Speed-up of isothermal forging processes of titanium aluminides by microstructure-adapted control of ram speed

通过微观结构适应的冲压速度控制来加速铝化钛的等温锻造过程

• 批准号: 325021729
• 负责人: Professor Dr.-Ing. Markus Bambach
• 金额: --
• 依托单位: Departement Maschinenbau und Verfahrenstechnik
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2017
• 资助国家: 德国
• 起止时间: 2016-12-31 至 2022-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/325021729?language=en
• 关键词: Speed; up; isothermal; forging; processes

The forging of intermetallic titanium aluminides (TiAl), used e.g. for turbine blades in modern aero engines, currently occurs as a two-stage isothermal forging process, at temperatures of at least 1200°C and process times of several minutes per stroke. This is associated with long process times, high loads on the extremely expensive molybdenum-based dies and high manufacturing costs, which prevents the wider use of TiAl. The aim of this project is to shorten the isothermal forging process of TNM-B1, which industrially takes place with constant ram speed, by targeted control of the ram movement. Results of the first funding period could demonstrate, both, experimentally and simulatively, that the forming process can be accelerated significantly, without causing increased damage to the material, by a previous heat treatment. So far, only the material-specific hardening / softening behavior has been taken into account and not the high variance of the material, the influence of the workpiece geometry or the complex structure and damage development of the material during deformation. In order to include these factors, a process control will be developed that combines a machine learning (ML) algorithm, the existing FEM model and a damage model. FEM is used to simulate the variable material behavior and the different workpiece geometries during the forming process. A Gurson-Tvergaard-Needleman damage model will be adapted to titanium aluminides and coupled with the FEM model. The ML algorithm will then adjust the ram speed according to the workpiece geometry, the material variance and the predicted damage behavior. In order to gain more in-depth knowledge about the microstructure development of TNM-B1 during hot forming, in-situ pressure tests with accompanying structure and phase analysis (DESY - Petra III) will be carried out. The results will provide information about the exact phase composition and the initiation of recrystallization. Details of why the heat treatment results in decreased yield stress, regardless of the test conditions, are also expected. During the heat treatment of larger components, e.g. Turbine blades, a temperature gradient is formed. The areas close to the surface cool faster than the inside of the component, which leads to different globular, lamellar and pearlitic / cellular microstructures over the component cross-section. In order to evaluate the microstructure gradient, a forging blank will pass the entire process chain, both, for constant and for accelerated test conditions. In addition, a post-heat treatment will be developed to achieve the desired final microstructures and mechanical properties.

用于现代航空发动机涡轮叶片的金属间化合物钛铝化物(TiAl)的锻造目前是一种两阶段等温锻造工艺，锻造温度至少为1200°C，每次冲程的加工时间为几分钟。这与较长的加工时间、对极其昂贵的钼基模具的高负荷以及较高的制造成本有关，这阻碍了TiAl的更广泛使用。该项目的目的是通过有针对性地控制顶头运动，缩短TNM-B1的等温锻造过程，该过程在工业上是以恒定的顶头速度进行的。第一个资助期的结果可以通过实验和模拟证明，通过先前的热处理，可以显著加快成形过程，而不会增加对材料的损害。到目前为止，只考虑了材料特定的硬化/软化行为，而没有考虑材料的高方差、工件几何形状的影响或材料在变形过程中的复杂结构和损伤发展。为了包含这些因素，将开发一种结合了机器学习(ML)算法、现有的有限元模型和损伤模型的过程控制。用有限元方法模拟了成形过程中材料的变化行为和不同的零件几何形状。Gurson-Tvergaard-Needleman损伤模型将适用于钛铝化物，并与有限元模型相耦合。然后，ML算法将根据工件的几何形状、材料变化和预测的损伤行为来调整冲压速度。为了更深入地了解TNM-B1在热成形过程中的组织发展，将进行伴随结构和物相分析的原位压力试验(DESY-Petra III)。这一结果将提供关于准确的相组成和再结晶开始的信息。无论试验条件如何，热处理导致屈服应力降低的细节也将被预期。在较大部件(如涡轮叶片)的热处理过程中，会形成温度梯度。靠近表面的区域比部件内部的冷却速度快，这导致部件横截面上形成了不同的球状、片状和珠光体/胞状组织。为了评估显微组织梯度，锻造毛坯将通过整个工艺链，无论是在恒定条件下还是在加速试验条件下。此外，还将开发一种后热处理，以获得所需的最终组织和机械性能。