Machine learning-based support for machining process predictive maintenance

The optimization of machining processes through predictive maintenance has gained significant traction in modern manufacturing. However, the existing analytical methods of predictive maintenance often fail to fully leverage high frequency sensor data, leading to suboptimal predictions of tool wear and failure. This study addresses these limitations by integrating wavelet energy analysis with advanced machine learning models to enhance predictive accuracy. A systematic approach to feature extraction and frequency band selection is employed, ensuring that the most relevant signal components are utilized for modeling. The effectiveness of various predictive algorithms, including random forests and gradient boosting, is evaluated to determine their suitability for tool wear prediction. Experimental results demonstrate that incorporating wavelet-based features significantly improves prediction performance, providing a robust framework for more efficient and cost-effective machining operations. This research contributes to advancing data-driven maintenance strategies by bridging the gap between signal processing techniques and machine learning applications in industrial settings.