Zhibing Liu

TL;DR: In this paper, a series of grinding experiments under the presence/absence of ultrasonic vibration assistance are performed, and the grinding forces and work-surface roughness are measured, while the wheel surface is examined too.

TL;DR: In this paper, the acceleration signals acquired by sensor were decomposed into a series of intrinsic mode functions (IMFs) by the adaptive analysis method named ensemble empirical mode decomposition (EEMD), and the IMFs which contain the feature information of milling process were selected as the analyzed signals.

TL;DR: The results of milling experiment show that the proposed chatter identification method can recognize early milling chatter effectively and a support vector machine chatter identification model is obtained based on the multi-indicators.

TL;DR: In this article, the combined effect of clamping and milling on machined surface errors is taken into account, and finite element analysis is developed to analyze the workpiece deformation.

TL;DR: Based on third-order Newton interpolation polynomial and direct integration scheme (DIS), a method to generate stability lobe diagram in milling process is proposed and proved to be an accurate and efficient method by the comparison results.