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360 results for “steel”
Risk and anomaly sensor for the steel production [CSS5]
<p> </p> <p> </p> <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>
Scale Soft Sensor for steel semi-products [CSS4]
<p> </p> <p>During production of steel bars in hot rolling mills there is a formation of scale on the surface of the products. It is differentiated between two scale types: primary scale and secondary scale. During the reheating of the product in the furnace the scale is called primary scale. After reheating and before rolling the scale is removed by a descaler, for instance with high water pressure. After rolling, while the product is located on the cooling bed, the secondary scale grows up.</p>
Three-Dimensional Characterization of Deformation-induced Damage in Dual Phase Steel using Deep Learning
<p>High performance sheet metals with a multi-phase microstructure suffer from deformation induced damage formation during forming in the constituent phases but importantly also where these intersect. To capture damage in terms of the physical processes in three dimensions (3D) and its stochastic nature during deformation, two challenges remain to be tackled: First, bridging high resolution analysis towards large scales to consider statistical data and, second, characterising in 3D with a resolution appropriate for sub-micron sized voids at a large scale. Here, we present how this can be achieved using panoramic scanning electron microscopy (SEM), metallographic serial sectioning, and deep-learning assisted automatic image analysis. This brings together the 3D evolution of active damage mechanisms with volumetric and environmental information for thousands of individual damage sites. We also assess potential surface preparation artefacts in 2D analyses. Overall, we find that for the material considered here, a dual phase (DP800) steel, martensite cracking is the dominant but not sole origin of deformation induced damage and that for a quantitative comparison of damage density, metallographic preparation can induce additional surface damage density far exceeding what is commonly induced between uniaxial straining steps.</p> <p>https://doi.org/10.1016/j.matdes.2023.112108</p>
GEOLAB-PEBSTER-Deltares: Small-scale experiments on Piled Embankments with Basal Steel Mesh Reinforcement - Measurements.
<p><strong>DOI: 10.5281/zenodo.12627309</strong></p> <h1><strong>Small-scale experiments on basal steel-mesh reinforced piled embankments (PEBSTER, a transnational access project of GEOLAB)</strong></h1> <p>Welcome to the GEOLAB-PEBSTER-Deltares project on Zenodo! This repository contains the measurement data from four small-scale experiments on Piled Embankments with Basal Steel Reinforcement (PEBSTER). Pile-supported (PS) embankments with basal geosynthetic reinforcement (GR) are commonly built in soft soil areas (van Eekelen and Han, 2020). Steel mesh reinforcement (SR) is particularly appealing for high embankments (Topolnicki et al., 2019). Its high axial stiffness, compared to geosynthetics, reduces horizontal deformation at the embankment base, minimizes bending moments in the piles, and ultimately enhances embankment stability.</p> <h2>PEBSTER</h2> <p>This dataset includes measurements from four small-scale tests on steel-reinforced piled embankments, conducted in the Deltares laboratory (van Eekelen et al., 2024a,b). These tests are part of a broader research initiative called Piled Embankments with Basal Steel Reinforcement (PEBSTER), which also includes a large-scale test (Schneider et al., 2024a,b). The small-scale tests were conducted at Deltares, Delft, Netherlands, while the large-scale test was conducted in Darmstadt, Germany, at the Institute of Geotechnics, Technical University of Darmstadt.</p> <h2>GEOLAB</h2> <p>GEOLAB is a project of the European Union’s Horizon 2020 research and innovation program under Grant Agreement No. 101006512, addressing Europe's Critical Infrastructure (CI) challenges in the water, energy, urban, and transport sectors.</p> <p>The GEOLAB Research Infrastructure (RI) consists of 11 unique installations across Europe to study subsurface behavior and its interaction with structural CI elements and the environment. During the GEOLAB Transnational Access (TA), users outside the consortium gained access to the GEOLAB installations to perform research and innovation.</p> <h2>Dataset of four small-scale experiments</h2> <p>Here, we share the measurement data from the small-scale experiments that were part of one of the GEOLAB TA projects: PEBSTER. Four piles (diameter 0.1 m, centre to centre 0.55m) passed through a steel plate, that supported a foam cushion, that was sealed and soaked. A tap allowed for drainage of the foam cushion, simulating the consolidation of the subsoil between the piles. The 0.55 m high embankment consisted of medium coarse sand, and was reinforced at its base with a steel mesh reinforcement. A surcharge load up to 100 kPa was applied with a water cushion.<br><br><span>The load distribution is measured by pressure cells and load transducers. </span>Soil strains and displacements were monitored at five elevations within the fill, utilizing distributed fibre optic sensing (DFOS) technology from the Nerve-Sensors family, as depicted in Figure 2. Additionally, the steel mesh reinforcement was extensively instrumented with optic fibres.</p> <h2>PEBSTER Research group</h2> <p>The project was conducted by a research group that includes Deltares, Netherlands, the Institute of Geotechnics of the Technical University of Darmstadt, Germany, Keller (Germany, France, Poland), SHM System, Poland, and FOLAB, Germany.</p> <p> </p>
A Dataset for In-situ synchrotron tomography experiments to investigate anisotropic damage of line pipe steel
<p>In this study, anisotropic ductility and associated damage mechanisms of a grade X100 line pipe steel were investigated using in-situ synchrotron-radiation computed tomography (SRCT) of notched round bars. Line pipe materials have anisotropic mechanical properties, such as tensile strength, ductility and toughness. Specimens were tested for loading along both rolling (L) and transverse (T) directions. The <em>in-situ</em> data collected allowed quantifying both specimen deformation (evolution of the cross section) and microscopic damage parameters such as porosity, void shape and void orientation. The data sets provide here are related to the paper <em>"On the origin of the anisotropic damage of X100 line pipe steel, Part I: in-situ synchrotron tomography experiments"</em> being published in <a href="https://www.springer.com/journal/40192">Integrating Materials and Manufacturing Innovation</a>. For each testing direction, dataset are provided using hdf5 and xdmf standarded exchange format. A compressed file is also provided in connection with the analyses explained in the article.</p>
Experiment to measure the thickness loss due to corrosion of a 5mm uncoated steel sample exposed close to the shore during 6 months
<p>This is the data obtained during a experiment conducted in Grand Canary Island close to the shore to collect data using CEIT's ultrasound sensor node attached to a 5mm uncoated steel sample. We collected during 6 months the measured thickness taking two measures per day. The sample was considerably degraded during that time. The estimated Corrosion Rate (134um/year) was in coherence with the typical corrosion rates in real environments (100-200um/year). </p>
Flow behaviour of magnetic steel powder
<p>These are the data used in the article "Flow behaviour of magnetic steel powder". This dataset includes:</p> <ul> <li>Optical micrographs used to determine the mean size and circularity in each size class after sieving <ul> <li>Scale bars are included for two images and may be used to determine the scale of all micrographs included - all images were taken using the same microscope, camera and software. All images are as-recorded.</li> </ul> </li> <li>X-ray diffractograms used to determine the fraction of martensite present in each sample <ul> <li>Calibration data for the diffraction instrument are included</li> </ul> </li> <li>Vibrating sample magnetometry data, used to determine the saturation and remanent magnetisation.</li> <li>Shear cell metrics derived automatically form the shear cell tests</li> <li>Hall flow times, both with and without drying</li> <li>Angle of repose dat</li> <li>Videos of all angle of repose tests</li> </ul> <p>Other data can be provided on request.</p>
Nanobeam electron diffraction dataset from ion irradiated DIN 1.4970 austenitic stainless steel with G-phase precipitates collected on pixelated TVIPS detector
<p><strong>Summary</strong></p> <p>This is a 4D scanning transmission electron microscopy (4D STEM) dataset collected in near-parallel beam mode (NBED) from a sample of ion irradiated austenitic (FCC) stainless steel of the DIN 1.4970 specification, collected on a high quality pixelated detector inside a transmission electron microscope (TEM). The dataset is represented by a 4D array, comprising a 2D grid of scan points, with each scan point mapping to an electron diffraction spot pattern. From this kind of dataset it is possible to derive local crystal orientations and strains. The dataset is in the .hspy format, the native hdf5 format of the <a href="https://zenodo.org/record/5082777">HyperSpy</a> library.</p> <p>The main features in this dataset are:</p> <ul> <li>a single crystal of the matrix is sampled, close to a 110 zone axis</li> <li>inside the matrix, irradiation induced G-phase precipitates of 10-20 nm in size can be found which contribute weakly to some of the diffraction patterns. From these patterns it is possible to derive the orientation relationship of the precipitates with respect to the matrix.</li> <li>irradiation also resulted in the formation of faulted frank loops, which also show up in some diffraction patterns.</li> </ul> <p><strong>Material and sample preparation</strong></p> <p>The sample was prepared from DIN 1.4970 steel (composition by weight: 15% Ni, 15% Cr, 1.8% Mn, 1.2% Mo, 0.5% Ti, 0.5% Si, 0.1% C, Fe Bal.) with the intended application of nuclear fuel cladding material. The material was originally in the shape of thin walled tubes and cold worked to 24% (measured by cross sectional area reduction). The material was aged for 2 hours at 800 °C. It was then irradiated to 40 dpa surface damage as calculated using the SRIM program and the Kinchin and Pease model with displacement energy of 40 eV, using 4.5 MeV Fe<sup>2+</sup> ions with a flux of arround 9x10<sup>11</sup> ions.s<sup>-1</sup>.cm<sup>-2</sup>. The irradiation was performed at 600 °C. Full details on the material, irradiation conditions, and context can be found in:</p> <p>Cautaerts, N., Delville, R., Stergar, E., Pakarinen, J., Verwerft, M., Yang, Y., Hofer, C., Schnitzer, R., Lamm, S., Felfer, P., & Schryvers, D. (2020). The role of Ti and TiC nanoprecipitates in radiation resistant austenitic steel : A nanoscale study. <em>Acta Materialia</em>, <em>197</em>, 184–197. https://doi.org/10.1016/j.actamat.2020.07.022</p> <p>A TEM sample was prepared by regular focused ion beam (FIB) lift-out techniques in a Ga-ion FIB. Additional details on the dataset can be found in the paper and supplementary materials of</p> <p>Cautaerts, N., Rauch, E. F., Jeong, J., Dehm, G., & Liebscher, C. H. (2021). Investigation of the orientation relationship between nano-sized G-phase precipitates and austenite with scanning nano-beam electron diffraction using a pixelated detector. <em>Scripta Materialia</em>, <em>201</em>, 113930. https://doi.org/10.1016/j.scriptamat.2021.113930</p> <p><strong>Microscopy parameters and data collection</strong></p> <p>NBED was performed in a JEM-2200FS TEM (JEOL) operating at 200 kV. The microscope was operated in nanobeam diffraction mode with the smallest spot size (Spot 5). The probe diameter was ~ 1 nm with a semi-convergence angle of ~0.5 mrad. Data was collected on a TemCam-XF416 pixelated CMOS detector (TVIPS). The camera length as indicated in the operating software was 80 cm, and collected images were 1024 by 1024 in size (hardware binning of 4). The dataset comprises 260x200 scan points and pixel depth is 2 bytes (unsigned 16 bit integers).</p> <p><strong>Data processing</strong></p> <p>The raw data was collected in the .tvips format. The original dataset was about 50 GB in size and can be shared upon request to the author. This dataset was converted to the .hspy format using the <a href="https://zenodo.org/record/4288857">TVIPSconverter</a> tool. In the conversion, the images were binned by an additional factor of 4 to a final size of 256x256. A median filter was also applied to the data to remove pixel noise.</p> <p><strong>Data characteristics</strong></p> <p>Scan shape: 260 x 200 pixels</p> <p>Image shape: 256 x 256 pixels</p> <p>Pixel dtype: uint16</p> <p>Scan pixel size: about 1 nm, scan dimensions were never calibrated</p> <p>Image pixel size: 0.01261 Angstrom<sup>-1</sup> / pixel</p> <p>Note that scale factors are not stored in the dataset! The dataset can be read with HyperSpy using the load function (please see the HyperSpy documentation) and the pixel scale can be set through the axes manager. It is highly recommended to have a working installation of <a href="https://zenodo.org/record/5075520">Pyxem</a> as well to process the data.</p> <p><strong>Additional notes</strong></p> <p>Data was collected with the TVIPS scan generator which can be quite buggy. The scan lines show "jitters" due to the unstable snake-scan pattern, hysteresis and instability.</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>
In-situ Heating-Stage EBSD Validation of Algorithms for Prior-Austenite Grain Reconstruction in Steel
<p>High temperature EBSD and dilatometry data from the manuscript "In-situ Heating-Stage EBSD Validation of Algorithms for Prior-Austenite Grain Reconstruction in Steel". This includes Gifs of the martensitic and bainitic phase transformations, individual frames as Tiff files and as CTF files. It also includes thermocouple read outs from the in-situ crucible and the raw data from the dilatometry experiments.</p>
Atom probe tomography data collection from DIN 1.4970 (15-15Ti) austenitic stainless steel irradiated with Fe ions
<p>This dataset comprises a large collection of atom probe tomography datasets collected from DIN 1.4970 alloy that was irradiated with Fe ions at different conditions. The DIN 1.4970 alloy is an austenitic stainless steel with 15 wt% Cr, 15 wt% Ni, a small addition of Ti. The full composition and characterization of our material can be found published elsewhere [1,2].</p> <p>Some of our material was subjected to ageing heat treatments at different temperatures for different times. Small samples of our original material and aged material was irradiated at the Michigan Ion Beam Laboratory in 2017 with 4.5 MeV Fe ions up to 40 dpa at an average dose rate of <span class="math-tex">\(2 \times 10^{-4}\)</span> dpa/s. This was done at three different temperatures: 300, 450, and 600 ºC. Atom probe samples were made of the irradiated layers (approximately 1.5 micron deep) with focused ion beam and mounted on Microtip coupons. APT measurements took place on three CAMECA LEAP-HR systems located at CAES in Idaho Falls, USA (files beginning with R33), at Montanuniversität Leoben in Leoben, Austria (R21) and at Friedrich–Alexander University in Erlangen, Germany (R56).</p> <p>The contents of this archive are:</p> <ul> <li>A folder containing the raw RHIT files</li> <li>A folder containing all the reconstructions and miscelaneous analysis files made by the author</li> <li>An excel file which indicates which measurement number stands for what material</li> <li>A suggested range file</li> </ul> <p>The RHIT files can only be used if one has access to the full IVAS 3.x version in order to make new reconstructions.</p> <p>The reconstructions and analysis folder can be useful to anyone. The folder buildup structure is similar to a project folder created by IVAS and should be directly importable into IVAS. Most folders are simply named after the RHIT file they were constructed from, though some have slightly modified names to include date of creation, extra information,... Inside all these folders you will find the recons folder and inside multiple reconstructions. At the deepest level you will find .pos files which can be read into free software such as python or <a href="http://threedepict.sourceforge.net/">3depict</a>. The range file that will give decent results on all these measurements is given at the top level; slight modifications may need to be applied for each measurement. Inside all folders you will also find numerous files (csv, png, jpg, ...) that were created by analyzing the data in IVAS. Sometimes the file names are very descriptive, sometimes less so. Sometimes these files were not saved to the default analysis folder but elsewhere on my drive. To be complete, I have moved all of these files into the top level folder. Therefore, besides the imagoAnalysis and recons folders, you will sometimes find additional folders and files in the folder. By different merging procedures, there may be multiple copies of the same files present as well. Unfortunately, the reconstructions and analysis folder is rather chaotic, as is the nature of file creation by IVAS.</p> <p>It is most instructive to start with the excel file at the top level of the archive. The first sheet contains some information, mostly the same as mentioned here. The second sheet pertains to the ion irradiations that were performed. The table colunms are self explanatory. Each irradiated sample was given a particular alias (first column), which relates it to the slot in the storage box in which it is stored. 5 different materials appear in the irradiations:</p> <ul> <li>T24 = tube, 24% cold worked. This represents the material as it was received from the manufacturer.</li> <li>T24-800C2h = the as-received material with an ageing heat treatment of 2 hours for 800 ºC applied.</li> <li>T24-600C4h = the as-received material with an ageing heat treatment of 4 hours for 600 ºC applied.</li> <li>T24-600C2868h = the as-received material with an ageing heat treatment of 2868 hours for 600 ºC applied.</li> <li>T46 = tube 46% cold worked. This represents another material received from the manufacturer</li> <li>AIM1 = another related material with a higher P and Si content obtained from another research institute</li> </ul> <p>All these materials were irradiated under different conditions as given in the subsequent columns. The irradiation parameters were drawn directly from reports produced by the lab, but we suspect some typos slipped into the reports. We do know for certain that the samples were irradiated up to a surface dose of 40 dpa, at least according to a <a href="http://www.srim.org/">SRIM calculation</a> with the K-P model. Atom probe results only pertain to T24 and T24-800C2h. A few measurements were conducted on T24-600C4h material but this material was not irradiated.</p> <p>The last sheet gives an overview of all the APT measurements included in this archive. The first column pertains to the sample alias in sheet 2: the irradiated disc from which the samples were made. The sample detail column details the history of the sample for convenience: T24 - <heat treatment conditions> - <irradiation conditions>. When in doubt, one can look up the sample alias in sheet 2. The filename pertains to the APT measurement RHIT file. For the 3 measurements performed in Leoben, RHIT files are not included in this archive. Finally a few details such as approximate ion count and some comments are included for some measurements.</p> <p>Funding: This work was supported by ENGIE [contract number 2015-AC-007 e BSUEZ6900]; the U.S. Department of Energy, Office of Nuclear Energy under DOE Idaho Operations Office Contract DE-AC07- 051D14517 as part of a Nuclear Science User Facilities experiment; and by the MYRRHA program in development at SCK-CEN, Belgium. Funding of the Austrian BMVIT (846933) in the framework of the program "Production of the future" and the "BMVIT Professorship for Industry" is gratefully acknowledged.</p> <p> </p> <p><a href="https://www.sciencedirect.com/science/article/pii/S0022311518300485">[1] N. Cautaerts, R. Delville, E. Stergar, D. Schryvers, M. Verwerft, Tailoring the Ti-C Nanoprecipitate Population and Microstructure of Titanium Stabilized Austenitic Steels, J. Nucl. Mater. 507 (2018) 177–187. doi:10.1016/j.jnucmat.2018.04.041.</a></p> <p> </p> <p><a href="https://www.sciencedirect.com/science/article/pii/S1359645418308103">[2] N. Cautaerts, R. Delville, E. Stergar, D. Schryvers, M. Verwerft, Characterization of (Ti,Mo,Cr)C Nanoprecipitates in an Austenitic Stainless Steel on the Atomic Scale, Acta Mater. 164 (2018) 90–98. doi:10.1016/J.ACTAMAT.2018.10.018.</a></p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Residual stresses in clad pressure vessel steel measured by contour method
<p>Data from contour method cut surfaces of low alloy steel plates clad in stainless steel. Two plates, each measuring 300 mm (length) x 200 mm (width) x 20 mm (thickness), were extracted from the outer cylindrical structure of a nuclear steam generator. The material was forged 18MND5 (French designation equivalent to A 508 Gr.3 Cl. 1). Stainless steel beads were then deposited, by submerged arc strip cladding, on the plates. One plate was clad in a single layer of AISI 309L, the second one was clad with a double layer, 309L followed by 308L. The datasets are in the form of lists of x, y, z coordinates, with one point per line, whitespace delimited in millimetres. Each cut has four files associated to it, two for each cut surface. For each surface, there is an outline file identifying the cut surface periphery and a points file containing the points lying on the surface. Two .mat files have also been uploaded, with the results from the analyses on the single and double layer clad plates.</p> <p>These measurements are part of a broader experimental investigation to better understand the role of residual stresses in underclad cracking. The contour method was used to characterise residual stresses in conjunction with neutron diffraction measurements. The details of the experimental procedure and other information will be found in the paper “Internal stresses in a clad pressure vessel steel during post-weld heat treatment and their relevance to underclad cracking" Cattivelli et al., soon to be published.</p>
Variation of texture anisotropy and hardness with build parameters and wall height in directed-energy-deposited 316L steel
<p>Raw data associated with a paper submission.<br> " Variation of texture anisotropy and hardness with build parameters and wall height in directed-energy-deposited 316L steel" submitted to Additive Manufacturing.</p> <p>Contained are all the raw images used in figures, as well as csv's of any data pltoted in graphs.</p> <p>Raw images captured during printing of various processing parameters<br> EBSD scans (.ctf) of all disucssed samples </p> <p>Wall definitions (EBSD compared to paper)<br> Wall 1 - Wall A1 300 W 2750 mm/s<br> Wall 2 - Wall D 500 W 2250 mm/s<br> Wall 3 - Wall B 300 W 2250 mm/s<br> Wall 4 - Wall C 500 W 2750 mm/s<br> Wall 5 - Wall A2 300 W 2750 mm/s</p>
EBSD datasets for cross-sectioned structural steel hardness indentations - Adaptive Domain Misorientation
<p>Open access datasets for structural steel hardness indentations from the following publication: Ultramicroscopy 2021, Volume 222: <a href="https://doi.org/10.1016/j.ultramic.2021.113203">https://doi.org/10.1016/j.ultramic.2021.113203</a></p> <p>Files included:</p> <ul> <li>Adaptive domain misorientation calculated for Indentation 1 and 2 using misorientation thresholds (Delta theta) 0.5deg and 2deg, corresponding to dense dislocation walls and sub-grain boundaries</li> <li>Indentation 2: Raw dataset and associated mask file for excluding the edge of the data</li> </ul> <p>The methodology for analysing and plotting of the data is found at: <a href="https://doi.org/10.5281/zenodo.4430623">https://doi.org/10.5281/zenodo.4430623</a></p> <p>For further information visit: Aalto University Wiki - <a href="https://wiki.aalto.fi/display/EMDIDS">https://wiki.aalto.fi/display/EMDIDS</a></p>
Data set from ambient vibration monitoring and static loading of a steel stringer bridge subject to imposed damage
<p>This data set contains structural response measurements acquired from the Route 345 Bridge over Big Sucker Brook in Waddington, NY prior to demolition and replacement. This steel stringer bridge was instrumented with dual-axis accelerometers and strain transducers and response measurements were obtained prior to and following mechanically imposed damages. Accelerometer measurements were obtained under vehicle passes and strain measurements were acquired during static loading of the span with a truck positioned at three prescribed locations. All measurement data is contained in a single h5-file (hierarchical data format version 5). The data is shared with the intent of promoting the advancement of structural health monitoring and vibration-based damage detection. </p> <p><strong>Version 2 Note: </strong>This version corrects the strain measurement data. Due to an index counter error in the script that compiled the strain measurement data into the h5-file, the strain measurement data in the Version 1 data were incorrectly sourced from a single scenario. The strain measurements are corrected in this new version. No other changes were made in the h5-file.</p>
Exiobase HYBRID | Green steel version
<h2>Description</h2> <p>This repository contains all data and code to extend the <a href="../records/10148587" target="_blank" rel="noopener">hybrid-units version of EXIOBASE</a> to account for new innovative steelmaking routes envisaged to be deployed in the EU to meet decarbonization targets for the steel industry. The new model was built by adopting the <a href="https://doi.org/10.5334/jors.473">MARIO</a> open-source framework. </p> <p>The database is an improved version of the one described in the following open-access paper (DOI: <a href="https://doi.org/10.1088/1748-9326/ad5bf1">https://doi.org/10.1088/1748-9326/ad5bf1</a>)</p> <h2>What's new</h2> <ul> <li>The new technologies have been characterized for all regions, assuming each inventory to be the same in all regions but differentiated by regional import patterns of each commodity.</li> <li>A slight aggregation on electricity production activities and commodities have been also performed, to nowcast electricity production mixes to 2024 based on <a href="https://ember-climate.org/data/data-tools/data-explorer/">Ember data.</a> Data from Ember have been rearranged to calculate electricity mixes by year and Exiobase regions</li> <li>The list of steel production technologies have been extended. Full list in the table below</li> </ul> <p>The database implements in the EU the following new activities and commodities:</p> <table> <tbody> <tr> <td><strong>New activities</strong></td> <td><strong>New commodities</strong></td> </tr> <tr> <td>Manufacturing of steam reformer</td> <td>Steam reformer</td> </tr> <tr> <td>Manufacturing of electrolyser</td> <td>Electrolyser</td> </tr> <tr> <td>Hydrogen production with steam reforming</td> <td>Steam reforming hydrogen</td> </tr> <tr> <td>Hydrogen production with electrolysis</td> <td>Electrolysis hydrogen</td> </tr> <tr> <td>DRI-EAF-NG</td> <td> </td> </tr> <tr> <td>DRI-EAF-NG-CCS</td> <td> </td> </tr> <tr> <td>DRI-EAF-COAL</td> <td> </td> </tr> <tr> <td>DRI-EAF-COAL-CCS</td> <td> </td> </tr> <tr> <td>DRI-EAF-H2</td> <td> </td> </tr> <tr> <td>DRI-EAF-BECCS</td> <td> </td> </tr> <tr> <td>DRI-SAF-BOF-NG</td> <td> </td> </tr> <tr> <td>DRI-SAF-BOF-H2</td> <td> </td> </tr> <tr> <td>DRI-SAF-BOF-BECCS</td> <td> </td> </tr> <tr> <td>SR-BOF</td> <td> </td> </tr> <tr> <td>SR-BOF-CCS</td> <td> </td> </tr> <tr> <td>BF-BOF-CCS-73%</td> <td> </td> </tr> <tr> <td>BF-BOF-CCS-86%</td> <td> </td> </tr> <tr> <td>BF-BOF-BECCSmax</td> <td> </td> </tr> <tr> <td>BF-BOF-BECCSmin</td> <td> </td> </tr> <tr> <td>AEL-EAF</td> <td> </td> </tr> <tr> <td>MOE</td> <td> </td> </tr> </tbody> </table> <p> </p> <p>Extended documentation of database adjustment and extension methodology available among the files in this repository. </p> <h2> </h2> <h2>Instructions</h2> <p>To use the database, please install MARIO following the <a href="https://mario-suite.readthedocs.io/en/latest/intro.html#installation">instructions.</a> The database can be parsed by using the following command<br><br></p> <div> <div>db = mario.parse_from_txt(</div> <div> path='PATH/TO/THE/FOLDER/WHERE/DATA/FROM/THIS/REPOSITORY/ARE/STORED',</div> <div> mode='coefficients',</div> <div> table='SUT',</div> <div>)</div> </div> <p> </p>
Dataset of Hyperspectral Melt Pool Signatures and Thermal Anomalies in DED of 316L steel
<p><strong>Description of the dataset</strong><br>The dataset includes in-situ melt pool signatures (hyperspectral NIR images) during the Directed Energy Deposition of 316L steel for several classes of thermal anomalies. Thermal anomalies were created during the process by varying the scanning speed.</p> <p>Samples were printed on the MiCLAD machine at the Vrije Universiteit Brussel (Belgium).</p> <p>Process and acquisition parameters:</p> <ul> <li>Hardware: <ul> <li>Machine: MiCLAD (Vrije Universiteit Brussel)</li> <li>Laser: High-YAG BIMO 1064nm, 2.55mm fibre, flat-top</li> <li>Nozzle: Harald-Dickler HighNo 4.0</li> </ul> </li> <li>Process parameters: <ul> <li>Laser power: 600 W</li> <li>Scanning speed: 500/700/900/1100/1300 mm/min</li> <li>Powder: 316L 45-105 um</li> <li>Powder flow rate: 3.5 g/m</li> <li>Layer thickness: 0.2 mm</li> </ul> </li> <li>Image characteristics: <ul> <li>Camera: 3D-One Avior AX-M25NIR</li> <li>Hyperspectral filter layout: 5x5 (25 wavelengths per image)</li> </ul> </li> </ul> <p><strong>Description of the files</strong></p> <ul> <li>CSV dataset (hyperspectral_nir_meltpool_dataset.csv): List of filename, sample, label, time (ms), X and Z position (mm) and local scanning speed (mm/min) for all melt pool signatures. Thermal anomalies are labelled accordingly: <ul> <li>0 : baseline</li> <li>1 : edge</li> <li>2 : underheat</li> <li>3 : strong underheat</li> <li>4 : overheat</li> <li>5 : strong overheat</li> </ul> </li> <li>Melt pool signatures (hyperspectral_nir_meltpool_images_*.zip): Raw .tif thermal images of the melt pool taken in-situ. The raw images must debayered to retrieve the spectral information, see the Python function and example script. </li> <li>Python debayer function (debayer.py): Debayering function to retrieve the spectral information from the raw images. </li> </ul>
Initial Simulation Results Using ModularBuildingPy on a High-Rise Modular Steel Building
<p>These are output files from 'ModularBuildingPy', a Python-based tool designed for numerical modeling and analysis of volumetric modular steel buildings. Refer to <a href="https://mbbatukan.github.io/ModularBuildingPy/">the documentation</a> for more information. </p>
X-ray scattering tensor-tomography dataset for a steel wire using the austenitic {220}-peak.
<p>Experimental data from a scanning-probe wide angle scattering experiment performed at the cSAXS beamline at teh Swiss Light Source at the Paul Scherrer Institure in Villigen, Switzerland.</p> <p>The file-format is that used in by the software package mumott (mumott.org).</p> <p>The sample is a tangled knot of hard-tempered steel. The detector images have been azimuthally re-grouped and only the intensity of the austeinte {220} peak is included in 48 separrate azimuthal bins.</p>
Creep and stress relaxation data for a martensitic steel at 500°C
<p>The files here uploaded describe the results of creep and stress relaxation tests performed on a martensitic steel at 500 °C. Data are provided both as .txt files and as excel files.</p> <p>Tests were performed on cylindrical sample, with a gauge length of 28 mm and diameter 5.6 mm</p> <p>Four creep tests were performed under 210, 230, 250 and 270 MPa stresses and stopped after 1% creep strain. Then they were unloaded and the anelastic contraction was recorded. Data are given in the form of creep strain vs. time.</p> <p>Two repeated stress relaxation tests were performed, with initial stresses of 270 MPa and 300 MPa. Samples were re-loaded and subsequently relaxed for a few times. In the 270 MPa test, before relaxation the sample was crept for a 0.1% strain.</p> <p>These data were used for a paper published on Metals.</p> <p> </p> <p> </p>
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