Auswahl der wissenschaftlichen Literatur zum Thema „Early-chatter detection“
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Zeitschriftenartikel zum Thema "Early-chatter detection"
Yang, Kai, Guofeng Wang und Junyu Cong. „Milling chatter monitoring based on sparse representation and image similarity measurement“. Insight - Non-Destructive Testing and Condition Monitoring 64, Nr. 3 (01.03.2022): 146–54. http://dx.doi.org/10.1784/insi.2022.64.3.146.
Der volle Inhalt der QuelleGupta, Pankaj, und Bhagat Singh. „Analyzing chatter vibration during turning on computer numerical control lathe using ensemble local mean decomposition and probabilistic approach“. Noise & Vibration Worldwide 52, Nr. 6 (12.03.2021): 168–80. http://dx.doi.org/10.1177/0957456521999871.
Der volle Inhalt der QuelleLiu, Yao, Xiufeng Wang, Jing Lin und Wei Zhao. „Early chatter detection in gear grinding process using servo feed motor current“. International Journal of Advanced Manufacturing Technology 83, Nr. 9-12 (18.08.2015): 1801–10. http://dx.doi.org/10.1007/s00170-015-7687-9.
Der volle Inhalt der QuelleYang, Bin, Kai Guo, Qian Zhou und Jie Sun. „Early chatter detection in robotic milling under variable robot postures and cutting parameters“. Mechanical Systems and Signal Processing 186 (März 2023): 109860. http://dx.doi.org/10.1016/j.ymssp.2022.109860.
Der volle Inhalt der QuelleCao, Hongrui, Kai Zhou, Xuefeng Chen und Xingwu Zhang. „Early chatter detection in end milling based on multi-feature fusion and 3σ criterion“. International Journal of Advanced Manufacturing Technology 92, Nr. 9-12 (18.05.2017): 4387–97. http://dx.doi.org/10.1007/s00170-017-0476-x.
Der volle Inhalt der QuelleYan, Shichao, und Yuwen Sun. „Early chatter detection in thin-walled workpiece milling process based on multi-synchrosqueezing transform and feature selection“. Mechanical Systems and Signal Processing 169 (April 2022): 108622. http://dx.doi.org/10.1016/j.ymssp.2021.108622.
Der volle Inhalt der QuelleOcean, Allyson J., Niraj Jaysukh Gusani, Muhammad Shaalan Beg, Anirban Maitra, Julissa Viana, Allison Rosenzweig, Fatima Zelada-Arenas et al. „Introduction of #PancChat: A novel Twitter platform to inform and engage the pancreatic cancer community.“ Journal of Clinical Oncology 36, Nr. 4_suppl (01.02.2018): 242. http://dx.doi.org/10.1200/jco.2018.36.4_suppl.242.
Der volle Inhalt der QuelleMa, Lei, Shreyes N. Melkote und James B. Castle. „A Model-Based Computationally Efficient Method for On-Line Detection of Chatter in Milling“. Journal of Manufacturing Science and Engineering 135, Nr. 3 (24.05.2013). http://dx.doi.org/10.1115/1.4023716.
Der volle Inhalt der QuelleMatthew, Dialoke Ejiofor, Jianghai Shi, Maxiao Hou und Hongrui Cao. „CHATTER DETECTION IN VIBRATION SIGNALS USING TIME-FREQUENCY ANALYSIS“. MM Science Journal 2023, Nr. 4 (15.11.2023). http://dx.doi.org/10.17973/mmsj.2023_11_2023099.
Der volle Inhalt der QuelleHynynen, Katja M., Juho Ratava, Tuomo Lindh, Mikko Rikkonen, Ville Ryynänen, Mika Lohtander und Juha Varis. „Chatter Detection in Turning Processes Using Coherence of Acceleration and Audio Signals“. Journal of Manufacturing Science and Engineering 136, Nr. 4 (21.05.2014). http://dx.doi.org/10.1115/1.4026948.
Der volle Inhalt der QuelleDissertationen zum Thema "Early-chatter detection"
Zhao, Yanqing. „Contributions à la détection précoce de chatter et à l’identification des bifurcations de période-N basée sur une approche de diagnostic cumulatif“. Electronic Thesis or Diss., Université de Lorraine, 2020. http://www.theses.fr/2020LORR0250.
Der volle Inhalt der QuelleCumulative diagnosis of dynamic systems requires the detection, identification, and characterization of incipient degradations. Its application to high-speed machining, for instance, could rely on period-N bifurcations phenomena analysis to detect and identify early-chatters and improve the quality of milling products and processes. Up to now, many efficient methods were proposed to detect early-chatter and identify period-N bifurcations. But these methods are struggling to implement these tasks reliably and accurately due to the complex nonlinear characteristics of their dynamic behaviors, the noise, and the variation of their operating conditions. The present thesis aims to develop and implement methods of early-chatter detection and period-N bifurcations identification within a real-time cumulative diagnosis approach. Aimed at early-chatter detection, we proposed three detection methods and one identification method for the cumulative diagnosis. The first method can be used to detect early-chatters remotely. The second one detects early-chatter quickly under specific operating and measuring conditions. However, in practice, the operating and measuring conditions are complex and variable. To adapt to different operating and measuring conditions, we proposed a third method, and the latter detects early-chatter reliably. It is also noted that in milling processes, the early-chatter can give rise to a bifurcation of period-N or Hopf type. The machining quality under the bifurcation process of the period-N type is less critical than that under the Hopf bifurcation type. To improve machining productivity and ensure the required machining quality, we can mill the workpiece under the condition of period-N bifurcations. Thus, it is compulsory to identify the early period-N bifurcations for improving machining productivity. For that purpose, we developed a method for identifying the type and size of the period-N bifurcations. We also proved the effectiveness of the proposed methods, using two benchmark milling process models. Besides, the proposed methods can be used for fault diagnosis of other dynamic systems, such as the pulse energy conversion systems or bearing or gearing systems
Konferenzberichte zum Thema "Early-chatter detection"
Zhao, Yanqing, Kondo H. Adjallah, Alexandre Sava und Zhouhang Wang. „Early Chatter Detection using MaxEnt and SPRT“. In 2019 6th International Conference on Control, Decision and Information Technologies (CoDIT). IEEE, 2019. http://dx.doi.org/10.1109/codit.2019.8820670.
Der volle Inhalt der QuelleMa, Lei, Shreyes Melkote und James Castle. „A Model Based Computationally Efficient Method for On-Line Detection of Chatter in Milling“. In ASME 2013 International Manufacturing Science and Engineering Conference collocated with the 41st North American Manufacturing Research Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/msec2013-1031.
Der volle Inhalt der QuelleDing, Longyang, Yuxin Sun und Zhenhua Xiong. „Early chatter detection based on logistic regression with time and frequency domain features“. In 2017 IEEE International Conference on Advanced Intelligent Mechatronics (AIM). IEEE, 2017. http://dx.doi.org/10.1109/aim.2017.8014158.
Der volle Inhalt der QuelleWan, Shaoke, Xiaohu Li, Wenjun Su und Jun Hong. „Investigation on Adaptive Filter for On-Line Detection and Active Control of Chatter Vibration in Milling Process“. In ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/detc2019-97290.
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