PhD Research Project: Topological evolution of metallic crystals via 4D imaging and data-science ap

Crystals with fascinating topologies often control the functional and mechanical properties of engineering materials. For instance, high temperature properties of Ni-based superalloys used for turbine blades are essentially determined by crystal arrays with dendritic topology. This makes the study of topological evolution in metals a critical part in determining optimal materials processing trajectories and enabling controllability in materials manufacturing.

High-speed X-ray tomography, available at synchrotron facilities such as the Diamond Light Source, has enabled the capture of 3D images in real-time (seconds per tomography), which has been applied to study crystal growth in 4D (3D plus time) in metallic alloys. The large amount of data collected makes it difficult to visualize and correctly quantify the data. This PhD project will apply advanced imaging analytical and statistical methodologies for the interpretation, analyses, quantification and visualization of crystal growth measured by high-speed synchrotron tomography. This will lead to the development of a benchmark approach for analytics of large microstructure datasets, which can be used widely in the materials community and beyond. In this project, you will develop a machine-learning based image processing pipeline including sematic segmentation to automatically segment the collected 4D data with high reliability on high performance computer clusters. You will develop algorithms to determine statistical quantities for the crystal growth from the large datasets, such as the shape factor of the solid-liquid interface and the velocity. Two-point data approach will be applied to determine the spatial correlations of the 3D topographic features.

The project will involve aspects of materials science, computational programming and data science, and will provide extensive training in a range of research techniques in those aspects. You will have the opportunity to perform experiments at large science facilities such as Diamond Light Source and spend some time of your PhD at Alan Turing Institute in London.

Candidates should have or expect to receive a first or upper second (2.1) honours degree (or equivalent) in computer science or materials science or other related discipline.

Funding Notes

To support students the Turing offers a generous tax-free stipend of £20,500 per annum, a travel allowance and conference fund, and tuition fees for a period of 3.5 years.

References

• B. Cai*, J. Wang, A. Kao, K. Pericleous, A. Phillion, R. Atwood, P.D. Lee*. 4D Synchrotron X-ray Tomographic Quantification of the Transition from Cellular to Dendrite Growth during Directional Solidification. Acta Mater., 2016(117): 160-169.
• B.Cai, P.D. Lee*, S.Karagadde, T.J.Marrow, T. Connolley. Time-resolved synchrotron tomographic quantification of deformation during indentation of an equiaxed semi-solid granular alloy. Acta Mater. 2016, 105, 338-346
• H.C. Wang*, Y. Kashyap, B. Cai, K. Sawhney. Smart phase and dark-field X-ray imaging at high energy. Scientific Report. 2016.
• A. Kao*, B. Cai, P.D. Lee, K. Pericleous. The Effects of Thermoelectric Magnetohydrodynamics in Directional Solidification under a Transverse Magnetic Field. Journal of Crystal Growth, 2016, Accepted.