Partial Dataset from Dry Machining Experiments on Ti6Al4V for Tool Wear Prediction Using LIME and SHAP

This dataset contains a subset of the data used in the study "Explainable Machine Learning for Wear Classification in Ti6Al4V Machining: An SHAP and LIME Approach for Decision Support" (Souza et al., 2024). The original raw dataset was collected and published in Klippel et al. (2024) under the title "Large-scale investigation of dry orthogonal cutting experiments ti6al4v and ck45", published in The International Journal of Advanced Manufacturing Technology.The subset uploaded here includes only the machining conditions and tool wear classifications used for training and testing machine learning models in the mentioned study. It has been processed and filtered for use with LIME and SHAP interpretability techniques.DOI of the original dataset:https://doi.org/10.1007/s00170-024-14597-2