Chatter Detection in Variable Cutting Depth Side Milling Using VMD and Vibration Characteristics Analysis

Zhao, Na; Su, Yingxin; Wang, Shijuan; Xia, Min; Liu, Changfu

doi:10.3390/electronics11223779

Cited by 3 publications

(3 citation statements)

References 27 publications

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“…Simulated signals have been widely used as a convenient input to assess signal processing algorithms for chatter detection or to validate some proposed chatter features, for instance, as shown in [120,121,130,141,164,194,234,235,[261][262][263]. Using model-based simulation for AI training and experimental signals for testing has also been reported, such as by Ozgur and Sener [89] and Vashisht and Peng [199].…”

Section: Other Sensors and Simulated Signalsmentioning

confidence: 99%

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

Navarro-Devia

Chen

Dao

et al. 2023

Int J Adv Manuf Technol

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Among the diverse challenges in machining processes, chatter has a significant detrimental effect on surface quality and tool life, and it is a major limitation factor in achieving higher material removal rate. Early detection of chatter occurrence is considered a key element in the milling process automation. Online detection of chatter onset has been continually investigated over several decades, along with the development of new signal processing and machining condition classification approaches. This paper presents a review of the literature on chatter detection in milling, providing a comprehensive analysis of the reported methods for sensing and testing parameter design, signal processing and various features proposed as chatter indicators. It discusses data-driven approaches, including the use of different techniques in the time–frequency domain, feature extraction, and machining condition classification. The review outlines the potential of using multiple sensors and information fusion with machine learning. To conclude, research trends, challenges and future perspectives are presented, with the recommendation to study the tool wear effects, and chatter detection at dissimilar milling conditions, while utilization of considerable large datasets—Big Data—under the Industry 4.0 framework and the development of machining Digital Twin capable of real-time chatter detection are considered as key enabling technologies for intelligent manufacturing.

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Section: Other Sensors and Simulated Signalsmentioning

confidence: 99%

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

Navarro-Devia

Chen

Dao

et al. 2023

Int J Adv Manuf Technol

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“…Chatter is regarded as a bottleneck that severely reduces productivity and surface quality, wears down tools, and produces undesirable noise during the machining process [1][2][3][4]. Due to the characteristics of low stiffness, chatter can occur during the whole machining process when milling thin-walled parts [5,6].…”

Section: Introductionmentioning

confidence: 99%

Identification of end-milling chatter based on comprehensive feature fusion

Matthew,

Cao,

Shi

2024

Eng. Res. Express

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The main barrier impeding the advancement of high-speed milling is chatter, which has a detrimental effect on the dimensional accuracy and quality of the finished workpiece. A reliable and precise chatter identification method is essential to improving the quality of machining. This paper presents a novel method for chatter identification using a comprehensive feature fusion of the Short-Time Fourier Transform (STFT) and the Fourier Synchrosqueezing Transform (FSST). The Wavelet Packet Transform (WPT) was used to pre-process the collected vibration and force signals. Wavelet packets with rich chatter information were then selected and reconstructed for further analysis. To reduce the effects of the rotating frequency and generate a hybrid spectrum with high resolution, a Gabor time-frequency filter is employed. As chatter indicators, standard deviation, skewness, and root mean square are computed. The proposed method's result shows superiority over conventional STFT and FSST across vibration and force signals, and we concluded that it is suitable and reliable for identifying chatter and useful for machining monitoring.

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“…In engineering practice, the vibration of the device is often effectively controlled by avoiding resonance. At present, many scholars have done a lot of research on modal analysis and vibration testing methods [2]- [4].…”

Section: Introductionmentioning

confidence: 99%

Modal analysis of binding device and multi-target optimization design

Gao

Zhang

et al. 2023

Third International Conference on Mechanical Design and Simulation (MDS 2023)

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Aiming at the cracking of support frame caused by vibration of automatic binding device, the vibration characteristics of the device were studied by means of vibration test and modal analysis, and the structure of the device was optimized. Firstly, modal simulation analysis was used to solve the first six-order natural frequencies and the corresponding mode shapes of the device. On this basis, vibration test was completed. By analyzing the acceleration spectrum of four measuring points of the device, the relationship between vibration characteristics and natural frequencies was studied. Finally, choose multi-target optimization method to optimize the device frame structure.The results show that the vibration amplitudes of the measuring points from high to low are as follows: the upper left frame of the device, the beam of the motor, the upper part of the middle frame and the middle of the middle frame. By comparing the mode shapes with the vibration tests, it is found that the mode that has a great influence on the vibration of the device occurs in the second-order, which is manifested as bending vibration, and the device resonates at 9.02Hz. The first and second-order frequencies after the optimization of the bundle device are 5.60Hz and 12.58Hz. Compared with the optimization before,the frequencies increase -10.4% and +39.5%, it has greatly improved the safe operating range of the bundle device.

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Chatter Detection in Variable Cutting Depth Side Milling Using VMD and Vibration Characteristics Analysis

Cited by 3 publications

References 27 publications

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

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

Identification of end-milling chatter based on comprehensive feature fusion

Modal analysis of binding device and multi-target optimization design

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