TY - JOUR
T1 - Predicting the Parabolic Rate Constants of High-Temperature Oxidation of Ti Alloys Using Machine Learning
AU - Bhattacharya, Somesh Kr
AU - Sahara, Ryoji
AU - Narushima, Takayuki
N1 - Funding Information:
SKB is thankful to Dr. Sayan Sarcar, Assistant Professor, Faculty of Library Information and Media Science, University of Tsukuba, Tsukuba, Japan for fruitful discussions. This work was supported by the Council for Science, Technology and Innovation under the Cross-ministerial Strategic Innovation Promotion Program. This study was also partially supported by the Japan Society for the Promotion of Science KAKENHI Grant (Number 18H01718), ISIJ Research Promotion Grant and NIMS-Tohoku cross appointment system.
Funding Information:
SKB is thankful to Dr. Sayan Sarcar, Assistant Professor, Faculty of Library Information and Media Science, University of Tsukuba, Tsukuba, Japan for fruitful discussions. This work was supported by the Council for Science, Technology and Innovation under the Cross-ministerial Strategic Innovation Promotion Program. This study was also partially supported by the Japan Society for the Promotion of Science KAKENHI Grant (Number 18H01718), ISIJ Research Promotion Grant and NIMS-Tohoku cross appointment system.
Publisher Copyright:
© 2020, Springer Science+Business Media, LLC, part of Springer Nature.
PY - 2020/10/1
Y1 - 2020/10/1
N2 - Abstract: In this study, we attempt to build a statistical (machine) learning model to predict the parabolic rate constant (kP) for the high-temperature oxidation of Ti alloys. Exploring the experimental studies on high-temperature oxidation of Ti alloys, we built our dataset for machine learning. Apart from the alloy composition, we included the constituent phase of the alloy, temperature of oxidation, time for oxidation, oxygen and moisture content, remaining atmosphere (gas except O2 gas in dry atmosphere), and mode of oxidation testing as the independent features while the parabolic rate constant (kP) is set as the target feature. We employed three different ML models to predict the ‘kP’ for Ti alloys. Among the regression models, the gradient boosting regressor yields the coefficient of determination (R2) of 0.92 for kP. The knowledge gained from this study can be used to design novel Ti alloys with excellent resistance towards high-temperature oxidation. Graphic Abstract: [Figure not available: see fulltext.]
AB - Abstract: In this study, we attempt to build a statistical (machine) learning model to predict the parabolic rate constant (kP) for the high-temperature oxidation of Ti alloys. Exploring the experimental studies on high-temperature oxidation of Ti alloys, we built our dataset for machine learning. Apart from the alloy composition, we included the constituent phase of the alloy, temperature of oxidation, time for oxidation, oxygen and moisture content, remaining atmosphere (gas except O2 gas in dry atmosphere), and mode of oxidation testing as the independent features while the parabolic rate constant (kP) is set as the target feature. We employed three different ML models to predict the ‘kP’ for Ti alloys. Among the regression models, the gradient boosting regressor yields the coefficient of determination (R2) of 0.92 for kP. The knowledge gained from this study can be used to design novel Ti alloys with excellent resistance towards high-temperature oxidation. Graphic Abstract: [Figure not available: see fulltext.]
KW - High-temperature oxidation
KW - Machine learning
KW - Python
KW - Regression
KW - Titanium alloys
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U2 - 10.1007/s11085-020-09986-3
DO - 10.1007/s11085-020-09986-3
M3 - Article
AN - SCOPUS:85087503728
SN - 0030-770X
VL - 94
SP - 205
EP - 218
JO - Oxidation of Metals
JF - Oxidation of Metals
IS - 3-4
ER -