Journal of Intelligent and Robotic Systems

, Volume 17, Issue 1, pp 81–99 | Cite as

Dynamic simulation and neural network compliance control of an intelligent forging center

  • K. W. Lilly
  • A. S. Melligeri


Automation of forging processes is important for both safety and efficiency in today's advanced manufacturing operations. This work supports the development of an Intelligent Open Die Forging System which will integrate state-of-the-art modelling techniques, automatic die selection and sequencing, full system dynamic simulation, automatic machine programming and coordination, and sensor-based process control to enable the production of more general and complex workpiece geometries than are achievable using current forging methods. Effective automation of this open die forging system requires the coordination and control of the major system components: press, robot, and furnace. In particular, forces exerted on the robot through its manipulation of the workpiece during forging must be minimized to avoid damage to the manipulator mechanism. In this paper, the application of neural networks for compliance control of the forging robot to minimize these forces is investigated. Effectiveness of the neural network-based compliance control module is evaluated through a full dynamic system simulation, which will later form a central part of the complete Intelligent Forging System. Dynamic simulation of the robot is achieved using an efficient O(N) recursive algorithm, while material flow of the workpiece is modeled with a finite element approach. Simulation and timing results for the complete processing system for a specific open die forging example are presented.

Key words

forging automation compliance control neural networks dynamic simulation finite element models recursive algorithms intelligent manufacturing 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Ang, C. S.: Implementation of the virtual environment force control concept an industrial robot, MS Thesis, Department of Mechanical Engineering, Penn State University, 1994.Google Scholar
  2. 2.
    Appleton, E., Higginbotham, W. B. and Law, D.: Open die forging with an industrial robot, The Industrial Robot (December 1979), 191–194.Google Scholar
  3. 3.
    Appleton, E.: Open-die forging operations by industrial robot, Robotic Technology 15 (1983), 160–168.Google Scholar
  4. 4.
    Asada, H.: Teaching and learning of compliance using neural nets: Representation and generation of nonlinear compliance, in Proc. 1990 IEEE Int. Conf. Robotics and Automation, 1990, pp. 1237–1244.Google Scholar
  5. 5.
    Brandl, H., Johanni, R. and Otter, M.: A very efficient algorithm for the simulation of robots and similar multibody systems without inversion of the mass matrix, Proc. IFAC/IFIP/IMACS Int. Symp. Theory of Robots, 1986.Google Scholar
  6. 6.
    Cha, D. H.: Compliance control of telerobot systems using a recurrent neural network, in Proc. Japan-USA Symp. Flexible Automation, Part 1, 1992, pp. 271–274.Google Scholar
  7. 7.
    Connoly, T. H.: Neural network hybrid position/force control, in Proc. 1993 Int. Conf. Intelligent Robots and Systems, 1993, pp. 240–244.Google Scholar
  8. 8.
    Featherstone, R.: Robot Dynamics Algorithms, Kluwer, Boston, 1987.Google Scholar
  9. 9.
    FIA, Open Die Forging Manual, 3rd edn, Forging Industry Association, Cleveland, OH, 1982.Google Scholar
  10. 10.
    Funahashi, K. I.: On the approximate realization of continuous mappings by neural networks, Neural Networks 2 (1989), 183–192.Google Scholar
  11. 11.
    Han, C. S., Gandhi, R. V. and Srinivasan, R.: Optimum design of forging die shapes using nonlinear finite element analysis, AIAA J. 31(4) (1993), 774–781.Google Scholar
  12. 12.
    Her, M. G.: Automated robotic deburring of parts using compliance control, ASME J. Dynamic Systems, Measurement, and Control 113(1) (1991), 60–66.Google Scholar
  13. 13.
    Kazerooni, H.: Compliance control and stability analysis of cooperating robot manipulators, Robotica 7(3) (1989), 191–198.Google Scholar
  14. 14.
    Kobayashi, S., Oh, S. and Altan, T.: Metal Forming and the Finite Element Method, Oxford University Press, New York, 1989.Google Scholar
  15. 15.
    Kwan, C. M.: Robust force/motion control of constrained robots using neural networks, in Proc. IEEE Conf. Decision and Control, Vol. 2, 1994, pp. 1862–1867.Google Scholar
  16. 16.
    Lee, C. H. and Kobayashi, S.: New solution to rigid plastic deformation problems using a matrix method, ASME Trans. J. Engrg. Industry 95 (1973), 865–873.Google Scholar
  17. 17.
    Lilly, K. W.: Efficient Dynamic Simulation of Robotic Mechanisms, Kluwer, Boston, 1993.Google Scholar
  18. 18.
    Lilly K. W. and Jablokow A. G.: Intelligent open die forging, Int. J. Industrial Engrg. 2(3) (1995).Google Scholar
  19. 19.
    Lilly, K. W. and Melligeri, A. S.: Efficient dynamic simulation of an integrated robot/forge near net-shape processing center, in R. P.Judd and N. A.Kheir (eds), Intelligent Manufacturing Systems, Pergamon, New York, 1992, pp. 347–351.Google Scholar
  20. 20.
    Lilly, K. W. and Orin, D. E.: Efficient dynamic simulation of a single closed-chain manipulator, in Proc. 1991 IEEE Int. Conf. Robotics and Automation, Vol. 1, 1991, pp. 210–215.Google Scholar
  21. 21.
    Maekawa, A.: Compliance control of an articulted manipulator, JSME Int. J. Ser. C: Dynamics, Control, Robotics, Design and Manufacturing 37(1) (1994), 155–161.Google Scholar
  22. 22.
    Melligeri, A. S. and Lilly, K. W.: Application of neural networks in compliance control of an integrated robot/forge processing center, in R.Sodhi (ed.), Advances in Manufacturing Systems: Design, Modeling and Analysis, Elsevier, New York, 1993, pp. 445–450.Google Scholar
  23. 23.
    Orin, D. E. and McGhee, R. B.: Dynamic computer simulation of robotic mechanisms, Int. J. Robotics Res. 7(5) (1981), 32–47.Google Scholar
  24. 24.
    Raibert, M. and Craig, J.: Hybrid position/force control of manipulators, ASME J. Dynamic Systems, Measurement, and Control 102(2) (1981), 126–133.Google Scholar
  25. 25.
    Roberson, R. E. and Schwertassek, R. F., Introduction to the Dynamics of Multibody Systems, Springer, Berlin, 1987.Google Scholar
  26. 26.
    Rodiguez, G., Jain, A. and Kreutz-Delgado, K.: A spatial operator algebra for manipulator modeling and control, Int. J. Robotics Res. 10 (1991), 371–381.Google Scholar
  27. 27.
    Rumelhart, D. E. and McClelland, J. L.: Parallel Distributed Processing, MIT Press, 1986.Google Scholar
  28. 28.
    Venkataraman, S. T., Gulati, S., Barhen, J. and Toomarian, N.: Parameter learning and compliance control using neural networks, in G. A.Bekey and K. Y.Goldberg (eds), Neural Networks in Robotics, Kluwer, Boston, 1993, pp. 193–216.Google Scholar
  29. 29.
    Vitscheff, V.: A programmable manipulator for closed die forging, Proc. 9th Int. Drop Forging Convention, Kyoto, Japan, 1977.Google Scholar
  30. 30.
    Waibel, B. J.: Theory and experiments on the stability of robot compliance control, IEEE Trans. Robotics and Automation 7(1) (1991), 95–104.Google Scholar
  31. 31.
    Walker, M. W. and Orin, D. E.: Efficient dynamic computer simulation of robotic mechanisms, ASME J. Dynamic Systems, Measurement, and Control 104 (1982), 205–211.Google Scholar
  32. 32.
    Wasserman, P. D., Neural Computing, Van Nostrand/Reinhold, New York, 1989.Google Scholar
  33. 33.
    Westmeyer, W. B.: Modernization speeds open die operation, Forging, Winter Issue (1991), 30–34.Google Scholar
  34. 34.
    Wright, J. R.: Investing in Excellence, Forging, Winter Issue (1991), 16–19.Google Scholar

Copyright information

© Kluwer Academic Publishers 1996

Authors and Affiliations

  • K. W. Lilly
    • 1
  • A. S. Melligeri
    • 2
  1. 1.Department of Mechanical EngineeringThe Pennsylvania State UniversityUniversity ParkU.S.A.
  2. 2.StatDesign, Inc.DearbornU.S.A.

Personalised recommendations