Integrated Environment for Design and Analysis of Parallel Robotic Machine

  • Dan ZhangEmail author


Because of the recent trend toward high-speed machining HSM, there is a demand to develop parallel kinematic machine with high dynamic performance, improved stiffness, and reduced moving mass [2, 11, 93, 148]. However, as researchers at Giddings and Lewis have indicated, full integration of standard automation components, CAD, and a user interface are required before making its parallel kinematic machine readily available for the general market. A virtual environment that can be used for PKM design, analysis, and simulation is urgently demanded. Several efforts have been done on this topic. Pritschow [122] proposed a systematic methodology for the design of different PKM topologies. Merlet [106] developed the software for the optimal design of a specific PKM class – Stewart platform-based mechanisms. Jin and Yang [79, 80] proposed a method for topology synthesis and analysis of parallel manipulators. Huang et al. [75] made some efforts on conceptual design of 3dof translational parallel mechanisms. Nevertheless, there is no complete virtual system existing for PKM design and analyze from the literature.

With the objective of developing a practical methodology and related virtual environment for PKM analysis and design, several activities have been conducted at Integrated Manufacturing Technologies Institute of National Research Council of Canada. PKM is a key component of reconfigurable manufacturing systems in different industrial sectors. It is very important for PKM designers to design and analysis the potential PKM with an integrated virtual environment before fabrication. The virtual environment is used for modeling, simulation, planning, and control of the proposed PKM.


Virtual Environment Scene Graph Parallel Kinematic Machine Reconfigurable Manufacturing System Parallel Machine Tool 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 2.
    Ares J, Brazales A, Busturia JM (2001) Tuning and validation of the motion platform washout filter parameters for a driving simulator. In: Driving simulation conference, pp 295–304Google Scholar
  2. 3.
    Gosselin CM, Guillot M (1991) The synthesis of manipulators with prescribed workspace. 123 ASME J Mech Des 113:451–455Google Scholar
  3. 4.
    Michalewicz Z (1994) Genetic algorithms + data structures = evolution programs. AI Series. 223 Springer, New YorkGoogle Scholar
  4. 5.
    Holland JH (1975) Adaptation in natural and artificial systems. The University of Michigan Press, Ann Arbor, MIGoogle Scholar
  5. 11.
    Ares J, Brazales A, Busturia JM (2001) Tuning and validation of the motion platform washout filter parameters for a driving simulator. In: Driving simulation conference, pp 295–304Google Scholar
  6. 17.
    Barrilleaux J (2001) 3D user interfaces with java 3D. Manning Publications, Greenwich, CTGoogle Scholar
  7. 75.
    Huang T, Li Z, Li M, Chetwynd D, Gosselin CM (2004) Conceptual design and dimensional synthesis of a novel 2-dof translational parallel robot for pick-and-place operations. ASME J Mech Des 126:449–455CrossRefGoogle Scholar
  8. 79.
    Jin Q, Yang TL (2004) Synthesis and analysis of a group of 3-degree-of-freedom partially decoupled parallel manipulators. ASME J Mech Des 126:301–306CrossRefGoogle Scholar
  9. 80.
    Jin Q, Yang TL (2004) Theory for topology synthesis of parallel manipulators and its application to three-dimension-translation parallel manipulators. ASME J Mech Des 126:625–639CrossRefGoogle Scholar
  10. 93.
    Lauffer JP, Hinnerichs TD, Kuo CP, Wada B, Ewaldz D, Winfough B, Shankar N (1996) Milling machine for the 21st century – goals, approach, characterization and modeling. In: Proceedings of SPIE – the international society for optical engineering smart structures and materials 1996: industrial and commercial applications of smart structures technologies, San Diego, vol 2721, pp 326–340Google Scholar
  11. 103.
    Masory O, Wang J (1995) Workspace evaluation of stewart platforms. Adv Robot 9(4): 443–461CrossRefGoogle Scholar
  12. 106.
    Merlet JP (2000) Parallel robots. Kluwer, New YorkzbMATHGoogle Scholar
  13. 122.
    Pritschow G, Wurst KH (1997) Systematic design of hexapods and other parallel link systems. CIRP Ann Manuf Technol 46(1):291–295CrossRefGoogle Scholar
  14. 136.
    Sowizral H, Rushforth K, Deering (2001) The java 3D API specification. Addison-Wesley, Reading, MAGoogle Scholar
  15. 142.
    Thomson WT (1993) Theory of vibration with applications. Prentice Hall, Upper Saddle River, NJGoogle Scholar
  16. 148.
    Valenti M (1995) Machine tools get smarter. ASME Mech Eng 117:70–75Google Scholar
  17. 150.
    Wang L, Wong B, Shen W, Lang S (2002) Java 3d enabled cyber workspace. Commun ACM 45:45–49Google Scholar
  18. 159.
    Xi F, Zhang D, Xu Z, Mechefske C (2003) Comparative study of tripod-type machine tools. Int J Mach Tools Manuf 43(7):721–730CrossRefGoogle Scholar
  19. 170.
    Zhang D (2000) Kinetostatic analysis and optimization of parallel and hybrid architectures for machine tools. Laval University, CanadaGoogle Scholar
  20. 174.
    Zhang D, Han W, Lang S (2003) On the kinetostatic analysis and dynamic modeling of a 3dof parallel kinematic machine. In: CIRP 2nd international conference on agile, reconfigurable manufacturingGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Faculty of Engineering and Applied ScienceUniversity of Ontario Institute of Technology (UOIT)OshawaCanada

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