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287 results for “alloy”
High Temperature Compression Studies of a Zr-2.5Nb Alloy using Deformation Dilatometer
<p>Data recorded in uniaxial compression for a Zr-2.5Nb alloy deformed at temperatures of 650C, 675C, 700C, 725C, 750C, 775C, 800C, 825C and 850C, at strain rates of 10-2.5, 10-2, 10-1.5, 10-1, 10-0.5 and 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 Zr-2.5Nb specimens were machined from the centre of an as-received forged plate manufactured at Wah Chang, with a beta-transformed starting microstructure. Si3N43 platens were used for all tests, with graphite lubricant applied at the ends of the sample to minimise friction. Tests were conducted in an inert He gas atmosphere. Temperature was controlled using an S-Type thermocouple spot-welded to the centre of the samples.</p> <p>Data recorded at high acquisition frequency during deformation is stored in the 'deformation_files' folder and saved with the format: ' test number (001 to 191)_temperature_log(strain rate)_repeat number (01 or 02)'. Data in the 'basic_files' folder is recorded at a lower acquisition frequency, but includes recording of the entire themomechanical cycle, including both heating and cooling stages, as well as deformation. The 'software_files' folder includes metadata stored in the form of a parameter file (.par and .pad), along with a Windows data file (.D5D) that can be loaded and analysed within the dilatometer user interface.</p> <p>An <a href="https://doi.org/10.5281/zenodo.3673105">accompanying python script</a> will allow the user to plot the stress-strain data using the Jupyter Notebook application, along with generating 'processing maps' of the material. A critical assessment of the application of 'processing maps' is included in the accompanying paper;</p> <p>C. S. Daniel, P. Jedrasiak, C. J. Peyton, J. Quinta da Fonseca, H. R. Shercliff, L. Bradley, and P. D.Honniball, “Quantifying Processing Map Uncertainties by Modeling the Hot-Compression Behavior of a Zr-2.5Nb Alloy,” in Zirconium in the Nuclear Industry: 19th International Symposium, ed. A. T. Motta and S. K. Yagnik (West Conshohocken, PA: ASTM International, 2021), 93–122. <a href="https://doi.org/10.1520/STP162220190031">10.1520/STP162220190031</a></p>
Monolayer doping of silicon-germanium alloys: A balancing act between phosphorus incorporation and strain relaxation
<p>This paper presents the application of monolayer doping (MLD) to silicon-germanium (SiGe). This study was carried out for phosphorus dopants on wafers of epitaxially grown thin films of strained SiGe on silicon with varying concentrations of Ge (18%, 30%, and 60%). The challenge presented here is achieving dopant incorporation while minimizing strain relaxation. The impact of high temperature annealing on the formation of defects due to strain relaxation of these layers was qualitatively monitored by cross-sectional transmission electron microscopy and atomic force microscopy prior to choosing an anneal temperature for the MLD drive-in. Though the bulk SiGe wafers provided are stated to have 18%, 30%, and 60% Ge in the epitaxial SiGe layers, it does not necessarily mean that the surface stoichiometry is the same, and this may impact the reaction conditions. X-ray photoelectron spectroscopy (XPS) and angle-resolved XPS were carried out to compare the bulk and surface stoichiometry of SiGe to allow tailoring of the reaction conditions for chemical functionalization. Finally, dopant profiling was carried out by secondary ion mass spectrometry to determine the impurity concentrations achieved by MLD. It is evident from the results that phosphorus incorporation decreases for increasing mole fraction of Ge, when the rapid thermal annealing temperature is a fixed amount below the melting temperature of each alloy.</p>
Al-Co-Cu alloy - melt-spun ribbons and powder - SEM and TEM microstructure
<p>This set contains SEM and TEM images with EDS chemical composition data for Al-Co-Cu alloy in a melt-spun ribbon form, which was applied as a catalyst for the phenylacetylene hydrogenation reaction. </p> <p>The material preparation and microstructural analyses were performed at the Institute of Metallurgy and Materials Science of the Polish Academy of Sciences.</p> <p>The experimental procedure for material preparation, instrumentation, data collection and results analysis were described in the work: https://doi.org/10.1007/s43452-024-00904-x</p> <p> </p> <p>Preparation of materials: Amelia Zięba</p> <p>TEM images collection (FEI Tecnai G2, ThermoFisher Titan Themis G2 200 Probe Cs-Corrected): Amelia Zięba, Lidia Lityńska-Dobrzyńska</p> <p>SEM images acquisition (FEI E-SEM XL-30): Amelia Zięba</p> <p> </p> <p>Files description code:</p> <p>sem_rib_2000_1 - SEM BSE image of a melt-spun ribbon_magnification_image no</p> <p>sem_pwdr_1000_1 - SEM BSE image of pulverised melt-spun ribbons_magnification_image no</p> <p>sem_pwdr_ar_1000_1 - SEM BSE image of pulverised melt-spun ribbons recovered after use as a catalyst in the phenylacetylene hydrogenation reaction_magnification_image no</p> <p>tem_bf_1 - TEM bright field image of a melt-spun ribbon sample (FIB lamella)_image no</p> <p>tem_dyf_5 - selected area electron diffraction of a melt-spun ribbon sample - the number indicates a corresponding image number</p> <p>EDS-HAADF_img_1 - STEM image of a melt-spun ribbon sample (FIB lamella) with EDS corresponding maps and line analyses</p> <p>TEM_eds_point_analysis.txt - results of point analyses for EDS-HAADF_img_x series</p> <p>stem_pwdr_ar_1 - STEM images of powder recovered after reaction with EDS analysis results: eds_spec_stem_pwdr_ar_1</p> <p> </p> <p><em><strong>Acknowledgements</strong></em></p> <p><strong><em>The work was financially supported by the National Science Centre (NCN), Poland, project No. 2021/41/N/ST8/02533.</em></strong></p> <p> </p>
MSCA SHINE_Project: Numerical results of the soldification-melting of a Si-based alloy (Ultra-high temperature storage)
<p>Project 101145914 — SHINE has received funding withing the MSCA framework. The purpose of this project is to construct a generic model describing the solidification-melting process of ultra-high temperature latent heat thermal energy storage systems based on data-driven analysis and rigorous CFD models. Within the initial steps of the project a preliminary database is being created based on available results from previous UPM’s projects (Thermobat Project) as well as preliminary simulations by using a 2D CFD model. The present dataset contains information of FeSiB alloy solidification-melting close to 1250 oC inside a cylindrical container, under various heating conditions. Analysis of the present data can be also found in the manuscript entitled as ¨Numerical analysis on the state of charge of an ultra-high temperature latent heat energy storage system, SoraPaces, Rome, 2024¨</p>
Dataset for publication: Statistically Equivalent Virtual Microstructures for Modeling of Complex Polycrystalline Alloys Using a Generative Adversarial Network (GAN)-Enabled Computational Platform
<p>This dataset provides the necessary data to get the images and results shown in the paper "Statistically Equivalent Virtual Microstructures for Modeling of Complex Polycrystalline Alloys Using a Generative Adversarial Network (GAN)-Enabled Computational Platform". </p> <p>Source Data Raw.zip has the entire data set used to generate the images.</p> <p>Source Data.zip contains the processed data from "Source Data Raw.zip". </p> <p>Files with extension .dream3d are accompained by a file with extension .xdmf. This files can be opened with Paraview. And their data can be accesible using python or matlab.</p> <p>For more information contact Proffesor Somnath Ghosh at Johns Hopkins University, Civil and Systems Engineering Department.</p>
Dataset for article "Interplay between disorder and electronic correlations in compositionally complex alloys"
<p>Dataset for article "Interplay between disorder and electronic correlations in compositionally complex alloys", https://doi.org/10.1038/s41467-024-52349-8. Source data for figures is described by name and axes and unites are described inside each data file.</p>
CsSn(Cl/Br/I)3 Perovskite Alloy DFT Dataset for Machine Learning
<p>This upload contains density functional theory (DFT) calculations of CsSn(Cl/Br/I)3 perovskite alloy. The calculations were performed for a study, where the DFT data was used to train an energy predicting machine learning model for CsSn(Cl/Br/I)3. The code related to the study is available through GitLab (https://gitlab.com/cest-group/learnsolar-cssnclbri).</p> <p>The data is divided into four data sets. For each set, the atomic structure data with total energies and forces has been separated into an ASE (Atomic Simulation Environment) extended XYZ file. Additional information on the atomic structures (e.g. space groups) is provided in JSON format. The data sets are:</p> <p><strong>sp_train_set</strong><br>Single point DFT calculations of 16 000 algorithmically generated CsSn(Cl/Br/I)3 structures of four different space groups: Pm-3m, P4/mbm, I4/mcm, and Pnma. Lattice parameters and atomic positions are determined through Vegard's law, but random deviations have been added to the atom positions, tilting angles of the Sn coordination octahedra, cell volume, cell height-to-width ratio, and some lattice vector angles. Cl/Br/I configurations are randomized. This data set was used to fit an initial machine learning model. The atomic structures included were selected using a clustering algorithm to accelerate learning.</p> <p><strong>sp_test_set</strong><br>Single point DFT calculations of 2 600 atomic structures similar to sp_train_set. The Cl/Br/I compositions are uniformly represented, having two atomic structures per composition and space group. This data was used for testing the machine learning model. </p> <p><strong>al_data</strong><br>DFT relaxation structure snapshots from the active learning run that was performed to improve the machine learning model's structure relaxation accuracy. There are 4230 structure snapshots in total.</p> <p><strong>relax_test_set</strong><br>100 DFT relaxations used for testing the machine learning relaxation accuracy. There are 2881 structure snapshots in total. Both initial (relax_test_set_initial.xyz) and final (relax_test_set_relaxed.xyz) atomic geometries are included.</p>
Data for "Two-phase mixture of iron-nickel-silicon alloys in the Earth's inner core"
<p>This file is the dataset used in the article "Two-phase mixture of iron-nickel-silicon alloys in the Earth's inner core", <em>Commun. Earth Environ.</em> <strong>2</strong>, 225 (2021). https://doi.org/10.1038/s43247-021-00298-1</p>
Characterization of a Nickel-Titanium (55.3 Ni wt%) shape memory alloy wire
<p>Characterization of a Nickel-Titanium (55.3 Ni wt%) shape memory alloy (SMA) wire supplied by Memry (USA).</p> <p>Diameter : 0.50mm</p> <p>Data given by the supplier : Nitinol wire, Hard black oxide, thermally straightened, As = 53°C</p> <p>Differential Scanning Calorimetry (DSC) test (as received) : a virgin sample of NiTi wire (7.9mg) is submitted to an initial heating up to 250°C, followed by 2 cooling-heating cycles (250°C/-90°C/+250°C). Test speed : 10°C per minute. The DSC apparatus is a DSC 250 (TA Instruments) with nitrogen gas flow. The test files for the software Trios are supplied as well as the .csv data.</p> <p>Differential Scanning Calorimetry (DSC) test (50 cycles) : the NiTi wire maintained in a water bath at a temperature of 70°C is submitted to 50 mechanical cycles of loading up to 90N and unloading down to 0.5N (superelastic cycles). A sample of this wire (8.1mg) is submitted to a DSC with an initial heating up to 250°C, followed by 2 cooling-heating cycles (250°C/-90°C/+250°C). Test speed : 10°C per minute.</p> <p>Isothermal traction at 25 degrees : a specific testing apparatus (illustration in the file Apparatus.jpg) has been set up. The SMA wire is tested in a box containing water, a water pumping/heating/cooling apparatus enables to control the water temperature in order to precisely control the SMA wire temperature. One side of the box is made of transparent PMMA in order to monitor the wire strains by Digital Image Correlation (DIC). The tested part of the wire finds itself in water, and its upper end is clamped in the cylinder jaws of a displacement controlled ADAMEL DY31 testing machine. The water temperature is monitored by two type K thermocouples at two different locations inside the water volume. The load in the wire is registered using a 100N load cell and the deformations of the wire were measured by DIC on images acquired by a Pike F421B camera (Allied Vision Technologies). A Matlab routine has been set up to acquire simultaneously the deformation images with the load values. A virgin wire of the same spool has been submitted to axial traction in 25°C water with a speed of 10^-4 s^-1. The first traction test presents a strain localization along the wire. We were not interested in this phenomenon and only present here the 2nd, 3rd and 4th test which show a homogeneous strain along the observed zone of the wire. The strain has been measured with the software GOM Correlate, by computing the mean value of the axial strain on a rectangular zone of several centimeters on the wire. The GOM Correlate files are supplied, as well as the .csv values. Between each test, the wire has been extracted from the testing chamber, placed in a furnace at 100°C during several minutes and then placed in a deep freezer at -20°C during several minutes in order to always test a martensitic wire.</p> <p>Thermal cycling at 25°C : With the same apparatus, a load of 25N has been applied on the wire at 85°C and then maintained constant with the help of a PID controller in the TestWorks software. The wire being maintained at this constant load, the water has been cooled down to 15°C and heated up to 85°C. The strain field could not be measured accurately, and for this reason the strain named Axial Strain 1, Axial Strain 2 and Axial Strain 3 are computed with virtual gauges in GOM Correlate (only between two points). The strain may be heterogeneous along the wire.</p>
Green and Controllable Preparation of Cu/Zn Alloys Using Combined Electrodeposition and Redox Replacement
<p>Dataset of journal paper </p> <p>Green and Controllable Preparation of Cu/Zn Alloys Using Combined Electrodeposition and Redox Replacement</p> <p> </p>
BAM reference data: results of ASTM E139 -11 creep tests on a reference material of Nimonic 75 nickel-base alloy
<p>Results of creep tests on a certified reference material at T = 600°C and a tensile creep load of 160 MPa are provided. The raw data are available in ASCII format (*.lis files). <br> The file "Inhalt_Content_V1.1.pdf" contains further information about the files provided.<br> The evaluated results include the times to reach 2% and 4% creep strain, respectively, and the creep rate after 400 h.</p> <p>The tests were carried out in an accredited test laboratory. The calibrations of all measurands and test and measuring equipment are documented. The calibrations meet the requirements of the test procedure and are metrologically traceable.</p>
Data for "Rapid mapping of alloy surface phase diagrams via Bayesian evolutionary multitasking"
<p>For the ORR study, the final datasets of the DFT-relaxed adsorbate-alloy configurations for the Pd-Ag(111) surface are stored in <strong>ads_PdAg_111_dft.db. </strong>For the SMR study, the final datasets of the DFT-relaxed adsorbate-alloy configurations for the Pt-Ni(111), (100) and (311) surfaces are stored in <strong>ads_PtNi_111_dft.db</strong>, <strong>ads_PtNi_100_dft.db</strong> and <strong>ads_PtNi_311_dft.db</strong>, respectively.</p> <p>The 76,265 tasks (combining 15,253 SMR conditions with 5 exploration parameters) used for the BEM runs in the SMR study can be found in <strong>bem_smr_tasks.csv</strong>.</p> <p>All the input files and scripts for BEM high-throughput screening (for both ORR and SMR studies), DFT calculations, EMT benchmarks, SGCMC simulations, structure generation and plotting (e.g. surface free energy diagrams and 2D phase diagrams) are all provided in <strong>inputs_and_scripts.zip</strong>.</p>
compatibility for recycling of alloys
<p>Open access to simulated and experimental data generated by the ReINTEGRA project (GA 886609, H2020, European Union, through Clean Sky 2 JU) along the research, at single-stringer coupon level, of the End-of-Life of novel welded Al-Li aerostructures.Research pertaining to Task 3.1 (WP3), Deliverable D3.2 </p> <p>Al-Li alloys Compatibility-Recyclability predictions generated by the first version of the Excel tool (ReINTEGRA scrap recyclability tool v01.xlsx) developed by AZTERLAN for 18 scrap fractions of coupons; and predictions made by the second version of the software (ReINTEGRA scrap recyclability tool v02.xlsx) for 12 scrap fractions generated during full validation of the recommended EoL route for each coupon reference. Outputs of the excel tool printed as pdf files.</p> <p>Experimental data of the scrap chemical composition originated from chemical analysis by ICP-OES conducted by AZTERLAN on test samples of remelted scrap fractions and on test samples of pre-scrap material fractions. Scrap fractions of coupons supplied by SONACA by cutting (radial saw) coupons following one of four possible cutting strategies (0C, 1C, 2C, 3C).</p> <p>Each pdf file contains the description of the scrap being evaluated, the measured elemental composition of the scrap (%wt) and the results of the following evaluations: (1) scrap composition vs composition ranges of four registered Al-Li alloys (2060, 2196, 2099, 2198); (2) quantification of excess/deficit of elements in scrap vs composition of the four registered alloys; (3) chemical compatibility with each alloy; (4) critical chemical element for compatibility; (5) recyclability as max % scrap acceptable as raw material to manufacture each alloy; (6) alloying addition needs for recycling.</p> <p>The ReINTEGRA scrap recyclability tool has been developed as part of WP3 activities (Task 3.1).</p> <p>The first version of the tool only evaluates compatibility/recyclability of scrap with the four Al-Li alloys used in ecoTECH coupons. The user must enter the chemical composition data of the scrap to be evaluated. The second version of the tool implements corrections to the algorithm to identify the critical element in excess for setting max. % of scrap in the charge.</p> <p>The coupon scrap samples have been used for investigating cutting strategies (WP2), remelting set-up (WP3) and full validation of recommended EoL routes (WP3). Pre-scrap materials have been used in pre-scrap characterisation protocols (WP4).</p>
Accompanying data for paper Plasticity and ductility of an anisotropic recrystallized AA2198 Al-Cu-Li alloy in T3 and T8 conditions during proportional and non-proportional loading paths: simulations and experiments
<p>Links</p> <ul> <li>Data DOI: <a href="https://doi.org/10.5281/zenodo.7729452">10.5281/zenodo.7729452</a></li> <li>Article <em>Plasticity and ductility of an anisotropic recrystallized AA2198 Al-Cu-Li alloy in T3 and T8 conditions during proportional and non-proportional loading paths: simulations and experiments</em>, DOI: <a href="https://doi.org/10.46298/jtcam.8913">10.46298/jtcam.8913</a></li> </ul> <p>Authors</p> <ul> <li>Xiang Kong, <a href="mailto:xiang.kong@minesparis.psl.eu">xiang.kong@minesparis.psl.eu</a>, MINES Paris, PSL University, Centre des Matériaux, CNRS UMR 7633, Evry France, ORCID: <a href="https://orcid.org/0000-0002-0835-3826">0000-0002-0835-3826</a></li> <li>Jianqiang Chen, Pratt & Whitney Canada, 1000 Boul. Marie-Victorin, Longueuil, QC J4G 1A1 Canada</li> <li>Yazid Madi, <a href="mailto:yazid.madi@minesparis.psl.eu">yazid.madi@minesparis.psl.eu</a>, MINES Paris, PSL University, Centre des Matériaux, CNRS UMR 7633, Evry France, ORCID: <a href="https://orcid.org/0000-0002-3530-8668">0000-0002-3530-8668</a></li> <li>Djamel Missoum-Benziane, <a href="mailto:djamel.missoum-benziane@minesparis.psl.eu">djamel.missoum-benziane@minesparis.psl.eu</a>, MINES Paris, PSL University, Centre des Matériaux, CNRS UMR 7633, Evry France, ORCID: <a href="https://orcid.org/0000-0002-9877-8261">0000-0002-9877-8261</a></li> <li>Jacques Besson, <a href="mailto:jacques.besson@minesparis.psl.eu">jacques.besson@minesparis.psl.eu</a>, MINES Paris, PSL University, Centre des Matériaux, CNRS UMR 7633, Evry France, ORCID: <a href="https://orcid.org/0000-0003-1975-2408">0000-0003-1975-2408</a></li> <li>Thilo F. Morgeneyer, <a href="mailto:thilo.morgeneyer@minesparis.psl.eu">thilo.morgeneyer@minesparis.psl.eu</a>, MINES Paris, PSL University, Centre des Matériaux, CNRS UMR 7633, Evry France, ORCID: <a href="https://orcid.org/0000-0002-0278-9565">0000-0002-0278-9565</a></li> </ul> <p>Language</p> <ul> <li>English</li> </ul> <p>Licence</p> <ul> <li>CC BY 4</li> </ul> <p>Contributions</p> <ul> <li>Conception and design of study, revising the manuscript critically for important intellectual content: TFM, JB.</li> <li>Acquisition of data: XK, JC, YM.</li> <li>Analysis and/or interpretation of data: XK, DMB, TFM.</li> </ul> <p>Associated article</p> <p>Xiang Kong, Jianqiang Chen, Yazid Madi, Djamel Missoum-Benziane, Jacques Besson, Thilo Morgeneyer "Plasticity and ductility of an anisotropic recrystallized AA2198 Al-Cu-Li alloy in T3 and T8 conditions during proportional and non-proportional loading paths: simulations and experiments" Journal of Theoretical, Computational and Applied Mechanics (JTCAM), March 13, 2023, DOI: <a href="https://doi.org/10.46298/jtcam.8913">10.46298/jtcam.8913</a>, HAL: <a href="https://hal.science/hal-03497233v3">hal-03497233v3</a></p> <p>Keywords</p> <ul> <li>plastic anisotropy</li> <li>mechanical testing</li> <li>non-proportional loading</li> <li>static loading</li> <li>ductile fracture</li> </ul> <p>Data collection: period and details</p> <ul> <li>Sept 2018 - June 2022, PhD period of Xiang Kong</li> <li>Mechanical experiments mainly were performed at the Centre des Materiaux in Evry, France, except the laminographic experiment which was performed at the ESRF ID19b in Grenoble, France, while numerical simulations were performed in <a href="http://www.zset-software.com/">Z-set/Zebulon</a> on the cluster at the Centre des Materiaux.</li> </ul> <p>Recommended citation line for the data</p> <p>Xiang Kong, Jianqiang Chen, Yazid Madi, Djamel Missoum-Benziane, Jacques Besson, & Thilo F. Morgeneyer. (2023). Accompanying data for paper Plasticity and ductility of an anisotropic recrystallized AA2198 Al-Cu-Li alloy in T3 and T8 conditions during proportional and non-proportional loading paths: simulations and experiments [Data set]. <a href="https://doi.org/10.5281/zenodo.7729452">10.5281/zenodo.7729452</a></p> <p>Funding sources</p> <ul> <li>ANR (Lambda project: ANR17-CE08-0051 and Alicandte project)</li> </ul> <p>Data structure and information</p> <ul> <li>The output data were used to produce Figures 6-9, 16-19, 23 from the associated article.</li> <li>Folder/files structure: <ul> <li><code>Experiments_Simulations_results/</code> <ul> <li><code>2198T3R/</code> - folder containing results for material AA2198T3R</li> <li><code>T3R_EXP_*.csv</code> - experimental data files</li> <li><code>T3R_SIM_*.csv</code> - simulation output</li> <li><code>README.md</code></li> <li><code>2198T8R/</code> - folder containing results for material AA2198T8R</li> <li><code>T8R_EXP_*.csv</code> - experimental data files</li> <li><code>T8R_SIM_*.csv</code> - simulation output</li> <li><code>README.md</code></li> <li><code>plot_T3R.py</code> - python script plotting results for material AA2198T3R</li> <li><code>plot_T8R.py</code> - python script plotting results for material AA2198T8R</li> <li><code>README.md</code></li> </ul> </li> <li><code>Simulation_input_files/</code> <ul> <li><code>2198T3R.mat,2198T8R.mat,steel.mat</code> - material properties files for Z-set/Zébulon</li> <li><code>postprocess.inp</code> - postprocessing input file for Z-set/Zébulon</li> <li><code>*.inp</code> - FE solver input files for Z-set/Zébulon</li> <li><code>post.py</code> - Python script to plot processed data</li> <li><code>README.md</code></li> <li><code>mesh_files/</code> <ul> <li><code>scale.inp</code> - Z-set/Zébulon mesh modifier</li> <li><code>ST_*.geof</code> - mesh files in Z-set/Zébulon format</li> <li><code>README.md</code></li> </ul> </li> </ul> </li> <li><code>.solidipes/</code> - curation tool metadata (not a part of the dataset)</li> </ul> </li> </ul>
Data from: Correlation of Microstructure and Local Mechanical Properties Along Build Direction for Multi-layer Friction Surfacing of Aluminum Alloys
<p>This dataset contains the data for the publication " Correlation of Microstructure and Local Mechanical Properties Along Build Direction for Multi-layer Friction Surfacing of Aluminum Alloys".</p>
State-of-the-Art Review on the Aspects of Martensitic Alloys Studied via Machine Learning
<p>Description</p> <p>The dataset for the review paper titled "State-of-the-Art Review on the Aspects of Martensitic Alloys Studied via Machine Learning" consists of the four files with the names (i) alloy_names.csv, (ii) machine_learning_methods.csv, (iii) nomenclature.csv, and (iv) ptmc_terminologies.csv.</p> <p><strong>(i) alloy_names.csv </strong>: This file presents the summarized list of alloys' names which have been discussed in the review paper. The list thus provides the names of the alloys for which data-driven studies have been attempted to explore one of the effects - martensitic transformation, phase transformation or shape memory effect. </p> <p><strong>(ii) machine_learning_methods.csv</strong> : The machine learning methods that have been discussed in the review paper in relation to the simulation, modeling or prediction tasks in martensitic alloys are listed in this file. This csv file conssits of three columns. The first column "Methods" lists the names of the machine learning methods whereas the second column "Purpose" briefly reveals the objective of the use of the named machine learning method. The final column "Reference" provides the information about the original work (source) from which the data is obtained. </p> <p><strong>(iii) nomenclature.csv </strong>: This file lists all of the acronyms utilized in the review paper, and provides their corresponding full forms. </p> <p><strong> (iv) ptmc_terminologies.csv</strong> : One of the major theories considered significant in the study of martensitic alloys and shape memory effects is phenomenological theory of martensite crystallography (PTMC). The review paper discusses this theory. The different concepts that might be helpful in understanding PTMC , have been assembled in the form of terminologies. </p>
NEMARCO project: Dataset for the publication "Development of a new manufacturing route for NiCrSiFeB alloys by Direct Energy Deposition Laser Beam process (LMD)"
<p><strong>LMD dataset</strong></p> <p>This dataset gathers data from different parts of the Laser Metal Deposition metal Additive Manufacturing process (DED-LB). The dataset covers not only the process development data for samples manufacturing and monitored data of the melt pool size during the process, but also the metrics associated to the powder feedstock consumption, energy consumption and process efficiency.</p> <p><strong>Motivation</strong></p> <p>Nickel-based NiCrSiFeB alloy (Ni-Cr-Si-B self-fluxing family) are excellent candidates for replacing Cobalt-based alloys in aeronautical components such as sealing rings, valve seats, sliding bearing seats, etc. In this type of components, commonly manufactured by centrifugal casting and conventional processes, high temperature wear and stiffness under complex thermo-mechanical stresses cause lack of sealing and an increase in the wear rate. Metal additive manufacturing by direct laser metal deposition with powder (p-LMD) is presented as a potential manufacturing route for the complex processing of this type of alloys. This research work deals with the development of a new manufacturing route using p-LMD that ranges from the proper selection of the chemical composition for the starting powders, the development of the LMD process parameters to tackle the challenges associated to the wide solidification range and crack susceptibility of Ni-Cr-Si-B alloys, its monitoring and control, as well as the post- processing required to achieve the manufacture of aeronautical components.</p>
NEMARCO project: Dataset for the publication "Solidification Path, Strengthening Mechanisms and Hardness of Ni-Cr-Si-Fe-B Self-Fluxing Alloys Obtained by Laser-Directed Energy Deposition (LMD)"
<p><strong>LMD dataset</strong></p> <p>This dataset gathers data from different parts of the Laser Metal Deposition metal Additive Manufacturing process (DED-LB). The dataset covers not only the process development data for samples manufacturing and monitored data of the melt pool size during the process, but also the metrics associated to the powder feedstock consumption, energy consumption and process efficiency.</p> <p><strong>Motivation</strong></p> <p>Nickel-based Ni-Cr-Si-B self-fluxing alloys are excellent candidates to replace Cobalt-based alloys in aeronautical components. In this work, metal additive manufacturing by directed energy deposition using a laser beam (DED-LB, also known as LMD) and gas-atomized powders as a material feedstock is presented as a potential manufacturing route for the complex processing of these alloys. This research deals with the advanced material characterization of these alloys obtained by LMD and the study and understanding of their solidification paths and strengthening mechanisms.</p>
Dataset Electrochemical Growth of Ag/Zn Alloys from Zinc Process Solutions and Their Dealloying Behavior
<p>Dataset of journal paper <em>Electrochemical Growth of Ag/Zn Alloys and Their Dealloying Behavior</em></p>
Effect of Nd on high temperatures deformation and corrosion behavior of AZE Mg alloy
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OpenNeuro
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