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 the capability to reduce development cost, risk, and time. However, so far, limited efforts have been made to predict alloy oxidation kinetics and spallation behavior via ML due to the lack of consistently measured and sufficient experimental data and the inherent complexity in oxidation behavior of multicomponent high-temperature alloys. A previous study reported the ability of ML to predict oxidation kinetics of NiCr-based alloys as a function of alloy composition and operating conditions. In the current work, the performance of a ML model in predicting rate constants and spallation probability was evaluated in light of the roles of the data distribution of the experimental dataset (data analytics), the alloy composition, the exposure environment and the chosen oxidation approach to extracting kinetic values from the measured mass changes (but using either a simple parabolic law or a statistical cyclic oxidation model). Potential strategies to improve the predictions and enhance the extrapolative capability of the previously trained model will be discussed.

与仅基于有限的实验研究或基于物理的模拟的传统材料开发方法相比，机器学习 (ML) 在预测材料特性方面具有许多优势，并且能够降低开发成本、风险和时间。然而，到目前为止，由于缺乏一致测量和足够的实验数据以及多组分高温合金氧化行为的固有复杂性，通过 ML 预测合金氧化动力学和散裂行为的努力有限。之前的一项研究报告了 ML 预测 NiCr 基合金氧化动力学的能力，作为合金成分和操作条件的函数。在目前的工作中，ML 模型在预测速率常数和散裂概率方面的性能根据实验数据集的数据分布（数据分析）、合金成分、暴露环境和选择的从测量的质量变化（但使用简单的抛物线定律或统计循环氧化模型）。将讨论改进预测和增强先前训练模型的外推能力的潜在策略。