Developing a "microstructural fingerprint" of titanium alloys - metallurgy in the information age
The properties of the metallic materials that we rely on in almost every aspect of our lives are highly dependent on their microstructures. The patterns of grain and phase boundaries within the metals, the grain shapes and the distribution of defects within the material (such as stacking faults (2-dimensional), dislocations (1-d) and point defects (0-d)) are hugely important in determining these properties. Much of the complexity in modern engineering alloys in terms of composition and processing (the ingredients and steps of the alloy recipe) is a result of the need to achieve highly optimised properties for deployment in very challenging service environments.
It is therefore curious that we have no universally agreed language for describing material microstructure. Often, for example, a pattern of grains in a polycrystal might be described by no more than an average grain size and some measure of the grain shape. Clearly this misses most of the information inherent in the detailed microstructure. Modern high-resolution, high-throughput experimental characterisation equipment can generate detailed images of microstructure at a very high rate, but most of the information is effectively thrown away immediately: the raw data files are too big to handle (and often too big even to retain) and we lack a descriptive language for capturing the essence of the microstructure in detail.
This PhD project will begin to address this deficiency. You will work to develop a methodology in which the tools of computer vision and image analysis are used alongside machine learning methods to produce a "microstructural fingerprint" of an alloy system. The project is sponsored by Rolls-Royce and will take as an example material the Ti6/4 alloy used for fan blades in jet engines. This material has a rich microstructure, requiring description on multiple length scales. Furthermore, the microstructure directly influences several key performance characteristics and Rolls-Royce has available a large database of material and properties and performance data.
Possible applications for a robust method of microstructural fingerprinting would include:
- Rapid characterisation of material along the supply and production chain permitting improved quality control;
- Development of "digital twins" at the alloy microstructure level. These are computer models evolved alongside real components to help identify possible issues or opportunities for improvement;
- Linking of composition and processing to key microstructural features or of these features to alloy properties and performance. Machine learning tools might then be used to relate composition and processing to properties via microstructure. A sound way to describe microstructure is a key step in this process.
Clearly, the methods developed as part of this project would be equally applicable to other alloy systems and to non-metallic poly-crystalline materials.
This 4 year PhD is sponsored by EPSRC. Funding covers tuition fees and annual maintenance payments of at least £14,777 per year for eligible UK and EU applicants. EU nationals must have lived in the UK for 3 years prior to the start of the programme to be eligible for a full award (fees and stipend). The proposed start date is 1st October 2019.
Applicants should have or expect to achieve at least a 2.1 honours degree in Physics, materials science, mathematics or similar and have demonstrated an aptitude for computational and theoretical aspects of their degree