Explaining hardness modeling with XAI of C45 steel spur-gear induction hardening

Abstract

This work presents an interpretability study with XAI tools to explain an XGBoost model for hardness prediction in the simultaneous double-frequency induction hardening. Experiments were carried out on C45 steel spur-gear. In order to explain the model, firstly, the built-in tool of the XGBoost library was used to interpret the feature importance. Then, a more advanced approach with the SHAP library was employed to highlight local and global explanations. Finally, the implementation of an interpretable surrogate model allowed to illustrate rules for prediction, making the explanation, although approximate, clear. This study proposes a relevant approach of AI to explain the results obtained by black box models which is currently a major element for the industry allowing to justify the quality of the results in a clear way. It is concluded that the model is consistent with physical principles.