Monitoring and Adaptive Control Technologies for Machining Performance Improvement in EtherCAT-Based Machine Tools

Real-time monitoring and adaptive control are essential for suppressing abnormal phenomena during machining to maintain product quality. Traditional machine tools rely on external sensors for chatter detection and process monitoring, which increases system complexity and cost. This study proposes an EtherCAT-based machine tool system that utilizes spindle current signals for real-time chatter detection and adaptive control without additional sensors. A suitable current feature was selected through experiments, enabling effective chatter detection. Upon detecting chatter, the spindle speed was adjusted to a stable region to suppress vibration. Additionally, machining load was estimated from the current, and the feed rate was adaptively controlled to maintain constant load conditions. This approach successfully reduced overall machining time and improved productivity. The proposed method demonstrates that internal current data combined with EtherCAT communication can provide a cost-effective and practical solution for intelligent machining systems requiring real-time monitoring and control.