Skip to main navigation menu Skip to main content Skip to site footer

Articles

Vol. 3 No. 1 (2016)

Industrial Robot Trajectory Stiffness Mapping for Hybrid Manufacturing Process

DOI
https://doi.org/10.15377/2409-9694.2016.03.01.4
Submitted
August 10, 2016
Published
10.08.2016

Abstract

The application of using industrial robots in hybrid manufacturing is promising, but the heavy external load applied on robot system, including the weight of deposition extruder or the cutting force from machining process, affects the operation accuracy significantly. This paper proposed a new method for helping robot to find the best position and orientation to perform heavy duty tasks based on the current system stiffness. By analyzing the robot kinematic and stiffness matrix properties of robot, a new evaluation formulation has been established for mapping the trajectory¢‚¬„¢s stiffness within the robot¢‚¬„¢s working volumetric. The influence of different position and orientation for hybrid manufacturing working path in different scale has been discussed. Finally, a visualized evaluation map can be obtained to describe the stiffness difference of a robotic deposition working path at different positions and orientations. The method is important for improving the operation performance of robot system with current stiffness capability.

References

  1. Zha and Xuan F. Optimal pose trajectory planning for robot manipulators. Mechanism and Machine Theory 2002; 37(10): 1063-1086. http://dx.doi.org/10.1016/S0094-114X(02)00053-8
  2. Kim, Taejung, and Sanjay E. Sarma. Toolpath generation along directions of maximum kinematic performance; a first cut at machine-optimal paths. Computer-Aided Design 2002; 34.6: 453-468. http://dx.doi.org/10.1016/S0010-4485(01)00116-6
  3. Schneider, Ulrich, et al. Combining holistic programming with kinematic parameter optimisation for robot machining. ISR/Robotik 2014; 41st International Symposium on Robotics; Proceedings of VDE 2014.
  4. Urbanic RJ, Hedrick R and Ana M. Djuric. A Linkage Based Solution Approach for Determining 6 Axis Serial Robotic Travel Path Feasibility. SAE International Journal of Materials and Manufacturing 2016; 0336: 444-456.
  5. Matsuoka, Shin-ichi, et al. High-speed end milling of an articulated robot and its characteristics. Journal of materials processing technology 1999; 95.1: 83-89.
  6. Pan, Zengxi, et al. Chatter analysis of robotic machining process. Journal of materials processing technology 2006; 173.3: 301-309. http://dx.doi.org/10.1016/j.jmatprotec.2005.11.033
  7. Wang, Guifeng, et al. Dynamic cutting force modeling and experimental study of industrial robotic boring. The International Journal of Advanced Manufacturing Technology 2015: 1-12.
  8. Mekaouche, Adel, Frédéric Chapelle and Xavier Balandraud. FEM-based generation of stiffness maps. IEEE Transactions on Robotics 2015; 31.1: 217-222. http://dx.doi.org/10.1109/TRO.2015.2392351
  9. Zhang, Pu, Zhenqiang Yao and Zhengchun Du. Global performance index system for kinematic optimization of robotic mechanism. Journal of Mechanical Design 2014; 136.3: 031001.
  10. Nawratil Georg. New performance indices for 6R robots. Mechanism and machine theory 2007; 42.11: 1499-1511. http://dx.doi.org/10.1016/j.mechmachtheory.2006.12.007
  11. Olds and Kevin C. Global Indices for kinematic and force transmission performance in parallel robots. IEEE Transactions on Robotics 2015; 31.2: 494-500. http://dx.doi.org/10.1109/TRO.2015.2398632
  12. Mansouri I and Ouali M. A new homogeneous manipulability measure of robot manipulators, based on power concept. Mechatronics 2009; 19.6: 927-944. http://dx.doi.org/10.1016/j.mechatronics.2009.06.008
  13. Chen, Yonghua and Fenghua Dong. Robot machining: recent development and future research issues. The International Journal of Advanced Manufacturing Technology 2013; 66.9-12: 1489-1497.
  14. Guérin, David, et al. Optimal measurement pose selection for joint stiffness identification of an industrial robot mounted on a rail. 2014 IEEE/ASME International Conference on Advanced Intelligent Mechatronics. IEEE, 2014. http://dx.doi.org/10.1109/AIM.2014.6878332
  15. Klimchik, Alexandr, et al. Accuracy improvement of robotbased milling using an enhanced manipulator model. Advances on Theory and Practice of Robots and Manipulators. Springer International Publishing 2014; 73-81.
  16. Abele E, Weigold M and Rothenbücher S. Modeling and identification of an industrial robot for machining applications. CIRP Annals-Manufacturing Technology 2007; 56.1: 387-390. http://dx.doi.org/10.1016/j.cirp.2007.05.090
  17. Moosavian, Amin and Fengfeng Jeff Xi. Design and analysis of reconfigurable parallel robots with enhanced stiffness. Mechanism and Machine Theory 2014; 77: 92-110. http://dx.doi.org/10.1016/j.mechmachtheory.2014.02.005
  18. Robin, Vincent, Laurent Sabourin and Grigore Gogu. Optimization of a robotized cell with redundant architecture. Robotics and Computer-Integrated Manufacturing 2011; 27.1: 13-21. http://dx.doi.org/10.1016/j.rcim.2010.06.010
  19. Pitt E. Bryn, Nabil Simaan and Eric J. Barth. An Investigation of Stiffness Modulation Limits in a Pneumatically Actuated Parallel Robot with Actuation Redundancy. ASME/BATH 2015 Symposium on Fluid Power and Motion Control. American Society of Mechanical Engineers 2015.
  20. Gosselin and Clement. Stiffness mapping for parallel manipulators. Robotics and Automation, IEEE Transactions on 1990; 6.3: 377-382.
  21. Majou, Félix, et al. Parametric stiffness analysis of the Orthoglide. Mechanism and Machine Theory 2007; 42.3: 296-311. http://dx.doi.org/10.1016/j.mechmachtheory.2006.03.018
  22. Ruggiu and Maurizio. Cartesian stiffness matrix mapping of a translational parallel mechanism with elastic joints. International Journal of Advanced Robotic Systems 2012; 9.
  23. Pinto, Charles, et al. A methodology for static stiffness mapping in lower mobility parallel manipulators with decoupled motions. Robotica 2010; 28.05: 719-735. http://dx.doi.org/10.1017/S0263574709990403