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机部件对于现代喷气涡轮机的效率至关重要。尽管有一个非常先进和严格控制的制造过程，仍然有许多未确定的和相互作用的变量会影响零件的产量。其中一个关键因素是铸造用陶瓷模具的脱壳操作。材料配方和工艺变量的控制对于制造具有无缺陷铸造所需的正确性能和尺寸的模具是必不可少的。三维尺寸表征和信息技术的最新进展过程粘度测量提供了生成“大数据”池的机会，可用于更好地响应过程中的变化。这些数据也可以被输入到先进的流程模型或“数字孪生模型”中，从而使下游能够理解变化的影响。作为HTRC内的工程师，您将开发数据收集流程，并利用大数据来开发和验证数字孪生模型中的外壳构建效果。这将需要一个基本的了解壳配方的影响，对尺寸建设和材料性能和间接影响的铸造缺陷称为高角边界。

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.