Texture in materials plays a crucial role in metallic products. A thorough study of the underlying morphology and its evolution is relevant for producing cast parts of innovative technological products. The formation of grain boundaries during directional solidification of grains with different crystallographic orientations affect its deformation and mechanical response.
The cellular automaton (CA) method aims at modelling complex phenomena taking place at the scale of interest through the use of simple laws applied at a smaller scale. The goal is to predict the grain structure formed in large simulation domains. This scale-bridging is necessary as the development of the structure depends on phenomena taking place at large spatial distances all being influenced by time-dependent boundary conditions. Coupling of the CA method with a relevant crystal plasticity finite element (CPFE) method will allow for a physics-based computational framework with a unique capability to model crystal growth and recrystallization in a bio-compatible magnesium alloy.
The approach will be used to predict the mechanical response of meso-scaled components made of magnesium alloy including next-generation bio-compatible stents.
Primary supervisor: Professor Anish Roy
Secondary supervisor: Dr Konstantinos Baxevanakis
Entry requirements for United Kingdom
At least a 2:1 honours degree (or equivalent international qualification) in mechanical engineering, materials engineering, aerospace engineering, civil engineering or a related subject. A relevant master's degree and/or experience in one or more of the following would be an advantage: mechanical engineering, product design, materials engineering, aerospace engineering, civil engineering or a related subject.
English language requirements
Applicants must meet the minimum English language requirements. Further details are available on the International website.
Find out more about research degree funding
How to apply
All applications should be made online. Under programme name, select ‘Mechanical and Manufacturing Engineering’. Please quote reference number: UF-AR-2022-2