Predicting the Parabolic Rate Constants of High-Temperature Oxidation of Ti Alloys Using Machine Learning

Somesh Kr Bhattacharya, Ryoji Sahara, Takayuki Narushima

研究成果: Article査読

3 被引用数 (Scopus)

抄録

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.]

本文言語English
ページ(範囲)205-218
ページ数14
ジャーナルOxidation of Metals
94
3-4
DOI
出版ステータスPublished - 2020 10 1

ASJC Scopus subject areas

  • 無機化学
  • 金属および合金
  • 材料化学

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