Volume 20, Issue 4 (April 2020)                   Modares Mechanical Engineering 2020, 20(4): 973-986 | Back to browse issues page

XML Persian Abstract Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Fallah M, Moetakef-Imani B. Application of Normalized Least Mean Square Adaptive Filter for Chatter Vibration Control. Modares Mechanical Engineering 2020; 20 (4) :973-986
URL: http://mme.modares.ac.ir/article-15-31853-en.html
1- Mechanical Engineering Department, Engineering Faculty, Ferdowsi University of Mashhad, Mashhad, Iran
2- Mechanical Engineering Department, Engineering Faculty, Ferdowsi University of Mashhad, Mashhad, Iran , imani@um.ac.ir
Abstract:   (2238 Views)
In this paper, a new active vibration control system has been proposed for the elimination of boring bar chatter in the internal turning process. The system is composed of a boring bar equipped with electromagnetic actuator and accelerometer, as well as a novel adaptive control algorithm that is widely used in the field of active noise control. The controller is known as feedback FxNLMS and is composed of two finite impulse response adaptive filters. One of the filters is known as a model filter, which predicts the dynamic model of actuator-boring bar assembly. The other is known as the control filter and anticipates the inverse model of forwarding path dynamics. The weight vector of the adaptive filter is adjusted by using the normalized least mean square algorithm. Firstly, the impact test is conducted in the presence of an adaptive controller. It is observed that the magnitude of the dominant mode on the forward path’s frequency response function is drastically suppressed by 36 dBs. Secondly, the internal turning tests are conducted on Aluminum alloy 6063-T6, to investigate the performance of the adaptive controller for the purpose of chatter mitigation. Due to the optimal performance of the adaptive controller, the dominant magnitude of the boring bar’s power spectral density is successfully attenuated up to 68 dBs, and the critical limiting depth of cut is increased by 10 folds. Also, the roughness of the machined surface is remarkably improved by 8 folds compared to the control-off cutting test. Moreover, the actuator cost is considerably reduced by 3 folds in comparison to the optimal constant-gain integral controller.
Full-Text [PDF 1139 kb]   (2064 Downloads)    
Article Type: Original Research | Subject: Mechatronics
Received: 2019/04/15 | Accepted: 2019/08/6 | Published: 2020/04/17

References
1. Neugebauer R, Denkena B, Wegener K. Mechatronic systems for machine tools. CIRP Annals. 2007;56(2):657-686. [Link] [DOI:10.1016/j.cirp.2007.10.007]
2. Quintana G, Ciurana J. Chatter in machining processes: A review. International Journal of Machine Tools and Manufacture. 2011;51(5):363-376. [Link] [DOI:10.1016/j.ijmachtools.2011.01.001]
3. Lee DG, Hwang HY, Kim JK. Design and manufacture of a carbon fiber epoxy rotating boring bar. Composite Structures. 2003;60(1):115-124. [Link] [DOI:10.1016/S0263-8223(02)00287-8]
4. Liu X, Liu Q, Wu S, Liu L, Gao H. Research on the performance of damping boring bar with a variable stiffness dynamic vibration absorber. The International Journal of Advanced Manufacturing Technology. 2017;89:2893-2906. [Link] [DOI:10.1007/s00170-016-9612-2]
5. Ema S, Marui E. Suppression of chatter vibration of boring tools using impact dampers. International Journal of Machine Tools and Manufacture. 2000;40(8):1141-1156. [Link] [DOI:10.1016/S0890-6955(99)00119-4]
6. Mei D, Kong T, Shih AJ, Chen Z. Magnetorheological fluid-controlled boring bar for chatter suppression. Journal of Materials Processing Technology. 2009;209(4):1861-1870. [Link] [DOI:10.1016/j.jmatprotec.2008.04.037]
7. Matsubara A, Maeda M, Yamaji I. Vibration suppression of boring bar by piezoelectric actuators and LR circuit. CIRP Annals. 2014;63(1):373-376. [Link] [DOI:10.1016/j.cirp.2014.03.132]
8. Fallah M, Moetakef-Imani B. Analytical prediction of stability lobes for passively damped boring bars. Journal of Mechanics. 2107;33(5):641-654. [Link] [DOI:10.1017/jmech.2017.22]
9. Glaser DJ, Nachtigal CL. Development of a hydraulic chambered actively controlled boring bar. Journal of Manufacturing Science and Engineering. 1979;101(3):362-368. [Link] [DOI:10.1115/1.3439519]
10. Min BK, O'Neal G, Koren Y, Pasek Z. A smart boring tool for process control. Mechatronics. 2002;12(9-10):1097-1114. [Link] [DOI:10.1016/S0957-4158(02)00020-X]
11. Abele E, Haydn M, Grosch T. Adaptronic approach for modular long projecting boring tools. CIRP Annals. 2016;65(1):393-396. [Link] [DOI:10.1016/j.cirp.2016.04.104]
12. Hanson RD, Tsao TC. Reducing cutting force induced bore cylindricity errors by learning control and variable depth of cut machining. Journal of Manufacturing Science and Engineering. 1998;120(3):547-554. [Link] [DOI:10.1115/1.2830158]
13. Akesson H, Smirnova T, Claesson I, Hakansson L. On the development of a simple and robust active control system for boring bar vibration in industry. International Journal of Acoustics and Vibration. 2007;12(4). [Link] [DOI:10.20855/ijav.2007.12.4216]
14. Fallah M, Moetakef-Imani B. Updating boring bar's dynamic model using particle swarm optimization. Modares Mechanical Engineering. 2017;16(12):479-489. [Persian] [Link]
15. Fallah M, Moetakef-Imani B. Identification of dynamic model for an active boring bar. Modares Mechanical Engineering. 2019;19(8):1917-1928. [Persian] [Link]
16. Fallah M. Chatter vibration control for stability improvement in deep internal turning [dissertation]. Mashhad: Ferdowsi University of Mashhad; 2018. [Persian] [Link]
17. Haykin S. Adaptive filter theory. 3rd Edition. New York: Prentice Hall; 1996. [Link]
18. Altintas Y. Manufacturing automation. 2nd Edition. Vancouver: Cambridge University Press; 2012. [Link]
19. Kuo SM, Morgan DR. Active noise control: A tutorial review. Proceedings of the IEEE. 1999;87(6):943-973. [Link] [DOI:10.1109/5.763310]
20. Kajikawa Y, Gan WS, Kuo SM. Recent advances on active noise control: Open issues and innovative applications. APSIPA Transactions on Signal and Information Processing. 2012;1. [Link] [DOI:10.1017/ATSIP.2012.4]
21. Madisetti VK. The digital signal processing handbook. 2nd Edition. New York: CRC Press; 2009. [Link] [DOI:10.1201/9781420046076]
22. Elliott S. Signal processing for active control. London: Academic Press; 2006. [Link]

Add your comments about this article : Your username or Email:
CAPTCHA

Send email to the article author


Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.