Research on milling chatter monitoring and suppression based on IWPEE and VASS dual indicators

Chatter is harmful for cutting processing, which can cause the surface quality of the workpiece to decline, violent tool wear, even broken tools and workpiece scrap. In order to achieve real-time monitoring and suppression of chatter during the machining process, a milling machining is employed as the research object in this study, and an online monitoring and suppression system for chatter are proposed. Firstly, an improved wavelet packet energy entropy (IWPEE) algorithm is proposed; the recognition accuracy and robustness are improved by this algorithm while the computational complexity is reduced. Secondly, in order to ensure the robustness of the monitoring system in practical application, a variance algorithm for the milling signal sampling is synchronized with the spindle speed (VASS) is proposed by analyzing milling cutter motion through Poincare section. In addition, the position of the spindle rotation is indirectly obtained through mathematical calculations; the signal sampling is ensured synchronized with the spindle rotation period, and the sensors are not necessary so the generalization besides economy is improved. Furthermore, VASS overcomes the defects of IWPEE which is vulnerable to high-frequency noise interference, thereby improving the reliability of chatter monitoring. Then, a dynamic modeling of the cutting system is conducted, and a chatter suppression method based on speed iteration is obtained. Finally, the effectiveness of the proposed chatter monitoring and suppression system is verified through milling experiments.