Combining Sensor Fusion and a Machine Learning Framework for Accurate Tool Wear Prediction During Machining

Effective cutting tool condition monitoring (TCM) is critical for achieving precision, cost efficiency, and minimizing unplanned downtime. This study proposes a sophisticated sensor fusion framework for accurate tool fault prediction during machining. Experimental data were collected while turning AISI 410-grade steel bars with uncoated carbide inserts under dry-cutting conditions. Force and vibration signals were captured across five tool health states (one healthy and four faulty) using a sensor network and data acquisition systems. The raw signals were decomposed using discrete wavelet transform, and key statistical features were extracted. Three distinct input datasets are constructed: Dataset I comprises statistical parameters extracted exclusively from the force signals, Dataset II consists of statistical parameters derived from the vibration signals, and Dataset III integrates the individual statistical parameters from both force and vibration signals through feature-level fusion. These datasets are then utilized for training ML classifiers (Support Vector Machine, Random Forest, and Naive Bayes) to perform feature learning and subsequent classification. Among the considered classifiers, the RF classifier yielded better classification accuracies of 96% and 97% while discriminating among the tool health scenarios through dataset I and II. Also, the RF and SVM classifiers achieved a classification accuracy of 98% and 88% in distinguishing tool health scenarios for dataset III. This method demonstrates exceptional suitability for real-time, in situ fault diagnostics and provides a strong foundation for developing online TCM systems, advancing the objectives of Industry 4.0 and smart manufacturing.