TCP-based calibration in robot-assisted belt grinding of aero-engine blades using scanner measurements

  • Xiaohu Xu
  • Dahu Zhu
  • Haiyang Zhang
  • Sijie Yan
  • Han Ding
ORIGINAL ARTICLE
First Online:
Received:
Accepted:

Abstract

Calibration in the robot-assisted belt grinding of complex blades is regarded as one of the key bottlenecks of measurement accuracy. To enhance the accuracy, a TCP-based (tool center point) calibration method is proposed in this paper to calibrate the relationship between the precalibrated 3D laser scanner and the robot end-effector by using criterion spheres as the calibration object. Based on the description of the robot-scanning system from the perspectives of coordinates and scanner measurement modes, the calibration strategies on translational and rotational motions of the robot are provided to determine the translation vector and the orientation matrix. Calibration experiments on the criterion sphere are performed, both the calibration errors (positioning error and orientation error) and sphere fitting error are calculated. A typical case on the robotic belt grinding of 84K-2R1 aviation blade is conducted to validate the calibration results. Finally, the key factors influencing the calibration accuracy are analyzed. It has been demonstrated that the TCP-based calibration method proposed is effective, concise, and time-saving, and can be widely applied in the robot-assisted belt grinding operation.

Keywords

TCP-based calibration Robot-assisted belt grinding Scanner measurement Aero-engine blade Criterion sphere 
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References

  1. 1.
    Zhu D, Luo S, Yang L, Chen W, Yan S, Ding H (2015) On energetic assessment of cutting mechanisms in robot-assisted belt grinding of titanium alloys. Tribol Int 90:55–59CrossRefGoogle Scholar
  2. 2.
    Zha XF, Chen XQ (2004) Trajectory coordination planning and control for robot manipulators in automated material handling and processing. Int J Adv Manuf Technol 23(11):831–845Google Scholar
  3. 3.
    Zhao P, Shi Y (2014) Posture adaptive control of the flexible grinding head for blisk manufacturing. Int J Adv Manuf Technol 70(9):1989–2001CrossRefGoogle Scholar
  4. 4.
    Je HW, Baek JY, Lee MC (2011) Current based compliance control method for minimizing an impact force at collision of service robot arm. Int J Precis Eng Man 12(2):251–258CrossRefGoogle Scholar
  5. 5.
    Fang HC, Ong SK, Nee AYC (2013) Orientation planning of robot end-effector using augmented reality. Int J Adv Manuf Technol 67(9):2033–2049CrossRefGoogle Scholar
  6. 6.
    Song Y, Liang W, Yang Y (2012) A method for grinding removal control of a robot belt grinding system. J Intell Manuf 23(5):1903–1913CrossRefGoogle Scholar
  7. 7.
    Gong C, Yuan J, Ni J (2000) Nongeometric error identification and compensation for robotic system by inverse calibration. Int J Mach Tool Manu 40(14):2119–2137CrossRefGoogle Scholar
  8. 8.
    Sun Y, Giblin DJ, Kazerounian K (2009) Accurate robotic belt grinding of workpieces with complex geometries using relative calibration techniques. Robotic Cim-Int Manuf 25(1):204–210CrossRefGoogle Scholar
  9. 9.
    Majarena AC, Santolaria J, Samper D, Aguilar JJ (2013) Analysis and evaluation of objective functions in kinematic calibration of parallel mechanisms. Int J Adv Manuf Technol 66(5):751–761CrossRefGoogle Scholar
  10. 10.
    Durupt A, Remy S, Ducellier G (2011) Reverse engineering of a piston using knowledge based reverse engineering approach. In: Bernard A (ed) Global product development. Springer, Berlin Heidelberg, pp. 683–690CrossRefGoogle Scholar
  11. 11.
    Fan KC, Lee MZ, Mou JI (2002) On-line non-contact system for grinding wheel wear measurement. Int J Adv Manuf Technol 19(1):14–22CrossRefGoogle Scholar
  12. 12.
    Conte J, Majarena AC, Aguado S, Acero R, Santolaria J (2016) Calibration strategies of laser trackers based on network measurements. Int J Adv Manuf Technol 83(5):1161–1170CrossRefGoogle Scholar
  13. 13.
    Zhan Q, Wang X (2012) Hand–eye calibration and positioning for a robot drilling system. Int J Adv Manuf Technol 61(5):691–701CrossRefGoogle Scholar
  14. 14.
    Kosarevsky S (2010) Practical way to measure large-scale 2D parts using repositioning on coordinate-measuring machines. Measurement 43(6):837–841CrossRefGoogle Scholar
  15. 15.
    Gerbino S, Del Giudice DM, Staiano G, Lanzotti A, Martorelli M (2015) On the influence of scanning factors on the laser scanner-based 3D inspection process. Int J Adv Manuf Technol. doi: 10.1007/s00170-015-7830-7 Google Scholar
  16. 16.
    Abderrahim M, Khamis A, Garrido S, Moreno L (2007) Accuracy and calibration issues of industrial manipulators. In: Huat LK (ed) Industrial robotics: programming, simulation and applications. Pro Literatur Verlag, Germany, pp. 131–146Google Scholar
  17. 17.
    Dombre E, Khalil W (2007) Robot manipulators: modeling, performance analysis and control. WileyGoogle Scholar
  18. 18.
    Muruganantham C, Jawahar N, Ramamoorthy B, Giridhar D (2009) Optimal settings for vision camera calibration. Int J Adv Manuf Technol 42(7):736–748CrossRefGoogle Scholar
  19. 19.
    Hartley R, Zisserman A (2003) Multiple view geometry in computer vision. University Press, Cambridge, pp. 168–183MATHGoogle Scholar
  20. 20.
    Li D, Shen L, Xie D, Zhang L (2012) Camera linear calibration algorithm based on features of calibration plate. Advances in Electric and Electronics 155:689–697CrossRefGoogle Scholar
  21. 21.
    Leali F, Vergnano A, Pini F, Pellicciari M, Berselli G (2014) A workcell calibration method for enhancing accuracy in robot machining of aerospace parts. Int J Adv Manuf Technol. doi: 10.1007/s00170-014-6025-y Google Scholar
  22. 22.
    Johansson H, Runesson K, Larsson F (2007) Calibration of a class of non-linear viscoelasticity models with adaptive error control. Comput Mech 41(1):107–119MathSciNetCrossRefMATHGoogle Scholar
  23. 23.
    Seo JK, Hwang YH, Hong HK (2004) Structure and motion recovery using two step sampling for 3D match move. In: Monroy R, Arroyo-Figueroa G, Sucar LE, Sossa H (eds) MICAI 2004: advances in artificial intelligence 2972:652–661. Springer, Berlin HeidelbergGoogle Scholar
  24. 24.
    Jiang ZT, Liu SC (2011) The self-calibration of varying internal camera parameters based on image of dual absolute quadric transformation. In: Information and Automation, vol 86. Springer Berlin Heidelberg, pp 452–461Google Scholar
  25. 25.
    Li J, Zhu J, Guo Y, Lin X, Duan K, Wang Y, Tang Q (2008) Calibration of a portable laser 3-D scanner used by a robot and its use in measurement. Opt Eng 47(1):017202–017202CrossRefGoogle Scholar
  26. 26.
    Ren Y, Yin S, Zhu J (2012) Calibration technology in application of robot-laser scanning system. Opt Eng 51(11):–114204Google Scholar
  27. 27.
    Yin S, Ren Y, Zhu J, Yang S, Ye S (2013) A vision-based self-calibration method for robotic visual inspection systems. Sensors 13(12):16565–16582CrossRefGoogle Scholar
  28. 28.
    Chang WC, Wu CH (2015) Eye-in-hand vision-based robotic bin-picking with active laser projection. Int J Adv Manuf Technol. doi: 10.1007/s00170-015-8120-0 Google Scholar
  29. 29.
    Li WL, Xie H, Zhang G, Yan SJ, Yin ZP (2015) Hand-eye calibration in visually-guided robot grinding. IEEE T Cybernetics. doi: 10.1109/TCYB.2015.2483740 Google Scholar
  30. 30.
    Zhao Y (2009) Robot grinding blade key technology research. Jilin University, Ph.D. Thesis. (in Chinese)Google Scholar
  31. 31.
    Chen J, Kang X, Liu Y, Wang ZJ (2015) Median filtering forensics based on convolutional neural networks. IEEE Signal Proc Let 22(11):1849–1853CrossRefGoogle Scholar
  32. 32.
    Zhang Z, Yuan L (2012) Building a 3D scanner system based on monocular vision. Appl Opt 51(11):1638–1644CrossRefGoogle Scholar
  33. 33.
    Krooks A, Kaasalainen S, Hakala T, Nevalainen O (2013) Correction of intensity incidence angle effect in terrestrial laser scanning. ISPRS Ann Photogramm Remote Sens Spat Inf Sci 2:145–150CrossRefGoogle Scholar
  34. 34.
    Brandstötter M, Gruber C, Hofbaur M (2015) A method to estimate the encoder dependent repeatability of general serial manipulators. In: Recent Advances in Mechanism Design for Robotics. Springer International Publishing, pp 99–110Google Scholar
  35. 35.
    Li J, Chen M, Jin X, Chen Y, Dai Z, Ou Z, Tang Q (2011) Calibration of a multiple axes 3-D laser scanning system consisting of robot, portable laser scanner and turntable. Optik 122(4):324–329CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2016

Authors and Affiliations

  • Xiaohu Xu
    • 1
    • 2
  • Dahu Zhu
    • 3
    • 4
    • 2
  • Haiyang Zhang
    • 2
  • Sijie Yan
    • 1
    • 2
  • Han Ding
    • 1
    • 2
  1. 1.State Key Laboratory of Digital Manufacturing Equipment and TechnologyHuazhong University of Science and TechnologyWuhanChina
  2. 2.Blade Intelligent Manufacturing DivisionHUST-Wuxi Research InstituteWuxiChina
  3. 3.Hubei Key Laboratory of Advanced Technology for Automotive ComponentsWuhan University of TechnologyWuhanChina
  4. 4.Hubei Collaborative Innovation Center for Automotive Components TechnologyWuhan University of TechnologyWuhanChina

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