Skip to main content
SpringerLink
Log in

Integration of optimized feedrate into an online adaptive force controller for robot milling

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

Maximum resultant cutting force control provides a great benefit of improving productivity in machining tasks. This paper presents a new force control method for robot milling that can prevent force overshoots during abrupt part geometry changes. Firstly, the feedrates of the robot at critical cutter locations are optimized offline according to the cutting force model and the part geometry. Secondly, an online parameter self-adaptive proportional-integral (PI) controller is designed in consideration of the robot feed-direction dynamics and the time-varying first-order model of the cutting process. Finally, the offline scheduled feedrates are integrated into the online adaptive controller via a feedforward-like strategy. Experiments demonstrate the effectiveness and advantages of the proposed force control method.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Leali F, Vergnano A, Pini F et al (2016) A workcell calibration method for enhancing accuracy in robot machining of aerospace parts. Int J Adv Manuf Technol 85:47–55

    Article  Google Scholar 

  2. Lehmann C, Pellicciari M, Drust M, Gunnink JW (2013) Machining with industrial robots: the COMET project Approach. In: Communications in Computer and Information Science, pp 27–36

    Google Scholar 

  3. Surdilovic D, Zhao H, Schreck G, Krueger J (2012) Advanced methods for small batch robotic machining of hard materials. In: Proceedings of ROBOTIK 2012. Munich, pp 1–6

  4. He J, Pan Z, Zhang H (2007) Adaptive force control for robotic machining process, 1–6

  5. Sörnmo O, Olofsson B, Robertsson A, Johansson R (2012) Increasing time-efficiency and accuracy of robotic machining processes using model-based adaptive force control. IFAC Proc Vol 45:543–548

    Article  Google Scholar 

  6. Altintas Y, Aslan D (2017) Integration of virtual and on-line machining process control and monitoring. CIRP Ann 66:349–352

    Article  Google Scholar 

  7. Matsubara A, Ibaraki S (2009) Monitoring and control of cutting forces in machining processes : a review. Int J Autom Technol 3:445–456

    Article  Google Scholar 

  8. Fussell BK, Jerard RB, Hemmett JG (2001) Robust feedrate selection for 3-Axis NC machining using discrete models. J Manuf Sci Eng 123:214

    Article  Google Scholar 

  9. Ferry WB, Altintas Y (2008) Virtual five-axis flank milling of jet engine impellers—part I: mechanics of five-axis flank milling. J Manuf Sci Eng 130:011005

    Article  Google Scholar 

  10. Budak E, Kops L (2000) Improving productivity and part quality in milling of titanium based impellers by chatter suppression and force control. CIRP Ann 49:31–36

    Article  Google Scholar 

  11. Luo M, Hou Y, Zhang D (2016) Feedrate optimization for worn cutter with measured cutting force in rough milling. IEEE/ASME Int Conf Adv Intell Mechatronics, AIM 2016–Septe: 345–350

  12. Liu Y, Cheng T, Zuo L (2001) Adaptive control constraint of machining processes. Int J Adv Manuf Technol 17:720–726

    Article  Google Scholar 

  13. Landers RG, Ulsoy AG (2000) Model-based machining force control. J Dyn Syst Meas Control 122:521

    Article  Google Scholar 

  14. Rober SJ, Shin YC, Nwokah ODI (1997) A digital robust controller for cutting force control in the end milling process. J Dyn Syst Meas Control 119:146

    Article  Google Scholar 

  15. Landers RG, Ulsoy AG, Ma YH (2004) A comparison of model-based machining force control approaches. Int J Mach Tools Manuf 44:733–748

    Article  Google Scholar 

  16. Elbestawi MA, Sagherian R (1987) Parameter adaptive control in peripheral milling. Int J Mach Tools Manuf 27:399–414

    Article  Google Scholar 

  17. Elbestawi MA, Mohamed Y, Liu L (1990) Application of some parameter adaptive control algorithms in machining. J Dyn Syst Meas Control 112:611

    Article  Google Scholar 

  18. Altintas Y (2012) Manufacturing automation: metal cutting mechanics, machine tool vibrations, and CNC design. Cambridge university press

  19. Lauderbaugh LK, Ulsoy AG (1989) Model reference adaptive force control in milling. J Eng Ind 111:13

    Article  Google Scholar 

  20. Zhang H, Pan Z (2008) Robotic machining: material removal rate control with a flexible manipulator. IEEE Conf Robot Autom Mechatron 2008:30–35

  21. Sörnmo O, Olofsson B, Robertsson A, Johansson R (2015) Learning approach to cycle-time-minimization of wood milling using adaptive force control. J Manuf Sci Eng 138:011013

    Article  Google Scholar 

  22. Stemmler S, Abel D, Schwenzer M et al (2017) Model predictive control for force control in milling. IFAC-Papers OnLine 50:15871–15876

    Article  Google Scholar 

  23. Spence A, Altintas Y (1991) CAD assisted adaptive control for milling. J Dyn Syst Meas Control 113:444

    Article  Google Scholar 

  24. Richards ND, Fussell BK, Jerard RB (2002) Efficient Nc machining using off-line optimized feedrates and on-line adaptive control. 1–11

  25. Saturley PV, Spence AD (2000) Integration of milling process simulation with on-line monitoring and control. Int J Adv Manuf Technol 16:92–99

    Article  Google Scholar 

  26. Fussell BK, Srinivasan K (1989) On-line identification of end milling process parameters. J Eng Ind 111:322

    Article  Google Scholar 

  27. Altintaş Y (1994) Direct adaptive control of end milling process. Int J Mach Tools Manuf 34:461–472

    Article  Google Scholar 

  28. Xiong G, Ding Y, Zhu LM, Su CY (2017) A product-of-exponential-based robot calibration method with optimal measurement configurations. Int J Adv Robot Syst 14:1–12

    Article  Google Scholar 

  29. Xiong G, Ding Y, Zhu L (2019) Stiffness-based pose optimization of an industrial robot for five-axis milling. Robot Comput Integr Manuf 55:19–28

    Article  Google Scholar 

  30. Budak E, Altintaş Y, Armarego EJA (1996) Prediction of milling force coefficients from orthogonal cutting data. J Manuf Sci Eng 118:216

    Article  Google Scholar 

  31. Ghasemi M, Zhao S, Insperger T, Kalmár-Nagy T (2012) Act-and-wait control of discrete systems with random delays. Proc Am Control Conf:5440–5443

Download references

Funding

This work was supported by the National Natural Science Foundation of China [Grant No. 91648202, 51822506, 51535004, 51905345] and Shanghai Rising-Star Program [Grant No. 17QA1401900].

Author information

Authors and Affiliations

  1. State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, People’s Republic of China

    Gang Xiong, Zhou-Long Li, Ye Ding & LiMin Zhu

Authors
  1. Gang Xiong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhou-Long Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ye Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. LiMin Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to LiMin Zhu.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xiong, G., Li, ZL., Ding, Y. et al. Integration of optimized feedrate into an online adaptive force controller for robot milling. Int J Adv Manuf Technol 106, 1533–1542 (2020). https://doi.org/10.1007/s00170-019-04691-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-019-04691-1

Keywords

Search

Navigation