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

通过熔模铸造制造单晶涡轮部件对现代喷气涡轮的效率至关重要。尽管有一个非常先进和严格控制的制造过程，仍然有许多未知的和相互作用的变量会影响组件的产量。其中一个关键因素是脱壳操作，它为铸造制造陶瓷模具。控制材料配方和加工变量对于使模具具有正确的性能和尺寸以实现无缺陷铸造是必不可少的。3D维度表征和过程粘度测量的最新进展为生成“大数据”池提供了机会，这些大数据池可用于更好地响应过程中的变化。这些数据也可以输入到高级流程模型或“数字双胞胎”中，从而使下游能够了解变化的影响。作为一名在HTRC工作的工程师，你将开发数据收集流程，并利用大数据来开发和验证shellbuild在数字孪生中的效果。这将需要对外壳配方对尺寸构建和材料性能的影响以及对铸造缺陷（称为高角边界）的后续影响有基本的了解。

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.