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.

这项工作提出了使用 XAI 工具的可解释性研究，以解释同步双频感应淬火中硬度预测的 XGBoost 模型。在C45钢直齿轮上进行了实验。为了解释模型，首先使用XGBoost库的内置工具来解释特征重要性。然后，采用 SHAP 库的更高级方法来突出局部和全局解释。最后，可解释的替代模型的实现可以说明预测规则，使解释虽然近似但清晰。这项研究提出了一种相关的人工智能方法来解释黑盒模型获得的结果，黑盒模型目前是行业的一个主要元素，可以以清晰的方式证明结果的质量。