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Meeting Materials Science & Technology 2020
Symposium Computation Assisted Materials Development for Improved Corrosion Resistance
Presentation Title Machine Learning to Predict Cyclic Oxidation of NiCr-based alloys
Author(s) Jian Peng, Marie Romedenne, Rishi Pillai, Govindarajan Muralidharan, Bruce A. Pint, J. Allen Haynes, Dongwon Shin
On-Site Speaker (Planned) Jian Peng
Abstract Scope Machine learning (ML) can offer many advantages in predicting material properties over traditional materials development methods based solely on limited experimental investigations or physical-based simulations with respect to cost, risk, and time. However, thus far limited efforts have been published to predict alloy oxidation resistance via ML. In this presentation, we compare two different oxidation models (a simple parabolic law and a statistical cyclic-oxidation model) to represent the high-temperature oxidation of NiCr-based alloys in dry- and wet-air within the context of data analytics. We successfully trained ML models with highly ranked key features identified from the extensive correlation analysis. The performance of selected oxidation models in ML was compared and discussed. This research was sponsored by the Department of Energy, Vehicle Technologies Office, Propulsion Materials Program.
Proceedings Inclusion? Planned: At-meeting proceedings

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

Applying Machine Learning to Determine the Corrosion Resistance of Alloys
Assessing High Temperature Durability for Long-term Applications
Development of a Multiscale Corrosion Model for Valve Steels in a Gasoline Engine Environment
High Temperature Oxidation Lifetime Modeling of FeCr and NiCr Foils in Water Vapor
Introductory Comments: Computation Assisted Materials Development for Improved Corrosion Resistance
Machine Learning to Predict Cyclic Oxidation of NiCr-based alloys
Metal-Oxide Bond-energy Models for Bond Energies of Alloy Oxides in Corrosion
Simulation of Dissolution of \Gamma\Prime Precipitates in Ni-base Superalloys during Oxidation
Tailoring the Microstructure of Eutectoid Steels during Annealing for Improved Corrosion Resistance: Insights from Phase-field Simulations

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