Optimization of Milling Processes: Chatter Detection via a Sensor-Integrated Vice

Abstract

The future of the milling process is the fully autonomous operation of the machine tools. Developments in terms of automation and machine tool design are now enabling fully autonomous operation. However, the optimization and stability of the process itself still remains a challenge. Chatter is the most significant bottleneck, and as such, it should be constantly monitored to ensure a stable process. This work proposes a sensor-integrated milling vice using an MEMS accelerometer as a non-invasive monitoring solution for chatter detection. The system is comprised by low-cost, industrial-grade components suitable for implementation in real production scenarios. The dynamic analysis of the sensor-integrated vice enables the definition of the sensor-integration point to ensure measurement quality. The use of advanced signal process algorithms for the demodulation of the vibration signal, along with the use of artificial intelligence for chatter detection, led to a high-performance system at a low cost. A wide set of milling experiments that has been conducted showcased that the proposed solution enables continuous, real-time process optimization in milling through in-process chatter detection.