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opencc-by-4.0Nov 2022View details →

Synchrotron X-ray Diffraction Analysis - Measuring Bulk Crystallographic Texture from Differently-Orientated Ti-6Al-4V Samples

A dataset of synchrotron X-ray diffraction (SXRD) analysis files, recording the refinement of crystallographic texture from six differently orientated Ti-6Al-4V (Ti-64) samples. Two different refinement methods were used to fit a range of diffraction pattern ring intensities, for determining crystallographic texture in both α (hexagonal close packed, hcp) and β (body-centred cubic, bcc) phases. The first procedure was based on an established Rietveld refinement method, using the software package <a href="https://maud.radiographema.eu">MAUD (Materials Analysis Using Diffraction)</a>. The second procedure uses a new Fourier-based peak fitting method from the <a href="https://pypi.org/project/continuous-peak-fit/">Continuous-Peak-Fit</a> Python package. Both methods were used to calculate texture from each of the six different sample orientations, a combination of the six sample orientations, and in a batch processing method for calculating spatially-resolved texture variation from 387 individual X-Y stage-scan SXRD measurements across one of the samples. Material The Ti-64 material used in this study was pre-rolled to 87.5% reduction at 915ºC and then air-cooled to develop a characteristic texture. Six different rectangular samples were cut from this material and are referenced according to alignment with the original rolling directions (RD – rolling direction, TD – transverse direction, ND – normal direction), and alignment with the horizontal (X) and vertical (Y) axes of the synchrotron detector; <table align="center"> <caption>A table recording the SXRD run number and sample orientation analysed.</caption> <thead> <tr> <th scope="col">Run Number</th> <th scope="col">Sample Orientation Reference</th> <th scope="col"> Sample Orientation (Horizontal - Vertical) </th> </tr> </thead> <tbody> <tr> <td>103840</td> <td>Sample 6</td> <td>TD45ºRD - ND</td> </tr> <tr> <td>103841</td> <td>Sample 5</td> <td>RD - TD45ºND</td> </tr> <tr> <td>103842</td> <td>Sample 4</td> <td>TD - RD45ºND</td> </tr> <tr> <td>103843</td> <td>Sample 3</td> <td>RD - TD</td> </tr> <tr> <td>103844</td> <td>Sample 2</td> <td>RD - ND</td> </tr> <tr> <td>103845</td> <td>Sample 1</td> <td>TD - ND</td> </tr> </tbody> </table> Diffraction Pattern Averaging The .cbf images found in the <a href="https://doi.org/10.5281/zenodo.7311306">raw dataset</a> were first converted into .tiff images. The stage-scan images were then averaged together for each of the different sample orientations, using a Python notebook <a href="https://github.com/LightForm-group/sxrd-tiff-summer">sxrd-tiff-summer</a>, to produce six averaged .tiff images. These averaged .tiff image capture average diffraction peak intensities from an area of about 96.75 mm2 (equivalent to a total volume of around 193.5 mm3) from each piece, which is therefore representative of bulk crystallographic texture from six different sample orientations. MAUD Analysis To process data using MAUD the diffraction pattern images must first be caked, which converts the data into .dat files of intensity versus 2θ profiles, using 72 azimuthal cakes, each of 5° azimuthal width. Although MAUD has an in-built function to cake data, using ImageJ, it is not possible to cake data in MAUD with ImageJ in an automated way. Therefore, caking was done using <a href="https://pyfai.readthedocs.io/en/master/">pyFAI</a>, an open-source Python package, with the caking procedure recorded in a separate Python notebook, <a href="https://github.com/LightForm-group/pyFAI-integration-caking">pyFAI-integration-caking</a>. The caking was applied to each of the six averaged tiff images, as well as being applied to 387 individual X-Y stage-scan tiff images from Sample 1 (103845). The caking procedure was also applied to the CeO2 calibrant diffraction pattern, creating a .dat file that could be used for calibration of the instrument parameters within MAUD, before fitting the experimental data from the different samples. A separate package <a href="https://github.com/LightForm-group/MAUD-batch-analysis">MAUD-batch-analysis</a> was used to record the setup of the files and details of the refinement procedure. Details about the refinement procedure are also recorded in an accompanying paper reporting on these results. A number of refinement steps were used to fit the caked data from the six different sample orientations, and calculate texture. Texture was also calculated from a .dat file that combined all six sample orientations together. The crystallographic texture was refined using the E-WIMV algorithm, which was found to best reproduce quantitative texture intensity values with an orientation distribution function (ODF) resolution of 15º. The MAUD-batch-analysis package also contains details about how to setup and run MAUD in an automated batch processing mode. MAUD's batch mode was used to calculate texture from a series of 387 individual stage-scan diffraction patterns from Sample 1 (103845). A MAUD-batch-analysis script was first used to substitute caked data from the 387 diffraction patterns into template .par files, which contained an initial refinement of the volume fraction, crystal sizes and micro-strain, as a starting point. Both the crystal parameters and texture were then iteratively refined, in MAUD, using a .ins batch analysis script launched from the terminal. This was done to refine both α and then β phase texture. The texture data from the MAUD analysis was recorded as an ODF, with 15º resolution over all Euler space, and extracted in text format using a script from MAUD-batch-analysis. These text files can be loaded into <a href="https://mtex-toolbox.github.io">MTEX</a>, for plotting and analysing both the α and β phase crystallographic texture. Continuous-Peak-Fit Analysis A .poni calibration file was created using <a href="https://www.clemensprescher.com/programs/dioptas">Dioptas</a>, through a refinement matching peak intensities from a CeO2 standard diffraction pattern image. Dioptas was then used to determine peak bounds in 2θ for characterising a total of 21 α and 4 β lattice plane rings from the Ti-64 diffraction pattern images, which were recorded in a .py input script. Using these two inputs, Continuous-Peak-Fit automatically converts full diffraction pattern rings into profiles of intensity versus azimuthal angle, for each 2θ section, which can also include multiple overlapping α and β peaks. The Continuous-Peak-Fit refinement can then be launched in a notebook or from the terminal, to automatically calculate a full mathematical description, in the form of Fourier expansion terms, to match the intensity variation of each individual lattice plane ring. The results for peak position, intensity and half-width for all 21 α and 4 β lattice plane peaks were recorded at an azimuthal resolution of 1º and stored in a .fit output file. Details for setting up and running this analysis can be found in the <a href="https://github.com/LightForm-group/continuous-peak-fit-analysis">continuous-peak-fit-analysis</a> package. This package also includes a Python script for extracting lattice plane ring intensity distributions from the .fit files, matching the intensity values with spherical polar coordinates to parametrise the intensity distributions from each of the six different sample orientations, in the form of pole figures. The script can also be used to combine intensity distributions from different sample orientations. The final intensity variations are recorded for each of the lattice plane peaks as text files, which can be loaded into MTEX to plot and analyse both the α and β phase crystallographic texture. This method was also used to analyse all 387 individual diffraction patterns recorded across Sample 1 (S1 – 103845), to quantify the texture variation across the piece. Metadata An accompanying YAML text file contains associated processing metadata for both the MAUD and the Continuous-Peak-Fit analyses, recording information about the different packages used to process the data, along with details about the different files contained within this analysis dataset.

opencc-by-4.0Nov 2022View details →

Synchrotron X-ray Diffraction Dataset - Measuring Bulk Crystallographic Texture from Differently-Orientated Ti-6Al-4V Samples

A dataset of raw synchrotron X-ray diffraction (SXRD) images, recording crystallographic texture from two different pre-processed Ti-6Al-4V (Ti-64) materials, analysing six differently orientated samples from each material. The aim of the work was to provide a large dataset for testing and improving crystallographic texture refinement from SXRD patterns, with the use of different computational fitting methods. Prior to the experiment, the Ti-64 materials had been pre-rolled and then air-cooled to develop the microstructure, rolling to 50% and 87.5% reduction at 915ºC using a rolling mill at The University of Manchester. Rectangular samples (2 mm thick) were then machined from these rolled blocks. The samples were cut along different directions, three samples along different orthogonal rolling directions, and three at different angles to the rolling directions. The samples are referenced according to alignment of the rolling directions (RD – rolling direction, TD – transverse direction, ND – normal direction) with the long horizontal (X) axis and short vertical (Y) axis of the rectangular specimens.  Data was recorded using a high energy 99.8 keV synchrotron X-ray beam and a 5 second exposure at the detector. The slits were adjusted to give a 0.5 x 0.5 mm beam area, chosen to optimally resolve both the α (hexagonal close packed, hcp) and β (body-centred cubic, bcc) phase peaks. The SXRD data was recorded across each of the specimens by stage-scanning the beam in sequential X-Y positions at 0.5 mm increments, forming a rectangular grid of measurement points across each sample. A powder Ti-64 sample was also measured as a random texture standard. As well as the main experiment, 3 samples (sample 1, 2 and 3) were held together in different orders (1, 2, 3 ; 2, 1, 3 ; 2, 3, 1) and analysed through-thickness, to measure how beam attenuation might affect the bulk texture measurement. In addition, different detector exposure times (1 to 0.04 seconds) were also tested to analyse the impact of exposure time on overall intensity, to see how well the α and β peaks could be resolved from background noise at very fast acquisition frequencies. The raw data is in the form of synchrotron diffraction pattern images which has been separated according to experiment type. An accompanying YAML text file contains associated beamline metadata for each measurement. Further details of the experimental setup can be found in a pdf document. The material data folder contains further details about the material and sample orientations, including an electron backscatter diffraction (EBSD) map that can be used to verify the crystallographic texture.

opencc-by-4.0Dec 2022View details →

Measuring Bulk Crystallographic Texture from Ti-6Al-4V Hot-Rolled Sample Matrices using Synchrotron X-ray Diffraction (Analysis Dataset)

A dataset of synchrotron X-ray diffraction (SXRD) analysis files, recording the refinement of crystallographic texture from a number of Ti-6Al-4V (Ti-64) sample matrices, containing a total of 93 hot-rolled samples, from three different orthogonal sample directions. The aim of the work was to accurately quantify bulk macro-texture for both the α (hexagonal close packed, hcp) and β (body-centred cubic, bcc) phases across a range of different processing conditions. Material Prior to the experiment, the Ti-64 materials had been hot-rolled at a range of different temperatures, and to different reductions, followed by air-cooling, using a rolling mill at The University of Manchester. Rectangular specimens (6 mm x 5 mm x 2 mm) were then machined from the centre of these rolled blocks, and from the starting material. The samples were cut along different orthogonal rolling directions and are referenced according to alignment of the rolling directions (RD – rolling direction, TD – transverse direction, ND – normal direction) with the long horizontal (X) axis and short vertical (Y) axis of the rectangular specimens. Samples of the same orientation were glued together to form matrices for the synchrotron analysis. The material, rolling conditions, sample orientations and experiment reference numbers used for the synchrotron diffraction analysis are included in the data as an excel spreadsheet. SXRD Data Collection Data was recorded using a high energy 90 keV synchrotron X-ray beam and a 5 second exposure at the detector for each measurement point. The slits were adjusted to give a 0.5 x 0.5 mm beam area, chosen to optimally resolve both the α and β phase peaks. The SXRD data was recorded by stage-scanning the beam in sequential X-Y positions at 0.5 mm increments across the rectangular sample matrices, containing a number of samples glued together, to analyse a total of 93 samples from the different processing conditions and orientations. Post-processing of the data was then used to sort the data into a rectangular grid of measurement points from each individual sample. Diffraction Pattern Averaging The stage-scan diffraction pattern images from each matrix were sorted into individual samples, and the images averaged together for each specimen, using a Python notebook <a href="https://github.com/LightForm-group/sxrd-tiff-summer">sxrd-tiff-summer</a>. The averaged .tiff images each capture average diffraction peak intensities from an area of about 30 mm2 (equivalent to a total volume of ~ 60 mm3), with three different sample orientations then used to calculate the bulk crystallographic texture from each rolling condition. SXRD Data Analysis A new Fourier-based peak fitting method from the <a href="https://pypi.org/project/continuous-peak-fit/">Continuous-Peak-Fit</a> Python package was used to fit full diffraction pattern ring intensities, using a range of different lattice plane peaks for determining crystallographic texture in both the α and β phases. Bulk texture was calculated by combining the ring intensities from three different sample orientations. A .poni calibration file was created using <a href="http://www.clemensprescher.com/programs/dioptas">Dioptas</a>, through a refinement matching peak intensities from a LaB6 or CeO2 standard diffraction pattern image. Two calibrations were needed as some of the data was collected in July 2022 and some of the data was collected in August 2022. Dioptas was then used to determine peak bounds in 2θ for characterising a total of 22 α and 4 β lattice plane rings from the averaged Ti-64 diffraction pattern images, which were recorded in a .py input script. Using these two inputs, Continuous-Peak-Fit automatically converts full diffraction pattern rings into profiles of intensity versus azimuthal angle, for each 2θ section, which can also include multiple overlapping α and β peaks. The Continuous-Peak-Fit refinement can be launched in a notebook or from the terminal, to automatically calculate a full mathematical description, in the form of Fourier expansion terms, to match the intensity variation of each individual lattice plane ring. The results for peak position, intensity and half-width for all 22 α and 4 β lattice plane peaks were recorded at an azimuthal resolution of 1º and stored in a .fit output file. Details for setting up and running this analysis can be found in the <a href="https://github.com/LightForm-group/continuous-peak-fit-analysis">continuous-peak-fit-analysis</a> package. This package also includes a Python script for extracting lattice plane ring intensity distributions from the .fit files, matching the intensity values with spherical polar coordinates to parametrise the intensity distributions from each of the three different sample orientations, in the form of pole figures. The script can also be used to combine intensity distributions from different sample orientations. The final intensity variations are recorded for each of the lattice plane peaks as text files, which can be loaded into MTEX to plot and analyse both the α and β phase crystallographic texture. Metadata An accompanying YAML text file contains associated SXRD beamline metadata for each measurement. The raw data is in the form of synchrotron diffraction pattern .tiff images which were too large to upload to Zenodo and are instead stored on The University of Manchester's Research Database Storage (RDS) repository. The raw data can therefore be obtained by emailing the authors. The material data folder documents the machining of the samples and the sample orientations. The associated processing metadata for the Continuous-Peak-Fit analyses records information about the different packages used to process the data, along with details about the different files contained within this analysis dataset.

opencc-by-4.0Dec 2022View details →

Measuring Bulk Crystallographic Texture from Ti-6Al-4V Hot-Rolled Sample Matrices using Synchrotron X-ray Diffraction (Results Dataset)

A dataset of crystallographic texture results for both α (hexagonal close packed, hcp) and β (body-centred cubic, bcc) phases, measured from 31 different hot-rolled Ti-6Al-4V (Ti-64) materials and 3 differently orientated samples using synchrotron X-ray diffraction (SXRD). The aim of the work was to accurately quantify bulk macro-texture for both the α and β phases across a range of different processing conditions, and to compare results with electron backscatter diffraction (EBSD) measurements. The synchrotron intensities were extracted using a new Fourier-based peak fitting method from the <a href="https://pypi.org/project/continuous-peak-fit/">Continuous-Peak-Fit</a> Python package, and then directly used to calculate the pole figures, orientation distribution functions (ODFs) and numerical values for the texture indices in <a href="https://mtex-toolbox.github.io">MTEX</a> Material The Ti-64 materials had been hot-rolled at a range of different temperatures, and to different reductions, followed by air-cooling. Three samples of different orientation were cut from the centre of these rolled blocks, and from the starting material. The material and hot-rolling conditions are recorded in this <a href="https://doi.org/10.5281/zenodo.7438090">analysis dataset</a> as an excel spreadsheet and summarised in the table below. <table align="center"> <caption>A table recording the sample number and associated hot-rolling condition.</caption> <tbody> <tr> <td> Sample Number </td> <td> Rolling Condition </td> </tr> <tr> <td>1</td> <td>825ºC, 87.5% Reduction</td> </tr> <tr> <td>2</td> <td>865ºC, 87.5% Reduction</td> </tr> <tr> <td>3</td> <td>895ºC, 87.5% Reduction</td> </tr> <tr> <td>4</td> <td>915ºC, 87.5% Reduction</td> </tr> <tr> <td>5</td> <td>935ºC, 87.5% Reduction</td> </tr> <tr> <td>6</td> <td>950ºC, 87.5% Reduction</td> </tr> <tr> <td>7</td> <td>960ºC, 87.5% Reduction</td> </tr> <tr> <td>8</td> <td>975ºC, 87.5% Reduction</td> </tr> <tr> <td>9</td> <td>1020ºC, 87.5% Reduction</td> </tr> <tr> <td>10</td> <td>β-annealed, 825ºC, 87.5% Reduction</td> </tr> <tr> <td>11</td> <td>β-annealed, 915ºC, 87.5% Reduction</td> </tr> <tr> <td>12</td> <td>β-annealed, 975ºC, 87.5% Reduction</td> </tr> <tr> <td>13</td> <td>Reduced heating from 915ºC, 87.5% Reduction</td> </tr> <tr> <td>14</td> <td>Reduced heating from 975ºC, 87.5% Reduction</td> </tr> <tr> <td>15</td> <td>825ºC, 75% Reduction</td> </tr> <tr> <td>16</td> <td>865ºC, 75% Reduction</td> </tr> <tr> <td>17</td> <td>895ºC, 75% Reduction</td> </tr> <tr> <td>18</td> <td>915ºC, 75% Reduction</td> </tr> <tr> <td>19</td> <td>935ºC, 75% Reduction</td> </tr> <tr> <td>20</td> <td>950ºC, 75% Reduction</td> </tr> <tr> <td>21</td> <td>960ºC, 75% Reduction</td> </tr> <tr> <td>22</td> <td>975ºC, 75% Reduction</td> </tr> <tr> <td>23</td> <td>1020ºC, 75% Reduction</td> </tr> <tr> <td>24</td> <td>β-annealed, 825ºC, 75% Reduction</td> </tr> <tr> <td>25</td> <td>β-annealed, 915ºC, 75% Reduction</td> </tr> <tr> <td>26</td> <td>β-annealed, 975ºC, 75% Reduction</td> </tr> <tr> <td>27</td> <td>Reduced heating from 915ºC, 75% Reduction</td> </tr> <tr> <td>28</td> <td>Reduced heating from 975ºC, 75% Reduction</td> </tr> <tr> <td>29</td> <td>As-received</td> </tr> <tr> <td>30</td> <td>As-received, β-annealed</td> </tr> <tr> <td>31</td> <td>975ºC, 50% Reduction</td> </tr> </tbody> </table> MTEX Data Analysis The lattice plane intensities for 22 α and 4 β phase peaks were extracted from the Continuous-Peak-Fit analysis, also included in this <a href="https://doi.org/10.5281/zenodo.7438090">analysis dataset</a>, and saved as text files in the form of pole figures. The lattice intensity text files were analysed in MTEX using scripts from the <a href="https://github.com/LightForm-group/continuous-peak-fit-analysis">continuous-peak-fit-analysis</a> package, to plot pole figures and ODF slices, and to calculate pole figure maxima, ODF maxima, texture indices and texture component phase fractions. A kernel half-width of 10° was found to produce optimal data fitting, for highly accurate texture strength intensity values. Metadata An accompanying YAML text file contains associated processing metadata for the SXRD analysis, recording information about the packages used to process the data, along with details about the different files contained within this results dataset.

opencc-by-4.0Apr 2023View details →

BAM reference data: Temperature-dependent Young's and shear modulus data for additively and conventionally manufactured variants of Ti-6Al-4V

This BAM reference dataset reports the elastic properties (Young's modulus, shear modulus) of titanium alloy Ti-6Al-4V between room temperature and 400 °C in an additively manufactured variant (laser-based directed energy deposition with powder as feedstock, DED-LB/M) and from a conventional process route (hot rolled bar). It was generated in an accredited test laboratory using calibrated measuring equipment. The calibrations meet the requirements of the test procedure and are metrologically traceable. The dataset was audited as BAM reference data.

opencc-by-4.0Jan 2022View details →

Dataset for paper entitled, 'Confirmation of rapid-heating β recrystallization in wire-arc additively manufactured Ti-6Al-4V'.

Dataset for paper entitled, 'Confirmation of rapid-heating β recrystallization in wire-arc additively manufactured Ti-6Al-4V'. doi: https://doi.org/10.1016/j.mtla.2020.100857

opencc-by-4.0Jan 2022View details →

Dataset for paper entitled 'Quantification of strain fields and grain refinement in Ti-6Al-4V inter-pass rolled wire-arc AM by EBSD misorientation analysis'.

Dataset for paper entitled 'Quantification of strain fields and grain refinement in Ti-6Al-4V inter-pass rolled wire-arc AM by EBSD misorientation analysis'. doi: https://doi.org/10.1016/j.matchar.2020.110673

opencc-by-4.0May 2022View details →

EBSD Dataset of the Alpha and Beta Phase Orientations for Hot-rolled Ti-6Al-4V

This is an EBSD dataset for hot-rolled Ti-6Al-4V alloy. It includes the EBSD maps of the starting material, and materials have been rolled at nine different temperatures (825°C, 865°C, 895°C, 915°C, 935°C, 950°C, 960°C, 975°C, 1020°C) to three reductions (50%, 75%, and 87.5%). Each rolled material was sampled from transverse direction (TD) and rolling direction (RD) and EBSD maps were taken from both directions. Materials rolled at 825°C and 915°C were further rolled to 94% reduction and EBSD maps were taken from TD. The information of each EBSD data is given in the metadata file in each folder. For materials rolled above 915°C, the beta phase was reconstructed with a software based on Burger's orientation relationship. The Matlab script for plotting EBSD maps, pole figures and ODFs can be downloaded <a href="https://zenodo.org/record/8328717">here</a>.

opencc-by-4.0Sep 2021View details →

Real-time observation of alpha nucleation in Ti-6Al-4V

This video was captured during an in-situ heating stage SEM experiment using a secondary electron camera at the University of Manchester, UK. The Ti-6Al-4V sample was heated to 1000°C to the full β-phase field and then slowly cooled (0.3°C/s) through the β transus, and the α-phase nucleation was recorded by the contrast change in the secondary electron camera from topography development on the sample surface due to surface relief. Microscope operated by Dr Alec E Davis and Dr Jack Donoghue, sample preparation by Nick Byres. These results were published in an Acta Materialia paper in 2021: Acta Materialia paper: https://doi.org/10.1016/j.actamat.2021.117315 Researchgate (free peer reviewed preprint): https://bit.ly/3EHs7Na

opencc-by-4.0Nov 2022View details →

Synchrotron X-ray Diffraction Results - Measuring Bulk Crystallographic Texture from Differently-Orientated Ti-6Al-4V Samples

A dataset of crystallographic texture results for both α (hexagonal close packed, hcp) and β (body-centred cubic, bcc) phases, measured from six differently orientated Ti-6Al-4V (Ti-64) samples, using two different analysis techniques of synchrotron X-ray diffraction (SXRD) data. The texture results are produced from two refinement methods for fitting intensities from SXRD pattern images; an established Rietveld refinement method using the software package <a href="http://maud.radiographema.eu">MAUD (Materials Analysis Using Diffraction)</a> and a new Fourier-based peak fitting method from the <a href="https://pypi.org/project/continuous-peak-fit/">Continuous-Peak-Fit</a> Python package. The texture results were also compared with electron backscatter diffraction (EBSD) measurements from a single sample orientation. The SXRD and EBSD textures were analysed using <a href="https://mtex-toolbox.github.io">MTEX</a> to enable a direct comparison of the pole figures, orientation distribution functions (ODFs) and numerical values for the texture indices. The SXRD texture is calculated from each of the six different sample orientations, a combination of the six sample orientations, and in a batch processing method for calculating spatially-resolved texture variation from 387 individual X-Y stage-scan SXRD measurements across one of the samples. The texture variation measured using stage-scan SXRD is directly compared with EBSD, by splitting up the EBSD map into an equivalent grid matrix using an automated script in MTEX. Material The Ti-64 material used in this study was pre-rolled to 87.5% reduction at 915ºC and then air-cooled to develop a characteristic texture. The run numbers from the experiment reference six different sample orientations, according to their alignment with the original rolling directions (RD – rolling direction, TD – transverse direction, ND – normal direction), and alignment with the horizontal (X) and vertical (Y) axes of the synchrotron detector. MAUD / MTEX Analysis The α and β phase texture for each of the six different sample orientations was calculated using MAUD, included in this <a href="https://doi.org/10.5281/zenodo.7311323">analysis dataset</a>, which produced ODFs in the form of text files. The texture files were analysed in MTEX using scripts from the <a href="https://github.com/LightForm-group/MAUD-batch-analysis">MAUD-batch-analysis</a> package, for plotting of the pole figures and ODF slices, along with calculation of pole figure maxima, ODF maxima and texture indices. The same procedure was used to analyse texture from all six orientations together; using MTEX to fit a single ODF text file. And a series of ODF text files were analysed to calculate texture variation from an X-Y stage scan of Sample 1 (103845). Two different ODF resolutions of 5º and 15º were initially used to fit the texture in MAUD, with the same ODF resolution applied to analyse the data in MTEX. However, an ODF resolution of 15° was found to reproduce the most reasonable texture strength intensity values, with the closest match to the EBSD results. Continuous-Peak-Fit / MTEX Analysis The lattice plane intensities for 21 α and 4 β phase peaks were extracted from the Continuous-Peak-Fit analysis, included in this <a href="https://doi.org/10.5281/zenodo.7311323">analysis dataset</a>, and saved as text files in the form of pole figures. The lattice intensity text files were analysed in MTEX using scripts from the <a href="https://github.com/LightForm-group/continuous-peak-fit-analysis">continuous-peak-fit-analysis</a> package, to plot pole figures and ODF slices, and to calculate pole figure maxima, ODF maxima and texture indices. The same procedure was used to analyse texture from all six orientations together, along with combinations of different sample orientations, by fitting combined lattice intensity text files in MTEX. And a series of lattice intensity text files were analysed to calculate texture variation from the X-Y stage scan of Sample 1 (103845). Lattice plane intensity distributions which had been normalised to a Ti-64 powder sample measurement were also analysed, to see if this had any effect on the texture intensities. Nevertheless, the powder-corrected texture was found to exactly match the raw intensity measurements. Three different ODF resolutions of 5º, 10º and 15º were initially used to fit the texture in MTEX. However, a kernel half-width of 10° was found to produce optimal data fitting, for highly accurate texture strength intensity values. EBSD / MTEX Analysis The indexed α-phase EBSD measurements were recorded over an area of around 100 mm2, with an equivalent sized map of β-phase orientations reconstructed from the data. Both the α and the β phase maps were analysed using the <a href="https://github.com/LightForm-group/MTEX-texture-block-analysis">MTEX-texture-block-analysis</a> package, which was used to split up the map into 387 individual square sections, with equivalent dimensions to the SXRD stage-scan measurement grid. For each of the 387 sections, MTEX was used to plot pole figures and ODF slices, and to calculate pole figure maxima, ODF maxima and texture indices. Texture Variation Comparison The texture values calculated from the SXRD stage scan measurements, with the two analysis methods, were used for a direct comparison with the texture variation recorded using EBSD. This analysis was recorded in the <a href="https://github.com/LightForm-group/texture-strength-comparison">texture-strength-comparison</a> package. The results show differences in texture variation across the piece depending on the method used to analyse the SXRD data. The Continuous-Peak-Fit analysis method shows the closest match with EBSD, producing clear texture intensity spikes for the different α and β lattice plane pole figure intensities, ODF maxima and texture indices, at the centre of the piece. The results were also used to develop SXRD maps showing the distribution of texture intensities across the sample. Metadata An accompanying YAML text file contains associated processing metadata for the SXRD and EBSD analyses, recording information about the different packages used to process the data, along with details about the different files contained within this results dataset.

opencc-by-4.0Aug 2021View details →

Dataset for paper entitled, 'Tailoring equiaxed β-grain structures in Ti-6Al-4V coaxial electron beam wire additive manufacturing'

Dataset for paper entitled, 'Tailoring equiaxed β-grain structures in Ti-6Al-4V coaxial electron beam wire additive manufacturing'. Abstract: High-deposition-rate, directed-energy-deposition additive manufacturing (DED-AM) processes typically produce Ti-6Al-4V (Ti64) components with coarse columnar β-grain structures that lead to undesirable mechanical anisotropy, as well as a fine heterogeneous lamellar transformation microstructure, which is very different to that seen standard wrought products. This arises because of the intrinsic lack of constitutional undercooling at the solidification front, and the subsequent high cooling rates and rapid thermal cycling experienced by the deposited material. In this work, the more refined primary β-grain solidification structures and textures seen in components built with the novel coaxial electron beam wire DED AM (CEWAM) process have been characterised in detail, for the first time, with the aim of investigating the potential for this technology to directly replicate the β-annealed damage-tolerant microstructure used in large Ti64 aerospace forgings. Due to its different lower energy density solidification conditions, it has been confirmed, by electron backscatter diffraction (EBSD) analysis and β-grain reconstruction in three orthogonal cross-sections, that the CEWAM process changes the melt conditions to promote β-grain nucleation ahead of the solidification front, which can result in a highly refined, equiaxed, β-grain structure. However, the conditions for refinement were marginal and a mixed grain structure was commonly observed in thicker sections. Additionally, the subsequent grain-growth stability during β-annealing was investigated. It is shown that an equivalent microstructure can be achieved to that seen in a standard β-forged component, by grain structure homogenisation and slow cooling through the β transus, to promote α colony nucleation, allowing direct part substitution. This was made possible by the refined primary β-grain structure achieved during deposition with the CEWAM solidification conditions which, importantly, are also shown to lead to a weaker texture than in a typical forging. Paper doi: https://doi.org/10.1016/j.mtla.2021.101202

opencc-by-4.0Dec 2020View details →

Dataset for paper entitled, 'Isomorphic grain inoculation in Ti-6Al-4V during additive manufacturing'

Dataset for paper entitled, 'Isomorphic grain inoculation in Ti-6Al-4V during additive manufacturing'. Abstract: The potential for using isomorphic inoculation (ISI) to grain refine titanium alloys in additive manufacturing was investigated by adding TiAlNb particles to Ti-64 during building test samples. A surviving particle was identified and its crystallographic relationship with the matrix studied by transmission Kikuchi diffraction. The particle and bulk matrix grain were shown to have the same crystallographic orientation, demonstrating that the ISI mechanism of solidification bypasses the nucleation step in favour of direct epitaxial growth. Paper doi: https://doi.org/10.1016/j.mlblux.2020.100057

opencc-by-4.0Feb 2021View details →

Dataset for paper entitled, 'The potential for grain refinement of Wire-Arc Additive Manufactured (WAAM) Ti-6Al-4V by ZrN and TiN inoculation'

Dataset for paper entitled, 'The potential for grain refinement of Wire-Arc Additive Manufactured (WAAM) Ti-6Al-4V by ZrN and TiN inoculation'. Abstract: Wire-Arc Additive Manufacturing (WAAM) of large near-net-shape titanium components has the potential to reduce costs and lead-time in many industrial sectors including aerospace. However, with titanium alloys, such as Ti-6Al-4V, standard WAAM processing conditions result in solidification microstructures comprising large cm- scale, <001> fibre textured, columnar β grains, which are detrimental to mechanical performance. In order to reduce the size of the solidified β-grains, as well as refine their columnar morphology and randomise their texture, two cubic nitride phases, TiN and ZrN were investigated as potential grain refining inoculants. To avoid the cost of manufacturing new wire, experimental trials were performed using powder adhered to the surface of the deposited tracks. With TiN particle additions, the β grain size was successfully reduced and modified from columnar to equiaxed grains, with an average size of 300 µm, while ZrN powder was shown to be ineffective at low addition levels studied. Clusters of TiN particles were found to be responsible for nucleating multiple β Ti grains. By utilizing the Burgers orientation relationship, EBSD investigation showed that a Kurdjumov-Sachs orientation relationship could be demonstrated between the refined primary β grains and TiN particles. Paper doi: https://doi.org/10.1016/j.addma.2021.101928