Hostname: page-component-54dcc4c588-br6xx Total loading time: 0 Render date: 2025-09-11T10:27:10.722Z Has data issue: false hasContentIssue false
  • English
  • Français

Thermal drift and backlash issues for industrial robots positioning performance

Published online by Cambridge University Press:  28 March 2022

Adrien Le Reun Open the ORCID record for Adrien Le Reun [Opens in a new window] ,
Kévin Subrin ,
Anthony Dubois  and
Sébastien Garnier
Adrien Le Reun
Affiliation:
Nantes University, Ecole Centrale Nantes, CNRS, LS2N, UMR 6004, F-44000, Nantes, France CEA Tech Pays de la Loire et Bretagne, CEA, CEA Tech Pays de la Loire, Bouguenais, France
Kévin Subrin*
Affiliation:
Nantes University, Ecole Centrale Nantes, CNRS, LS2N, UMR 6004, F-44000, Nantes, France
Anthony Dubois
Affiliation:
CEA Tech Pays de la Loire et Bretagne, CEA, CEA Tech Pays de la Loire, Bouguenais, France
Sébastien Garnier
Affiliation:
Nantes University, Ecole Centrale Nantes, CNRS, LS2N, UMR 6004, F-44000, Nantes, France
*
*Corresponding author. E-mail: kevin.subrin@univ-nantes.fr
Get access Rights & Permissions [Opens in a new window]

Abstract

Robot positioning performance is studied in the scope of a robotized X-ray computed tomography application on a ABB IRB4600 robot. The robot has the “absolute accuracy” option, that is, the manufacturer has identified the manufacturing defects and included them in the robot control. Laser-tracker measurement on a 6.5-h long linear trajectory shows thermal drift and backlash issues, affecting the positioning unidirectional repeatability and bidirectional accuracy. A thermo-geometrical model with backlash compensation is developed. Geometrical calibration improves the forwards unidirectional mean accuracy from 1.39 to 0.06 mm between theoretical and optimized geometrical parameters with a stable thermal state. Thermo-geometrical calibration reduces the positioning scattering from a maximum of 0.15 to 0.05 mm (close to the repeatability of the robot). Backlash compensation improves the bidirectional mean accuracy from 1.53 to 0.07 mm.

Keywords

X-ray Computed Tomographyrobot positioningthermal driftbacklash

Information

Type
Research Article
Information
Robotica , Volume 40 , Issue 9 , September 2022 , pp. 2933 - 2952
Copyright
© The Author(s), 2022. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Holub, W., Brunner, F. and Schön, T., “Roboct-Application for In-Situ Inspection of Join Technologies of Large Scale Objects,” In: International Symposium on Digital Industrial Radiology and Computed Tomography, Fürth, Germany (2019).Google Scholar
Landstorfer, P., Hiller, J. and Herbst, M., “Investigation of Positioning Accuracy of Industrial Robots for Robotic-based x-Ray Computed Tomography,” In: 9th Conference of Industrial Computed Tomography (ICT 2019), Padova, Italy (2019) pp. 13–15.Google Scholar
ABB, Product specification - IRB 4600, 2020.Google Scholar
Kang, R., Probst, G., Slaets, P. and Dewulf, W., “Robot Accuracy and its Impact on Robot-CT Reconstruction Quality,” In: International Symposium on Digital Industrial Radiology and Computed Tomography, Fürth, Germany (2019).Google Scholar
Kang, R., Probst, G. M., Slaets, P. and Dewulf, W., “Investigation of the impact of various robot properties on a twin Robot-CT system,” Nondestruct. Test. Eval. 35(3), 276–286 (2020). doi: 10.1080/10589759.2020.1774581 CrossRefGoogle Scholar
Ametova, E., Ferrucci, M., Chilingaryan, S. and Dewulf, W., “Software-based compensation of instrument misalignments for X-ray computed tomography dimensional metrology,” Precis. Eng. 54, 233–242 (2018). doi: 10.1016/j.precisioneng.2018.05.016, https://linkinghub.elsevier.com/retrieve/pii/S0141635918300588 CrossRefGoogle Scholar
Banjak, H., X-ray computed tomography reconstruction on non-standard trajectories for robotized inspection, PhD thesis (Université de Lyon, 2016).Google Scholar
Kisner, S. J., Haneda, E., Bouman, C. A., Skatter, S., Kourinny, M. and Bedford, S.. “Limited View Angle Iterative CT Reconstruction,” In: Computational Imaging X, vol. 8296 (SPIE, Burlingame, CA, 2012) pp. 66–74.Google Scholar
Robin, V., Sabourin, L. and Gogu, G., “Optimization of a robotized cell with redundant architecture,” Robot. Comput. Integr. Manuf. 27(1), 13–21 (2011). doi: 10.1016/j.rcim.2010.06.010, https://www.sciencedirect.com/science/article/piiS073658451000061X CrossRefGoogle Scholar
Garnier, S., Subrin, K. and Waiyagan, K., “Modelling of robotic drilling,” Proc. CIRP 58, 416–421 (2017). doi: 10.1016/j.procir.2017.03.246, https://linkinghub.elsevier.com/retrieve/pii/S2212827117304298 CrossRefGoogle Scholar
Wu, Y., Klimchik, A., Caro, S., Furet, B. and Pashkevich, A., “Geometric calibration of industrial robots using enhanced partial pose measurements and design of experiments,” Robot Comput. Integr. Manuf. 35, 151–168 (2015). doi: 10.1016/j.rcim.2015.03.007, https://www.sciencedirect.com/science/article/pii/S0736584515000411 Google Scholar
Kamali, K., Joubair, A., Bonev, I. A. and Bigras, P., “Elasto-Geometrical Calibration of an Industrial Robot Under Multidirectional External Loads Using a Laser Tracker, In: 2016 IEEE International Conference on Robotics and Automation (ICRA), 2016) pp. 4320–4327, doi: 10.1109/ICRA.2016.7487630 CrossRefGoogle Scholar
Nubiola, A. and Bonev, I. A., “Absolute calibration of an ABB IRB, 1600 robot using a laser tracker,” Robot. Comput. Integr. Manuf. 29(1), 236–245 (2013). doi: 10.1016/j.rcim.2012.06.004, http://www.sciencedirect.com/science/article/pii/S0736584512000816 CrossRefGoogle Scholar
Heisel, U., Richter, F. and Wurst, K.-H., “Thermal behaviour of industrial robots and possibilities for error compensation,” CIRP Ann. 46(1), 283–286 (1997). doi: 10.1016/S0007-8506(07)60826-9 CrossRefGoogle Scholar
Karan, B. and Vukobratović, M., “Calibration and accuracy of manipulation robot models,” Mech. Mach. Theory 29(3), 479–500 (1994). doi: 10.1016/0094-114X(94)90130-9 CrossRefGoogle Scholar
Poonyapak, P., Hayes, M. J. D. and McDill, J. M. J., “Temperature-Induced Deformation in a Mechanical System,” In: Proceedings of the 12th IFToMM World Congress, Besancon, France (2007) pp. 17–21.Google Scholar
Reinhart, G., Gräser, R.-G. and Klingel, R., “Qualification of standard industrial robots to cope with sophisticated assembly tasks,” CIRP Ann. 47(1), 1–4 (1998). doi: 10.1016/S0007-8506(07)62772-3, http://www.sciencedirect.com/science/article/pii/S0007850607627723 CrossRefGoogle Scholar
Leitner, M., Thermal effects and the consequences for repeatability of an industrial robot, CANCAM 01 (2001). http://faculty.mae.carleton.ca/John_Hayes/Papers/robrep.pdf Google Scholar
Gong, C., Yuan, J. and Ni, J., “Nongeometric error identification and compensation for robotic system by inverse calibration,” Int. J. Mach. Tool Manuf. 40(14), 2119–2137 (2000). doi: 10.1016/S0890-6955(00)00023-7 CrossRefGoogle Scholar
Eastwood, S. and Webb, P., “Compensation of thermal deformation of a hybrid parallel kinematic machine,” Robot. Comput. Integr. Manuf. 25(1), 81–90 (2009). doi: 10.1016/j.rcim.2007.10.001, https://www.sciencedirect.com/science/article/pii/S0736584507001032 CrossRefGoogle Scholar
Li, R. and Zhao, Y., “Dynamic error compensation for industrial robot based on thermal effect model,” Measurement 88, 113–120 (2016). doi: 10.1016/j.measurement.2016.02.038, https://www.sciencedirect.com/science/article/pii/S0263224116001159 CrossRefGoogle Scholar
Mohnke, C., Reinkober, S. and Uhlmann, E., “Constructive methods to reduce thermal influences on the accuracy of industrial robots,” Procedia Manuf. 33, 19–26 (2019). doi: 10.1016/j.promfg.2019.04.004, https://linkinghub.elsevier.com/retrieve/pii/S2351978919304792 CrossRefGoogle Scholar
Slamani, M. and Bonev, I., “Characterization and experimental evaluation of gear transmission errors in an industrial robot,” Ind. Robot. Int. J. 40(5), 441–449 (2013). doi: 10.1108/IR-07-2012-387 CrossRefGoogle Scholar
Slamani, M., Nubiola, A. and Bonev, I. A., “Modeling and assessment of the backlash error of an industrial robot,” Robotica 30(7), 1167–1175 (2012). doi: 10.1017/S0263574711001287, https://www.cambridge.org/core/product/, identifier/S0263574711001287/type/journal_article CrossRefGoogle Scholar
Ming, A., Kajitani, M., Kanamori, C. and Ishikawa, J., “Measurement of transmission error including backlash in angle transmission mechanisms for mechatronic systems,” JSME Int. J. Ser. C Mech. Syst. Mach. Elem. Manufact. 44(1), 196–202 (2001).Google Scholar
Vocetka, M., Huňady, R., Hagara, M., Bobovský, Z., Kot, T. and Krys, V., “Influence of the approach direction on the repeatability of an industrial robot,” Appl. Sci. 10(23), 8714 (2020).CrossRefGoogle Scholar
ISO 9283, Manipulating industrial robots — Performance criteria and related test methods, (1998). Available at: https://www.iso.org/obp/ui/#iso:std:iso:9283:ed-2:v1:en Google Scholar
API, Radian 3D Laser-tracker - Product specification, 2018).Google Scholar
Davidzon, M. I., “Newton’s law of cooling and its interpretation,” Int. J. Heat Mass Trans. 55(21), 5397–5402 (2012). doi: 10.1016/j.ijheatmasstransfer.2012.03.035, https://www.sciencedirect.com/science/article/pii/S0017931012001846 CrossRefGoogle Scholar
Denavit, J. and Hartenberg, R. S., “A kinematic notation for lower-pair mechanisms based on matrices,” ASME J. Appl. Mech. 22(2), 215–221 (1955).CrossRefGoogle Scholar
Hayati, S. and Mirmirani, M., “Improving the absolute positioning accuracy of robot manipulators,” J. Robot. Syst. 2(4), 397–413 (1985). doi: 10.1002/rob.4620020406, https://onlinelibrary.wiley.com/doi/abs/10.1002/rob.4620020406 CrossRefGoogle Scholar
ESA - Esmat, Stainless Steel AISI 316L (2022). Available at: http://esmat.esa.int/Services/Preferred_Lists/Materials_Lists/a63.htm Google Scholar
Dieter Kraft, A software package for sequential quadratic programming, 1988). Technical Report DFVLR-FB 88-28, Institut fuer Dynamik der Flugsysteme, Oberpfaffenhofen. Available at: http://degenerateconic.com/wp-content/uploads/2018/03/DFVLR_FB_88_28.pdf Google Scholar
Ferrucci, M., Leach, R. K., Giusca, C., Carmignato, S. and Dewulf, W., “Towards geometrical calibration of x-ray computed tomography systems—A review,” Meas. Sci. Technol. 26(9), 092003 (2015). doi: 10.1088/0957-0233/26/9/092003, https://iopscience.iop.org/article/10.1088/0957-0233/26/9/092003 CrossRefGoogle Scholar