Advertisement

Novel servo-feed-drive model considering cutting force and structural effects in milling to predict servo dynamic behaviors

  • Chen-Jung Li
  • Hsiang-Chun TsengEmail author
  • Meng-Shiun Tsai
  • Chih-Chun Cheng
ORIGINAL ARTICLE
  • 85 Downloads

Abstract

This paper presents an integrated servo-feed-drive model including the cutting force and structural effect to predict tracking errors in an end-milling process. Most conventional approaches consider the cutting force to be an equivalent torque to servo feed drive. However, in addition to acting as the equivalent torque to the servo feed drive, cutting forces also cause the machine table to vibrate. This paper considers the aforementioned cutting-force effects to predict tracking errors and then verifies the tracking errors using experimental results. Experiments are conducted on a 3-axis computer numerical control (CNC) machining center to validate the tracking errors predicted by the proposed servo-feed-drive model. For one case study, the peak-to-peak tracking errors from the experimental, proposed, and traditional models are 3 μm, 2.8 μm, and 0.5 μm, respectively, for the x-axis, and 2.1 μm, 1.7 μm, and 0.4 μm, respectively, for the y-axis. The experimental results illustrate that the tracking errors predicted using the proposed model are more accurate than those predicted using the traditional model without consideration of the transmission path. Therefore, it can be concluded that the proposed integrated model provides much accurate tracking-error prediction, and thus, the ball-screw and machine-table flexibilities should be considered.

Keywords

Servo feed drive Cutting force Structural effect Tracking error End milling 

Notes

Funding information

The financial supports were provided by the Ministry of Science and Technology, R. O. C., under the grant MOST 106-2221-E-002-240-MY2, 107-2218-E-002-071 and by Precision Machinery Research and Development Center (PMC), R. O. C.

References

  1. 1.
    Ramesh R, Mannan MA, Poo AN (2005) Tracking and contour error control in CNC servo systems. Int J Mach Tools Manuf 45(3):301–326CrossRefGoogle Scholar
  2. 2.
    Ramesh R, Mannan MA, Poo AN (2000) Error compensation in machine tools — a review: part II: thermal errors. Int J Mach Tools Manuf 40(9):1257–1284CrossRefGoogle Scholar
  3. 3.
    Muhammad BB, Wan M, Feng J, Zhang WH (2017) Dynamic damping of machining vibration: a review. Int J Adv Manuf Technol 89(9):2935–2952CrossRefGoogle Scholar
  4. 4.
    Zhang GP, Huang YM, Shi WH, Fu WP (2003) Predicting dynamic behaviours of a whole machine tool structure based on computer-aided engineering. Int J Mach Tools Manuf 43(7):699–706CrossRefGoogle Scholar
  5. 5.
    Law M, Altintas Y, Phani AS (2013) Rapid evaluation and optimization of machine tools with position-dependent stability. Int J Mach Tools Manuf 68(1):81–90CrossRefGoogle Scholar
  6. 6.
    Wu J, Han Y, Xiong Z, Ding H (2017) Servo performance improvement through iterative tuning feedforward controller with disturbance compensator. Int J Mach Tools Manuf 117(1):1–10CrossRefGoogle Scholar
  7. 7.
    Ohishi K, Miyazaki T, Inomata K, Yanagisawa H, Koide D, Tokumaru H (2006) Robust tracking servo system considering force disturbance for the optical disk recording system. IEEE Trans Ind Electron 53(3):838–847CrossRefGoogle Scholar
  8. 8.
    Erkorkmaz K, Altintas Y (2001) High speed CNC system design part II modeling and identification of feed drives. Int J Mach Tools Manuf 41(10):1487–1509CrossRefGoogle Scholar
  9. 9.
    Chen Y, Huang P, Yen J (2002) Frequency-domain identification algorithms for servo system with friction. IEEE Trans Control Syst Technol 10(5):654–665CrossRefGoogle Scholar
  10. 10.
    Olsson H, Åström KJ, Canudas de Wit C, Gäfvert M, Lischinsky P (1998) Friction models and friction compensation. Eur J Control 4(3):176–195CrossRefGoogle Scholar
  11. 11.
    Nguyen BD, Aldo AF, Olivier AB (2007) Efficient simulation of a dynamic system with LuGre friction. J Comput Nonlinear Dyn 2(4):281–289CrossRefGoogle Scholar
  12. 12.
    Altintas Y, Weckb M (2004) Chatter stability of metal cutting and grinding. CIRP Ann 53(2):619–642CrossRefGoogle Scholar
  13. 13.
    Budak E, Altintas Y (1998) Analytical prediction of chatter stability in milling part I general formulation. J Dyn Syst Meas Control 120(1):22–30CrossRefGoogle Scholar
  14. 14.
    Schmitz TL, Smith KS (2009) Machining dynamics frequency response to improved productivity. Springer, New YorkGoogle Scholar
  15. 15.
    Tachikawa T, Iba D, Kurita N, Nakamura M, Moriwaki I (2017) Basic study on calculation of cutting forces useful for reducing vibration in skiving. J Mech Des 139(10):104501CrossRefGoogle Scholar
  16. 16.
    Altintas Y (1992) Prediction of cutting forces and tool breakage in milling from feed drive current measurements. J Eng Ind 114(4):386–392CrossRefGoogle Scholar
  17. 17.
    Lee CH, Yang MY, Oh CW, Gim TW, Ha JY (2015) An integrated prediction model including the cutting process for virtual product development of machine tools. Int J Mach Tools Manuf 90(1):29–43CrossRefGoogle Scholar
  18. 18.
    Zhang X, Zhang J, Zhang W, Liang T, Liu H, Zhao W (2018) Integrated modeling and analysis of ball screw feed system and milling process with consideration of multi-excitation effect. Mech Syst Signal Process 98(1):484–505CrossRefGoogle Scholar
  19. 19.
    Aslan D, Altintas Y (2018) Prediction of cutting forces in five-axis milling using feed drive current measurements. IEEE ASME Trans Mechatron 23(2):833–844CrossRefGoogle Scholar
  20. 20.
    Tsai MS, Huang YC (2016) A novel integrated dynamic acceleration/deceleration interpolation algorithm for a CNC controller. Int J Adv Manuf Technol 87(1):279–292CrossRefGoogle Scholar
  21. 21.
    Tsai MS, Nien HW, Yau HT (2011) Development of integrated acceleration/deceleration look-ahead interpolation technique for multi-blocks NURBS curves. Int J Adv Manuf Technol 56(5):601–618CrossRefGoogle Scholar
  22. 22.
    Lin MT, Yen CL, Tsai MS, Yau HT (2013) Application of robust iterative learning algorithm in motion control system. Mechatronics 23(5):530–540CrossRefGoogle Scholar
  23. 23.
    Ljung L (1999) System identification: theory for the user. Prentice Hall, New JerseyzbMATHGoogle Scholar
  24. 24.
    The MathWorks, Inc (2019) MATLAB system identification toolbox reference. MathWorks, MassachusettsGoogle Scholar
  25. 25.
    Levi EC (1959) Complex-curve fitting. IRE Trans Automat Contr AC-4(1):37–44CrossRefGoogle Scholar
  26. 26.
    The MathWorks, Inc (2019) MATLAB signal processing toolbox reference. MathWorks, MassachusettsGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  • Chen-Jung Li
    • 1
  • Hsiang-Chun Tseng
    • 2
    Email author
  • Meng-Shiun Tsai
    • 3
  • Chih-Chun Cheng
    • 2
  1. 1.Department of Mechatronics EngineeringNational Kaohsiung University of Science and TechnologyKaohsiung CityPeople’s Republic of China
  2. 2.Department of Mechanical EngineeringNational Chung Cheng UniversityChiayi CountyPeople’s Republic of China
  3. 3.Department of Mechanical EngineeringNational Taiwan UniversityTaipei CityPeople’s Republic of China

Personalised recommendations