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

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The manufacture of single crystal turbine components via investment casting is criticalto the efficiency of the modern jet turbine. Despite a very advanced and tightlycontrolled manufacturing process, there are still many unidentified and interactingvariables 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 amould with the correct properties and dimensions required for a defect free casting.Recent advances in 3D dimensional characterisation and in-process viscositymeasurement provide an opportunity to generate "big data" pools that can be used tobetter respond to changes in the process. This data can also be fed into advancedprocess models or "digital twins" that allow the effects of changes to be understooddownstream.As an EngD working within the HTRC you will develop data collection processes andmake use of big data that becomes available to develop and validate the effects of shellbuild within the digital twin. This will require a fundamental understanding of the shellformulation effects on dimensional build and material properties and subsequentimpact on a casting defect known as High Angle Boundaries.