Liuqing Yang

Bio: Liuqing Yang is an academic researcher from Shanghai Jiao Tong University. The author has contributed to research in topics: Sliding mode control & Machining. The author has an hindex of 1, co-authored 1 publications receiving 26 citations.

Active damping of milling chatter vibration via a novel spindle system with an integrated electromagnetic actuator

Abstract: Self-excited vibration, widely referred to as chatter, has always been a limitation and challenge in machining. To suppress milling chatter vibration and improve surface finishes, a novel spindle system is proposed in this study. A noncontact electromagnetic actuator with two degrees of freedom is developed and integrated into the designed spindle system compactly. A differential driving mode is utilized for the electromagnetic actuator to obtain a linear output of actuator force, making the actuator more applicable for vibration control. Displacement sensors mounted near the actuator measure the vibration of the rotating spindle shaft and provide feedback signals for the developed proportional-derivative controller. The active damping performance of the designed spindle system with an integrated electromagnetic actuator, in both x and y directions, is also validated with impact tests and milling experiments, and a maximum increase (by factors of 3.67 and 2.89 in x and y directions, respectively) of dynamic stiffness at the first modal frequency is obtained. Milling experiment results with and without active damping also illustrate that milling chatter vibration has been well damped actively with the developed spindle system.

Prediction Methods and Experimental Techniques for Chatter Avoidance in Turning Systems: A Review

Abstract: The general trend towards lightweight components and stronger but difficult to machine materials leads to a higher probability of vibrations in machining systems. Amongst them, chatter vibrations are an old enemy for machinists with the most dramatic cases resulting in machine-tool failure, accelerated tool wear and tool breakage or part rejection due to unacceptable surface finish. To avoid vibrations, process designers tend to command conservative parameters limiting productivity. Among the different machining processes, turning is responsible of a great amount of the chip volume removed worldwide. This paper reports some of the main efforts from the scientific literature to predict stability and to avoid chatter with special emphasis on turning systems. There are different techniques and approaches to reduce and to avoid chatter effects. The objective of the paper is to summarize the current state of research in this hot topic, particularly (1) the mechanistic, analytical, and numerical methods for stability prediction in turning; (2) the available techniques for chatter detection and control; (3) the main active and passive techniques.

An Active Control Method for Chatter Suppression in Thin Plate Turning

Abstract: This paper presents an active control method, consisting of an adaptive sliding-mode controller (ASMC) and a displacement field reconstruction (DFR) method, for chatter suppression in turning of thin-walled workpieces (such as compressor disks and casings in aircraft engines) where low workpiece stiffness renders machining with potential regenerative chatter. Due to the presence of multi-modal dynamics, variant modal parameters, and measurement difficulties, active chatter control of thin plate turning has been challenging. Unlike existing controls based on a lumped-parameter single degree-of-freedom cutting model, a distributed-parameter dynamic model of a rotating thin plate with multiple vibration modes is used to analyze the machining stability with the designed controller. Moreover, model parameters of the plate are not needed to construct the controller. The DFR is employed to capture the plate dynamic behavior for feedback to the ASMC during turning, overcoming the long existing difficulties to measure plate vibration at the cutting point. A fast tool servo is utilized in the control implementation. Theoretical analyses, numerical simulations, and experimental evaluation on a lathe demonstrate that chatter in thin plate turning can be effectively attenuated with the proposed active control method.

A Novel Unsupervised Machine Learning-Based Method for Chatter Detection in the Milling of Thin-Walled Parts.

Abstract: Data-driven chatter detection techniques avoid complex physical modeling and provide the basis for industrial applications of cutting process monitoring. Among them, feature extraction is the key step of chatter detection, which can compensate for the accuracy disadvantage of machine learning algorithms to some extent if the extracted features are highly correlated with the milling condition. However, the classification accuracy of the current feature extraction methods is not satisfactory, and a combination of multiple features is required to identify the chatter. This limits the development of unsupervised machine learning algorithms for chattering detection, which further affects the application in practical processing. In this paper, the fractal feature of the signal is extracted by structure function method (SFM) for the first time, which solves the problem that the features are easily affected by process parameters. Milling chatter is identified based on k-means algorithm, which avoids the complex process of training model, and the judgment method of milling chatter is also discussed. The proposed method can achieve 94.4% identification accuracy by using only one single signal feature, which is better than other feature extraction methods, and even better than some supervised machine learning algorithms. Moreover, experiments show that chatter will affect the distribution of cutting bending moment, and it is not reliable to monitor tool wear through the polar plot of the bending moment. This provides a theoretical basis for the application of unsupervised machine learning algorithms in chatter detection.