Developing methods for Big Data capture in support of the Digital Twin for Investment Casting Shelling

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 subsequent

impact on a casting defect known as High Angle Boundaries.