@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{EisentrautBolzSizovaetal., author = {Eisentraut, Mark and Bolz, Sebastian and Sizova, Irina and Bambach, Markus and Weiß, Sabine}, title = {Development of a heat treatment strategy for the γ-TiAl based alloy TNM-B1 to increase the hot workability}, series = {SN Applied Sciences}, volume = {1}, journal = {SN Applied Sciences}, number = {11}, issn = {2523-3963}, doi = {10.1007/s42452-019-1563-4}, pages = {8}, 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{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{SizovaSviridovBambachetal., author = {Sizova, Irina and Sviridov, Alexander and Bambach, Markus and Eisentraut, Mark and Hemes, Susanne and Hecht, Ulrike and Marquardt, Axel and Leyens, Christoph}, title = {A study on hot-working as alternative post-processing method for titanium aluminides built by laser powder bed fusion and electron beam melting}, series = {Journal of Materials Processing Technology}, volume = {291}, journal = {Journal of Materials Processing Technology}, issn = {0924-0136}, doi = {10.1016/j.jmatprotec.2020.117024}, pages = {14}, language = {en} }