Advertisement

Dynamic Behavior Analysis and Multi-sensor Modal Information Fusion for Robotic Milling System

  • Daxian HaoEmail author
  • Wei Wang
  • Gang Zhang
  • Qilong Wang
  • Chao Yun
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11745)

Abstract

Industrial robots have been proven to be more widely suitable for machining than CNC machines in many applications. However, their lack of rigidity and precision is still a limit for precision tasks. The dynamic behavior of robotic milling system has a significant effect on machining quality. The main difference of dynamic behavior between robot and CNC is that the robot’s modes greatly shift, depending on its varying dynamic parameters and joint configurations. Therefore, it is of great significance to study the dynamic characteristics of the robot to suppress the machining chatter and improve the machining accuracy of the robot. Firstly, a series of modal tests are conducted on a milling robot Considering that the robotic milling system is a time-varying system. The improving accuracy FRFs (frequency response function) are obtained by the low-frequency sensitive accelerometer and high-frequency sensitive accelerometer. The accelerometers are used to conduct corresponding modal tests on robot structure and tool-spindle of the system respectively. Then, in order to smooth the curve and splice the data of the two FRFs, a new method for multi-sensor modal information fusion using moving average method and weighted average method is presented. The experimental support for chatter-free prediction in robot high-speed milling by the regenerative chatter theory. The dynamic behavior for Robotic Milling System is analyzed at last.

Keywords

Robotic milling system Dynamic Experimental modal analysis MSIF (Multi-sensor Information Fusion) 

References

  1. 1.
    Axinte, D., Shirinzadeh, B.: MiRoR—Miniaturized robotic systems for holistic in-situ repair and maintenance works in restrained and hazardous environments. IEEE/ASME Trans. Mechatron. 23(2), 978–981 (2018)CrossRefGoogle Scholar
  2. 2.
    Tyapin, I., Kaldestad, K.B., Hovland, G.: Off-line path correction of robotic face milling using static tool force and robot stiffness. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5506–5511 (2015)Google Scholar
  3. 3.
    Makhanov, S.S., Batanov, D., Bohez, E., Sonthipaumpoon, K., Anotaipaiboon, W., Tabucanon, M.: On the tool-path optimization of a milling robot. Comput. Ind. Eng. 43(3), 455–472 (2002)CrossRefGoogle Scholar
  4. 4.
    Bisu, C., Cherif, M., Gerard, A., K’Nevez, J.Y.: Dynamic behavior analysis for a six axis industrial machining robot. Adv. Mater. Res. 423, 65–76 (2012)CrossRefGoogle Scholar
  5. 5.
    Li, J., Li, B., Shen, N.Y., Qian, H., Guo, Z.M.: Effect of the cutter path and the workpiece clamping position on the stability of the robotic milling system. Int. J. Adv. Manuf. Technol. 89, 2919–2933 (2016)CrossRefGoogle Scholar
  6. 6.
    Mejri, S., Gagnol, V., Le, T.P., Sabourin, L., Ray, P., Paultre, P.: Dynamic characterization of machining robot and stability analysis. Int. J. Adv. Manuf. Technol. 82(1–4), 351–359 (2016)CrossRefGoogle Scholar
  7. 7.
    Zaghbani, I., Songmene, V., Bonev, I.: An experimental study on the vibration response of a robotic machining system. Proc. Inst. Mech. Eng. Part B: J. Eng. Manuf. 227(6), 866–880 (2013)CrossRefGoogle Scholar
  8. 8.
    Pan, Z., Zhang, H., Zhu, Z., Wang, J.: Chatter analysis of robotic machining process. J. Mater. Process. Technol. 173(3), 301–309 (2006)CrossRefGoogle Scholar
  9. 9.
    Mousavi, S., Gagnol, V., Bouzgarrou, B.C., Ray, P., Mousavi, S., Gagnol, V., et al.: Stability optimization in robotic milling through the control of functional redundancies. Rob. Comput.-Integr. Manuf. 50(2018), 181–192 (2017)Google Scholar
  10. 10.
    Mousavi, S., Gagnol, V., Bouzgarrou, B.C., Ray, P.: Dynamic modeling and stability prediction in robotic machining. Int. J. Adv. Manuf. Technol. 88, 3053–3065 (2017)CrossRefGoogle Scholar
  11. 11.
    Rafieian, F., Liu, Z., Hazel, B.: Dynamic model and modal testing for vibration analysis of robotic grinding process with a 6DOF flexible-joint manipulator. In: International Conference on Mechatronics and Automation, vol. 47, pp. 2793–2798 (2009)Google Scholar
  12. 12.
    Mejri, S., Gangol, V., Le, T.P., Sabourin, L., Ray, P., Paultre, P.: Experimental protocol for the dynamic modeling of machining robots. In: Congrés Français De Mécanique, CFM (2013)Google Scholar
  13. 13.
    Tunc, L.T., Stoddart, D.: Tool path pattern and feed direction selection in robotic milling for increased chatter-free material removal rate. Int. J. Adv. Manuf. Technol. 89(9), 2907–2918 (2017)CrossRefGoogle Scholar
  14. 14.
    Altintas, Y.: Manufacturing Automation, 2nd edn. Cambridge University Press, Cambridge (2012)Google Scholar
  15. 15.
    Tunc, L.T., Shaw, J.: Experimental study on investigation of dynamics of hexapod robot for mobile machining. Int. J. Adv. Manuf. Technol. 84(5–8), 817–830 (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Daxian Hao
    • 1
    Email author
  • Wei Wang
    • 1
  • Gang Zhang
    • 2
  • Qilong Wang
    • 1
  • Chao Yun
    • 1
  1. 1.Beihang UniversityBeijingChina
  2. 2.HUST-Wuxi Research InstituteWuxiChina

Personalised recommendations