Developing a ’Microstructural Fingerprint’ of Titanium Alloys - Metallurgy in the Information Age [Sponsor: Rolls-Royce; FULLY FUNDED]

This PhD is part of the EPSRC Centre for Doctoral Training in Materials for Demanding Environments [M4DE CDT]; it is sponsored by Rolls-Royce, and will commence September 2018.

Background
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.

Project Outline
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.

About Rolls-Royce
Rolls-Royce is a global business providing power systems for use on land, at sea and in the air. The Group has a balanced business portfolio with leading positions in the civil and defence aerospace, marine and nuclear power generation sectors. Rolls-Royce has substantial investments in developing technology and capabilities that can be applied to products and services in a variety of markets. This project will be carried out in close collaboration with the Civil Nuclear business in Rolls-Royce which provides products and services to both the existing nuclear fleet and new build projects throughout the world.