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360 results for “steel”
Spherical Indentation tests on rail R260 steel
<p>This data set contains photos and measurement data from spherical indentation tests on R260 rail steel. <br>This data set belongs to the following journal article:</p> <p>Bettina Suhr, William A. Skipper, Roger Lewis, Klaus Six:<br>DEM modelling of surface indentations caused by granular materials: application to wheel–rail sanding,<br>Computational Particle Mechanics, 2024,<br>https://doi.org/10.1007/s40571-024-00816-w</p> <p>A flat specimen made of the R260 rail steel was indented by a stainless steel (AISI 440C) ball bearing of 8.73 mm diameter. <br>The following normal loads were applied for indentation tests: <br>100 N, 500 N, 1000 N, 2000 N, 3000 N, 4000 N, 5000 N, 6000 N, 7500 N. <br>For each load level, two tests were conducted to check the repeatability of the measurement. <br>After tests, the indent was analysed using the Alicona InfiniteFocusSL 3D optical profilometer. <br>The Alicona captured a 3D scan covering a 3.66 mm x 3.66 mm area (vertical resolution of 500 nm). </p> <p>Available data and file naming conventions</p> <p>For the test at 100 N load, the available files are named as follows:</p> <p>11-32__100N.jpg: image coloured by the indentation depth<br>11-32__100N_Diameter.jpg: image of the indent with visually measured diameter of the indent<br>11-32__100N.txt: point cloud of the analyzed surface</p> <p>The repetition measurements’ files at 100N load are named: 11-32__100N_a.jpg, 11-32__100N_Diameter_a.jpg, 11-32__100N_a.txt</p> <p>The files belonging to test with higher applied load are named accordingly.</p> <p><br>This research was funded in whole, or in part, by the Austrian Science Fund (FWF) project<br> P 34273: DEM modelling of adhesion in sanded wheel-rail contacts.</p> <p>This work was conducted at Virtual Vehicle Research GmbH in Graz, Austria. <br>The authors would like to acknowledge the financial support within the COMET K2 Competence Centers <br>for Excellent Technologies from the Austrian Federal Ministry for Climate Action (BMK), the <br>Austrian Federal Ministry for Labour and Economy (BMAW), the Province of Styria (Dept. <br>12) and the Styrian Business Promotion Agency (SFG). The Austrian Research Promotion <br>Agency (FFG) has been authorised for the programme management. </p> <p> </p>
Data for "Using physics-informed neural networks to predict the lifetime of laser powder bed fusion processed 316L stainless steel under multiaxial low-cycle fatigue loading"
<p>Title of dataset: Data for "Using physics-informed neural networks to predict the lifetime of laser powder bed fusion processed 316L stainless steel under multiaxial low-cycle fatigue loading".</p> <p>Name/institution/contact information: Dr. Michal Bartošák, Czech Technical University in Prague - Faculty of Mechanical Engineering, email: michal.bartosak@fs.cvut.cz.</p> <p>Date of data collection: The data were collected between 2021 and 2024.</p> <p>File name structure: The data consists of two files: "316L_fatigue_and_defects.xls," which contains fatigue lifetime data and defect characteristics, and an associated description file, "read_me.txt."</p> <p>See "https://doi.org/10.1016/j.ijfatigue.2024.108608" for the associated article and a detailed description of the methods.</p>
Residual stress in 316L stainless steel benchmark additively manufactured arches determined by neutron diffraction and snychtron X-ray diffraction
<p>Residual stress data recorded as part of the EASI-STRESS project. </p> <p>The data presented is the residual stress in three orthogonal directions determined by neutron diffraction (SALSA at ILL) and synchrotron X-ray diffraction (P07 and P61A at Desy operated by Hereon and ID15A at ESRF). The data is for residual stress in a benchmark 316L stainless steel arch manufactured by laser powder bed fusion using a MetalFAB1 additive manufacturing machine. The arch is square topped with dimensions of nominally 20 mm in both the x and y direction (in plane). The overhang which creates the arch shape runs parallel to the y direction. The height of the arch is nominally 10 mm with the ligament above the over hang being of 2 mm nominal thickness. </p> <p>Data for two lines is presented: line 1 runs from the centre of the top surface (defined at the origin) down into the arch (defined as positive z direction). Line 2 runs along to the x axis at a depth of 1 mm into the arch. </p> <p>The stress-free reference used was a reference comb cut from an identically made arch.</p> <p>Different gauge volume sizes and shapes were defined at each institution, all measurement locations indicate the centre of the gauge volume during each measurement. gauge volumes were: P07 200 x 200 µm2, ~1.9 mm, P61A 150 × 150 µm2 ~2.9 mm, ID15A 200 ´ 50 µm2 ~1.7 mm, SALSA 0.6 x 0.6 x2 mm3.</p>
As built and post heat treatment residual stresses in 316L stainless steel additively manufactured benchmark arches
<p>The data presented is the residual stress in three orthogonal directions determined by neutron diffraction (SALSA at ILL) and synchrotron X-ray diffraction (P07 and P61A at Desy operated by Hereon). The data is for residual stress in a benchmark 316L stainless steel arches manufactured by laser powder bed fusion using MetalFAB1 and EOS M290 additive manufacturing machines. These arches are square topped with dimensions of nominally 20 mm in both the x and y direction (in plane). The overhang which creates the arch shape runs parallel to the y direction. The height of the arch is nominally 10 mm with the ligament above the over hang being of 2 mm nominal thickness. </p> <p>Data for part in both an as built and after a 700 °C 2 hour heat treatment are presented. The measurement line runs from the centre of the top surface (defined at the origin) down into the arch (defined as positive z direction).</p> <p>The stress-free reference used was a reference comb cut from an identically made arches. For heat treated samples, heat treated combs were used.</p> <p>Different gauge volume sizes and shapes were defined at each institution, all measurement locations indicate the centre of the gauge volume during each measurement. gauge volumes were: P07 200 x 200 µm2, ~1.9 mm, P61A 150 × 150 µm2 ~2.9 mm, ID15A 200 x 50 µm2 ~1.7 mm, SALSA 0.6 x 0.6 x2 mm3.</p>
Fatigue life of S960 high strength steel with laser cladded functional surface layers
<p>This dataset to paper: Fatigue life of S960 high strength steel with laser cladded functional surface layers, which includes mainly raw data for S-N curves.</p>
Dataset for use with The Role of Hydrogen in Decarbonizing US Iron and Steel Production
<p>Sqlite file containing the database used with the Temoa model to produce the results presented in "The Role of Hydrogen in Decarbonizing US Iron and Steel Production"</p>
The Oberwolfach Steel-Profile benchmark revisited
<p>This archive features the matrix data for the reimplemented Steel Profile benchmark from the Oberwolfach Collection. In contrast to the original, it uses FENICS, rather than ALBERTA, for the finite element semi-discretization in space.</p> <ol> <li>Download and unzip all ZIP files for the dimensions you need (numbers in the names are dimensions of the system)</li> <li>add getrail.m to the same folder</li> <li>download mmread.m from https://math.nist.gov/MatrixMarket/mmio/matlab/mmiomatlab.html</li> </ol> <p>See `help getrail` for usage instructions.</p>
Figures 24–27. Rhinolaemus niueensis. 24 in A revision of the genus Rhinolaemus Steel (Coleoptera: Laemophloeidae)
Figures 24–27. Rhinolaemus niueensis. 24) Pronotum, SEM photomicrograph. 25) Abdominal segment VII. 26) Male genitalia. 27) Parameres and apex of median lobe.
Figures 16–19. Rhinolaemus niueensis, SEM photomicrographs. 16 in A revision of the genus Rhinolaemus Steel (Coleoptera: Laemophloeidae)
Figures 16–19. Rhinolaemus niueensis, SEM photomicrographs. 16) Head, ventral. 17) Mesocoxa. 18) Pro- and mesosternum. 19) Metasternum and abdomen.
Figures 9–11. Rhinolaemus tuberculatus. 9 in A revision of the genus Rhinolaemus Steel (Coleoptera: Laemophloeidae)
Figures 9–11. Rhinolaemus tuberculatus. 9) Female. 10) Male. 11) Illustration of holotype (Grouvelle 1878).
Figures 5–8 in A revision of the genus Rhinolaemus Steel (Coleoptera: Laemophloeidae)
Figures 5–8. Rhinolaemus spp., head and pronotum. 5) R. maculatus, female holotype. 6) R. maculatus, male. 7) R. niueensis, female. 8) R. niueensis, male.
Figures 20–23. Rhinolaemus spp., male genitalia. 20 in A revision of the genus Rhinolaemus Steel (Coleoptera: Laemophloeidae)
Figures 20–23. Rhinolaemus spp., male genitalia. 20) R. maculatus. 21) R. maculatus, parameres and apex of median lobe. 22) R. tuberculatus, genitalia. 23) R. tuberculatus, parameres and apex of median lobe.
Figures 12–15. Rhinolaemus niueensis, SEM photomicrographs. 12 in A revision of the genus Rhinolaemus Steel (Coleoptera: Laemophloeidae)
Figures 12–15. Rhinolaemus niueensis, SEM photomicrographs. 12) Head, dorsal. 13) Puncturation of head. 14) Antennal club antennomeres. 15) Elytra.
K-band FMCW Data for Steel in Sandstone
<p>This dataset contains FMCW signal returns in the K-band for a steel rod and bolt inserted into apertures in sandstone. 3 ml of fluid ingress data is also included. The associated ppt provides key equipment and experimental parameters.</p>
Risk and anomaly sensor for the steel production [CSS5] - Integrated
<p>The CAPRI risk and anomalies sensor for the steel production aims to provide an estimate of the processing risk for intermediate products at different stages of the processing chain. This risk estimation will be the basis for a decision support system, which will provide recommendations regarding the further processing of a semi-product. For instance, if an item will likely fail to meet the quality specification for its original customer order, the support system could recommend changing the target order the product will be assigned to, or it could recommend to immediately recycle the item or to do some reprocessing. The earlier we identify a problematic item, the less energy and time needs be wasted in its further processing, therefore the solution can lead to substantial savings both in cost and CO2 emissions.</p> <p>This video describes the integration of the risk and anomalies sensor into CAPRI's cognitive automation platform (CAP).</p>
Dynamical simulation of EBSD master pattern of chi-phase in steel
<p>Dynamical simulation of an electron backscatter diffraction (EBSD) master pattern of a chi-phase (Fe<sub>36</sub>Cr<sub>15</sub>Mo<sub>7</sub>) in steel (<em>I<span class="math-tex">\(\bar{4}\)</span>3m</em>, <em>a</em> = 8.854 Å) (see Kasper [1954], doi:<a href="https://doi.org/10.1016/0001-6160(54)90066-8">10.1016/0001-6160(54)90066-8)</a>. The master pattern was simulated with EMsoft v5.0. 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/steel_chi_mc_mp_20kv.h5") s.plot()</code></pre> <p>The PNG files show the stereographic projection of the upper and lower hemispheres of the master pattern from 20 kV. The remaining files are input and output files to the EMsoft programs EMmkxtal (output: steel_chi.xtal), EMMCOpenCL (input: steel_chi.xtal, mcopencl.nml; output: steel_chi_mc_mp_20kv.h5) and EMEBSDmaster (input: BetheParameters.nml, ebsdmaster.nml, steel_chi_mc_mp_20kv.h5; output: added to existing steel_chi_mc_mp_20kv.h5).</p>
Datasets describing optimization the cutting regime in the turning of AISI 316L steel based on the NSAG-II and NSAG-III multicriteria algorithms.
<p><em>This work shows the multi-criteria data analysis of the dry and MQL turning process of AISI 316L steel using the evolutionary algorithms of non-dominant class II and III (NSAG-II and NSAG-III). The wear of the cutting tool (VB), the energy consumption (E) and the machining time (t) are used as analysis variables, with the aim of minimizing the wear of the cutting tool based on the optimal selection of parameters. When comparing the results obtained from both methods, we found that NSAG-III was the best alternative for selecting parameters in the turning of specimens, with fewer tool wear and more efficient use of energy consumption.</em> <em>Interpretation of this data</em></p>
A Multimodal Dataset on Stainless Steel for Electrochemical Corrosion Studies: Optical Microscopy and Linear Sweep Voltammetry
<p>The upload includes optical and electrochemical data for corrosion experiments.</p> <p>This dataset presents the results of an experimental study conducted to investigate the electrochemical behavior of electropolished Stainless Steel 316L (SS316L) samples immersed in NaCl solutions. The combination of Linear Sweep Voltammetry (LSV) and optical microscopy techniques was employed to gather comprehensive insights into the electrochemical processes occurring on the surface of the stainless steel samples.</p> <p>The samples used in the experiment were electropolished SS316L, chosen for its widely recognized corrosion resistance properties and frequent application in various industrial sectors. LSV was performed on the samples in a potential range of -0.5V to 1.35V, (vs 3.4M KCl Ag/AgCl). NaCl solutions with concentrations of 5mM, 10mM, and 50mM were prepared to simulate different electrolyte conditions.</p> <p>Two different scan rates, 50mV/s and 100mV/s, were applied during the LSV experiments to observe the effect of varying scan rates on the electrochemical behavior of the SS316L samples. The scan rates were chosen to cover a range commonly encountered in electrochemical studies.</p> <p>List of experiments:</p> <ul> <li> 5 mM solution, 100mV/s scan rate</li> <li> 10 mM solution, 50mV/s scan rate</li> <li> 10 mM solution, 100mV/s scan rate</li> <li> 50 mM solution, 50mV/s scan rate</li> <li> 50 mM solution, 100mV/s scan rate</li> </ul> <p>The dataset is accompanied by animated plots. The top left plot shows electrochemistry data, bottom left - average normalized intensity and derivative of intensity. Top right - original optical images, bottom right - normalized images.</p> <p>The scale for optical images: 1px = 480 nm. Axes on images are in pixels</p> <p>Jupyter notebook with the code, used to create videos included. We recommend opening the Jupyter notebook file in a Python 3 environment.<br> </p>
Data from: Effect of pH regulation by microbes on corrosion behaviour of duplex stainless steel 2205 in acidic artificial seawater environment
Sulphate reducing bacteria (SRB) can regulate environmental pH because of their metabolism. Since local acidification results in pitting corrosion, the potential capacity of pH regulation by SRB would have important consequences for electrochemical aspects of the bio-corrosion process. This study focussed on identifying the effect of pH on the corrosion of duplex stainless steel (DSS) 2205 in a nutrient rich artificial seawater medium containing SRB species, Desulfovibrio vulgaris. DSS samples were exposed to the medium for 13 days at 37 0C at pH ranging from 4.0 to 7.4. The open circuit potential value (OCP), sulphide level, pH and number of bacteria in the medium were recorded daily. Electrochemical impedance spectroscopy (EIS) and potential dynamic polarization were used to study the properties of the films and corrosion behaviour of the material. Inductively coupled plasma mass spectrometry (ICPMS) was used to measure the concentration of cations Cr, Fe, Ni, Mo, Mn in the experimental solution after 13 days. Scanning electron microscopy (SEM) and Energy Dispersive X-Ray Spectroscopy (EDX) were used for surface analysis. The results showed the pH changed from acidic values set at the beginning of the experiment to approximately pH 7.5 after 5 days due to bacterial metabolism. After 13 days, the highest iron concentration was in the solution that was initially at pH 4 accompanied by pitting on the stainless steel. Sulphide was present on all specimens but with more sulphide corrosion products at pH 4. The results of this study suggest that the corrosion process for the first few days exposure at low pH was driven by pH in solution rather than by bacteria. The increasing pH during the course of the experiment slowed down the corrosion process of materials originally at low pH. The nature and mechanism of SRB attack on duplex stainless steel at different acidic environments are discussed.
Experimental data and videos of steel wide-flange and HSS columns tested under collapse-consistent loading histories
<p>The experimental dataset is comprised of the following items:</p> <p>(a) a <a href="https://zenodo.org/api/files/0e8fafb8-f08d-4e19-8515-643b675faa1d/Suzuki_Lignos-2020-Experimental_Program.pdf">summary table</a> that contains the geometric and material parameters for each test specimen along with deduced quantities of interest with regard to the collapse behavior of each specimen;</p> <p>(b) the deduced experimental data of <a href="https://zenodo.org/api/files/0e8fafb8-f08d-4e19-8515-643b675faa1d/Suzuki_Lignos-2020-Deduced_Data_Wide_Flange_Columns.xlsx">wide-flange specimens</a>;</p> <p>(c) the deduced experimental data of <a href="https://zenodo.org/api/files/0e8fafb8-f08d-4e19-8515-643b675faa1d/Suzuki_Lignos-2020-Deduced_Data_HSS_Columns.xlsx">hollow structural shape (HSS) specimens</a>.</p> <p>The deduced data contains normalized axial shortening, chord rotation and deduced moment (without member p-delta).</p> <p>(d) The <a href="https://zenodo.org/api/files/0e8fafb8-f08d-4e19-8515-643b675faa1d/Suzuki_Lignos-2020-Experimental_Program.pdf">summary report of the experimental program</a> along with an appendix of photos of each specimen is included together with the dataset.</p> <p>(e) characteristic videos for each specimen that demonstrate the member performance through the loss of their lateral load carrying capacity.</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.