Intelligent Tool Wear Prediction for Enhanced Sustainability in Milling of Ni-Based Superalloy

This research presents an integrated and systematically validated framework for predicting tool wear in milling Inconel X750 using multi-sensor fusion. In this study, an accelerometer and a dynamometer are integrated to achieve sensor fusion, along with cryogenically treated cutting tool inserts with different edge radii. Experiments were designed to analyse tool wear, with results evaluated using ANOVA tests. The study employs S-Golay filtered Stationary Wavelet Transform and the Largest Lyapunov Exponent to extract features from vibration and cutting force signals, enhancing prediction accuracy. Explainable AI (XAI) ensures model transparency, while the Extreme Learning Machine (ELM) effectively manages complex data relationships, yielding robust predictions. By combining sensor fusion with XAI, the study enhances interpretability and trust in AI-based decisions, making predictive maintenance more actionable for industrial applications. Results show the depth of cut has the highest mean Shapley values, achieving accurate metrics for tool inserts T1and T2. Furthermore the study achieves comparable accuracy metrics for cutting tool inserts T1and T2, with an RMSE of 2.27%, MAE of 1.47%, and |R95%| of 4.61% for cutting tool T1and an RMSE of 3.14%, MAE of 1.95%, and |R95%| of 5.1% for cutting tool T2. This research enhances machining practices, particularly in aerospace, improving tool life and efficiency.