Correlated electron diffraction and energy-dispersive X-ray for automated microstructure analysis

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In this study the effect of merging correlated energy dispersive X-ray (EDS) spectra and electron diffraction data on unsupervised machine learning (clustering) is explored. The combination of data allows second phase coherent precipitates to be identified, that could not be determined from either the individual EDS or diffraction data alone. In order to successfully combine these two distinct data types we leveraged a data fusion method where both data sets were normalised and combined using a robust scaler followed by variance equalisation. A machine learning pipeline was implemented which performs dimensional reduction with PCA and followed by fuzzy C-means clustering, as this allows signals from overlapping regions of the microstructure to be partitioned between different clusters. User control of this partition is used to confirm a change in the stoichiometry of the embedded second phase regions.