Speed-up of isothermal forging processes of titanium aluminides by microstructure-adapted control of ram speed
Mechanical Properties of Metallic Materials and their Microstructural Origins
Final Report Abstract
The overall objective of the research project is to achieve process acceleration in the isothermal forging of Titanaluminides using a microstructure-adaptive strain control. By determining the softening kinetics and forming limit changes under transient forming conditions, the softening of the material during forming after the yield stress peak could be used to accelerate the process. A higher-level process control system, which acts with the machine tool's machine control system, was first developed for the technical realization of process acceleration. This turn receives input data from the newly developed co-simulation process control based on a machine learning model. The machine learning model couples the FEM model of the forging process with the variance of the flow stress behavior of the material through appropriate sensor technology and generates the variable forming rate per cycle time. The softening behavior of the titanium alloy was taken into account in the FEM model by adapting a damage model (Gurson-Tvergaard-Needleman model) for steel, including a description of the pore formation. All steps of the accelerated process chain were successfully applied to several forged blanks and accompanied by microstructure analyses. Subsequently, tensile tests at different temperatures were carried out to determine the correlation between microstructure and the relevant mechanical properties for each individual step of the process chain. EBSD measurements obtained a more in-depth knowledge of the metallurgical processes occurring during accelerated forming. This made it possible to determine the exact phase composition and the degree of recrystallization for various formed states. With the GOS method, the recrystallization behavior of each phase could be investigated separately for the first time. For a multiphase material, such as TNM-B1, this results more accurately than determining the recrystallized fraction by orientation difference alone. For comparison, accelerated forming tests were also performed on TNB-V4. These have shown that, with appropriate adaptation, accelerated forming is also possible on TiAl alloys with a different composition of β-stabilizing alloying elements. The material analyses have shown that accelerated forming under transient conditions is possible for TNM-B1 and similar TiAl alloys and can lead to comparable results to the previously standard constant forming of these materials, but with significantly lower flow stress and thus less stress on tools and machines.
Publications
-
New Machine Tool Control for Hot Forming of Lightweight Materials, 36. Aachener Stahlkolloquium – Umformtechnik “Ideen Form geben“, Aachen, ISBN 978-3-95886-460-3. (2022)
Feistle, M.; Burger, S.; Li, R.; Bambach, M.; Thein, F. & Herty, M.
-
Developing an artificial neural network controller for accelerating the hot deformation of the titanium aluminide TNM-B1 using reinforcement learning and finite element simulations. Journal of Intelligent Manufacturing, 35(7), 3331-3352.
Stendal, J. A. & Bambach, M.
