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Software . 2023
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Software . 2023
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texture-strength-comparison: v1.0.0

Authors: Daniel, Christopher Stuart; Quinta da Fonseca, João;

texture-strength-comparison: v1.0.0

Abstract

A set of Python scripts for comparing the crystallographic texture strength output of synchrotron X-ray diffraction (SXRD) and electron backscatter diffraction (EBSD) data processed using MTEX, to produce texture intensity plots, 2D texture intensity maps, and 2D texture component phase fraction distributions. This package has been used to analyse crystallographic texture distributions and in-situ texture changes in Ti-6Al-4V materials through a number of different synchrotron diffraction experiments at DESY and Diamond Light Source beamlines. This package has been used to compare texture intensity variation from stage scan synchrotron measurements using MAUD and Continuous-Peak-Fit, with the texture variation recorded using an EBSD map that had been divided into equivalent sized squares as were used for the synchrotron analysis. Synchrotron data was first analysed using either the MAUD-batch-analysis or continuous-peak-fit-analysis packages, to measure the texture distribution across different samples, or to record the in-situ texture evolution during different high temperature deformation experiments. EBSD data was first analysed using the MTEX-texture-block-analysis package to divide the EBSD maps and measure the micro-texture distribution. Contents The following notebooks have been used to analyse different experimental data: 1. `texture_strength_comparison_diamond_2017.ipynb` - A notebook analysing texture intensity changes from a series of synchrotron measurements recorded during high temperature deformation of a Ti-6Al-4V tensile specimen. The texture data was fitted using two different methods, MAUD (a Rietveld refinement software) and Continuous-Peak-Fit (a Fourier series based peak analysis method). 2. `texture_strength_comparison_diamond_2021.ipynb` - A notebook analysing texture intensity changes from a series of synchrotron measurements recorded as a stage-scan (X-Y) across a pre-rolled Ti-6Al-4V sample. The texture data was used to compare texture intensity variation from stage scan synchrotron measurements using MAUD and Continuous-Peak-Fit, with the texture variation recorded using an EBSD map that had been divided into equivalent sized squares as were used for the synchrotron analysis. 3. `texture_strength_comparison_desy_2020-21.ipynb` - A notebook analysing texture intensity changes from a series of synchrotron measurements recorded during high temperature deformation of Ti-6Al-4V compression specimens, at different temperatures and strain rates. The texture data was fitted using Continuous-Peak-Fit. 4. `texture_strength_comparison_desy_2020-21_multihit.ipynb` - A notebook analysing texture intensity changes from a series of synchrotron measurements recorded during high temperature deformation of Ti-6Al-4V compression specimens, with multi-hit deformation and hold stages. The texture data was fitted using Continuous-Peak-Fit. 5. `texture_strength_comparison_diamond_2022.ipynb` - A notebook analysing texture intensity and texture component phase fraction changes from a series of synchrotron measurements recorded as a stage-scan (X-Y) across pre-rolled Ti-6Al-4V samples. The alpha and beta phase texture data was used to analyse texture intensity variation and texture component phase fraction variation from stage scan synchrotron measurements using Continuous-Peak-Fit. Note, the `example-data/` and `example-analysis/` folders contain instuctions for downloading data that can be used as an example analysis, but a clear external file structure should be setup to support the analysis of large synchrotron datasets. Installation Instructions for installing the Python libraries to run the notebooks can be found in the README.md file.

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Keywords

python, ti64, crystallographic texture, synchrotron diffraction

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This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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