This repository contains Jupyter notebooks and a MATLAB script necessary to reproduce the EBSD results in the conference paper "Intermetallic phase layers in cold metal transfer aluminium-steel joints with an Al-Si-Mn filler alloy" which was recently submitted to Materials Transactions - ICAA 18 special issue (doi):

@article{bergh2023intermetallic,
  author  = {Tina Bergh and Håkon Wiik Ånes and Ragnhild Aune and Sigurd Wenner and Randi Holmestad and Xiaobo Ren and Per Erik Vullum},
  title   = {Intermetallic Phase Layers in Cold Metal Transfer Aluminium-Steel Welds with an Al–Si–Mn Filler Alloy},
  doi     = {10.2320/matertrans.MT-LA2022046},
  number  = {2},
  pages   = {352-359},
  volume  = {64},
  groups  = {ED, Aluminium, Steel},
  journal = {MATERIALS TRANSACTIONS},
  year    = {2023},
}

The supplementary information including the raw EBSD data to reproduce the results are available on Zenodo (doi):

@dataset{bergh2023intermetallic_si,
  author    = {Bergh, Tina and Ånes, Håkon Wiik and Wenner, Sigurd},
  title     = {{Supplementary Information and EBSD data for 'Intermetallic Phase Layers in Cold Metal Transfer Aluminium-Steel Welds with an Al-Si-Mn Filler Alloy'}},
  doi       = {10.5281/zenodo.6634354},
  month     = aug,
  publisher = {Zenodo},
  year      = {2022},
}

The content in this repository is licensed under the GPLv3+, since many of the softwares used have this license.

The Jupyter notebook and Python files are numbered according to the steps taken in the EBSD analysis:

1. ebsd1_preprocess.ipynb: Increase the signal-to-noise ratio of patterns by background subtraction and averaging. Generate indexing-independent views of EBSD datasets (mean intensity map, image quality map, and average neighbour dot product map), and calibrate the detector-sample geometry via projection center (PC) optimization with the PyEBSDIndex Python package (cubic phases only!). An average PC is used in dictionary indexing.
2. ebsd2_dictionary_indexing.py: Obtain crystal orientations from the EBSD patterns via dictionary indexing (DI) as implemented in kikuchipy. Requires master patterns of each phase, generated with EMsoft.
3. ebsd3_orientation_refinement.py: Refine crystal orientations obtained from DI.
4. ebsd4_postprocess_indexing_results.ipynb: Create multi-phase crystal map from the single-phase maps obtained from DI.
5. ebsd5_plot_simulations.ipynb: Plot geometrical and dynamical simulations for each phase.

Python packages used in the notebooks and scripts are listed in requirements.txt and can be installed into a virtual or conda environment:

pip install -r requirements.txt

Analysis of the EBSD indexing results is done with the script orientation_analysis.m. MATLAB packages used are MTEX and export_fig.