Postgraduate research project

Combining deep learning and computational thermodynamics modelling to design novel functionally graded multimaterial components

Fully funded (UK and international)
Type of degree
Doctor of Philosophy
Entry requirements
A UK 2:1 honours degree View full entry requirements
Faculty graduate school
Faculty of Engineering and Physical Sciences
Closing date

About the project

This PhD project will lead to the conception of functionally graded advanced components, combining properties that are considered unattainable. 

Metal powder-based additive manufacturing is an emerging 3D printing technology that provides great topological freedom, allowing for component shapes unattainable via wrought technologies. However, the majority of the metal 3D printing has been on alloys designed for wrought conditions, and recent years have seen the emergence of alloy design efforts tailored specifically to this manufacturing technology, where extreme cyclic cooling and heating rates are present.

An additional breakthrough in metal 3D printing can be achieved by combining powder metals of distinct properties, such as heat resistance with low density, or corrosion resistance with ultra-high strength. The number of combinations is seemingly infinite, this requires a multiple approach combining computational thermodynamics and deep learning.

The work will be carried out in an international context, with collaboration with Kassel University (Germany) to perform builds and advanced characterisation down to the individual atom scale, and with the National Institute for Standards and Technology (NIST, USA) in data intensive methods analysis.

The key output of this project will be to find a range of novel functionally graded systems to satisfy emerging industries such as hydrogen storage and turbines, fusion energy, and space.