Property-controlled forging: analysis and experimental validation

在等温锻造中，工具被加热到工件温度，因此近净成形对于低可加工性的材料变得可行。等温锻造技术的进步使得在商用喷气发动机中批量生产金属间钛铝化物（TiAl）制成的涡轮机叶片成为可能。然而，目前，由于对最终几何形状的高要求以及对铸造结构的高度变形的要求，可以发现该技术的许多限制。采用目前的工艺和工装技术，涡轮机叶片的材料利用率往往低于10%。在第一阶段中，一个性能控制等温平板模具锻造过程与局部成形，因此进行了研究和一个合适的控制器已经推出。在第二阶段，我们专注于使用工件的局部加热在多个冲程中进行性能控制的平模锻造。合适的控制策略，使用水平集描述的变形的几何形状已被提出和分析。在第三阶段，我们的重点是目标几何形状的联合和鲁棒控制以及多行程控制下的变形程度。由M. Bambach专注于使用基于神经网络的代理模型预测材料微观结构。这些允许在接近生产条件下用作实时软传感器。计划实现目标几何形状和变形程度的联合预测，以便为最终生产工艺步骤生产工具。由M. Herty研究了基于可验证的代理软传感器的鲁棒控制器的数学方面的训练神经网络。一个公式使用贝叶斯定理的设计新的控制与错误的保证将开发。成形、几何、微观组织控制和数学控制理论的研究问题在各个方面都有着密切的联系。

In isothermal forging, the tools are heated to the workpiece temperature, so that near-net-shaping becomes feasible for materials of low workability. Advances in isothermal forging have recently made it possible to use turbine blades made of intermetallic titanium aluminides (TiAl) in serial production in commercial jet engines. Currently, however, many limitations of this technology can be found due to the high demands on final geometry as well as the demand on the high degree of deformation of the cast structure. With the current process and tooling technology, the material utilization of turbine blades is often less than 10%. In phase 1, a property-controlled isothermal flat tool forging process with local forming was therefore investigated and a suitable controller has been derived. In the 2nd phase we focused on property-controlled flat tool forging in several strokes using local heating of the workpiece. Suitable control strategy using a Level-Set description of the deformed geometry has been proposed and analysed. In the third phase the joint and robust control of target geometry as well as deformation degree under controls of multiple strokes will be our focus. The part of the proposal led by M. Bambach focusses on the prediction of the material micro-structure using surrogate models based on neural networks. Those allow to be used as real-time soft-sensors under near-production conditions. It is planned to achieve the joint prediction of target geometry and deformation degrees, such that tools can be produce for the final production process step. The part of the proposal lead by M. Herty investigates the mathematical aspects of robust controllers based on verifiable surrogate soft-sensors given by the trained neural networks. A formulation using Bayes’s theorem for the design of novel controls with error guarantees will be developed. The research questions in forming, geometry, microstructure control and mathematical control theory are closely linked in various aspects.