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• The investigation of surface mid-frequency waviness and machine tool vibration from diverse sources is conducted through micro-planning experiments. The research delves into the influence of cutting conditions on vibration and mid-frequency waviness. • The influence of various components within the cutting force on vibration and mid-frequency waviness in UPM is elucidated through the acquisition, processing, and analysis of cutting force data. • The study discovered a discrepancy in the influence of cutting force on the vibration of stationary and moving components. Cutting force serves as the main excitation source for the vibration of the stationary part, while it exhibits an impedance role in the vibration of the moving part. • The research revealed that the stability and attenuation of the waviness caused by the stationary and moving parts vibration are different spatially and temporally. In addition to the dynamic characteristics of the machine tool, cutting conditions significantly influences the vibration of the cutting system and mid-frequency waviness of the workpiece surface in ultra-precision machining (UPM). In this work, the effects of cutting conditions on machining vibration and surface waviness were investigated by high-speed micro-planning experiments. The origin of vibration and waviness were elucidated by frequency-domain processing of the vibration signal and surface waviness analysis. The dynamic characteristics of machine tool are determined using modal test and analysis. The results revealed that the cutting force has different contributions to the workpiece vibration and tool vibration. In the time domain, distinct differences can be found in stability and attenuation of various waviness components. The divergent effects of cutting force components in different vibration forms are confirmed through a novel cutting force model of micro-planing. The investigation can further reveal the formation mechanisms of cutting system vibration and surface waviness during the micro-cutting process, which can provide practical guidance for suppressing mid-frequency waviness on machined surfaces. [ABSTRACT FROM AUTHOR]

Thin-walled micro parts are widely used in many fields, such as aviation, aerospace, precision engineering, and even micro-molds. Compared with other micro-fabrication technology, micro-milling can be considered as one of the most efficient 3D fabrication techniques. However, due to the vulnerable stiffness of thin-walled micro parts and micro tools, regenerative chatter is prone to occur during the micro-milling process. Severe chatter can lead to large fluctuations in cutting forces, resulting in deterioration of surface roughness/integrity and decreasing in tool life. Therefore, an in-process chatter detection strategy based on the feature extraction of micro-milling forces is proposed in this paper. Micro-milling force signals are measured and processed by advanced algorithms developed. After variational mode decomposition (VMD) for each group of cutting forces, the optimal intrinsic mode function (IMF) is adopted based on the Laplacian score (LS). The multi-scale permutation entropy (MSPE) values of the optimal IMF of each group are calculated, wherein the scale factor s , the embedding dimension m , and the delay time t are obtained by the genetic algorithm (GA). The obtained MSPE values are used as input vectors to train the support vector machine (SVM) classifier, which is used to monitor the stability states of the micro-milling processes. The effectiveness of the proposed strategy is compared with the other classic chatter detection methods and indicators in the micro-milling experiments of thin-walled parts. The results show that the method resulted from the strategy can extract cutting force features effectively and in-process detect chatters accurately in micro-milling thin-walled parts processes. [ABSTRACT FROM AUTHOR]