Online reliable chatter detection of milling flexible part using synthetic criterion

In machining processes, the self-excited vibration between the cutting tool and the workpiece is an important issue that can result in undesirable effects, for example, poor quality of the final surface, low dimensional accuracy, breakage of the tool, and excessive noise. To anticipate this problem, statistical features of the vibration signal, such as mean, variance, and standard deviation, have been extracted from online measurements. The synthesis criterion (SC), which is based on the standard deviation (STD) and the one-step autocorrelation function (OSAF), has been employed to detect quickly the threshold of chatter vibration. In this article, flexible workpieces with varying cutting depths have been selected to detect online chatter vibrations during milling operations. In order to collect an analog vibration signal, an STM32 card has been selected with a sampling rate up to 20 kSPS. A high-bandwidth, lightweight film piezoelectric sensor is attached to the workpiece. Unlike other sensors, such as load cells or acceleration sensors, the film piezoelectric sensors do not alter the dynamics of the system. In this research, cost-effective hardware is also developed to capture vibration signals reliably and efficiently. The experimental results confirm that the developed SC algorithm can efficiently predict the onset of chatter vibration as it was able to detect the onset of chatter vibrations within 0.18 sec. Thus, the SC algorithm can considerably enhance the milling operations of flexible parts.