Postgraduate research project

Rapid 3D printing of multi-material critical defence components

Funding
Competition funded View fees and funding
Type of degree
Doctor of Philosophy
Entry requirements
2:1 honours degree View full entry requirements
Faculty graduate school
Faculty of Engineering and Physical Sciences
Closing date

About the project

This project tackles the challenge of rapidly manufacturing multi material defence components using advanced 3D printing. You will develop new joining strategies for metals previously considered unprintable together, using cutting edge additive manufacturing, modelling, and materials characterisation to enable fast, distributed production of critical national security hardware across the UK.

A rapid and flexible manufacturing capability is essential for UK national security, where defence components may need to be produced quickly and in geographically dispersed locations. Multi material metal additive manufacturing (AM) offers a transformative solution, allowing complex components—such hot chambers, nozzles, tanks, and injectors—to be printed on demand. However, many of the required alloys (e.g., aluminium, titanium, steel, nickel superalloys) are currently considered “unjoinable” due to incompatible metallurgical behaviour. Recent research shows that these barriers can be overcome using transitional alloy layers and advanced directed energy deposition (DED) AM systems.

This project  will address the core scientific and engineering challenges that must be solved to enable truly “single shot” printing of multi material defence components. You will:

  • develop and optimise new multi material joining strategies (e.g., Ni/Al, Ni/Ti, Ni/Fe, Fe/Al, Fe/Ti), using computational thermodynamics and phase kinetics modelling to design crack free transitional layers
  • manufacture and test multi material specimens using state of the art DED facilities at the Henry Royce Institute, followed by advanced microscopy, X ray CT, and mechanical testing at the University of Southampton
  • create a graph neural network based logistics framework to determine the optimal distribution of materials, stocks, and production sites across the UK under different scenarios

You will gain experience across materials science, advanced manufacturing, machine learning, and experimental characterisation. 

This project aligns directly with EPSRC and MOD priorities in national resilience, advanced manufacturing, and defence readiness, with industrial engagement opportunities under active development. This is an exciting opportunity to work at the frontier of multi material additive manufacturing with real world impact.