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287 results for “alloy”
Analytical Electron Microscopy of Grain Boundary Segregation: Application to Al-Zn-Mg-Cu (7xxx) Alloys - Supporting Data
<p>Data to reproduce the figures reported in the manuscript Analytical Electron Microscopy of Grain Boundary Segregation: Application to Al-Zn-Mg-Cu (7xxx) Alloys. Published open access in Materials Characterization <a href="https://doi.org/10.1016/j.matchar.2019.06.016">https://doi.org/10.1016/j.matchar.2019.06.016</a></p>
Rapid portabilization of elasto-chemical evolution data for dental Ti-Cr alloy microstructure through sparsification and tensor computation
<ol> <li><strong>c_theta_interpolation_function_data.csv</strong>: Interpolation function is utilized in phase field model to interpolate the material properties along the interface region. In this work, the corresponding material property that has been interpolated is the elasticity tensor. The csv file (c_theta_interpolation_function_data.csv) consists of the data of the interpolation function <strong>h(theta,c)</strong> given by the expression <em><strong>h = 1/(1+ exp(-theta(2c-1)))</strong></em>. The input feature "c" in the weighted input feature for the sigmoidal function is the mole fraction of Chromium in the two metastable phase regions in the binary Ti-Cr alloy undergoing phase decomposition. The "c" column in the table represents the data of Chromium composition. For the present study, the value of weighted parameter in the input feature is taken as equal to 10 i.e. theta = 10 . Hence, the column h(10,c) represents the data of the interpolation function used by the phase field simulation (Eq. 8 in the paper). To illustrate on how the choice of theta alters the values of h, this csv file also presents four additional columns of h corresponding to different constant values of theta (theta = 5, 15, 20 and 50). The steepness of the sigmoidal interpolation function increases as theta increases, and the graphical representation of the table can be accessed at <a title="c-theta-interpolation-function" href="https://interpolationfunction.streamlit.app/" target="_blank" rel="noopener">https://interpolationfunction.streamlit.app/</a>. </li> <li><strong>spatial-coordinates_composition_data_Figure4c.csv</strong>: This file contains the data of Fig. 4(c) which is the result of the microstructure reconstruction after Tucker decomposition with 10 % sparsification for t = 1 h 23 min 20s. The spatial distribution of Cr composition is sufficient to represent the microstructural information for the binary Ti-Cr alloy. That is the data of spatial coordinates and mole fraction of Cu at each coordinate is sufficient to represent this microstructure. Thus, the csv file consists of the following three columns : X-Coordinate (nm), Y-Coordinate (nm) and Cr_MoleFraction. The unit for the values of X and Y coordinates is nm. The mole fraction is unitless. </li> <li><strong>bulk_free_energy.csv</strong>: The file contains the data of the coefficients alpha, beta, gamma, delta, epsilon and ceq in the equation for bulk thermodynamic free energy ( F_{chem}) at T = 700.15 K. Mathematically, F_{chem} = f_{chem}* Vmol where Vmol is the molar volume of a phase. The expression for molar bulk chemical free energy is: <em><strong>F_{chem} = alpha*(epsilon*c - ceq)^2 + beta*(epsilon*c - ceq) + beta*(epsilon*c - ceq)^6 + delta</strong></em>. </li> <li><strong>statistical_metrics_comparison.zip</strong>:This folder consists of three files that compares the outcome of tensor impainting results of Canonical Polyadic (CP) and Tucker methods for three sparsity values ( 10 %, 15 % and 25%). Each of the files corresponds to the sparsity value, and so the names of of the files are statistical_metrics_10percent.csv, statistical_metrics_15percent.csv and statistical_metrics_20percent.csv. Four types of statistical techniques are considered: <strong>root mean square error (RMSE)</strong>, microstructural similarity (micro sim), blob detection via deviation quantified from <strong>Determinant of Hessian (DoH)</strong>, and Shape Index. The first three methods: RMSE, microstructural similarity and blob detection via DoH are used quantitatively to compare the tensor inpainted images with the benchmark image from phase field method. Shape index is used to perform qualitative analysis, and it has been inferred that both CP and Tucker methods based decomposition and subsequent reconstruction/inpainting are in the acceptable from the viewpoint of tracking the curvature of interfaces. For 10% sparsity, the Tucker method is found to produce better results even if both CP and Tucker produce acceptable ones.</li> </ol>
AlloyManufacturingNet for discovery and design of hardness-elongation synergy in multi-principal element alloys
<p>Description</p> <p>1. Models saved after training the neural networks:</p> <p>a. hardness_saved_models.zip</p> <p>b. ductility_saved_models.zip</p> <p>2.<strong> prediction_data_for_casting_process.zip</strong>: Alloy types and their composition variants are provided in the file <strong>multicomponent_alloys_variants_compositions.csv</strong>. The hardness prediction for casting process with alloys are given in <strong>hardness_prediction_alloys_CAT-A.csv</strong> file whereas elongation prediction for the alloys for the same process (manufacturing route) are provided in<strong> elongation_prediction_alloys_CAT-A.csv</strong>. The composition sets D1_{x}D2_{y}(ZrHfNb)_{1-x-y} are referred to as<strong> Alloy A</strong>, whereas D1_{x}D2_{y}(VNbTa)_{1-x-y} are denoted as <strong>Alloy B</strong> in the columns of the csv files. The numeric value after alloy type denotes the specific pairs of the dopants [D1,D2] . For example, the alloy variants Ti_{x}Ta_{y}(ZrHfNb)_{1-x-y}, W_{x}Ta_{y}(ZrHfNb)_{1-x-y}, Mo_{x}Ta_{y}(ZrHfNb)_{1-x-y}, and Cr_{x}W_{y}(ZrHfNb)_{1-x-y} are referred to as Alloy A1, Alloy A2, Alloy A3 and Alloy A4 respectively. Similarly, Alloy A1, Alloy A2, Alloy A3 and Alloy A4 respectively denote Cr_{x}W_{y}(VNbTa)_{1-x-y}, Zr_{x}W_{y}(VNbTa)_{1-x-y}, Hf_{x}W_{y}(VNbTa)_{1-x-y}, and Mo_{x}Ti_{y}(VNbTa)_{1-x-y}. Hence, if a column is represented as HV_A2, then it is the hardness prediction for the alloy systems W_{x}Ta_{y}(ZrHfNb)_{1-x-y}, and if the column header is EL_B3, then the elongation of Hf_{x}W_{y}(VNbTa)_{1-x-y} alloy systems is estimated.</p>
Data for "Integrated ab initio modelling of atomic order and magnetic anisotropy for rare-earth-free magnet design: effects of alloying additions in L1$_0$ FeNi."
<p>Data for "Integrated ab initio modelling of atomic order and magnetic anisotropy for rare-earth-free magnet design: effects of alloying additions in L1$_0$ FeNi", published in npj Comput. Mater. <strong>10</strong> 272 (2024).</p> <p>Version 2 contains additional results relating to Ni-rich systems.</p> <p>Version 3 contains data relating to vibrational considerations for the A1-L1$_0$ transition in equiatomic FeNi.</p> <p>Version 4 contains date pertaining to the Curie temperatures of the disordered, partially ordered, and fully ordered alloys considered in this work.</p>
Experimental data used in the article entitled "Engineering an ultra-fine grained microstructure, twins and stacking faults in PBF-LB/M Al-Si alloy via KoBo extrusion method"
<p>Dataset include</p> <p>EBSD results: KOB;O.ang and LPBF_condition.ang</p> <p>Tensile test results:</p> <p>KOBO-processed sample: KOBO.xls</p> <p>LPBF sample: SLM.xls</p>
Experimental dataset for the publication "Characterisation of Microstructure and Special Grain Boundaries in LPBF AlSi10Mg Alloy Subjected to the KoBo Extrusion Process"
<p>Dataset includes:</p> <p>EBSD data:</p> <p>KOBO.ang - KOBO-processed sample</p> <p>LPBF_condition.ang - As-built sample</p> <p>TKD data:</p> <p>TKD_KOBO.ang - KOBO-processed sample</p> <p>HRTEM images:</p> <p>Fig. 12(b).dm3 - KOBO-processed sample</p>
4D STEM acquisition on an atom probe specimen of an ultrafine grain Fe-51.4at% Cr alloy
<p>Material: Fe-51.4at% Cr alloy processed by high pressure torsion </p> <p>Microscope: JEOL F 200<br>Accelration voltage: 200 kV<br>Aperture: 10 µm<br>Camera length: 200 mm<br>Detector size in pixel: 512 x 512<br>Scanned area size in pixel: 250 x 150<br>Scanned area size in nm2: 625 x 375</p>
Data for a publication "Microstructure and mechanical properties of in-situ SiO2-reinforced mechanically alloyed CoCrFeNiMnX (X= 5, 20, 35 at.%) high-entropy alloys"
<p>Dataset contains data that has been used within the manuscript entitled: "Microstructure and mechanical properties of in-situ SiO2-reinforced mechanically alloyed CoCrFeNiMnX (X= 5, 20, 35 at.%) high-entropy alloys". For more information, please read the README.txt file.</p>
Data for the publication "Hydrogen penetration into the NiTi superelastic alloy investigated in-situ by synchrotron diffraction experiments"
<p>This dataset contains the data to the research paper "Hydrogen penetration into the NiTi superelastic alloy investigated in-situ by synchrotron diffraction experiments". The paper describes a<span> microstructural evolution caused by a hydrogen permeation into the NiTi superelastic alloy, which was investigated in-situ using the X-ray synchrotron diffraction. The diffraction data, electrochemical data, TEM pictures, lattice parameters for ab-initio DFT calculations and input parametrs for FEM calculations are included.</span></p>
The P-V-T dataset of liquid Fe-C alloys from FP-MD simulations
<p>The <em>P</em>-<em>V</em>-<em>T</em> dataset of liquid Fe-C alloys with different carbon contents (<em>X</em><sub>C</sub> = 0, 2.1, 4.7, 7.6, and 11.2 wt%) under the <em>P</em>-<em>T</em> conditions of the outer core (~136-330 GPa, 4000-6000 K) are obtained via first-principles molecular dynamics simulations.</p>
EBSD datasets for low-alloy steel weld metals - Arc, Laser, and Laser-hybrid welding
<p>Example open access datasets for low-alloy structural steel weld metals, previously published in ref. [1]. The datasets are also related to the International Institute of Welding Commission C-IX, document nr. IX-L-1239-2021.</p> <p>Files included:</p> <ul> <li>The nomenclature of the files corresponds to what is used in ref. [1].</li> <li>CV_3.crc/cpr: Conventional arc weld, 3/5 mm t-joint</li> <li>HY_1.crc/cpr: Laser-hybrid weld, 3mm butt-joint</li> <li>LA_1.crc/cpr: Laser weld, 3mm butt-joint</li> </ul> <p>The methodology for analysing grain size and dislocation sub-structures is found at: <a href="../record/5053377">https://zenodo.org/record/5053377</a> and <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/GSMUM">https://wiki.aalto.fi/display/GSMUM</a> and <a href="https://wiki.aalto.fi/display/EMDIDS">https://wiki.aalto.fi/display/EMDIDS</a></p> <p><strong>Refererences:</strong></p> <ul> <li>[1] Materials Science and Engineering: A, 2014; 592: 28-39, <a href="http://dx.doi.org/10.1016/j.msea.2013.10.094">http://dx.doi.org/10.1016/j.msea.2013.10.094</a></li> <li>[2] Welding in the World. 2016; 60: 673-678. <a href="http://dx.doi.org/10.1007/s40194-016-0318-8">http://dx.doi.org/10.1007/s40194-016-0318-8</a></li> <li>[3] 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></li> </ul>
Preparation of orthodontic arch wire by laser welding of Ti/TiNi dissimilar alloys
<p>TiNi shape memory alloy has good biocompatibility and superelasticity on the basis of certain strength and stiffness. In<br> the process of tooth correction, stainless steel of composite orthodontic arch wire is used as the support of supporting<br> teeth. TiNi alloy of composite orthodontic arch wire is used to correct the dislocation teeth. Compared with the traditional<br> stainless steel arch wire, composite orthodontic arch wire can reduce the pain of patients. In this experiment, laser welding<br> was carried out by filling Cu filler metal. Laser centering welding and laser offsetting welding were used respectively, and<br> the joint strength was compared. It was found that laser offsetting welding can effectively reduce the content of interface<br> intermetallic compounds and improve the joint strength.</p>
Ball-on-plate tribotesting of IVB element layers deposited on anodized aluminum alloys
<p>This dataset contains a summary file (this one) and two spreadsheet files with coefficients of friction of anodic coatings with or without deposited layers of IVB elements, their oxides or selected other elements. Aluminum alloys 1050 and 6082 are used as substrates with 50 μm anodic coatings and 15-75 nm or higher thickness of the deposited layers. Corundum ball of 6 mm OD is used as a counter-body at 2 cm/s velocity under 1 N and 10 N loads. In this file, methodology, charts with coefficients of friction, X-ray reflectance data and calculations are provided to quantify the deposition rates on polished surfaces.</p>
Precession electron diffraction dataset from nanocrystaline Cu-Ag (FCC) alloy 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 + precession mode (PED) of a nanocrystaline Cu-Ag sample, 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><strong>Material and sample preparation</strong></p> <p>The sample was prepared from a nanocrystalline Cu-Ag thin film. The details are described in the following publication:</p> <p>Oellers, Tobias, et al. "Thin-Film Microtensile-Test Structures for High-Throughput Characterization of Mechanical Properties." <em>ACS combinatorial science</em> 22.3 (2020): 142-149.</p> <p>The sample was prepared by punching a 3 mm diameter disc out, gluing this to a Cu single hole grid, and subsequent Ar+ ion milling until perforation at 2.5 kV using a PIPS II system (Gatan). The rough milling was followed with a 0.5 kV cleaning.</p> <p><strong>Microscopy parameters and data collection</strong></p> <p>PED 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) and a condenser aperture size of 10 μm. The probe diameter was ~ 1 nm with a convergence angle of ~2 mrad. A precession frequency of 100 Hz and a precession angle of 0.5° were applied during the nanobeam scanning. Data was collected on a TemCam-XF416 pixelated CMOS<br> detector (TVIPS). The camera length as indicated in the operating software was 15 cm, and collected images were 2k by 2k in size (hardware binning of 2). The dataset comprises 150x150 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 100 GB in size and is no longer available. 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 512x512. A median filter was also applied to the data.</p> <p><strong>Data characteristics</strong></p> <p>Scan shape: 150 x 150 pixels</p> <p>Image shape: 512 x 512 pixels</p> <p>Pixel dtype: uint16</p> <p>Scan pixel size: about 1 nm, scan dimensions were never calibrated</p> <p>Image pixel size: 0.01155 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). 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. A large number of the scan points are worthless. In addition, the detector background was not properly subtracted in the image, resulting in striped artifacts in the images.</p>
Electron backscatter diffraction data and backscatter electron images from four conditions from a cold-rolled and annealed Al-Mn alloy
<p>Raw electron backscatter diffraction (EBSD) datasets and backscatter electron (BSE) images acquired from four conditions from a cold-rolled and non-isothermally annealed Al-Mn alloy: as deformed, 175 C, 300 C and 325 C. The heating rate is 50 C/h. The material is recovered after 300 C and partly recrystallized after 325 C.</p> <p>The data forms part of the supplementary material to the paper "Orientation dependent pinning of (sub)grains by dispersoids during recovery and recrystallization in an Al-Mn alloy" (2023) published in Acta Materialia (https://doi.org/10.1016/j.actamat.2023.118761).</p> <p>The data was acquired in order to study the effect of particles on recovery and recrystallization in the Al-Mn alloy. The particles detected in the BSE images were inserted in the EBSD map after the EBSD map had been corrected for distortions by image registration using the BSE images.</p> <p>See the <em>GitHub</em> repository https://github.com/hakonanes/p-texture-al-mn-alloys for <em>Jupyter</em> notebooks and <em>MTEX</em> (<em>MATLAB</em>) and <em>ImageJ</em> scripts used to process and analyze the data.</p> <p>See the <em>README.txt </em>file for a description of the file contents.</p>
Investigation via Electron Microscopy and Electrochemical Impedance Spectroscopy of the Effect of Aqueous Zinc Ions on Passivity and the Surface Films of Alloy 600 in PWR PW at 320 C
<p>This upload includes the raw EDS data in Bruker Esprit format and the raw EIS data in excel dat format that is presented in the manuscript published in Corrosion and Materials Degradation entitled <em>Investigation via Electron Microscopy and Electrochemical Impedance Spectroscopy of the Effect of Aqueous Zinc Ions on Passivity and the Surface Films of Alloy 600 in PWR PW at 320 C.</em></p>
Datasets of binary and quaternary alloys with Curie temperature and magnetization for the eRSM
<pre>binary_alloys: A material dataset containing 100 transition-rare earth metal binary alloys, comprising nickel (Ni), manganese (Mn), cobalt (Co), or iron (Fe), and the corresponding Curie temperatures ($T_{C}$). This dataset was collected from the Atomwork database of the National Institute of Materials Science \cite{paulingfile,atomwork}. Each binary alloy in $\mathcal{D}_{binary}$ is represented using seven descriptors: (1,2) the atomic number of transition metal ($Z_T$) and rare-earth ($Z_R$) constituents; (3) projection of the spin magnetic moment onto the total angular moment of the $4f$ elections ($J_{4f}\left(1-g_{j}\right)$); (4, 5) covalent radius ($r_{covT}$) and first ionization (${IP}_T$) of the transition metal; (6, 7) concentration of the transition metal ($C_T$) and rare-earth metal ($C_R$). </pre> <pre>quaternary_alloys: A material dataset contains 990 equiatomic quaternary high-entropy alloys, which comprise $14$ transition metals {Ag, Cd, Co, Cr, Cu, Fe, Mn, Mo, Ni, Pd, Rh, Ru, Tc, Zn}, and the corresponding calculated magnetizations and Curie temperatures in the BCC phase. The dataset was collected from an original dataset of 147,630 equiatomic quaternary high-entropy alloys calculated using Korringa-Kohn-Rostoker coherent approximation method . Each alloy in the dataset is represented using 135 compositional descriptors, including the means, standard deviations, and covariance of the atomic representations of their constituent elements and four categorical features indicating the elements comprising the quaternary alloy. </pre>
Al-Cu-Li alloy SPED data
<p>100x100 SPED dataset of theta prime, T1 and Al phases. Convergence semi angle ~1.13mrad, precession angle ~1deg, precession frequency 100Hz. Medipix MerlinEM detector. Pixel exposure time 10ms, step size ~4.6nm.</p>
Supplementary material for "A Rapid, Open-Source CCT Predictor for Low Alloy Steels, and its Application to Compositionally Heterogeneous Material"
<p>The complete collection of measured, modelled and analysed data associated with the work "A Rapid, Open-Source CCT Predictor for Low Alloy Steels, and its Application to Compositionally Heterogeneous Material" and includes:</p> <ol> <li>Measured and analysed dilatometry data.</li> <li>Optical micrographs of as-cooled microstructures.</li> <li>Microhardness measurements of the as-cooled samples.</li> <li>PAG size analysis.</li> <li>Modelled CCT data.</li> <li>SA-540 EPMA data.</li> <li>Modelled results from adapting the model to consider SA-540 chemical heterogeneity.</li> <li>Full chemical analysis for each alloy examined.</li> <li>Modelled Thermo-Calc CCT data.</li> <li>Modelled JMatPro CCT data.</li> </ol>
Dataset and scripts for publication "Property design of extruded magnesium-gadolinium alloys through machine learning"
<p>Data and scripts accompanying publication "Property design of extruded magnesium-gadolinium alloys through machine learning"</p>
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OpenNeuro
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