Data Analytics Approach to Predict High-Temperature Cyclic Oxidation Kinetics of NiCr-based Alloys

TL;DR

This paper develops a machine learning-based data analytics framework to accurately predict high-temperature cyclic oxidation kinetics of NiCr-based alloys, addressing the complexity of multi-component alloy behavior.

Contribution

It introduces a novel ML approach trained on curated experimental data to predict oxidation rate constants across compositions and temperatures, advancing alloy oxidation modeling.

Findings

ML models accurately predict experimental parabolic rate constants

Correlation analysis identifies key features influencing oxidation kinetics

Potential to extend models for comprehensive cyclic oxidation behavior

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 (kp). This study demonstrates the potential of ML approaches to predict oxidation kinetics of alloys over a 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.