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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 -