Abstract
In milling process, chatter is one of the most unfavorable factors, which will reduce surface quality, limit tool life, accelerate tool wear, and decrease machining efficiency. To solve this problem, a great deal of research has been done in milling dynamic modeling and chatter suppression. In this paper, a new milling force calculation method considering helix angle and bending is presented, in which the instantaneous cutting area is calculated in an improved way. The milling dynamic equations are established based on the proposed model, and the stability limit is obtained with semi discretization method (SDM). Results show that tool bending and helix play important roles in stability lobe diagram (SLD). Subsequently, the stability prediction is verified in the milling experiment. Stability analysis can just provide the guidance for selection of milling parameters. In order to get higher efficiency and larger stable region, the time-domain least mean square (LMS) adaptive algorithm is constructed and implemented for chatter suppression in this article. For the sake of applying the method to experiments, the smart toolholder equipped with piezoelectric stack actuators is designed and mounted to a three-axis milling machine. The experimental results show that this method can suppress chatter effectively.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
References
Kayhan M, Budak E (2009) An experimental investigation of chatter effects on tool life. Proc Inst Mech Eng B J Eng Manuf 223(11):1455–1463. https://doi.org/10.1243/09544054JEM1506
Taylor FW (1907) On the art of cutting metals. Trans ASME 28:31–58
Tobias SA (1965) Machine tool vibration. Blackie and Sons, London
Koenigsbeger F, Tlusty J (1970) Machine tool structures. Pergamon Press, Oxford
Altintas Y, Budak E (1995) Analytical prediction of stability lobes in milling. CIRP Ann Manuf Technol 44(1):357–362. https://doi.org/10.1016/S0007-8506(07)62342-7
Insperger T, Muñoa J, Zatarain M, Peigné G (2006) Unstable islands in the stability chart of milling processes due to the helix angle. CIRP 2nd International Conference on High Performance Cutting, Vancouver, Canada, pp 12–13
Schmitz TL, Couey J, Marsh E, Mauntler N, Hughes D (2007) Runout effect in milling: surface finish, surface location error and stability. Int J Mach Tools Manuf 47(5):841–851. https://doi.org/10.1016/j.ijmachtools.2006.06.014
Tang AJ, Liu ZQ (2009) Three-dimensional stability lobe and maximum material removal rate in end milling of thin-walled plate. Int J Adv Manuf Technol 43(1-2):33–39. https://doi.org/10.1007/s00170-008-1695-y
Tyler C, Schmitz T (2013) Analytical process damping stability prediction. J Manuf Process 15(1):69–76. https://doi.org/10.1016/j.jmapro.2012.11.006
Balachandran B (2001) Nonlinear dynamics of milling processes. Philos Trans R Soc Lond A 359(1781):793–819. https://doi.org/10.1098/rsta.2000.0755
Balachandran B, Zhao MX (2000) A mechanics based model for study of dynamics of milling operations. Meccanica 35(2):89–109. https://doi.org/10.1023/A:1004887301926
Long X, Balachandran B (2010) Stability of up-milling and down-milling operations with variable spindle speed. J Vibration Control 16(16):1151–1168. https://doi.org/10.1177/1077546309341131
Yang Y, Zhang WH, Ma YC, Wan M (2016) Chatter prediction for the peripheral milling of thin-walled workpieces with curved surfaces. Int J Mach Tools Manuf 109:36–48. https://doi.org/10.1016/j.ijmachtools.2016.07.002
Totis G (2017) Breakthrough of regenerative chatter modeling in milling by including unexpected effects arising from tooling system deflection. Int J Adv Manuf Technol 89(9–12):2515–2534. https://doi.org/10.1007/s00170-016-9855-y
Totis G, Albertelli P, Torta M, Sortino M, Monno M (2017) Upgraded stability analysis of milling operations by means of advanced modeling of tooling system bending. Int J Mach Tools Manuf 113C:19–34
Cao H, Zhang X, Chen X (2017) The concept and progress of intelligent spindles: a review. Int J Machine Tools Manuf 112:21–52. https://doi.org/10.1016/j.ijmachtools.2016.10.005
Dohner JL, Lauffer JP, Hinnerichs TD, Shankar N, Regelbrugge M, Kwan CM, Xu R, Winterb B (2004) Mitigation of chatter instabilities by active structural control. J Sound Vib 269(1-2):197–211. https://doi.org/10.1016/S0022-460X(03)00069-5
Van Dijk NJM (2011) Active chatter control in high-speed milling processes. Dissertation, Eindhoven University of Technology
Huang T, Chen Z, Zhang HT, Ding H (2015) Active control of an AMBs supported spindle for chatter suppression in milling process. J Dyn Syst Meas Control 137(11):111003. https://doi.org/10.1115/1.4030841
Monnin J, Kuster F, Wegener K (2014) Optimal control for chatter mitigation in milling—part 1: modeling and control design. Control Eng Pract 24:156–166. https://doi.org/10.1016/j.conengprac.2013.11.010
Monnin J, Kuster F, Wegener K (2014) Optimal control for chatter mitigation in milling—part 2: experimental validation. Control Eng Pract 24:167–175. https://doi.org/10.1016/j.conengprac.2013.11.011
Verschuren T (2009) Active chatter control in high-speed milling using μ-synthesis. Dissertation, Eindhoven University of Technology
Dijk NJM, Wouw N, Doppenberg EJJ, Oosterling JAJ, Nijmeijer H (2012) Robust active chatter control in the high-speed milling process. IEEE Trans Control Syst Technol 20(4):901–917. https://doi.org/10.1109/TCST.2011.2157160
Zhang HT, Wu Y, He DF, Zhao H (2015) Model predictive control to mitigate chatters in milling processes with input constraints. Int J Mach Tools Manuf 91:54–61. https://doi.org/10.1016/j.ijmachtools.2015.01.002
Rashid A, Nicolescu CM (2006) Active vibration control in palletised workholding system for milling. Int J Mach Tools Manuf 46(12–13):1626–1636. https://doi.org/10.1016/j.ijmachtools.2005.08.020
Jia ZM, Xiang YK, Ji Huan GE, Nie WM (2017) Design and experimental study of cutting chatter control system based on filtered-X LMS. Mach Tool Hydraul 45(6):100–104. http://www.en.cnki.com.cn/Article_en/CJFDTotal-JCYY201706018.htm
Zhang X, Wang C, Gao RX, Yan R, Chen X, Wang S (2016) A novel hybrid error criterion-based active control method for on-line milling vibration suppression with piezoelectric actuators and sensors. Sensors 16(1):68. https://doi.org/10.3390/s16010068
Wang C, Zhang X, Liu Y, Cao H, Chen X (2018) Stiffness variation method for milling chatter suppression via piezoelectric stack actuators. Int J Mach Tools Manuf 124:53–66. https://doi.org/10.1016/j.ijmachtools.2017.10.002
Long XH, Balachandran B (2007) Stability analysis for milling process. Nonlinear Dyn 49(3):349–359. https://doi.org/10.1007/s11071-006-9127-8
Gross D, Ehlers W, Wriggers P, Schröder J, Müller R (2017) Mechanics of materials—formulas and problems. Springer, Berlin Heidelberg. https://doi.org/10.1007/978-3-662-53880-7
Altintas Y (2012) Manufacturing automation; metal cutting mechanics, machine tool vibrations and CNC design. Cambridge University Press, Cambridge
Stephenson DA, Agapiou JS (2016) Metal cutting theory and practice. CRC Press, Boca Raton. https://doi.org/10.1201/b19559
Insperger T, Stepan G (2004) Updated semi-discretization method for periodic delay-differential equations with discrete delay. Int J Numer Methods Eng 61(1):117–141. https://doi.org/10.1002/nme.1061
Funding
This work is supported by the National Natural Science Foundation of China (Nos. 51405370 and 51421004), the Project Funded by Key Laboratory of Product Quality Assurance & Diagnosis (No. 2014SZS14-P05), and the open foundation of Zhejiang Provincial Key Laboratory of Laser Processing Robot/Key Laboratory of Laser Precision Processing & Detection (lzsy-12).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Wang, C., Zhang, X., Cao, H. et al. Milling stability prediction and adaptive chatter suppression considering helix angle and bending. Int J Adv Manuf Technol 95, 3665–3677 (2018). https://doi.org/10.1007/s00170-017-1389-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00170-017-1389-4