Chinese Journal of Mechanical Engineering >

2022 , Vol. 35 >Issue 5: 115 - 115

DOI: https://doi.org/10.1186/s10033-022-00778-1

Variable Stiffness Identification and Configuration Optimization of Industrial Robots for Machining Tasks

  • Jiachen Jiao ,
  • Wei Tian ,
  • Lin Zhang ,
  • Bo Li ,
  • Junshan Hu ,
  • Yufei Li ,
  • Dawei Li ,
  • Jianlong Zhang
Expand
  • 1. Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China;
    2. Beijing Institute of Space Launch Technology, Beijing, 100076, China;
    3. Beijing Institute of Mechanical Equipment, Beijing, 100854, China

Received date: 2020-07-14

  Revised date: 2021-04-15

  Online published: 2023-04-24

Supported by

Supported by National Natural Science Foundation of China (Grant No. 51875287), National Defense Basic Scientific Research Program of China (Grant No. JCKY2018605C002) and Jiangsu Provincial Natural Science Foundation of China (Grant No. BK20190417).

Fold

Abstract

Industrial robots are increasingly being used in machining tasks because of their high flexibility and intelligence. However, the low structural stiffness of a robot significantly affects its positional accuracy and the machining quality of its operation equipment. Studying robot stiffness characteristics and optimization methods is an effective method of improving the stiffness performance of a robot. Accordingly, aiming at the poor accuracy of stiffness modeling caused by approximating the stiffness of each joint as a constant, a variable stiffness identification method is proposed based on space gridding. Subsequently, a task-oriented axial stiffness evaluation index is proposed to quantitatively assess the stiffness performance in the machining direction. In addition, by analyzing the redundant kinematic characteristics of the robot machining system, a configuration optimization method is further developed to maximize the index. For numerous points or trajectory-processing tasks, a configuration smoothing strategy is proposed to rapidly acquire optimized configurations. Finally, experiments on a KR500 robot were conducted to verify the feasibility and validity of the proposed stiffness identification and configuration optimization methods.

Key words： Industrial robot; Space gridding; Variable stiffness identification; Configuration optimization; Smooth processing

Cite this article

Jiachen Jiao , Wei Tian , Lin Zhang , Bo Li , Junshan Hu , Yufei Li , Dawei Li , Jianlong Zhang . Variable Stiffness Identification and Configuration Optimization of Industrial Robots for Machining Tasks[J]. Chinese Journal of Mechanical Engineering, 2022 , 35(5) : 115 -115 . DOI: 10.1186/s10033-022-00778-1

References

[1] C Lehmann, M Pelliciari, M Drust, et al. Machining with industrial robots: the COMET project approach. International Workshop on Robotics in Smart Manufacturing, Porto, Portugal, June 26-28, 2013: 27-36.
[2] S Garnier, K Subrin, K Waiyagan. Modelling of robotic drilling. Procedia CIRP, 2017, 58: 416-421.
[3] D Song, Z Kan, L Wenhe. Stability of lateral vibration in robotic rotary ultrasonic drilling. International Journal of Mechanical Sciences, 2018, 145: 346-352.
[4] Y Guo, H Dong, G Wang, et al. Vibration analysis and suppression in robotic boring process. International Journal of Machine Tools and Manufacture, 2016, 101: 102-110.
[5] E Abele, S Rothenbücher, M Weigold. Cartesian compliance model for industrial robots using virtual joints. Production Engineering, 2008, 2(3): 339-343.
[6] E Abele, M Weigold, S Rothenbücher. Modeling and identification of an industrial robot for machining applications. CIRP Annals - Manufacturing Technology, 2007, 56(1): 387-390.
[7] C Dumas, S Caro, M Cherif, et al. Joint stiffness identification of industrial serial robots. Robotica, 2012, 30(4): 649-659.
[8] J Zhou, H N Nguyen, H J Kang. Simultaneous identification of joint compliance and kinematic parameters of industrial robots. International Journal of Precision Engineering and Manufacturing, 2014, 15(11): 2257-2264.
[9] A Klimchik, B Furet, S Caro, et al. Identification of the manipulator stiffness model parameters in industrial environment. Mechanism & Machine Theory, 2015, 90: 1-22.
[10] G Alici, B Shirinzadeh. Enhanced stiffness modeling, identification and characterization for robot manipulators. IEEE Transactions on Robotics, 2005, 21(4): 554-564.
[11] L Sabourin, K Subrin, R Cousturier, et al. Redundancy-based optimization approach to optimize robotic cell behaviour: Application to robotic machining. Industrial Robot, 2015, 42(2): 167-178.
[12] G Xiong, Y Ding, L M Zhu. Stiffness-based pose optimization of an industrial robot for five-axis milling. Robotics and Computer-Integrated Manufacturing, 2019, 55: 19-28.
[13] Y Guo, H Dong, Y Ke. Stiffness-oriented posture optimization in robotic machining applications. Robotics and Computer-Integrated Manufacturing, 2015, 35: 69-76.
[14] U Schneider, M Drust, M Ansaloni, et al. Improving robotic machining accuracy through experimental error investigation and modular compensation. International Journal of Advanced Manufacturing Technology, 2016, 85(1-4): 3-15.
[15] A Klimchik, Y Wu, C Dumas, et al. Identification of geometrical and elastostatic parameters of heavy industrial robots. The IEEE International Conference on Robotics and Automation (ICRA), 2013 May 6-10, Karlsruhe, Germany. New York: IEEE, 2013: 3707-3714.
[16] W Tian, D Mei, P Li, et al. Determination of optimal samples for robot calibration based on error similarity. Chinese Journal of Aeronautics, 2015, 28(3): 946-953.
[17] K Yang, W Yang, G Cheng, et al. A new methodology for joint stiffness identification of heavy duty industrial robots with the counterbalancing system. Robotics and Computer-Integrated Manufacturing, 2018, 53: 58-71.
[18] Y Lin, H Zhao, H Ding. Posture optimization methodology of 6R industrial robots for machining using performance evaluation indexes. Robotics and Computer-Integrated Manufacturing, 2017, 48: 59-72.
[19] Y Zeng, W Tian, D Li, et al. An error-similarity-based robot positional accuracy improvement method for a robotic drilling and riveting system. International Journal of Advanced Manufacturing Technology, 2017, 88(9-12): 2745-2755.
[20] Y Zeng, W Tian, W Liao. Positional error similarity analysis for error compensation of industrial robots. Robotics and Computer-Integrated Manufacturing, 2016, 42: 113-120.
[21] J Jiao, W Tian, L Zhang, et al. Variable parameters stiffness identification and modeling for positional compensation of industrial robots. International Conference on Control Engineering and Artificial Intelligence, Singapore, January 17-19, 2020: 401-410.
[22] C Dumas, S Caro, S Gamier, et al. Joint stiffness identification of six-revolute industrial serial robots. Robotics and Computer-Integrated Manufacturing, 2011, 27: 881-888.
[23] Y Bu, W Liao, W Tian, et al. Modeling and experimental investigation of Cartesian compliance characterization for drilling robot. International Journal of Advanced Manufacturing Technology, 2017, 91(9-12): 3253-3264.
[24] Y Bu, W Liao, W Tian, et al. Stiffness analysis and optimization in robotic drilling application. Precision Engineering, 2017, 49: 388-400.
[25] M Cordes, W Hintze. Offline simulation of path deviation due to joint compliance and hysteresis for robot machining. International Journal of Advanced Manufacturing Technology, 2017, 90: 1075-1083.
[26] G Wang, H Dong, Y Guo, et al. Dynamic cutting force modeling and experimental study of industrial robotic boring. International Journal of Advanced Manufacturing Technology, 2016, 86(1-4): 179-190.
[27] W W Qu, P H Hou, G J Yang, et al. Research on the stiffness performance for robot machining system. Acta Aeronautica ET Astronautica Sinica, 2013, 34(11): 2823-2832.
[28] A Ajoudani, G T Nikos, A Bicchi. On the role of robot configuration in Cartesian stiffness control. IEEE International Conference on Robotics and Automation (ICRA), Washington, USA, May 26-30, 2015: 1010-1016.
[29] J Andres, L Gracia, J Tornero. Implementation and testing of a CAM postprocessor for an industrial redundant workcell with evaluation of several fuzzified Redundancy Resolution Schemes. Robotics and Computer-Integrated Manufacturing, 2012, 28(2): 265-274.
[30] H Xie, W Li, Z Yin. Posture optimization based on both joint parameter error and stiffness for robotic milling. International Conference on Intelligent Robotics and Applications, Newcastle, Australia, Aug 9-11, 2018: 277-286.
Options
Outlines

/

〈 〉

　 Copyright © Chinese Journal of Mechanical Engineering, All Rights Reserved.
Powered by Beijing Magtech Co. Ltd,.