Data analytics approach to predict high-temperature cyclic oxidation kinetics of NiCr-based Alloys

Peng, Jian; Pillai, Rishi; Romedenne, Marie; Pint, Bruce A.; Muralidharan, Govindarajan; Allen Haynes, J.; Shin, Dongwon

doi:10.1038/s41529-021-00188-z

Data analytics approach to predict high-temperature cyclic oxidation kinetics of NiCr-based Alloys

Journal Article · Mon Aug 09 00:00:00 EDT 2021 · npj Materials Degradation

DOI:https://doi.org/10.1038/s41529-021-00188-z· OSTI ID:1812408

Peng, Jian; Pillai, Rishi; Romedenne, Marie; Pint, Bruce A.; Muralidharan, Govindarajan; Allen Haynes, J.;

Abstract

Although of practical importance, there is no established modeling framework to accurately predict high-temperature cyclic oxidation kinetics of multi-component alloys due to the inherent complexity. We present a data analytics approach to predict the oxidation rate constant of NiCr-based alloys as a function of composition and temperature with a highly consistent and well-curated experimental dataset. Two characteristic oxidation models, i.e., a simple parabolic law and a statistical cyclic oxidation model, have been chosen to numerically represent the high-temperature oxidation kinetics of commercial and model NiCr-based alloys. We have successfully trained machine learning (ML) models using highly ranked key input features identified by correlation analysis to accurately predict experimental parabolic rate constants ( k _p ). This study demonstrates the potential of ML approaches to predict oxidation kinetics of alloys over wide composition and temperature ranges. This approach can also serve as a basis for introducing more physically meaningful ML input features to predict the comprehensive cyclic oxidation behavior of multi-component high-temperature alloys with proper constraints based on the known underlying mechanisms.

View Journal Article

Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE; USDOE Office of Energy Efficiency and Renewable Energy (EERE)

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1812408

Alternate ID(s):: OSTI ID: 1817562

Journal Information:: npj Materials Degradation, Journal Name: npj Materials Degradation Journal Issue: 1 Vol. 5; ISSN 2397-2106

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United Kingdom

Language:: English

References (55)

The role of Cr atom in the early steam oxidation of Fe‐based alloys: An atomistic simulation Qi, Jing; Xu, Hong; Liang, Zhiyuan Materials and Corrosion, Vol. 72, Issue 3 https://doi.org/10.1002/maco.202011774	journal	September 2020
Efficient Learning Machines Awad, Mariette; Khanna, Rahul https://doi.org/10.1007/978-1-4302-5990-9	book	January 2015
An Introduction to Statistical Learning James, Gareth; Witten, Daniela; Hastie, Trevor Springer Texts in Statistics https://doi.org/10.1007/978-1-4614-7138-7	book	January 2013
The high-temperature oxidation of iron-chromium-nickel alloys containing 0?30% chromium Croll, J. E.; Wallwork, G. R. Oxidation of Metals, Vol. 4, Issue 3 https://doi.org/10.1007/BF00613088	journal	September 1972
Microstructure, adhesion and growth kinetics of protective scales on metals and alloys Hindam, H.; Whittle, D. P. Oxidation of Metals, Vol. 18, Issue 5-6 https://doi.org/10.1007/BF00656571	journal	December 1982
Calculations of parabolic reaction rate constants Pieraggi, B. Oxidation of Metals, Vol. 27, Issue 3-4 https://doi.org/10.1007/BF00667057	journal	April 1987
Materials informatics Ramakrishna, Seeram; Zhang, Tong-Yi; Lu, Wen-Cong Journal of Intelligent Manufacturing, Vol. 30, Issue 6 https://doi.org/10.1007/s10845-018-1392-0	journal	January 2018
Long-Term Oxidation Testing and Lifetime Modeling of Cast and ODS FeCrAl Alloys Dryepondt, Sebastien; Turan, Josh; Leonard, Donovan Oxidation of Metals, Vol. 87, Issue 1-2 https://doi.org/10.1007/s11085-016-9668-2	journal	November 2016
Predicting the Parabolic Rate Constants of High-Temperature Oxidation of Ti Alloys Using Machine Learning Bhattacharya, Somesh Kr.; Sahara, Ryoji; Narushima, Takayuki Oxidation of Metals, Vol. 94, Issue 3-4 https://doi.org/10.1007/s11085-020-09986-3	journal	June 2020
Effect of Pressure and Thermal Cycling on Long-Term Oxidation in CO2 and Supercritical CO2 Pint, B. A.; Pillai, R.; Lance, M. J. Oxidation of Metals, Vol. 94, Issue 5-6 https://doi.org/10.1007/s11085-020-10004-9	journal	November 2020
Invited Review Paper in Commemoration of Over 50 Years of Oxidation of Metals: Addressing the Role of Water Vapor on Long-Term Stainless Steel Oxidation Behavior Pint, Bruce A. Oxidation of Metals, Vol. 95, Issue 5-6 https://doi.org/10.1007/s11085-020-10012-9	journal	January 2021
Oxidation Behavior of Candidate NiCr Alloys for Engine Exhaust Valves: Part I—Effect of Minor Alloying Elements Pillai, R.; Romedenne, M.; Haynes, J. A. Oxidation of Metals, Vol. 95, Issue 1-2 https://doi.org/10.1007/s11085-020-10017-4	journal	January 2021
Mapping Multivariate Influence of Alloying Elements on Creep Behavior for Design of New Martensitic Steels Verma, Amit K.; Hawk, Jeffery A.; Bruckman, Laura S. Metallurgical and Materials Transactions A, Vol. 50, Issue 7 https://doi.org/10.1007/s11661-019-05234-9	journal	April 2019
Prediction of the Composition and Hardness of High-Entropy Alloys by Machine Learning Chang, Yao-Jen; Jui, Chia-Yung; Lee, Wen-Jay JOM, Vol. 71, Issue 10 https://doi.org/10.1007/s11837-019-03704-4	journal	July 2019
Uncertainty Quantification of Machine Learning Predicted Creep Property of Alumina-Forming Austenitic Alloys Peng, Jian; Yamamoto, Yukinori; Brady, Michael P. JOM, Vol. 73, Issue 1 https://doi.org/10.1007/s11837-020-04423-x	journal	November 2020
River flow forecasting through conceptual models part I — A discussion of principles Nash, J. E.; Sutcliffe, J. V. Journal of Hydrology, Vol. 10, Issue 3 https://doi.org/10.1016/0022-1694(70)90255-6	journal	April 1970
Materials informatics Rajan, Krishna Materials Today, Vol. 8, Issue 10 https://doi.org/10.1016/S1369-7021(05)71123-8	journal	October 2005
Cyclic oxidation of coated and uncoated single-crystal nickel-based superalloy MC2 analyzed by continuous thermogravimetry analysis Raffaitin, A.; Monceau, D.; Andrieu, E. Acta Materialia, Vol. 54, Issue 17 https://doi.org/10.1016/j.actamat.2006.05.034	journal	October 2006
Modern data analytics approach to predict creep of high-temperature alloys Shin, D.; Yamamoto, Y.; Brady, M. P. Acta Materialia, Vol. 168 https://doi.org/10.1016/j.actamat.2019.02.017	journal	April 2019
Physical metallurgy-guided machine learning and artificial intelligent design of ultrahigh-strength stainless steel Shen, Chunguang; Wang, Chenchong; Wei, Xiaolu Acta Materialia, Vol. 179 https://doi.org/10.1016/j.actamat.2019.08.033	journal	October 2019
Advanced data science toolkit for non-data scientists – A user guide Peng, Jian; Lee, Sangkeun; Williams, Andrew Calphad, Vol. 68 https://doi.org/10.1016/j.calphad.2019.101733	journal	March 2020
The role of Cr on oxide formation in Ni-Cr alloys: A theoretical study Hong, Ki-Ha; Kim, Jeoung Han; Chang, Kunok Computational Materials Science, Vol. 142 https://doi.org/10.1016/j.commatsci.2017.09.056	journal	February 2018
Early stage oxidation of Ni–Cr binary alloy (111), (110) and (100) surfaces: A combined density functional and quantum chemical molecular dynamics study Das, Nishith Kumar; Shoji, Tetsuo Corrosion Science, Vol. 73 https://doi.org/10.1016/j.corsci.2013.03.020	journal	August 2013
Kinetics of breakaway oxidation of Fe–Cr and Fe–Cr–Ni alloys in dry and wet carbon dioxide Gheno, Thomas; Monceau, Daniel; Young, David J. Corrosion Science, Vol. 77 https://doi.org/10.1016/j.corsci.2013.08.008	journal	December 2013
Modeling carbide dissolution in alloy 602 CA during high temperature oxidation Chyrkin, A.; Pillai, R.; Ackermann, H. Corrosion Science, Vol. 96 https://doi.org/10.1016/j.corsci.2015.03.019	journal	July 2015
External α-Al2O3 scale on Ni-base alloy 602 CA – Part II: Microstructural evolution Pillai, R.; Chyrkin, A.; Galiullin, T. Corrosion Science, Vol. 127 https://doi.org/10.1016/j.corsci.2017.07.021	journal	October 2017
Modeling the oxidation kinetics of titanium alloys: Review, method and application to Ti-64 and Ti-6242s alloys Vaché, N.; Cadoret, Y.; Dod, B. Corrosion Science, Vol. 178 https://doi.org/10.1016/j.corsci.2020.109041	journal	January 2021
High temperature oxidation of fuel cladding candidate materials in steam–hydrogen environments Pint, B. A.; Terrani, K. A.; Brady, M. P. Journal of Nuclear Materials, Vol. 440, Issue 1-3 https://doi.org/10.1016/j.jnucmat.2013.05.047	journal	September 2013
Growth and adherence of chromia based surface scales on Ni-base alloys in high- and low-pO2 gases Zurek, J.; Young, D. J.; Essuman, E. Materials Science and Engineering: A, Vol. 477, Issue 1-2 https://doi.org/10.1016/j.msea.2007.05.035	journal	March 2008
Performance of advanced turbocharger alloys and coatings at 850–950°C in air with water vapor Pint, B. A.; Haynes, J. A.; Armstrong, B. L. Surface and Coatings Technology, Vol. 215 https://doi.org/10.1016/j.surfcoat.2012.08.099	journal	January 2013
Introduction to the High-Temperature Oxidation of Metals Birks, Neil; Meier, Gerald H.; Pettit, Frederick S. Cambridge University Press https://doi.org/10.1017/CBO9781139163903	book	January 2006
DFT Simulations of Titanium Oxide Films on Titanium Metal Ohler, Bastian; Prada, Stefano; Pacchioni, Gianfranco The Journal of Physical Chemistry C, Vol. 117, Issue 1 https://doi.org/10.1021/jp309827u	journal	December 2012
Random Forests Breiman, Leo Machine Learning, Vol. 45, Issue 1, p. 5-32 https://doi.org/10.1023/A:1010933404324	journal	January 2001
Continuous Thermogravimetry Under Cyclic Conditions Monceau, D.; Poquillon, D. Oxidation of Metals, Vol. 61, Issue 1/2 https://doi.org/10.1023/B:OXID.0000016281.25965.93	journal	February 2004
Machine learning in materials informatics: recent applications and prospects Ramprasad, Rampi; Batra, Rohit; Pilania, Ghanshyam npj Computational Materials, Vol. 3, Issue 1 https://doi.org/10.1038/s41524-017-0056-5	journal	December 2017
Recent advances and applications of machine learning in solid-state materials science Schmidt, Jonathan; Marques, Mário R. G.; Botti, Silvana npj Computational Materials, Vol. 5, Issue 1 https://doi.org/10.1038/s41524-019-0221-0	journal	August 2019
Machine learning enabled autonomous microstructural characterization in 3D samples Chan, Henry; Cherukara, Mathew; Loeffler, Troy D. npj Computational Materials, Vol. 6, Issue 1 https://doi.org/10.1038/s41524-019-0267-z	journal	January 2020
Coupling physics in machine learning to predict properties of high-temperatures alloys Peng, Jian; Yamamoto, Yukinori; Hawk, Jeffrey A. npj Computational Materials, Vol. 6, Issue 1 https://doi.org/10.1038/s41524-020-00407-2	journal	September 2020
Accelerating materials property predictions using machine learning Pilania, Ghanshyam; Wang, Chenchen; Jiang, Xun Scientific Reports, Vol. 3, Issue 1 https://doi.org/10.1038/srep02810	journal	September 2013
Data analytics approach for melt-pool geometries in metal additive manufacturing Lee, Seulbi; Peng, Jian; Shin, Dongwon Science and Technology of Advanced Materials, Vol. 20, Issue 1 https://doi.org/10.1080/14686996.2019.1671140	journal	October 2019
The oxidation of alloys Wallwork, G. R. Reports on Progress in Physics, Vol. 39, Issue 5 https://doi.org/10.1088/0034-4885/39/5/001	journal	May 1976
The 2019 materials by design roadmap Alberi, Kirstin; Nardelli, Marco Buongiorno; Zakutayev, Andriy Journal of Physics D: Applied Physics, Vol. 52, Issue 1 https://doi.org/10.1088/1361-6463/aad926	journal	October 2018
Combinatorial screening for new materials in unconstrained composition space with machine learning Meredig, B.; Agrawal, A.; Kirklin, S. Physical Review B, Vol. 89, Issue 9 https://doi.org/10.1103/PhysRevB.89.094104	journal	March 2014
The random subspace method for constructing decision forests No authors listed IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 20, Issue 8 https://doi.org/10.1109/34.709601	journal	January 1998
Detecting Novel Associations in Large Data Sets Reshef, D. N.; Reshef, Y. A.; Finucane, H. K. Science, Vol. 334, Issue 6062 https://doi.org/10.1126/science.1205438	journal	December 2011
Pearson's correlation coefficient Sedgwick, P. BMJ, Vol. 345, Issue jul04 1 https://doi.org/10.1136/bmj.e4483	journal	July 2012
Oxidation of Ni-Cr-Al Alloys Between 1000° and 1200°C Giggins, C. S.; Pettit, F. S. Journal of The Electrochemical Society, Vol. 118, Issue 11 https://doi.org/10.1149/1.2407837	journal	January 1971
The Effect of Oxide Volatilization on the Oxidation Kinetics of Cr and Fe-Cr Alloys Tedmon, C. S. Journal of The Electrochemical Society, Vol. 113, Issue 8 https://doi.org/10.1149/1.2424115	journal	January 1966
Bayesian Interpolation MacKay, David J. C. Neural Computation, Vol. 4, Issue 3 https://doi.org/10.1162/neco.1992.4.3.415	journal	May 1992
Modelling compositional changes in nickel base alloy 602 CA during high temperature oxidation Chyrkin, A.; Sloof, W. G.; Pillai, R. Materials at High Temperatures, Vol. 32, Issue 1-2 https://doi.org/10.1179/0960340914Z.00000000082	journal	January 2015
Influence of alloy additions on oxidation Stott, F. H. Materials Science and Technology, Vol. 5, Issue 8 https://doi.org/10.1179/mst.1989.5.8.734	journal	August 1989
Curse of dimensionality and related issues in nonparametric functional regression Geenens, Gery Statistics Surveys, Vol. 5, Issue none https://doi.org/10.1214/09-SS049	journal	January 2011
ASCENDS: Advanced data SCiENce toolkit for Non-Data Scientists Lee, Sangkeun; Peng, Jian; Williams, Andrew Journal of Open Source Software, Vol. 5, Issue 46 https://doi.org/10.21105/joss.01656	journal	February 2020
On the Laws of Inheritance in Man: I. Inheritance of Physical Characters Pearson, Karl; Lee, Alice Biometrika, Vol. 2, Issue 4 https://doi.org/10.2307/2331507	journal	November 1903
A Review of the Application of Machine Learning and Data Mining Approaches in Continuum Materials Mechanics Bock, Frederic E.; Aydin, Roland C.; Cyron, Christian J. Frontiers in Materials, Vol. 6 https://doi.org/10.3389/fmats.2019.00110	journal	May 2019

Similar Records

Lessons Learned in Employing Data Analytics to Predict Oxidation Kinetics and Spallation Behavior of High-Temperature NiCr-Based Alloys

Journal Article · Thu Aug 05 20:00:00 EDT 2021 · Oxidation of Metals · OSTI ID:1813193

Predicting the High-Temperature Oxidation Response of Nickel Superalloys Using CALPHAD-Enhanced Machine Learning

Conference · Wed Mar 18 20:00:00 EDT 2026 · OSTI ID:3024055

Related Subjects

36 MATERIALS SCIENCE

Data analytics approach to predict high-temperature cyclic oxidation kinetics of NiCr-based Alloys

Citation Formats

References (55)

Similar Records

Related Subjects