Machine Learning-Based Predictive Maintenance for Mechanical Equipment in Smart Manufacturing
In computer numerical control (CNC) systems, the remaining useful life (RUL) of cutting tools is an important factor to be considered to prevent unplanned tool stoppages, premature tool replacement, and to ensure machining quality. This research suggests a group-aware and explainable machine learning-based approach to normalized RUL prediction using vibration, current and operating condition features. In contrast to the traditional CNC RUL studies performed by observation level random splitting, the proposed method ensures complete separation of tools between training, validation, and independent test partitions to avoid any information leakage between partitions at the level of the tools. Seven stable predictors were identified: five vibration variables, one current variable and the hardness of the workpiece, by using the stability-based feature selection. Four methods of repeated group validation, formal validation, macro-tool error and worst-tool performance were used to compare baseline and nonlinear regression models. Random Forest was chosen as the final model, with the validation RMSE value of 0.1676 and test RMSE value of 0.2377 and test R^2of 0.3417. The framework showed that unseen-tool generalization could be measured, but there was a variation between test tools that showed the difficulty of cross-tool degradation variability. The features related to vibration accounted for 66.29% of the total model importance, while the workpiece hardness and current variability accounted for 26.72% and 6.99% respectively. When the dominant predictive information was extracted from the vibration, the best performance was achieved by sensor-group ablation, and the complete feature configuration gave the best performance in terms of validation.