TY - GEN A1 - Léonard, Fabien T1 - Overview of XCT data processing workflow for ammonium nitrate prills quantitative analysis N2 - This video presents the data processing workflow that was developed to perform the quantitative structural and morphological analysis of ammonium nitrate prills by x-ray computed tomography. KW - ANFO KW - Data processing KW - Explosives KW - Porosity KW - Surface area KW - XCT PY - 2020 DO - https://doi.org/10.5281/zenodo.3611339 PB - Zenodo CY - Geneva AN - OPUS4-51897 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Aßmann, Norman A1 - Bühling, Benjamin T1 - Impact-echo dataset "Radarplatte" N2 - This dataset contains raw data resulting from Impact-Echo measurements at the reference conrete block "Radarplatte", located at BAM (German Federal Institute for Materials Research and Testing). This specimen has been described in detail by Niederleithinger et al. (2021), who applied muon tomography, ultrasonic echo measurements, radar and X-ray laminography to visualize its internal structure. The Impact-Echo method is based on the excitation of the zero-group-velocity frequency of the first symmetric Lamb mode of a plate-like structure, in order to assess its thickness. Numerous publication elaborate on Impact-Echo theory, examples are (Gibson and Popovics 2005, Schubert and Köhler 2008 , Abraham and Popovics 2010). The measurements have been conducted using a setup that contains only commercially available components. The setup consists of an Olson CTG-2 concrete thickness gauge (Olsen Instruments, USA) for actuation and sensing and an 8-bit NI USB-5132 digital storage oscilloscope (National Instruments, USA) combined with the Echolyst software (Schweizerischer Verein für technische Inspektionen (SVTI), Switzerland) for data acquisition. Measurements were conducted using a grid of 23x23 points with a spacing of 50 mm. At each point 8192 samples were recorded at a sampling rate of 1 MS/s. The dataset contains the (X,Y) location in mm of the individual measurement points as well as the raw measurement data at those points. The data is provided in the formats *.mir/*.mhdr (Echolyst), *.npy (Python) and *.mat (Matlab) and *.csv to ease the import in various post-processing tools. KW - Nondestructive testing KW - Impact-echo KW - S1-ZGV mode KW - Concrete PY - 2021 DO - https://doi.org/10.7910/DVN/UNOH2U PB - Harvard College CY - Cambridge, MA, USA AN - OPUS4-53886 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Kudela, P. A1 - Radzienski, M. A1 - Moix-Bonet, M. A1 - Willberg, C. A1 - Lugovtsova, Yevgeniya A1 - Bulling, Jannis A1 - Tschöke, K. A1 - Moll, J. T1 - Dataset on full ultrasonic guided wavefield measurements of a CFRP plate with fully bonded and partially debonded omega stringer N2 - The fourth dataset dedicated to the Open Guided Waves platform presented in this work aims at a carbon fiber composite plate with an additional omega stringer at constant temperature conditions. The dataset provides full ultrasonic guided wavefields. A chirp signal in the frequency range 20-500 kHz and Hann windowed tone-burst signal with 5 cycles and carrier frequencies of 16.5 kHz, 50 kHz, 100 kHz, 200 kHz and 300kHz are used to excite the wave. The piezoceramic actuator used for this purpose is attached to the center of the stringer side surface of the core plate. Three scenarios are provided with this setup: (1) wavefield measurements without damage, (2) wavefield measurements with a local stringer debond and (3) wavefield measurements with a large stringer debond. The defects were caused by impacts performed from the backside of the plate. As result, the stringer feet debonds locally which was verified with conventional ultrasound measurements. The dataset can be used for benchmarking purposes of various signal processing methods for damage imaging. The detailed description of the dataset is published in Data in Brief Journal. KW - Open data KW - Ultrasonic guided waves KW - Experimental data PY - 2021 DO - https://doi.org/10.5281/zenodo.5105861 PB - Zenodo CY - Geneva AN - OPUS4-54504 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Grotelüschen, Bjarne A1 - Bühling, Benjamin T1 - Impact-Echo Dataset "IE Platte" N2 - This dataset contains raw data resulting from Impact-Echo measurements at the reference concrete block "IE Platte", located at BAM (German Federal Institute for Materials Research and Testing). The specimen contains three polystyrene slabs and one polyethylene foil to act as reflectors. The specimen was produced in a three-step process. First, the base plate was cast. Second, the reflectors were taped to the base plate. Finally, the upper layer was cast on top of base plate and reflectors. A drawing is contained in the dataset. The Impact-Echo method is based on the excitation of the zero-group-velocity frequency of the first symmetric Lamb mode of a plate-like structure, in order to assess its thickness. Numerous publications elaborate on Impact-Echo theory, examples are (Gibson and Popovics 2005, Schubert and Köhler 2008 , Abraham and Popovics 2010). The measurements have been conducted using a setup that contains only commercially available components. The setup consists of an Olson CTG-2 concrete thickness gauge (Olsen Instruments, USA) for actuation and sensing and an 8-bit NI USB-5132 digital storage oscilloscope (National Instruments, USA) combined with the Echolyst software (Schweizerischer Verein für technische Inspektionen (SVTI), Switzerland) for data acquisition. Measurements were conducted using a grid of 29x29 points with a spacing of 50 mm. At each point 8192 samples were recorded at a sampling rate of 1 MS/s. The dataset contains the (X,Y) location in mm of the individual measurement points as well as the raw measurement data at those points. The data is provided in the formats *.mir/*.mhdr (Echolyst), *.npy (Python) and *.mat (Matlab) and *.csv to ease the import in various post-processing tools. KW - Concrete KW - Impact-Echo KW - Nondestructive testing PY - 2022 DO - https://doi.org/10.7910/DVN/EH4E9G PB - Harvard College CY - Cambridge, MA, USA AN - OPUS4-55279 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Schumacher, David A1 - Hohendorf, Stefan T1 - How does a Moka Pot work? 2D X-Ray video gives insights! N2 - This sequence of X-Ray images shows how one of the most common Italian moka pots actually work! The sequence starts with a completely prepared moka pot (water in the bottom part, coffee in the middle and hot plate on). During the process the water starts to boil and the steam pressure pushes the hot water through the coffee into the bassin at the top of the pot. This video sequence and additional explanations can also be found on Wikipedia. KW - X-Ray KW - Moka Pot KW - Coffee KW - Radiography KW - Espressokanne PY - 2022 DO - https://doi.org/10.5281/zenodo.7003489 PB - Zenodo CY - Geneva AN - OPUS4-55558 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Schumacher, David A1 - Waske, Anja T1 - XCT data of metallic feedstock powder with pore size analysis N2 - X-Ray computed tomography (XCT) scan of 11 individual metallic powder particles, made of (Mn,Fe)2(P,Si) alloy. The data set consists of 4 single XCT scans which have been stitched together [3] after reconstruction. The powder material is an (Mn,Fe)2(P,Si) alloy with an average density of 6.4 g/cm³. The particle size range is about 100 - 150 µm with equivalent pore diameters up to 75 µm. The powder and the metallic alloy are described in detail in [1, 2]. KW - Additive Manufacturing KW - Feedstock powder KW - Powder Characterization KW - X-Ray Computed Tomography PY - 2022 DO - https://doi.org/10.5281/zenodo.5796487 PB - Zenodo CY - Geneva AN - OPUS4-55556 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Völker, Christoph A1 - Moreno Torres, Benjami T1 - SLAMD-FIB-Case-Study N2 - With 8% of man-made CO2 emissions, cement production is an important driver of the climate crisis. By using alkali-activated binders part of the energy-intensive clinker production process can be dispensed with. However, because numerous chemicals are involved in the manufacturing process here, the complexity of the materials increases by orders of magnitude. Finding a properly balanced cement formulation is like looking for a needle in a haystack. We have shown for the first time that artificial intelligence (AI)-based optimization of cement formulations can significantly accelerate research. The „Sequential Learning App for Materials Discovery“ (SLAMD) aims to accelerate practice transfer. With SLAMD, materials scientists have low-threshold access to AI through interactive and intuitive user interfaces. The value added by AI can be determined directly. For example, the CO2 emissions saved per ton of cement can be determined for each development cycle: the more efficient the AI optimization, the greater the savings. Our material database already includes more than 120,000 data points of alternative cements and is constantly being expanded with new parameters. We are currently driving the enrichment of the data with a life cycle analysis of the building materials. Based on a case study we show how intuitive access to AI can drive the adoption of techniques that make a real contribution to the development of resource-efficient and sustainable building materials of the future and make it easy to identify when classical experiments are more efficient. KW - Alkali activated concrete KW - Concrete PY - 2022 UR - https://github.com/BAMcvoelker/SLAMD-FIB-Case-Study#slamd-fib-case-study PB - GitHub CY - San Francisco, CA, USA AN - OPUS4-56637 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Chaudhuri, Somsubhro A1 - Stamm, Michael T1 - Infrared thermography of turbulence patterns of operational wind turbine rotor blades supported with high-resolution photography: KI-VISIR Dataset N2 - With increasing wind energy capacity and installation of wind turbines, new inspection techniques are being explored to examine wind turbine rotor blades, especially during operation. A common result of surface damage phenomena (such as leading-edge erosion) is the premature transition of laminar to turbulent flow on the surface of rotor blades. In the KI-VISIR (Künstliche Intelligenz Visuell und Infrarot Thermografie – Artificial Intelligence-Visual and Infrared Thermography) project, infrared thermography is used as an inspection tool to capture so-called thermal turbulence patterns (TTP) that result from such surface contamination or damage. To compliment the thermographic inspections, high-resolution photography is performed to visualise, in detail, the sites where these turbulence patterns initiate. A convolutional neural network (CNN) was developed and used to detect and localise the turbulence patterns. A unique dataset combining the thermograms and visual images of operational wind turbine rotor blades has been provided, along with the simplified annotations for the turbulence patterns. Additional tools are available to allow users to use the data requiring only basic Python programming skills. KW - Reference Data KW - Infrared Thermography KW - Wind energy KW - Dataset PY - 2024 DO - https://doi.org/10.5281/zenodo.13771899 PB - Zenodo CY - Geneva AN - OPUS4-61102 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Friedrich, Alexander T1 - Simulation data for eddy current rail testing - simulation accuracy and evaluation uncertainty quantification N2 - This dataset serve to quantify the simulation error and the evaluation uncertainties in the context of eddy current rail testing. It was obtained during the AIFRI project (Artificial Intelligence for Rail Inspection) with the Faraday software by INTEGRATED Engineering Software, using its BEM Solver. KW - Non-destructive testing KW - Eddy current testing KW - Eddy current simulstion PY - 2024 DO - https://doi.org/10.5281/zenodo.13970374 PB - Zenodo CY - Geneva AN - OPUS4-61470 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Völker, Tobias A1 - Mensing, Friedrich A1 - Kruschwitz, Sabine T1 - Estimation of cement content in concrete by spatially resolved laser induced breakdown spectroscopy – Dataset N2 - This dataset includes both simulated and raw measurement data as detailed in the journal paper [1]. File names follow the format SampleNo_Type, where SampleNo corresponds to the sample identifier listed in Table 1, and Type specifies the data type: either syn (synthetic) or exp (experimental). For experimental data, a slice index is appended to the file name in the format SampleNo_Type_Slice, identifying the specific subsample. [1] https://doi.org/10.1016/j.cemconres.2024.107714 KW - LIBS KW - Spectroscopy KW - Cement content KW - Elemental mapping PY - 2024 DO - https://doi.org/10.5281/zenodo.14246581 PB - Zenodo CY - Geneva AN - OPUS4-62394 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Maack, Stefan A1 - Küttenbaum, Stefan A1 - Bühling, Benjamin T1 - Low-frequency ultrasound data (pulse-echo technique) with shear horizontal and longitudinal waves on the step-shaped concrete specimen “Pk050” N2 - This dataset contains raw data acquired in ultrasound measurements on a reference specimen made of concrete at Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin (Germany). The internal specimen identifier is “Pk050”. The measurements were conducted using the pulse-echo method. The upper surface of the specimen was defined as measuring area. The aim of the measurements is to determine the geometrical dimensions (thickness) of the specimen “Pk050”. In addition to this, a dataset of a second specimen with identifier “Pk266” has been acquired. Pk266 has the same geometrical dimensions and concrete recipe as Pk050, but contains tendons [Reference: https://doi.org/10.7910/DVN/NUU0WZ]. KW - Ultrasound KW - Pulse echo method KW - Synthetic Aperture Focusing Technique KW - Validation KW - Elastic wave PY - 2023 DO - https://doi.org/10.7910/DVN/9EID5D PB - Harvard College CY - Cambridge, MA, USA AN - OPUS4-57231 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Gebauer, D. A1 - Beltrán Gutierrez, R. A1 - Marx, S. A1 - Maack, Stefan A1 - Küttenbaum, Stefan A1 - Pirskawetz, Stephan A1 - Breit, W. A1 - Mechtcherine, V. A1 - Grahl, K. A1 - Thiel, T. A1 - Schickert, M. A1 - Krüger, M. T1 - Interrelated Data Set from Nondestructive and Destructive Material Testing of Concrete Compressive Strength Specimens N2 - "This data set contains three different data types obtained from concrete specimens. For each specimen, the rebound numbers, ultrasonic data (ultrasonic velocity, time of flight), and destructive concrete strength are given. Two kind of specimen geometries were tested: cubes and drilled cores. The files are labeled according to the specimen geometry as "cube" or "core" and the type of measurement data as "compressive_strength", "rn_R" and "rn_Q" for rebound numbers as well as "us" for ultrasonic data. The ultrasonic data were generated by six independent laboratories, the rebound numbers by five independent laboratories and the destructive tests by one laboratory. The designation of each specimen establishes the relationship between the different data types." KW - Compressive Concrete Strength KW - Rebound Hammer KW - Ultrasonic Pulse Velocity PY - 2023 DO - https://doi.org/10.7910/DVN/AFCITK PB - Harvard College CY - Cambridge, MA, USA AN - OPUS4-57223 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Klewe, Tim A1 - Strangfeld, Christoph A1 - Ritzer, Tobias A1 - Kruschwitz, Sabine T1 - GPR Dataset of Moisture Measurements on Building Floors in Laboratory and On-Site N2 - Related work Laboratory Study: Combining Signal Features of Ground-Penetrating Radar to Classify Moisture Damage in Layered Building Floors https://doi.org/10.3390/app11198820 On-Site Study: TBA Doctoral Thesis: Non-destructive classification of moisture deterioration in layered building floors using ground penetrating radar https://doi.org/10.14279/depositonce-19306 Measurement Parameters The GPR measurements were carried out with the SIR 20 from GSSI and a 2 GHz antenna pair (bandwidth 1 GHz to 3 GHz) in common-offset configuration. Each B-Scan consists of N A-Scans, each including 512 samples of a 11 ns time window. Survey lines were recorded with 250 A-Scans/ meter, which equals a 4 mm spacing between each A-Scan No Gains were applied. Folder Description: Lab_dry, Lab_insulDamage, Lab_screedDamage - each contain 168 Measurements (B-Scans) in .csv on 84 dry floors, floors with insulation damage and screed damage. - each floor setup was measured twice on two orthogonal survey lines, indicated by _Line1_ and _Line2_ in the file name. - the file names encode the building floor setup e.g. CT50XP100 describes a 50 mm cement screed with 100 mm extruded polystyrene below - the material codes are CT: cement screed, CA: anhydrite screed, EP: expanded polystyrene, XP: extruded polystyrene, GW: glass wool, PS: perlites further information can be found in the publication https://doi.org/10.3390/app11198820 OnSite_ - 5 folders containing B-Scans on 5 different practical moisture damages - the building floor setup is encoded according to the lab with an additional measurement point numbering at the start and a damage case annotation at the end of the file name with _dry, _insulationDamage and_screedDamage File Description: B-Scans, Measurement files - no header - dimension: 512 x N data point with N beeing the number of A-Scans including 512 samples of a 11 ns time window. - survey lines were recorded with 250 A-Scans/ meter, which equals a 4 mm spacing between each A-Scan Moisture References - Moist_Reference of On-Site Locations include the columns MeasPoint: Measurement point, wt%Screed: moisture content of screed layer in mass percent; wt%Insul: moisture content of insulation layer in mass percent. References were obtained by drilling cores with 68 mm diameter in the center of each survey line. - Moist_Reference_Screed of Lab data include the columns Screed: Screed material and thickness in mm, wt%Screed moisture content of screed layer in mass percent - Moist Reference_Insul of Lab data include the columns Insulation: Insulation material and thickness in mm, water addition in l: water added to the insulation layer in liters, V%Insulation: water added to the insulation layer in volume percent, RH%: resulting relative humidy in the insulation layer during measurement. These References are only avaible for Lab measurements on insulation damages. KW - GPR KW - Material Moisture KW - Building Floors KW - Laboratory KW - On-Site PY - 2024 DO - https://doi.org/10.5281/zenodo.10776684 PB - Zenodo CY - Geneva AN - OPUS4-59609 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Gebauer, D. A1 - Gutierrez, B. A1 - Marx, S. A1 - Maack, Stefan A1 - Küttenbaum, Stefan A1 - Pirskawetz, Stephan A1 - Breit, W. A1 - Mechtcherine, V. A1 - Grahl, K. A1 - Thiel, T. A1 - Schickert, M. A1 - Krüger, M. T1 - Interrelated data set from nondestructive and destructive material testing of concrete compressive strength specimens N2 - This data set contains three different data types obtained from concrete specimens. For each specimen, the rebound numbers, ultrasonic data (ultrasonic velocity, time of flight), and destructive concrete strength are given. Two kind of specimen geometries were tested: cubes and drilled cores. The files are labeled according to the specimen geometry as "cube" or "core" and the type of measurement data as "compressive_strength", "rn_R" and "rn_Q" for rebound numbers as well as "us" for ultrasonic data. The ultrasonic data were generated by six independent laboratories, the rebound numbers by five independent laboratories and the destructive tests by one laboratory. The designation of each specimen establishes the relationship between the different data types. KW - Non-destructive testing KW - Rebound number KW - Ultrasonic pulse velocity KW - Compressive concrete strength KW - Interlaboratory comparison PY - 2023 DO - https://doi.org/10.7910/DVN/AFCITK PB - Harvard College CY - Cambridge, MA, USA AN - OPUS4-57499 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Maack, Stefan A1 - Küttenbaum, Stefan A1 - Bühling, Benjamin T1 - Low-frequency ultrasound data (pulse-echo technique) with shear horizontal and longitudinal waves on the step-shaped concrete specimen “Pk266” with tendons N2 - This dataset contains raw data acquired in ultrasound measurements on a reference specimen made of concrete at Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin (Germany). The internal specimen identifier is “Pk266”. The measurements were conducted using the pulse-echo method. The upper surface of the specimen was defined as measuring area. The aim of the measurements is to determine both the geometrical dimensions (thickness) and the position of tendons to the measuring area. In addition to this, a second dataset of a second specimen with identifier is existing named “Pk050” has been acquired. Pk050 has the same geometrical dimensions and concrete recipe as Pk266 recipe but does not contain tendons [Reference: https://doi.org/10.7910/DVN/9EID5D]. KW - Ultrasound KW - Pulse echo method KW - Synthetic Aperture Focusing Technique KW - Validation KW - Elastic wave PY - 2023 DO - https://doi.org/10.7910/DVN/NUU0WZ PB - Harvard College CY - Cambridge, MA, USA AN - OPUS4-57232 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Bühling, Benjamin T1 - Acoustic and flow data of an ultrasonic fluidic switch and an ultrasonic piezoelectric transducer N2 - This dataset contains acoustic and flow data of an ultrasonic fluidic switch, which have been acquired using a microphone, a hot-wire anemometer and a pitot tube. Furthermore, acoustic data of a commercial piezoelectric transducer is provided. KW - Fluidics KW - Air-coupled ultrasound KW - Ultrasound KW - Non-destructive testing KW - Acoustic-flow interaction KW - Piezoelectric transducer PY - 2020 DO - https://doi.org/10.7910/DVN/OQYPC9 PB - Harvard College CY - Cambridge, MA, USA AN - OPUS4-52392 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Maack, Stefan T1 - Low-frequency ultrasound data (pulse-echo technique) with shear horizontal and longitudinal waves on a reference polyamide specimen “BAM-Pk218” N2 - This dataset contains raw data observed with ultrasound measurements on a polyamide reference specimen at Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin. The internal specimen identifier is „Pk218“. The measurements were conducted using the pulse-echo method. KW - Nondestructive testing KW - Ultrasound KW - Pulse-echo technique KW - Validation KW - Reference material KW - Reconstruction algorithm PY - 2022 DO - https://doi.org/10.7910/DVN/KVN7CY PB - Harvard College CY - Cambridge, MA, USA AN - OPUS4-54959 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Grohmann, Maria T1 - Low-frequency ultrasound data (pulse-echo technique) of shear horizontal and longitudinal waves acquired on the concrete step specimen “Pk401” with embedded polystyrene foam cuboids N2 - This dataset includes raw pulse-echo ultrasound measurements of a stepped concrete specimen (ID “Pk401”) from BAM, Berlin. The specimen (2000 × 800 mm) has four thicknesses—573.8, 453.4, 333.1, 210.4 mm—and contains polystyrene cuboids (120 × 120 × 60 mm) centered at depths of 270, 210, 150, and 90 mm. Measurements utilized both shear (M2502) and longitudinal (M2503) probes, combining commercial and in-house BAM equipment. KW - Reference data KW - Non destructive testing and evaluation KW - Validation KW - Ultrasonic PY - 2025 DO - https://doi.org/10.7910/DVN/0HHJFF PB - Harvard College CY - Cambridge, MA, USA AN - OPUS4-63912 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Kern, Simon A1 - Liehr, Sascha A1 - Wander, Lukas A1 - Bornemann-Pfeiffer, Martin A1 - Müller, S. A1 - Maiwald, Michael A1 - Kowarik, Stefan T1 - Training data of quantitative online NMR spectroscopy for artificial neural networks N2 - Data set of low-field NMR spectra of continuous synthesis of nitro-4’-methyldiphenylamine (MNDPA). 1H spectra (43 MHz) were recorded as single scans. Two different approaches for the generation of artificial neural networks training data for the prediction of reactant concentrations were used: (i) Training data based on combinations of measured pure component spectra and (ii) Training data based on a spectral model. Synthetic low-field NMR spectra First 4 columns in MAT-files represent component areas of each reactant within the synthetic mixture spectrum. Xi (“pure component spectra dataset”) Xii (“spectral model dataset”) Experimental low-field NMR spectra from MNDPA-Synthesis This data set represents low-field NMR-spectra recorded during continuous synthesis of nitro-4’-methyldiphenylamine (MNDPA). Reference values from high-field NMR results are included. KW - NMR spectroscopy KW - Real-time process monitoring KW - Artificial neural networks KW - Online NMR spectroscopy KW - Automation KW - Process industry PY - 2020 DO - https://doi.org/10.5281/zenodo.3677139 PB - Zenodo CY - Geneva AN - OPUS4-50456 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Liehr, Sascha T1 - ANNforPAT - Artificial Neural Networks for Process Analytical Technology N2 - This code accompanies the paper "Artificial neural networks for quantitative online NMR spectroscopy" published in Analytical and Bioanalytical Chemistry (2020). KW - Artificial neural networks KW - Automation KW - Online NMR spectroscopy KW - Process industry KW - Real-time process monitoring PY - 2020 UR - https://github.com/BAMresearch/ANNforPAT PB - GitHub CY - San Francisco AN - OPUS4-54481 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Lecompagnon, Julien T1 - Thermographic Reference Dataset: Experimentally simulated Gaussian-shaped internal defects in GFRP N2 - We introduce a thermographic reference dataset consisting of 100 experimentally recorded temperature fields that replicate defect-like heat signatures in glass fiber reinforced polymer (GFRP) composites. Instead of relying on the fabrication of numerous damaged specimens, the dataset was generated by directly imprinting defect signatures onto an intact laminate using a near-infrared laser projector with spatial light modulation. The projected patterns are derived from parameterized Gaussian distributions, enabling systematic variation in defect size, shape, and orientation. The resulting steady-state thermal responses were captured with infrared thermography, providing high-resolution temperature distribution measurements for each case. This novel approach ensures that the heat transfer physics are preserved experimentally, while no special samples with different defects need to be manufactured to acquire datasets for different defect parameters. The dataset is ultimately designed as a resource for the benchmarking of thermographic non-destructive testing techniques, the validation of numerical heat transfer simulations, and the training of data-driven algorithms for defect detection in composite materials. KW - Thermography KW - Temperature KW - Composites KW - GFRP KW - Reference Data KW - Laser Projector KW - Nondestructive Testing PY - 2025 DO - https://doi.org/10.5281/zenodo.17378681 PB - Zenodo CY - Geneva AN - OPUS4-64472 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Han, Ying T1 - Segmented primary phases of Al-alloy EN AW-2618A in the T61 state using synchrotron computed tomography N2 - The video file contains data from a synchrotron computed tomography measurement of the primary phases of the Al-alloy EN AW-2618A in the T61 state. The volume fraction of the primary phases was determined. KW - Aluminum alloy KW - EN AW-2618A KW - Computed tomography KW - Primary phases PY - 2024 DO - https://doi.org/10.5281/zenodo.12730717 PB - Zenodo CY - Geneva AN - OPUS4-60657 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Völker, Christoph T1 - WEBSLAMD N2 - The objective of SLAMD is to accelerate materials research in the wet lab through AI. Currently, the focus is on sustainable concrete and binder formulations, but it can be extended to other material classes in the future. 1. Summary Leverage the Digital Lab and AI optimization to discover exciting new materials Represent resources and processes and their socio-economic impact. Calculate complex compositions and enrich them with detailed material knowledge. Integrate laboratory data and apply it to novel formulations. Tailor materials to the purpose to achieve the best solution. Workflow Digital Lab Specify resources: From base materials to manufacturing processes – "Base" enables a detailed and consistent description of existing resources Combine resources: The combination of base materials and processes offers an almost infinite optimization potential. "Blend" makes it easier to design complex configurations. Digital Formulations: With "Formulations" you can effortlessly convert your resources into the entire spectrum of possible concrete formulations. This automatically generates a detailed set of data for AI optimization. AI-Optimization Materials Discovery: Integrate data from the "Digital Lab" or upload your own material data. Enrich the data with lab results and adopt the knowledge to new recipes via artificial intelligence. Leverage socio-economic metrics to identify recipes tailored to your requirements. KW - Materials informatics KW - Scientific software KW - Sequential learning PY - 2022 UR - https://github.com/BAMresearch/WEBSLAMD DO - https://doi.org/10.26272/opus4-56640 PB - GitHub CY - San Francisco, CA, USA AN - OPUS4-56640 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Klewe, Tim T1 - Synthetic Dataset for Sequential Learning-Based Optimisation of Bio-Ash Binder Formulations under Seasonal Availability Constraints N2 - This dataset accompanies the study on sequential learning–based optimisation of bio-ash–cement binder formulations under seasonally varying material availability. It provides a fully synthetic but chemically inspired benchmark design space for evaluating data-driven optimisation strategies in cementitious materials research. The dataset comprises 5,006 unique binder formulations, each defined by the mass fractions of cement and five bio-based ash components (A1–A5). Ash components represent generic bio-ash types derived from agricultural residues (e.g. rice husk ash, cassava peel ash), and their internal proportions are systematically varied under mass-balance constraints. Cement content ranges from 0 to 100 wt% in discrete steps. To reflect dynamic supply conditions, the dataset includes season-specific ash usage metrics for four seasons (S1–S4), expressing the fraction of available ash resources consumed by each formulation. A synthetic compressive strength value is assigned to every formulation using a nonlinear scoring function based on chemically inspired descriptors, with added noise to generate a structured yet non-trivial optimisation landscape. These strength values do not represent calibrated physical predictions and are intended solely as a hidden objective function for benchmarking sequential learning algorithms. The dataset is designed for in silico benchmarking, reproducibility studies, and methodological comparisons of optimisation and active learning strategies. It enables systematic evaluation of algorithmic performance without the need for physical experiments. KW - Sequential learning KW - Bio-based ashes KW - Low-clinker cement KW - Mixture optimisation KW - Supplementary cementitious materials PY - 2026 DO - https://doi.org/10.5281/zenodo.18389681 PB - Zenodo CY - Geneva AN - OPUS4-65420 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -