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

开发大数据捕获方法以支持熔模铸造脱壳的数字孪生

• 批准号: 2889986
• 金额: --
• 依托单位: University of Birmingham
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2889986
• 关键词: Developing; methods; Big; Data; capture

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.

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。尽管制造工艺非常先进且受到严格控制，但仍然存在许多未知且相互作用的变量，这些变量会影响组件的良率。一个关键因素是为铸造陶瓷模具创建脱壳操作。材料配方和加工变量的控制对于制造模具至关重要具有无缺陷铸造所需的正确属性和尺寸。3D 尺寸表征和过程中粘度测量的最新进展提供了生成“大数据”池的机会，可用于更好地响应过程中的变化。这些数据还可以输入到先进的流程模型或“数字双胞胎”中，以便在下游了解变化的影响。作为 HTRC 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中 shellbuild 的影响。这需要对壳配方对尺寸结构和材料特性的影响以及随后对称为高角度边界的铸造缺陷的影响有一个基本的了解。