest Particle Deformation and Powder Flow Behavior in Mechano-Fusion: Linking Microscopic Structure to Macroscopic Performance"
    (Technische Universität Bergakademie Freiberg, 2025-09-09) Seyffer, Judith Miriam
    The research data set comprises the process data of mechano-fusion experiments (machine raw data and experiment meta data) and measurement data of different characterization methods (laser diffraction, BET specific surface area, powder flowability) for the investigated particle samples after mechano-fusion. The data corresponds to the publication "Guest Particle Deformation and Powder Flow Behavior in Mechano-Fusion: Linking Microscopic Structure to Macroscopic Performance" by Seyffer et al. (2025). Further information can be found in the provided Readme file and in the corresponding publication.
  • ItemOpen Access
    Research data for: “Forces during film drainage and detachment of NMC and spherical graphite in particle-bubble interactions quantified by CP-AFM and modeling to understand the salt flotation of battery black mass”
    (Technische Universität Bergakademie Freiberg, 2024-12-09) Nicklas, Jan
    This dataset characterizes the particle-bubble interaction for single battery black mass particles (NMC 622 and spherical graphite) in sodium chloride solutions (0.001 mol/L to 0.750 mol/L) for pH 3 to pH 10. The interaction of black mass particles with gas bubbles in the AFM-geometry gives information about the likeliness of particle-bubble-attachment and detachment in salt flotation. The research data consists of two parts: A) the Experimental Atomic Force Microscopy data for the interaction of black mass particles (NMC 622 (NMC) and spherical graphite (SG)) with sessile gas bubbles in salt solutions and B) the Data for the key figures of “Forces during film drainage and detachment of NMC and spherical graphite in particle-bubble interactions quantified by CP-AFM and modeling to understand the salt flotation of battery black mass”.
  • ItemOpen Access
    Sensitivity of Filter Cake Permeability to Systematic Variations in Particle Shape and Size: A Bottom-Up Stochastic Analysis
    (Technische Universität Bergakademie Freiberg, 2026-04-28) Löwer, Erik
    The data belongs to the corresponding publication "Sensitivity of Filter Cake Permeability to Systematic Variations in Particle Shape and Size: A Bottom-Up Stochastic Analysis" with the following abstract. A key question in filtration process design is understanding the filtration properties of a specific particle system. Current methods, like the Carman-Kozeny equation, struggle to accurately predict specific cake resistance and capillary pressure from a given particle size distribution, leading to reliance on limited empirical correlations. This poses challenges for process simulation, as the transition from particle characteristics to filter cake properties remains unclear. This work aims to correlate the distribution of multi-dimensional particle properties—both size and shape—with the properties of the resulting 3D filter cake morphology. We use tomographic image data of real filter cakes to validate a stochastic 3D model which describes the relationship between particle properties and multiphase filter cake characteristics using generated virtual particles that statistically resemble the actual data. Validation is provided by filter cakes from two different particle systems: spherical glass and broken quartz particles ranging in size from 40 to 300 μm with sphericity values from 0.5 to 1. Artificial, but realistic particles following the same particle property distributions are utilized to construct virtual filter cake structures using a forced bias algorithm, which statistically represent actual cake structures. The generated virtual cake structures allow for deriving trends in permeability by systematically varying particle properties virtually. The dataset provides the used raw data and the generated artifical cake structures which were used for the correlation of several particle, structure and process parameters of the investigated filtration process.
  • ItemOpen Access
    Supplementary data of publication: "In-line Image Analysis of Particulate Processes with Deep Learning: Optimizing Training Data Generation via Copy-Paste Augmentation"
    (Technische Universität Bergakademie Freiberg, 2024-04-26) Daus, Sarah
    This dataset contains the supplementary data of the open-access publication "In-line Image Analysis of Particulate Processes with Deep Learning: Optimizing Training Data Generation via Copy-Paste Augmentation". It provides all the necessary data and scripts to replicate the results of the study and to further use the synthetic data generation to train one's own deep learning image segmentation model. The original scripts have been slightly modified to make them more suitable for general use, e.g. parameters have been passed to an argument parser instead of hard-coding them whenever possible. Abstract of Paper: Monitoring particle properties directly in the process using in-situ microscopy can provide valuable input to control loops, improve process understanding and facilitate process optimization. However, obtaining reliable information from these images remains a challenge, especially for higher solids concentrations or agglomerating systems. Recent studies have successfully applied deep learning models to extract particle characteristics from in-situ image data. Despite these advances, the problem of generating training data has not been properly addressed. Manual annotation is time-consuming and prone to bias due to high particle counts, particle overlap, and out-of-focus objects. This paper presents a new approach to generate training data for segmentation models by combining conventional segmentation methods with copy-paste augmentation. A case study was conducted in which depth filtration experiments were performed on irregularly shaped alumina particles. An instance segmentation model trained on data generated using the proposed approach successfully detected and characterized particles, even at high solids concentrations.
  • ItemOpen Access
    X-ray CT Data of Lunar Regolith Simulants and Gray Value Sensitive Simulation Data
    (Technische Universität Bergakademie Freiberg, 2026-02-18) Ditscherlein, Ralf
    This dataset provides reconstructed X-ray microtomography data of four lunar regolith simulants together with simulated data for method evaluation. The dataset enables reproducibility of the particle fingerprint visualization technique, supports exploration of imaging artifacts, and provides reference implementations in Python. It is intended for use in particle system characterization, image analysis, and method benchmarking.