(Fusion Power CDT) Advanced Fusion Metallurgy through Machine Learning – Enhanced Electron Hyperspectral Imaging: Bridging the gap from Electronic Density of States to Functional Integrity in Fusion components

About the Project

This project is available through the Fusion CDT. Please check https://fusion-cdt.ac.uk/project/advanced-fusion-metallurgy-through-machine-learning/ for full details.

Abbreviated summary: The project aims to answer the questions the following question: Could SE images, which also contain SE spectral information hold the key for in-situ evaluation as a universal solution for rapidly optimising the most challenging e-beam processing of advanced materials for fusion applications?

Secondary Electron Hyperspectral Imaging (SEHI) delivers SE spectra (rather than just the flux of SEs) for each incident point. SEHI was pioneered by the Rodenburg group. For lithium compounds, some precious metals and carbon materials (including graphite), SE peak positions have been shown to be related to the Density of States (DOS) as obtained from Density Functional Theory (DFT) models. Peak widths and position can be related to local disorder, surface chemical changes on the micron to the nanometre scale, stress, or changes in SE spectra due to beam damage. Thus, SEHI is ideally placed to connect fundamental science that could improve metallurgical workflows in fusion reactor applications. However, SEHI has not yet been explored in this context. This project aims to understand the SE emission spectra of metal alloys used in fusion reactors and their relationships to structural changes (atomic, nano, micro through to weld joint scale) and functional property changes.

Objectives and Expected Key Outcomes:

• DFT calculations to determine the electronic density of states for the alloys of interest as a function of alloy chemistry, disorder and strain
• ML framework for SEHI features extraction that enables mapping of alloy chemistry variation, disorder and strain
• Establish relationships between electron emission spectra and alloy ab-initio models and performance (engineering properties and/or degradation behaviour), through local density of state (DOS) uniformity/variations derived from experimental SEHI data
• Testing and validation of relationships, through controlled ion beam damage, metallurgical characterisation and localised mechanical property testing
• A trained graduate with transferable skills and knowledge of electron microscopy, metallurgy, modelling and or machine learning, and Hyperspectral Imaging techniques for process monitoring in the context of fusion reactor components.

Funding Notes

Fully Funded EPSRC Fusion CDT and CMBE 4 year Studentship (Fees and Stipend)

Applicants should have a minimum of an upper second-class honour’s degree in Materials Science and Engineering, or any closely related Engineering or Science subject. Applications from other equivalent qualifications and experiences and non-traditional career paths are encouraged.

If English is not your first language then you must have an International English Language Testing System (IELTS) average of 6.5 or above with at least 6.0 in each component, or equivalent. Please see this link for further information: View Website

Please see this link for information on how to apply: View Website. Please include the name of your proposed supervisor and the title of the PhD project within your application.