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Estimation and compensation of angular misalignment at robot end brush roller-workpiece contact interface via elastic contact force perception

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Abstract

When the non-standard customized brush roller tool is used for robotic grinding of large-scale components, the clamping and positioning error of the brush roller at the end of the robot is extremely easy to cause misalignment at the brush roller-workpiece contact interface, which will affect the machining accuracy and surface quality. In order to ensure the parallel contact between the brush roller and the workpiece surface during the machining process, a calculation model of the angular misalignment at the brush roller-workpiece contact interface is proposed based on the elastic contact force perception, and then the accurate positioning of the robot end brush roller is realized by a fast compensation method. Firstly, according to the geometric force relationship between the brush roller and the workpiece, as well as the determined brush roller material property parameters, the estimation model of angular misalignment is established. Secondly, both the axial force and normal torque at the time of initial contact detected by the force-controlled sensor are regarded as the input parameters in the model. Furthermore, the calculated brush roller-workpiece contact offset is used as the geometric error compensation amount, and the brush roller is deflected to achieve error compensation by the robot RAPID program control command. The finite element simulation results are compared with the theoretical calculation values, and the average relative error is 15.1%. The experiment on robotic grinding and brushing of high-speed rail body indicates that the compensated angle can be reduced to 0.024° from an average of 0.179° before compensation, coupled with uniform material removal depth. The proposed method can significantly improve the contour accuracy of large-scale components.

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The data and materials of this manuscript are available from the corresponding author on reasonable request.

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References

  1. Zhu D, Feng X, Xu X, Yang Z, Li W, Yan S (2020) Ding H. Robotic grinding of complex components: a step towards efficient and intelligent machining – challenges, solutions, and applications. Robot Cim-Int Manuf 65:101908.

  2. Li W, Xie H, Yin Z, Ding H (2021) The research of geometric error modeling of robotic machining: I spatial motion chain and error transmission. J Mech Eng 57(7):154–168

    Article  Google Scholar 

  3. Li W, Xie H, Yin Z, Ding H (2021) The research of geometric error modeling of robotic machining: II parameter identification and pose optimization. J Mech Eng 57(7):169–184

    Article  Google Scholar 

  4. Jiang Z, Huang M, Tang X, Guo Y (2021) A new calibration method for joint-dependent geometric errors of industrial robot based on multiple identification spaces. Robot Cim-Int Manuf 71(1):102175

    Article  Google Scholar 

  5. Luo G, Zou L, Wang Z, Lv C, Huang Y (2021) A novel kinematic parameters calibration method for industrial robot based on Levenberg-Marquardt and Differential Evolution hybrid algorithm. Robot Cim-Int Manuf 71(1):102165

    Article  Google Scholar 

  6. Li W, Xie H, Zhang G, Yan S, Yin Z (2015) Hand–eye calibration in visually-guided robot grinding. IEEE Trans Cybern 46(11):2634–2642

    Article  Google Scholar 

  7. Li M, Du Z, Ma X, Dong W, Gao Y (2021) A robot hand-eye calibration method of line laser sensor based on 3D reconstruction. Robot Cim-Int Manuf 71(2017):102136

    Article  Google Scholar 

  8. Xu X, Zhu D, Wang J, Yan S, Han D (2018) Calibration and accuracy analysis of robotic belt grinding system using the ruby probe and criteria sphere. Robot Cim-Int Manuf 51:189–201

    Article  Google Scholar 

  9. Sun Y, Giblin D, Kazerounian K (2009) Accurate robotic belt grinding of workpieces with complex geometries using relative calibration techniques. Robot Cim-Int Manuf 25(1):204–210

    Article  Google Scholar 

  10. Zhu D, Xu X, Jiang C, Li W (2021) Research progress in robotic grinding technology for complex blades. Acta Aeronautica et Astronautica Sinica 42(10):524265

    Google Scholar 

  11. Ren Y, Yin S, Zhu J (2012) Calibration technology in application of robot-laser scanning system. Opt Eng 51(11):114204

    Article  Google Scholar 

  12. Ma L, Bazzoli P, Sammons P, Landers R, Bristow D (2018) Modeling and calibration of high-order joint-dependent kinematic errors for industrial robot. Robot Cim-Int Manuf 50:153–167

    Article  Google Scholar 

  13. Luo X, Xie F, Liu X, Xie Z (2021) Kinematic calibration of a 5-axis parallel machining robot based on dimensionless error mapping matrix. Robot Cim-Int Manuf 70:102115

    Article  Google Scholar 

  14. Nubiola A, Bonev I (2013) Absolute calibration of an ABB IRB 1600 robot using a laser tracker. Robot Cim-Int Manuf 29(1):236–245

    Article  Google Scholar 

  15. Zhao D, Dong C, Guo H, Tian W (2018) Kinematic calibration based on the multicollinearity diagnosis of a 6-DOF polishing hybrid robot using a laser tracker. Math Probl Eng 5602397

  16. Huang T, Zhao D, Yin F, Tian W, Chetwynd D (2019) Kinematic calibration of a 6-DOF hybrid robot by considering multicollinearity in the identification Jacobian. Mech Mach Theory 131:371–384

    Article  Google Scholar 

  17. Boby R, Klimchik A (2021) Combination of geometric and parametric approaches for kinematic identification of an industrial robot. Robot Cim-Int Manuf 71:102142

    Article  Google Scholar 

  18. Cen L, Melkote S, Castle J, Appelman H (2016) A wireless force-sensing and model-based approach for enhancement of machining accuracy in robotic milling. IEEE-ASME T Mech 21(5):2227–2235

    Article  Google Scholar 

  19. Latifinavid M, Konukseven E (2017) Hybrid model based on energy and experimental methods for parallel hexapod-robotic light abrasive grinding operations. Int J Adv Manuf Technol 93(9–12):3873–3887

    Article  Google Scholar 

  20. Latifinavid M, Donder A, Konukseven E (2018) High-performance parallel hexapod-robotic light abrasive grinding using real-time tool deflection compensation and constant resultant force control. Int J Adv Manuf Technol 96(9–12):3403–3416

    Article  Google Scholar 

  21. Ye C, Yang J, Zhao H, Ding H (2021) Task-dependent workpiece placement optimization for minimizing contour errors induced by the low posture-dependent stiffness of robotic milling. Int J Mech Sci 205:106601

    Article  Google Scholar 

  22. Wang Q, Wang W, Zheng L, Yun C (2021) Force control-based vibration suppression in robotic grinding of large thin-wall shells. Robot Cim-Int Manuf 67:102031

    Article  Google Scholar 

  23. Xu X, Chen W, Zhu D, Ding H (2021) Hybrid active/passive force control strategy for grinding marks suppression and profile accuracy enhancement in robotic belt grinding of turbine blade. Robot Cim-Int Manuf 67:102047

    Article  Google Scholar 

  24. Popov V (2010) Contact mechanics and friction: physical principles and applications. Springer-Verlag, Berlin Heidelberg

    Book  Google Scholar 

  25. Qu C, Lv Y, Yang Z, Xu X, Zhu D, Yan S (2019) An improved chip-thickness model for surface roughness prediction in robotic belt grinding considering the elastic state at contact wheel-workpiece interface. Int J Adv Manuf Technol 104(5):3209–3217

    Article  Google Scholar 

  26. Zhu D, Wen Z, Xi W, Zhang M (2020) Parametric evaluation method for surface texture of high-speed railway body in white drawing. J Huazhong Univ Sci Technolog (Natural Science Edition) 48(11):48–53

    Google Scholar 

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Funding

This study is financially supported by the National Nature Science Foundation of China (No. 51975443) and the Hubei Province Key R&D Program (No. 2020BAA025).

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Authors

Contributions

Xiaozhi Feng: conceptualization, investigation, writing—original draft, writing—review and editing, methodology. Rui Lv: methodology, validation, writing. Chen Qian: methodology, writing—reviewing and editing. Yudi Wang: investigation, writing—reviewing and editing. Linli Tian: methodology, writing—reviewing and editing. Dahu Zhu: funding acquisition, writing—reviewing and editing.

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Correspondence to Linli Tian.

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Feng, X., Lv, R., Qian, C. et al. Estimation and compensation of angular misalignment at robot end brush roller-workpiece contact interface via elastic contact force perception. Int J Adv Manuf Technol 121, 367–377 (2022). https://doi.org/10.1007/s00170-022-09282-1

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  • DOI: https://doi.org/10.1007/s00170-022-09282-1

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