Cluster Analysis of Combined EDS and EBSD Data to Solve Ambiguous Phase Identifications

Area of Science:

• Materials Science
• Analytical Chemistry
• Data Science

Background:

• Differentiating phases with similar crystal structures but different chemistries is a challenge in analytical scanning electron microscopy (SEM).
• Electron backscatter diffraction (EBSD) and X-ray energy dispersive spectroscopy (EDS) are commonly used but lack automated, unbiased methods for phase differentiation when EBSD responses are similar.
• Existing methods struggle to integrate EBSD and EDS data effectively for unambiguous phase identification.

Purpose of the Study:

• To develop and present a novel, automated, and unbiased data analytics method for differentiating material phases.
• To merge and analyze simultaneously acquired EDS and EBSD data for enhanced phase identification.
• To provide a reproducible method using open-source code and data.

Main Methods:

• A data analytics approach combining singular value decomposition and cluster analysis was employed.
• The method integrates crystallographic data from EBSD with elemental data from EDS.
• Simultaneously acquired EDS + EBSD data were merged for comprehensive analysis.

Main Results:

• The developed method successfully differentiates crystallographically ambiguous but chemically distinct phases.
• The approach was validated using hexagonal TiB2 ceramic contaminated with cubic phases.
• Automated phase determination from combined crystal and elemental data was achieved.

Conclusions:

• The proposed data analytics method offers an open, automated, and unbiased solution for phase differentiation in SEM-EBSD/EDS analysis.
• This technique enhances the capability to identify complex material microstructures.
• The provided Python 3 Jupyter Notebook and data allow for replication and further application.