This research presents a method that utilizes explainability techniques to amplify the performance of machine learning (ML) models in forecasting the quality of milling processes, as demonstrated in this paper through a manufacturing use case. The methodology entails the initial training of ML models, followed by a fine-tuning phase where irrelevant features identified through explainability methods are eliminated. This procedural refinement results in performance enhancements, paving the way for potential reductions in manufacturing costs and a better understanding of the trained ML models. This study highlights the usefulness of explainability techniques in both explaining and optimizing predictive models in the manufacturing realm.

翻译：本研究提出了一种利用可解释性技术提升机器学习（ML）模型在铣削加工质量预测中性能的方法，并通过制造场景用例加以验证。该方法首先完成ML模型的初始训练，随后进入微调阶段——借助可解释性方法识别并剔除无关特征。这一过程优化显著提升了模型性能，不仅可能降低制造成本，还能深化对已训练ML模型的理解。本研究凸显了可解释性技术在制造领域中解释并优化预测模型的双重价值。