A chatter detection method in milling of thin-walled TC4 alloy workpiece based on auto-encoding and hybrid clustering

2021 ◽  
Vol 158 ◽  
pp. 107755
Author(s):  
Yichao Dun ◽  
Lida Zhu ◽  
Boling Yan ◽  
Shuhao Wang
2019 ◽  
Vol 9 (17) ◽  
pp. 3576 ◽  
Author(s):  
Yang ◽  
Wang ◽  
Yang

Thin-walled tubes are a kind of pressure vessel formed by a stamping and drawing process, which must withstand a great deal of sudden pressure during use. When microcrack defects of a certain depth are present on its inner and outer surfaces, severe safety accidents may occur, such as cracking and crushing. Therefore, it is necessary to carry out nondestructive testing of thin-walled tubes in the production process to eliminate the potential safety hazards. To realize the rapid detection of microcracks in thin-walled tubes, this study could be summarized as follows: (i) Because the diameters of the thin-walled tubes were much larger than their thicknesses, Lamb wave characteristics of plates with equal thicknesses were used to approximate the dispersion characteristics of thin-walled tubes. (ii) To study the dispersion characteristics of Lamb waves in thin plates, the detection method of the mode was determined using the particle displacement–amplitude curve. (iii) Using a multi-channel parallel detection method, rapid detection equipment for Lamb wave microcracks in thin-walled tubes was developed. (iv) The filtering peak values for defect signal detection with different depths showed that the defect detection peak values could reflect the defect depth information. (v) According to the minimum defect standard of a 0.045-mm depth, 100,000 thin-walled tubes were tested. The results showed that the missed detection rate was 0%, the reject rate was 0.3%, and the detection speed was 5.8 s/piece, which fully meets the actual detection requirements of production lines. Therefore, this study not only solved the practical issues for the rapid detection of microcracks in thin-walled tubes but also provided a reference for the application of ultrasonic technology for the detection of other components.


Measurement ◽  
2018 ◽  
Vol 127 ◽  
pp. 356-365 ◽  
Author(s):  
Yun Chen ◽  
Huaizhong Li ◽  
Liang Hou ◽  
Jun Wang ◽  
Xiangjian Bu

2020 ◽  
Vol 106 (9-10) ◽  
pp. 3881-3895 ◽  
Author(s):  
Weiguo Zhu ◽  
Jichao Zhuang ◽  
Baosu Guo ◽  
Weixiang Teng ◽  
Fenghe Wu

Author(s):  
Katja M. Hynynen ◽  
Juho Ratava ◽  
Tuomo Lindh ◽  
Mikko Rikkonen ◽  
Ville Ryynänen ◽  
...  

Chatter is an unfavorable phenomenon in turning operation causing poor surface quality. Active chatter elimination methods require the chatter to be detected before the control reacts. In this paper, a chatter detection method based on a coherence function of the acceleration of the tool in the x direction and an audio signal is proposed. The method was experimentally tested on longitudinal turning of a stock bar and facing of a hollow bar. The results show that the proposed method detects the chatter in an early stage and allows correcting control actions before the chatter influences the surface quality of the workpiece. The method is applicable both to facing and longitudinal turning.


Author(s):  
Adam K. Kiss ◽  
Daniel Bachrathy ◽  
Gabor Stepan

In this contribution, a chatter detection method is investigated for milling operations. The proposed approach can give not only qualitative condition (stable or unstable), but a quantitative measure of stability. For this purpose, it requires an external excitation of stable machining condition. Transient vibration of the perturbation is captured by means of stroboscopic section, and the corresponding monodromy operator is approximated by its projection to the subspace of the dominant modes. The monodromy matrix is determined with the application of homogeneous coordinate representation. Then, the periodic solution and the dominant characteristic multipliers are calculated and their modulus determines the quantitative measure of stability condition.


2020 ◽  
Vol 135 ◽  
pp. 106385 ◽  
Author(s):  
Kai Li ◽  
Songping He ◽  
Bin Li ◽  
Hongqi Liu ◽  
Xinyong Mao ◽  
...  

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