Developing methods for Big Data capture in support of the Digital Twin for Investment Casting Shelling
Lead Research Organisation:
University of Birmingham
Department Name: Chemical Engineering
Abstract
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.
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.
People |
ORCID iD |
Christopher Windows-Yule (Primary Supervisor) | |
Rahul Arath (Student) |
Studentship Projects
Project Reference | Relationship | Related To | Start | End | Student Name |
---|---|---|---|---|---|
EP/S023070/1 | 01/10/2019 | 31/03/2028 | |||
2889986 | Studentship | EP/S023070/1 | 01/10/2023 | 30/09/2027 | Rahul Arath |