@misc{BambachSizovaSviridovetal., author = {Bambach, Markus and Sizova, Irina and Sviridov, Alexander and Stendal, Johan Andreas and G{\"u}nther, Martin}, title = {Batch Processing in Preassembled Die Sets - A New Process Design for Isothermal Forging of Titanium Aluminides}, series = {Journal of Manufacturing and Materials Processing}, volume = {2}, journal = {Journal of Manufacturing and Materials Processing}, number = {1}, issn = {2504-4494}, doi = {doi:10.3390/jmmp2010001}, pages = {14}, language = {en} } @misc{StendalEisentrautSizovaetal., author = {Stendal, Johan Andreas and Eisentraut, Mark and Sizova, Irina and Bolz, Sebastian and Weiß, Sabine and Bambach, Markus}, title = {Accelerated hot deformation and heat treatment of the TiAl alloy TNM-B1 for enhanced hot workability and controlled damage}, series = {Journal of Materials Processing Technology}, volume = {Vol. 291}, journal = {Journal of Materials Processing Technology}, issn = {0924-0136}, doi = {10.1016/j.jmatprotec.2020.116999}, pages = {12}, language = {en} } @misc{EisentrautStendalBolzetal., author = {Eisentraut, Mark and Stendal, Johan Andreas and Bolz, Sebastian and Bambach, Markus and Weiß, Sabine}, title = {Applying a softening adapted acceleration to the hot deformation of TNM-B1}, series = {MRS Fall Meeting 2020}, journal = {MRS Fall Meeting 2020}, abstract = {Hot isostatically forged TiAl turbine blades made of TNM-B1 are commercially used in aircraft engines, as they offer significantly lower weight than the traditional nickel-based blades while exhibiting similar strength. Like other TiAl alloys, TNM-B1 displays high peak stress followed by a strong softening behavior (i.e. stress reduction) during hot deformation. This softening can be used to accelerate the deformation process by reducing the processing time and in turn the costs for TNM-B1 parts. In order to avoid increased damage during the accelerated process, a pre-heat treatment (HT) for the hot isostatically pressed material (HIP) is required. To simulate the accelerated forming process, hot compression tests were performed with a DIL805A/D/T dilatometer from TA Instruments (New Castle, Delaware, USA) with different strain rates (0.0013, 0.005, 0.01 and 0.05) and temperatures (T=1150, 1175 and 1200°C). Deformation of the heat-treated state revealed lower flow stress (in both, peak stresses and steady state stresses) and fewer voids compared to the HIP state (Fig. 1.a). The compression test data were used to develop material and temperature specific strain rate profiles based on a material model. Subsequently, hot compression tests were performed with different strain rate profiles (starting strain rates 0.0013 and 0.0052) for the HIP and the HT state. The results were evaluated with regard to their microstructure, deformation, and damage behavior. A reduction of the processing time for all tested strain rates profiles by factors 2-3 could be achieved compared to constant strain rates. Furthermore, the results indicated that the deformation with strain rate profiles (compared to constant strain rates) did not significantly change the resulting microstructure or damage tolerance of the HT state.}, language = {en} } @misc{StendalBambachEisentrautetal., author = {Stendal, Johan Andreas and Bambach, Markus and Eisentraut, Mark and Sizova, Irina and Weiß, Sabine}, title = {Applying Machine Learning to the Phenomenological Flow Stress Modeling of TNM-B1}, series = {Metals}, volume = {9}, journal = {Metals}, number = {2}, issn = {2075-4701}, doi = {10.3390/met9020220}, pages = {18}, abstract = {Data-driven or machine learning approaches are increasingly being used in material science and research. Specifically, machine learning has been implemented in the fields of materials discovery, prediction of phase diagrams and material modelling. In this work, the application of machine learning to the traditional phenomenological flow stress modelling of the titanium aluminide (TiAl) alloy TNM-B1 (Ti-43.5Al-4Nb-1Mo-0.1B) is investigated. Three model types were developed, analyzed and compared; a physics-based phenomenological model (PM) originally developed for steel by Cingara and McQueen, a purely data-driven machine learning model (MLM), and a hybrid model (HM), which uses characteristic points predicted by a learning algorithm as input for the phenomenological model. The same amount of data was used to both fit the PM and train the MLM and HM. The models were analyzed and compared based on the accuracy of their predictions, development and computing time, and their ability to predict on interpolated and extrapolated inputs. The results revealed that for the same amount of experimental data, the MLM was more accurate than the PM. In addition, the MLM was better able to capture the characteristic peak stress in the TNM-B1 the flow curves, and could be developed and computed faster. Furthermore, the MLM was able to make realistic predictions for inputs outside the experimental data used for training. The HM showed comparable accuracy to the PM for the experimental conditions. However, the HM was able to produce a better fit for input conditions outside the training data.}, language = {en} } @misc{StendalEisentrautSizovaetal., author = {Stendal, Johan Andreas and Eisentraut, Mark and Sizova, Irina and Bolz, Sebastian and Bambach, Markus and Weiß, Sabine}, title = {Effect of heat treatment on the workability of hot isostatically pressed TNM-B1}, series = {AIP Conference Proceedings}, volume = {2113}, journal = {AIP Conference Proceedings}, number = {1}, isbn = {978-0-7354-1847-9}, doi = {10.1063/1.5112544}, language = {en} } @misc{EisentrautStendalBolz, author = {Eisentraut, Mark and Stendal, Johan Andreas and Bolz, Sebastian}, title = {Accelerating the isothermal forging process of titanium aluminides by microstructure adaptive speed control, MSE, Darmstadt, 2018}, pages = {1}, abstract = {Titanium aluminides (TiAl) are attractive materials due to their low weight, high strength, high temperature- and corrosion resistance. Currently, TiAl turbine blades produced by isothermal forging are being used in commercial aircraft jet engines. However, low ram speeds, costly dies and a required protective atmosphere makes the process relatively expensive, restricting implementation into other areas. Reducing the processing time can cut costs and in turn lead to a much wider range of applications for TiAl forgings. This project focuses on the TiAl alloy TNM-B1. During preliminary testing, the material displayed a pronounced peak in flow stress followed by a strong softening response. If this behavior can be utilized by accelerating the ram during softening, the processing time can be significantly reduced. The impact on microstructure and limits of such an acceleration are important to consider. Compression tests at constant and variable speeds are to be performed to map the flow behavior, microstructure development and damage criteria. Based on the results, a material model will be developed and used in simulations. The aim is to establish a process design, which can adapt and optimize the forging speed to a given microstructure within the limits of the material. TNM-B1 is a complex multiphase alloy, with highly varied deformation and recrystallization behaviors across the different phases. This implies the importance of a suitable heat treatment strategy to achieve high deformability during forging, as well as the desired final microstructure. Heat treatment procedures will be investigated with the aim of generating highly deformable and damage resistant microstructures prior to forging, and application specific microstructures after forging.}, language = {en} }