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Metals 2019, 9(2), 220; https://doi.org/10.3390/met9020220

Applying Machine Learning to the Phenomenological Flow Stress Modeling of TNM-B1

1
Chair of Mechanical Design and Manufacturing, Brandenburg University of Technology Cottbus-Senftenberg, Konrad-Wachsmann-Allee 17, D-03046 Cottbus, Germany
2
Department of Metallurgy and Materials Technology, Brandenburg University of Technology Cottbus-Senftenberg, Konrad-Wachsmann-Allee 17, D-03046 Cottbus, Germany
*
Author to whom correspondence should be addressed.
Received: 23 November 2018 / Revised: 19 January 2019 / Accepted: 29 January 2019 / Published: 13 February 2019
(This article belongs to the Special Issue Constitutive Modelling for Metals)
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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. View Full-Text
Keywords: machine learning; phenomenological modeling; hybrid modeling; titanium aluminide; TNM-B1; hot isothermal forging machine learning; phenomenological modeling; hybrid modeling; titanium aluminide; TNM-B1; hot isothermal forging
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Stendal, J.A.; Bambach, M.; Eisentraut, M.; Sizova, I.; Weiß, S. Applying Machine Learning to the Phenomenological Flow Stress Modeling of TNM-B1. Metals 2019, 9, 220.

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