Elucidating Precipitation in FeCrAl Alloys through Explainable AI: A Case Study

Abstract

Primary challenge of using FeCrAl in high temperature industrial setting is the formation of α’- precipitates that causes brittleness in the alloy, resulting in failure through fracture. The precipitation causes hardness change during thermal aging which is sensitive to both alloy composition and experimental condition (i.e., temperature and time of heat treatment). A Gaussian Process Regression (GPR) model is built on the hardness data collected at GE Research. Subsequently, for the first time, SHapley Additive exPlanations (SHAP) built upon the GPR is used as an Explainable Artificial Intelligence (XAI) tool to understand the effect of feature values in driving the hardness change. Several key insights are affirmed and obtained from a material science perspective.