Online chatter detection in robotic machining based on adaptive variational mode decomposition

Abstract

Chatter is the main problem that limits the application of industrial robots in the field of machining process. It is critical important to establish an adaptive chatter detection solution for robot machining process and realize the online detection of chatter. However, different from machine tool chatter, the chatter in robotic machining process is more complex to be detected due to the variable stiffness characteristics and weaker stiffness of normal industrial robot, and the existing literature has less research on this problem. This paper presents a comprehensive solution for online chatter detection in robotic machining process. Firstly, in order to detect the chatter in robotic machining process and avoid mode mixing problem in variational mode decomposition (VMD) process, an adaptive variational mode decomposition (AVMD) method based on kurtosis and instantaneous frequency is proposed, which realizes the adaptive selection of the decomposition parameter. Secondly, optimal decomposition parameters are calculated by using genetic algorithm. By optimizing the discrete step length of decomposition parameter, it greatly reduces the optimization time. Last but not least, approximate entropy, energy entropy and proposed entropy drift coefficient are extracted to distinguish chatter and stable machining state. Simulation and experimental results show that the proposed method can meet the real-time requirements of online detection and detect the occurrence of chatter effectively.

Chatter detection in milling processes—a review on signal processing and condition classification

“…Cyclostationary-based indicators were proposed in the angular domain from the periodic and residual parts of angular speed and cutting force signals for chatter detection, and the indicator based on IAS is recommended as it does not require additional sensors [84]. The application of adaptive variational mode decomposition for chatter detection has been lately reported in [142] and [198]. Mishra and Singh [87,[340][341][342][343]] investigated a spline-based local mean decomposition technique, while Zhang et al [137] used a morphological empirical wavelet transform (EWT).…”

“…Chen et al [162] employed the threshold value of an entropy-based feature which was previously normalized as a function of the cutting parameters. Yang et al [133] extracted chatter features from the filtered signal to reduce the cutting parameter effect, while Cheng et al [198] proposed a coefficient indicator instead of using an absolute threshold. Entropy theories have been also applied as criteria for feature ranking and selection, as in [258].…”

“…Approximate entropy [88,133,169,170,183] Sample entropy and others [133,141,142,162,171,186,198,223] Power spectral entropy [90,111,121,134,138,157,175,176] Permutation entropy [134,157] Complexity and hand-crafted [76, 78, 80, 83, 84, 90, 115, 118, 120, 121, 123, 126, 128, 129, 132, 144, 146, 147, 149, 150, 163-165, 169, 174, 177, 179, 180, 184, 193, 197, 202, 204, 217, 220, 227, 239, 244, 255, 259, 326, 353] showed that the accuracy of each model varies according to the utilized feature, and SVM had the highest susceptibility compared to the other methods, while the probabilistic neural network (PNN) had a slightly lower accuracy. The performance of NNs is similar to SVM, and it has some advantages such as lower training time [145,180].…”

Digital twin of the technological process for grinding helical flutes of a cutting tool

“…Machining error prediction and compensation refers to the theoretical modeling and simulation of the cutting process [48,49]. Process optimization mainly refers to cutting force modeling to predict the mechanical deformation of the workpiece [50][51][52][53], which depends on the accuracy of the models. Thus, one should take into account the deformation of both the workpiece and the cutting tool, the degree of its wear, and errors when rolling screw surfaces [54][55][56][57][58][59][60][61][62].…”