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

Duran, E.C., Kho, Z., Einsle, J. F. , Azaceta, I., Cavill, S.A., Kerrigan, A., Lazarov, V.K. and Eggeman, A.S. (2023) Correlated electron diffraction and energy-dispersive X-ray for automated microstructure analysis. Computational Materials Science, 228, 112336. (doi: 10.1016/j.commatsci.2023.112336)

Abstract

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.

• Item Type: Articles
• Additional Information: This work was supported by the Henry Royce Institute for Advanced Materials, funded through EPSRC grants EP/R00661X/1, EP/S019367/1, EP/P025021/1, EP/S021531/1 and EP/P025498/1. ECD acknowledges financial support from the Republic of Türkiye Ministry of National Education. ASE acknowledges financial support from the Royal Society. ZK thanks the EPSRC for the studentship provided to them through the Department of Materials Doctoral Training Account. IA acknowledges funding through EPSRC grant EP/K03278X/1.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Einsle, Dr Joshua Franz

Creator Roles

Einsle, J. F.: Formal analysis, Methodology, Writing – review and editing

• Authors: Duran, E.C., Kho, Z., Einsle, J. F., Azaceta, I., Cavill, S.A., Kerrigan, A., Lazarov, V.K., and Eggeman, A.S.
• College/School: College of Science and Engineering > School of Geographical and Earth Sciences > Earth Sciences
• Journal Name: Computational Materials Science
• Publisher: Elsevier
• ISSN: 0927-0256
• ISSN (Online): 1879-0801
• Published Online: 28 June 2023
• Copyright Holders: Copyright © 2023 The Authors
• First Published: First published in Computational Materials Science 228:112336
• Publisher Policy: Reproduced under a Creative Commons license