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64 results for “aluminium”
Hyperspectral X-ray CT datasets of an aluminium phantom containing three metal-based powders
<p><strong>General Data description:</strong></p> <p>This is a set of two hyperspectral (energy-resolved) X-ray CT projection datasets of a multi-phase phantom. It was acquired in a custom-built, laboratory micro-CT scanner with an energy-sensitive HEXITEC detector in the Henry Moseley X-ray Imaging Facility at The University of Manchester.</p> <p>The following data contains all the files necessary for reconstruction, following two hyperspectral scans of a metal, multi-phase phantom. The phantom consists of an external aluminium cylinder, with three holes, each filled with a different metal-based powder (CeO<sub>2</sub>, ZnO, Fe). Each powder provides a unique attenuation signal, with CeO<sub>2</sub> in particular producing a distinct spectral marker which can be measured by an energy-sensitive detector. Two identical scans were acquired, with only the exposure time per projection changed.</p> <p>Note: Zenodo Version 2 of this dataset contains the incorrect version of the 180s, 180 projection phantom dataset, if wishing to analyse the dataset used in the associated hyperspectral paper. This version (Version 3) contains the correct dataset from the paper.</p> <p><strong>File descriptions:</strong></p> <p>Contained is an image (.jpg) of the sample, along with five MATLAB (.mat) data files, as well as a single text (.txt) file. Where necessary, the files have been named to match the dataset they belong to, based on the different exposure times used for each dataset.</p> <p>Phantom_design_measurements.jpg contains a photograph of the physical phantom, combined with a diagram showing full sample measurements.</p> <p>Powder_phantom_scan_geometry.txt gives a breakdown of the full sample and detector geometry used when acquiring the raw projections for both scans.</p> <p>Powder_phantom_30s_30Proj_sinogram.mat contains the 4D sinogram constructed following flatfield normalisation of the raw projection data, where an exposure time of 30 s was used for each projection. The 4D array contains the total number of energy channels acquired during scanning, followed by vertical and horizontal pixel number, and finally total projections angles acquired during scanning. The total number of channels in the file is 200.</p> <p>Powder_phantom_180s_180Proj_sinogram.mat is the 4D sinogram for the dataset, when exposure times of 180 s were used for each projection, following flatfield normalisation. A discontinuity occurs at projection 137 due to an interruption in the scan procedure. The total number of channels in the file is 200.</p> <p>Energy_axis.mat provides a direct conversion between the energy channels, and the energies (in keV) that they correspond to, following a calibration procedure prior to scanning. This is the same for both datasets.</p> <p>FF_30s.mat contains the 4D flatfield data acquired when no sample was present, in the case of 30 s exposure times. This data was used to normalise the projection datasets, as the sinogram was constructed. The first 200 channels are included.</p> <p>FF_180s.mat contains the 4D flatfield data for the dataset where 180 s exposure times were used. The first 200 channels are included.</p>
Scanning electron diffraction tilt series data of an aluminium-steel interface region
<p>This dataset contains scanning electron diffraction (SED) data used in the publication entitled "<strong>Microstructural and mechanical characterisation of a second generation hybrid metal extrusion & bonding aluminium-steel butt joint</strong>". The data denoted “SED_HYB_...” were recorded from an aluminium-steel interface region that includes aluminium and steel grains, an interfacial Al-Fe-Si layer, and dispersoids and some oxide particles located within the aluminium region. The nanoscale interfacial intermetallic phase layer is polycrystalline, and to increase the probability of recording data from intermetallic phase crystals oriented close to zone axes, the data were recorded in a tilt series covering 30 degrees, in steps of 1 degree. The file names give the goniometer x-tilt values in degrees, e.g. " SED_HYB_TX-150.hdf5" denotes an x-tilt of -15.0 degrees. SED data recorded from an Au cross-grating specimen, named "SED_AuX.hdf5", and from a MoO3 specimen, named "SED_MoO3.hdf5", are also included for calibration purposes.</p>
Characterization of SRF (XRF portable analyser) prepared for an aluminium scrap pre-heating system (REVaMP project)
<p>Open access to experimental data generated by the REVaMP project (GA 869882, Horizon 2020, European Union) along the research of the combustion of a SRF, prepared from ASR, to be used as alternative fuel in a scrap pre-heater at an aluminium refinery plant. Research pertaining to WP1, Deliverable D1. <br> Underlying data for the publication Acha, E. et al. Combustion of a Solid Recovered Fuel (SRF) Produced from the Polymeric Fraction of Automotive Shredder Residue (ASR). Polymers 2021, 13, 3807. https://doi.org/10.3390/polym13213807. Data related to Figure 1 in the article.</p> <p>Subject: Representative samples of SRF were manually sorted into categories of plastics, wood, textile, foam and others, and directly analyzed by the Thermo Fisher Scientific portable analyser Niton™, X-Ray Fluorescence (XRF).</p>
Supplementary Information and EBSD data for 'Intermetallic phase layers in cold metal transfer aluminium-steel welds with an Al-Si-Mn filler alloy'
<p>Supplementary information and electron backscatter diffraction (EBSD) data for the article entitled 'Intermetallic phase layers in cold metal transfer aluminium-steel joints with an Al-Si-Mn filler alloy'. There are three EBSD datasets, I-III, named "I_EBSD.dat" - "III_EBSD.dat", each with corresponding calibration and background patterns, as well as secondary electron scanning electron microscopy images showing the scanned area and text files containing the acquisition parameters. The data analysis workflow has been published on GitHub, see References.</p>
Data for "First Principle Calculation on Pressure Dependent Yielding in Solute Strengthened Aluminium Alloys"
<p>The dataset contains the DFT results which is the basis for the results and discussions in the related article, "First principle calculations of pressure dependent yielding in solute strengthened aluminium alloys". The details of the DFT calculations are written in the article.</p> <p>The two different file-name conventions are explained below.</p> <p>OUTCAR_Al_R1_HSP<br> OUTCAR_X_HSP_POS</p> <p>Al-files represent the pure aluminium models, showing the dislocation energies at various hydrostatic pressures. X (Cu, Si, Mg) represents the solute specie in the full model showing the configurational energy. R1 is the radius of the relaxed region. HSP represents the superimposed hydrostatic pressure, which is given by (-560+HSP*80) MPa. POS is the atomic index in the OUTCAR files, where the solute is substituted.</p>
I-BiDaaS - CRF - Aluminium die-casting Synthetic Dataset
<p>The dataset has been generated after receiving unstructured sets of a large amount of heterogeneous data from several sources and levels from the production line. CRF analysed all information and selected seventeen parameters (e.g. piston speed in the first and second phase, piston stroke, intensification pressures) of the production of the engine block by die-casting. The synthetic dataset was generated in order to analyse the provided parameters and try to cluster them by identifying those that are most representative of each cluster. The clusterization is useful to identify the behaviour of parameters and help to understand those that affect the quality of the process and products. </p>
Thickness dependence of the mechanical properties of piezoelectric high-Q_m nanomechanical resonators made from aluminium nitride
<p>Data used for figures in "Thickness dependence of the mechanical properties of piezoelectric high-Q_m nanomechanical resonators made from aluminium nitride" Anastasiia Ciers et.al <a href="https://doi.org/10.1088/2633-4356/ad9b64">Materials for Quantum Technology <strong>4</strong>, 046301 (2024)</a>; <a href="https://doi.org/10.48550/arXiv.2410.03944">arXiv:2410.03944 [cond-mat.mes-hall] (2024)</a></p>
TGA and LOI measurements within the thermal degradation study of a SRF prepared for an aluminium scrap pre-heating system (REVaMP project)
<p>Underlying data (related to Figure 2) for the publication Acha, E.; Lopez-Urionabarrenechea, A.;Delgado, C.; et al. Combustion of a Solid Recovered Fuel (SRF) Produced from the Polymeric Fraction of Automotive Shredder Residue (ASR). Polymers <strong>2021</strong>, 13, 3807. <a href="https://doi.org/10.3390/polym13213807">https://doi.org/10.3390/polym13213807</a></p> <p>Experimental data generated by the REVaMP project (GA 869882, Horizon 2020, European Union) along the research of the combustion of a SRF, prepared from ASR, to be used as alternative fuel in a scrap pre-heater at an aluminium refinery plant. Research pertaining to Task 1.1 (WP1), Deliverable D1. </p> <p>Subject: Thermal degradation study performed in air to measure the mass loss of SRF samples with time and temperature during a continuous heating process (two TGA measurements and determination of variation of LOI with T). The results indicate the different stages in the thermal decomposition of the prepared SRF and the temperature range of its combustion. Useful information for designing the operation conditions of the SRF combustion chamber of the scrap pre-heater.</p> <p> </p>
Vibration assisted drilling (VAD) application to the manufactured maraging steel X3NiCoMoTi18-9-5 (1.2709) and aluminium AlSi10Mg (EN AC-43000) parts
<p>Repository containing data from vibration assisted drilling (VAD) experiments on steel and Aluminum 3D powderbed manufactured parts.</p> <p>Please refer to README.MD (or .PDF), which contains a brief description of the chosen materials, parts and tools An explanation of the data aquisition and processing methods, together with the used nomenclature is given as well.</p>
High Temperature Compression Studies of a 6082.50 Aluminium Alloy using Deformation Dilatometer
<p>Data recorded in uniaxial compression for a 6082.50 aluminium alloy deformed at temperatures of 490C, 520C and 560C, at strain rates of 10, 1, and 0.1 s-1, to 50% height reduction, using TA Instruments DIL 805 A/D/T Quenching and Deformation Dilatometer. The cylindrical samples measured 5 mm diameter and 10 mm height. The 6082.50 aluminium specimens were machined from an as-cast billet. Two homogenisation have been performed on this material, one at 590C for 8h and one at 520C for 2h. These are identified as homogenisation 1 and homogenisation 2 in the data. Al2O3 platens were used for all tests, with Mo discs superglued at both ends of the sample to maximise thermal contact. Tests were conducted in an inert He gas atmosphere under a vacuum of 1e-5 mbar. The temperature was controlled using an K-Type thermocouple spot-welded to the centre of the samples with another K-Type thermocouple welded halfway between the centre and end to measure the thermal gradient. Not all of these second thermocouples provided data due to breakages. </p> <p>Data recorded at high acquisition frequency during deformation is stored in the 'data_deformation' folder and saved with the format: ' test number (002 to 038)_Compression_Daniel_Al_(homogenisation treatment - see above)_SHT_540C_Deform_(Deformation temperature)_(deformation strain rate)_QuenchHeGas__1. Here SHT_540C refers to the solutionising treatment and QuenchHeGas__1 refers to the method of cooling after deformation. This dataset does not include readings from the off-centre thermocouple. Data in the 'data_basic' folder is recorded at a lower acquisition frequency but includes a recording of the entire thermomechanical cycle, including both heating and cooling stages, as well as deformation. The 'method' folder includes contains the temperature and deformation profiles used. 'pictures' includes images taken of the samples and set up during the experiment. </p>
Estimated particle parameters of an aluminium matrix composite
<p>The dataset consists of section profile parameters in tabular form (assumed to come from prolate spheroids) of ellipses fitted to measured particles of an aluminium matrix composite from metallographic analysis. The dataset can be used to reconstruct the trivariate spatial spheroid (prolate form) distribution.</p>
Diffraction Contrast Tomography reconstruction of an Aluminium-Lithium tension specimen
<p>This data set is the reconstruction of the polycrystalline microstructure of a small tension specimen made of Aluminium-Lithium alloy. The specimen is approximately 0.5 mm in cross section. The data is the result of 3 DCT scans merged together. The spatial resolution is 1.4 micrometer. The data was acquired at the ID11 beamline at The European Synchrotron Radiation Facility and reconstructed using the DCT software available at https://sourceforge.net/projects/dct/</p> <p>The data is provided in HDF5 format, compatible with open source packages such as Paraview and DREAM3D. The laboratory coordinate system XYZ corresponds to the sample position when all the diffractometer rotations are zero.</p>
Dispersoid Composition in Zirconium Containing Al-Zn-Mg-Cu (AA7010) Aluminium Alloy - Supporting Data
<p>Data related to measured dispersoid compositions and calculated dispersoid volume fractions:</p> <p>Dispersoid Composition in Zirconium Containing Al-Zn-Mg-Cu (AA7010) Aluminium Alloy<br> A.M. Cassell, J. D. Robson, C. P. Race, A. Eggeman, T. Hashimoto, M. Besel.</p> <p>Submitted to Acta Materialia.</p> <p>Datafile of compositions to reproduce Fig.8 in paper (.mat Matlab format)</p> <p>Datafile of predicted dispersoid volume fractions on which calculations were performed to produce Fig. 10 in paper (comment in datafile provides further details)</p>
Dynamical simulation of EBSD master pattern of aluminium
<p>Dynamical simulation of an electron backscatter diffraction (EBSD) master pattern of aliminium (<em>Fm<span class="math-tex">\(\bar{3}\)</span>m</em>, <em>a</em> = 4.04 Å). The master pattern was simulated with EMsoft v4.1. The HDF5 file includes master patterns of the upper and lower hemispheres, in both the stereographic projection and the square Lambert projection, of accelerating voltages from 10 to 20 kV with an increment of 1 kV.</p> <p>The HDF5 file can be opened with any HDF5 reader, e.g. the applications HDFView and HDFCompass or the Python library h5py. The file can also be read and plotted with the Python library kikuchipy (https://kikuchipy.org). Assuming Python and the library is installed, the stereographic projection of the master pattern with all energies can be read and plotted with the following commands:</p> <pre><code class="language-python">import kikuchipy as kp s = kp.load("/path/to/am_mc_mp_20kv.h5") s.plot()</code></pre> <p>The PNG file shows the stereographic projection of the upper hemisphere of the master pattern from 20 kV. The al.xtal file was produced with the EMsoft program EMmkxtal, contains a complete description of the crystal structure, and is used as input to the EMsoft programs EMMCOpenCL and EMEBSDmaster. The latter two programs produced the al_mc_mp_20kv.h5 file.</p>
Impact hammer test of an Aluminium 6082 T6 plate, 260 x 246 x 1.5 mm, free boundary conditions
<p>Plate data</p> <p>------------</p> <p>E = 70 GPa</p> <p>ν = 0.33</p> <p>ρ = 2700 kg/m^3</p> <p>h = 1.50 mm (±0.01mm)</p> <p>width = 246 mm</p> <p>length = 260 mm</p> <p> </p> <p>Experiment description</p> <p>-----------------------------</p> <p>- a Cartesian coordinate system (XYZ) is defined in one of the corners of the plate, with XY laying on the mid-plane of the plate;</p> <p>- the X axis is aligned with the length direction;</p> <p>- the Y axis is aligned with the width direction;</p> <p>- the free boundary conditions are approximated experimentally by means of soft cotton wires, and the plate is tested while hanging on the cotton wires that are fixed at two corners, by means of adhesive tapes;</p> <p>- a mesh of 5 by 5 points is defined on the plate, with the following coordinates (in meters):</p> <pre><code class="language-python">1, [0., 0.] 2, [0.065, 0.] 3, [0.13, 0.] 4, [0.195, 0.] 5, [0.26, 0.] 6, [0., 0.0615] 7, [0.065, 0.0615] 8, [0.13, 0.0615] 9, [0.195, 0.0615] 10, [0.26, 0.0615] 11, [0., 0.123] 12, [0.065, 0.123] 13, [0.13, 0.123] 14, [0.195, 0.123] 15, [0.26, 0.123] 16, [0., 0.1845] 17, [0.065, 0.1845] 18, [0.13, 0.1845] 19, [0.195, 0.1845] 20, [0.26, 0.1845] 21, [0., 0.246] 22, [0.065, 0.246] 23, [0.13, 0.246] 24, [0.195, 0.246] 25, [0.26, 0.246]</code></pre> <p>- the accelerometer is fixed at point 5;</p> <p>- the time-response to the hammer impact is obtained for each point by hitting with the impact hammer at each point;</p> <p>- three time-response data points are obtained at each point;</p> <p>- for all measurements a total of 2^15 = 32768 time samples are obtained, corresponding to a time interval of 0 <= t <= 3.840 s, and a minimum sampling rate of 8000 Hz. </p> <p> </p> <p>Details about the hardware</p> <p>----------------------------------</p> <p>1) Data Acquisition Module NI 9234</p> <p>With four input channels simultaneously acquire at rates up to 51.2 kS/s (kilosamples per second). The operating temperature limits are from -40 °C to 70 °C, maximum of 5 g vibration, and maximum of 50 g shock.</p> <p>2) Impact hammer with load cell 086C02</p> <p>The first channel corresponds to the impact hammer load cell measurements, in Newtons [N]. The hardware sensitivity (+-15%) is 50 mV/lbf or 11.2 mV/N.</p> <p>3) Unidirectional Accelerometer 352A24</p> <p>The second channel corresponds to the accelerometer measurements, in gravity units [g], being 1 gravity unit = 9.81 m/s^2. The hardware sensitivity (+-10%) is 100 mV/g or 10.2 mV/(m/s^2).</p> <p> </p> <p> </p> <p>Reading the experimental data</p> <p>--------------------------------------</p> <p>I use the https://nptdms.readthedocs.io/en/stable/quickstart.html module to read the experimental data saved with the "*tdms" extension. The following example illustrates how to read the data at all the points:</p> <pre><code class="language-python">from nptdms import TdmsFile # reading from the experimental results # - the hammer impact force in [N] as a function of time from df.iloc[:, 0] # - the accelerations in [g] as a function of time from df.iloc[:, 1] times = {} hammer_impacts = {} accelerometer = {} for point in usedpoints: tdms = TdmsFile('WaveForms Point%02d.tdms' % point) df = tdms.as_dataframe() df.index = df.index/df.shape[0] * tmax times[point] = df.index hammer_impacts[point] = df.iloc[:, 0] accelerometer[point] = df.iloc[:, 1]</code></pre> <p> </p> <p> </p> <p>Plate model</p> <p>----------------</p> <p>In file "plate_model.py" you can find a validated vibration model of the plate, based on the Python package:</p> <blockquote> <p>Castro, S.G.P. Supporting code for Stability and Analysis of Structures II, Department of Aerospace Structures and Materials, Delft University of Technology, 2023. <a href="https://doi.org/10.5281/zenodo.2583004">DOI: 10.5281/zenodo.2583004</a>.</p> </blockquote>
iSDAsoil: soil extractable Aluminium for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths
<p>iSDAsoil dataset soil extractable Aluminium (Al) log-transformed predicted at 30 m resolution for 0–20 and 20–50 cm depth intervals. Data has been projected in WGS84 coordinate system and compiled as <a href="https://gdal.org/drivers/raster/cog.html">COG</a>. Predictions have been generated using multi-scale Ensemble Machine Learning with 250 m (MODIS, PROBA-V, climatic variables and similar) and 30 m (DTM derivatives, Landsat, Sentinel-2 and similar) resolution covariates. For model training we use a pan-African compilations of soil samples and profiles (<a href="https://www.isda-africa.com/national-soil-services/">iSDA points</a>, <a href="https://www.isric.org/projects/africa-soil-profiles-database-afsp">AfSPDB</a>, <a href="https://landpotential.org/data-portal/">LandPKS</a>, and other national and regional soil datasets). Cite as:</p> <p>Hengl, T., Miller, M.A.E., Križan, J. <em>et al.</em> African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. <em>Sci Rep</em> <strong>11, </strong>6130 (2021). <a href="https://doi.org/10.1038/s41598-021-85639-y">https://doi.org/10.1038/s41598-021-85639-y</a></p> <p>To open the maps in QGIS and/or directly compute with them, please use the <a href="https://gitlab.com/openlandmap/africa-soil-and-agronomy-data-cube"><strong>Cloud-Optimized GeoTIFF version</strong></a>.</p> <p>Layer description:</p> <ul> <li>sol_log.al_mehlich3_m_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil extractable Aluminium mean value,</li> <li>sol_log.al_mehlich3_md_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil extractable Aluminium model (prediction) errors,</li> </ul> <p>Model errors were derived using bootstrapping: md is derived as standard deviation of individual learners from 5-fold cross-validation (using spatial blocking). The model 5-fold cross-validation (<a href="https://mlr.mlr-org.com/reference/makeStackedLearner.html">mlr::makeStackedLearner</a>) for this variable indicates:</p> <pre><code>Variable: log.al_mehlich3 R-square: 0.881 Fitted values sd: 0.872 RMSE: 0.321 Random forest model: Call: stats::lm(formula = f, data = d) Residuals: Min 1Q Median 3Q Max -5.7042 -0.1036 0.0059 0.1189 3.3777 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.675492 2.771906 -0.244 0.807 regr.ranger 0.879567 0.005464 160.969 <2e-16 *** regr.xgboost 0.071537 0.005813 12.306 <2e-16 *** regr.cubist 0.150157 0.004553 32.979 <2e-16 *** regr.nnet 0.087603 0.431261 0.203 0.839 regr.cvglmnet -0.084440 0.003182 -26.534 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.3208 on 63551 degrees of freedom Multiple R-squared: 0.8808, Adjusted R-squared: 0.8808 F-statistic: 9.391e+04 on 5 and 63551 DF, p-value: < 2.2e-16</code></pre> <p>To back-transform values (y) to ppm use the following formula:</p> <pre><code>ppm = expm1( y / 10 )</code></pre> <p>To submit an issue or request support please visit <a href="https://isda-africa.com/isdasoil"><strong>https://isda-africa.com/isdasoil</strong></a></p>
Atom Probe Tomoghraphy Pure Aluminium Dataset
<p>This dataset presents Atom Probe Tomography (APT) data for pure aluminum, acquired using the Oxcart instrument—a titanium APT system. The experiment was conducted and recorded by the PyCCAPT control module.</p> <p>The primary data file, "2382_Jan-10-2025_15-12_NiC9_Al.h5," collects raw data captured by the PyCCAPT control module.</p> <p>The dataset includes a calibrated files: "1748_Al.h5" and a range file "1748_Al_range.h5." The former contains calibrated APT data, while the latter provides information on the range data.</p> <p> </p>
Modelling dynamic precipitation in pre-aged aluminium alloys
<p>Calculation of precipitation kinetics under deformation</p> <p>This repository contains the data plotted in https://doi.org/10.1016/j.actamat.2022.118036 as well as the code used to generate them.</p> <p>The code can be run reading the instructions contained in README.txt</p>
Tensile test data of a 9 microns thick aluminium foil
<p>Material testing data from tensile tests performed on an aluminium AA8079 alloy. The specimens are 100mm long and have a cross section of 10x0.009mm^2. Specimens are cut tested in 0°, 10°, ...40°, 45°, 50°, 60°, ....90° clock wise to the direction of rolling during manufacturing of the foil. Unfortunately the precise meaning of "clock wise" vis á vis the rolling direction and side of the foil is not known but is consistent in this text. The naming of the files reveal the direction of the test and the specimen no. for the respective direction, e.g "45_RawDataySpecimen_2" is to the second specimen tested in a direction 45° clock wise to the rolling direction.</p> <p>The tests are described in detail in B. Käck & C. Malmberg, Master thesis: Aluminium foil at multiple length scales, mechanical tests and numerical simulations in abaqus (2015), div. of Solid Mechanics, Lund Institute of Technology, Sweden.</p> <p>The tests were evaluated in "On the stiffness tensor in AA8079 at small and intermediate strains" by E. Andreasson, W. Reheman, P. Ståhle and S. Kao-Walter, submitted for publication. The major discoveries were that 1) plastic deformation appear almost immediately, i.e., practically at zero load, 2) the compliances increase linearly with strain, from the value of the inverse elastic modulus to almost five times that 3) principal material directions are not along the rolling direction but rather 5° to 15° anti-clock wise from the rolling direction and finally 4) there is a minimum stress in the region of between 35° to 45° clock wise from the rolling direction and for symmetry reasons also at 55° to 65° in the anti-clock wise direction.</p>
Data for the prediction of chatter vibrations in robotic milling of aluminium parts based on previous experiences using neural network
<p>This data has been used for the validation of the software developed by DFKI in collaboration with IDEKO for the prediction of stability in robotic milling of aluminium parts, in the framework of COROMA research project funded by the European Union. www.coroma-project.eu</p> <p>The source of information is stability lobes obtained from FRFs obtained mixing by receptance coupling experimental FRFs of the robot, spindle and toolholder with FRFs of the tool obtained analitycally using beams theory. Real machinings have not been done since they would be very time consuming. Once the stability lobes where available random sampling has been done in the lobes between certain boundaries of axial depth of cut and spindle speed to represent machining with different conditions.</p> <p>The information contained here includes:</p> <p>- Data sets for different conditions, with tools of different diameters and different number of cutting teeth. (in the naming of the folder D represents diameter, Z represents number of teeth).</p> <p>- Most of the data sets also include figures with the milling stability lobe charts for different radial depths of cut and different diameters and number of teeth. In these figures the random sampling representing machining tests has been marked with a black X.</p> <p>- There are also versions of the data sets with different number of samples (20 or 40) in order to test the prediction algorithm with a different number of information.</p> <p>- In the data sets an extended version has been created, representing the know-how of the operator that if a machining is unstable all the machinings with higher axial depth of cut will be unstable, and if a machining is stable all the machinings with lower axial depth of cut will be stable.</p> <p>- Companion documents in PDF format in order to provide more detailed information on the datasets and results.</p> <p>Keywords: Milling, machining, vibration, chatter, stability, prediction, neural network, robot, robotic, AI, artificial intelligence.</p> <p>www.ideko.es<br> www.dfki.de</p> <p>Asier Barrios<br> IDEKO research centre<br> Arriaga Kalea, 2<br> Elgoibar 20870, Spain<br> Phone: +34 943748000<br> abarrios@ideko.es</p> <p>October 2019</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.