Data Acquisition and Chatter Recognition Based on Multi-Sensor Signals for Blade Whirling Milling

Bladed components are essential in engines and propulsion systems, but their thin structures, complex geometries, and significant material removal rates during machining make them challenging to manufacture. This study investigates the chatter phenomenon in blade whirling milling, a promising method for improving machining efficiency. Multi-sensor signals, including vibration and acoustic emission signals, are collected during roughing and finishing machining. Time-domain, frequency-domain, and time-frequency features are extracted, filtered, and fused using principal component analysis (PCA) to retain relevant information while ensuring computational efficiency. The features are then input into an MLGRU-based chatter recognition model, incorporating a self-attention mechanism (SAM) for enhanced performance. The experimental results show that the proposed model achieves an average recognition accuracy of 89.16%, with a response time within 0.4 s, reflecting its effectiveness and timeliness in chatter detection. The findings also validate that the blade edge regions are more prone to chatter, especially during rough machining, due to their lower rigidity and greater sensitivity to external excitations.