Performance Evaluation of Five-Axis CNC Milling via Spindle Current and Vibration Monitoring

The digitalization of machining processes is increasingly recognized as essential for achieving higher productivity, reliability, and traceability. However, access to reliable in-process sensor data remains limited, particularly in multi-axis CNC machining, where dimensional accuracy and surface integrity strongly depend on stable and optimized process conditions. This study investigates sensor-based monitoring as a practical approach for evaluating process performance in five-axis CNC milling. Electric current and vibration signals were acquired during three machining operations, under distinct cutting parameters, using current clamps and a plug-and-play MEMS accelerometer. The signals were processed using the root mean square method to assess the correlation between sensor data and machining conditions. Dimensional inspection of each workpiece was carried out to verify geometric conformity. The results show that spindle current measurements exhibit a strong linear correlation with material removal rate and cutting power, supporting their use as indicators of cutting forces and energy consumption. Vibration signals revealed pronounced dynamic behaviour for specific tool orientations, particularly in transverse to tool axis direction. The proposed methodology provides a simple and low-cost framework for integrating sensor-based monitoring into five-axis CNC milling, particularly relevant for semi-roughing operations, and offers a basis for future studies on process optimization and real-time condition monitoring.