Developing a digital twin for next generation forging of high-value titanium alloy components

该项目的目标是开发用于制造高性能钛合金部件的最先进的开式和闭式模具热锻工艺的数字孪生。这将通过开发新的实验信息多尺度材料模拟来实现，以建立锻造行为图作为制造变量的函数。性能决定微观变化将使用基于物理的相场模型进行评估，该模型将把局部材料性能数据提供给部件锻造的整体有限元模型。钛合金的结构完整性对微观组织的变化尤为敏感。部件通常由较大的钢坯的部分封闭模锻而成，钢坯由铸锭通过一系列开放式模锻步骤成形。需要开式模锻来打破前驱铸锭的粗糙组织，而前驱铸锭本身是通过一系列熔化和再凝固步骤来生产的，以提高其纯度和均匀性。从早期阶段保留的任何不一致和缺陷通常在漫长的加工序列的后续步骤中继承。此外，由于现有的应力和温度梯度以及残余应力，当制造商试图锻造更大的部件时，实现高度的微观结构控制变得越来越困难。由于微观结构不一致，多达70%的材料必须被加工掉，这要求坯料比最终零件大得多。此外，为了允许可能的微观结构不一致，部件可能会制造成更保守的安全边际和更简单的几何形状，这增加了它们的重量。这显然不利于航空制造商的燃油效率，而航空制造商是钛锻件的主要用户。为了解决这些挑战，数字孪生所使用的部件尺度变形模型必须反映真实钛合金的微观组织灵敏度。以下任务将构成该项目的主要目标：开发新的相场模型，可以准确地复制钛合金在热锻造过程中控制性能的微观结构转变；开发一种专门的建模算法，将并发的微观和部件比例模型联系起来。这些算法将分别负责相场和有限元模型之间的属性和变形条件数据的交换，以及作为数字孪生框架的新模型的必要RVEs验证的生成和管理。这将涉及在一系列加工条件下对真实部件进行合适的开式和/或闭式模锻的模拟。这将用于绘制出最终产品的微观和宏观特性作为各种工艺参数的函数。这样的地图将适用于告知制造商关于最佳热变形条件和锻造顺序的故障排除，以及随着新技术能力的出现有效地优化组件的几何形状。

The goal of the project is to develop a digital twin of state-of-the-art open and closed die hot forging processes used for manufacturing high-performance titanium alloy components. This will be achieved by developing novel experimentally informed multi-scale materials simulations to build up forging behaviour maps as a function of the manufacturing variables. Property-determining microscopic changes will be evaluated using physics-based phase field models, which will feed local material property data into global finite-element models of component forging. The structural integrity of titanium alloys is especially sensitive to variations in the microstructure. Components are typically closed die forged from sections of larger billets, and the billets are shaped from cast ingots using a series of open die forging steps. Open die forging is needed to break up the coarse microstructure of the precursor ingots, which themselves are produced using a sequence of several melting and re-solidification steps to increase their purity and homogeneity. Any non-uniformities and defects retained from the earlier stages are most often inherited throughout the subsequent steps in the long processing sequence. Furthermore, achieving a high degree of microstructural control becomes progressively more difficult as manufacturers try to forge larger components, due to existing stress and temperature gradients and residual stresses. As much as 70% of material must be machined away due to microstructural inconsistencies, requiring billets to be considerably larger than the final parts. Furthermore, to allow for possible microstructural inconsistencies, components may be manufactured to satisfy more conservative safety margins and simpler geometries, which increases their weight. This is a clear detriment to fuel efficiency for the aerospace manufacturers - which are major users of titanium forgings. To solve these challenges, it is essential that the component-scale deformation models used by the digital twin reflect the microstructural sensitivity of real titanium alloys. The following tasks will form the main objectives of the project: development of novel phase field models that can accurately replicate the property-controlling microstructural transformations in titanium alloys during hot forging development of a specialised modelling algorithm that will link the concurrent microscopic and component scale models. The algorithms will be responsible for the exchange of property and deformation condition data between the phase field and finite element models respectively, as well as the generation and administration of the necessary RVEs validation of the new models as a framework for the digital twin. This will involve the simulation of suitable open and/or closed die forging of real components over a range of processing conditions. This will be used to map out the microscopic and macroscopic properties of the final product as a function of the various process parameters. Such maps would then be suitable for informing manufacturers regarding the optimal hot- deformation conditions and troubleshooting the forging sequences, as well as effectively optimising the component geometries as new technological capabilities emerge.