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

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...

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Bibliographic Details
Published in:Metals (Basel ) 2019-02, Vol.9 (2), p.220
Main Authors: Stendal, Johan, Bambach, Markus, Eisentraut, Mark, Sizova, Irina, Weiß, Sabine
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Alloys
Computing time
Fuzzy logic
Heat treating
hot isothermal forging
hybrid modeling
Machine learning
Microscopy
Modelling
Molybdenum
Neural networks
Phase diagrams
phenomenological modeling
Physics
Steel
titanium aluminide
Titanium aluminides
Titanium base alloys
TNM-B1
Training
Yield strength
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Summary: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.
ISSN:2075-4701
2075-4701
DOI:10.3390/met9020220