TY - GEN A1 - Stendal, Johan Andreas A1 - Bambach, Markus A1 - Eisentraut, Mark A1 - Sizova, Irina A1 - Weiß, Sabine T1 - Applying Machine Learning to the Phenomenological Flow Stress Modeling of TNM-B1 T2 - Metals N2 - 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. KW - machine learning KW - phenomenological modeling KW - titanium aluminide KW - hot isothermal forging Y1 - 2019 UR - https://www.mdpi.com/2075-4701/9/2/220 U6 - https://doi.org/10.3390/met9020220 SN - 2075-4701 VL - 9 IS - 2 ER - TY - GEN A1 - Eisentraut, Mark A1 - Bolz, Sebastian A1 - Sizova, Irina A1 - Bambach, Markus A1 - Weiß, Sabine T1 - Development of a heat treatment strategy for the γ-TiAl based alloy TNM-B1 to increase the hot workability T2 - SN Applied Sciences Y1 - 2019 U6 - https://doi.org/10.1007/s42452-019-1563-4 SN - 2523-3963 SN - 2523-3971 VL - 1 IS - 11 ER - TY - GEN A1 - Stendal, Johan Andreas A1 - Eisentraut, Mark A1 - Sizova, Irina A1 - Bolz, Sebastian A1 - Bambach, Markus A1 - Weiß, Sabine T1 - Effect of heat treatment on the workability of hot isostatically pressed TNM-B1 T2 - AIP Conference Proceedings Y1 - 2019 SN - 978-0-7354-1847-9 U6 - https://doi.org/10.1063/1.5112544 VL - 2113 IS - 1 ER - TY - GEN A1 - Stendal, Johan Andreas A1 - Eisentraut, Mark A1 - Sizova, Irina A1 - Bolz, Sebastian A1 - Weiß, Sabine A1 - Bambach, Markus T1 - Accelerated hot deformation and heat treatment of the TiAl alloy TNM-B1 for enhanced hot workability and controlled damage T2 - Journal of Materials Processing Technology Y1 - 2021 U6 - https://doi.org/10.1016/j.jmatprotec.2020.116999 SN - 0924-0136 VL - Vol. 291 ER - TY - GEN A1 - Sizova, Irina A1 - Sviridov, Alexander A1 - Bambach, Markus A1 - Eisentraut, Mark A1 - Hemes, Susanne A1 - Hecht, Ulrike A1 - Marquardt, Axel A1 - Leyens, Christoph T1 - A study on hot-working as alternative post-processing method for titanium aluminides built by laser powder bed fusion and electron beam melting T2 - Journal of Materials Processing Technology Y1 - 2021 U6 - https://doi.org/10.1016/j.jmatprotec.2020.117024 SN - 0924-0136 SN - 1873-4774 VL - 291 ER -