EPSRC supported EngD: Developing methods for Big Data capture in support of the Digital Twin for Investment Casting Shelling Operations

The manufacture of single crystal turbine components via investment casting is critical to the efficiency of the modern jet turbine. Despite a very advanced and tightly controlled manufacturing process, there are still many unidentified and interacting variables that can affect component yields.

One key factor is the shelling operation which creates the ceramic mould for casting. Control of the material formulation and processing variables are essential in making a mould with the correct properties and dimensions required for a defect-free casting.

Recent advances in 3D dimensional characterisation and in-process viscosity measurement provide an opportunity to generate “big data” pools that can be used to better respond to changes in the process. This data can also be fed into advanced process models or “digital twins” that allow the effects of changes to be understood downstream.

As an EngD working within the HTRC, you will develop data collection processes and make use of big data that becomes available to develop and validate the effects of shell build within the digital twin. This will require a fundamental understanding of the shell formulation effects on dimensional build and material properties and the subsequent impact on a casting defect known as High Angle Boundaries.

At this time we can only accept UK or Home-based students. For informal enquiries please contact [Email Address Removed] , together with a CV. Candidates must have at least a 2.1 in a relevant degree discipline or a 2.2 plus an MSc. For details on the Engineering Doctorate scheme visit the homepage: https://www.birmingham.ac.uk/schools/chemical-engineering/postgraduate/eng-d/available-projects.aspx