  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.
