Advertisement

Genetic Optimisation of Machine Tool Paths

  • M. K. A. Mohd Ariffin
  • N. D. Sims
  • K. Worden
Conference paper

Abstract

Many aircraft components are designed from monolithic structures to reduce manufacturing cost, reduce weight, and increase stiffness. To enable machining of such structures it is necessary to perform high-speed machining that can remove a large amount of material within a shorter time. However, the performance of high speed-machining operations is limited by the static and dynamic stiffness of the tool and part, which can cause problems such as regenerative chatter and push-off.

The tool path plays a key role in avoiding these problems as it helps to minimise the workpiece vibration during machining. This work aims to optimise the tool path by simulating the removal of material in a finite element environment, which is controlled by a Genetic Algorithm. To simulate the physical removal of material during machining, a finite element model is designed to represent a thin walled workpiece. The target was to reduce the deflection after each element was removed, according to the sequence suggested by the Genetic Algorithm.

As a first step, a cantilever beam is created, meshed and numbered. There are 6 elements to remove, representing the tool path sequence for a physical machining process. A Genetic Algorithm was used to find the element removal sequence that gave the greatest workpiece stiffness in the cutting region. It is concluded that tool path optimisation can be performed successfully by the proposed technique.

Keywords

Genetic Algorithm Tool Path Travelling Salesman Problem Chip Thickness Uncut Chip Thickness 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    J. Tlusty, “Manufacturing processes and equipment.” Upper Saddle River, NJ: Prentice Hall, 2000, pp. 559–604.Google Scholar
  2. 2.
    D. Montgomery and Y. Atlintas, “Mechanism of cutting force and surface generation in dynamic milling,” Journal of Engineering for Industry, vol. 113, pp. 160–168, 1991.CrossRefGoogle Scholar
  3. 3.
    J. Tlusty, “Dynamics of High-Speed Milling,” Journal of Engineering for Industry, pp. 59–67, 1986.Google Scholar
  4. 4.
    S. Smith and D. Dvorak, “Tool path strategies for high speed milling aluminum workpieces with thin webs,” Mechatronics, vol. 8, pp. 291–300, 1998.CrossRefGoogle Scholar
  5. 5.
    M. Monreal and C. A. Rodriguez, “Influence of tool path strategy on the cycle time of high-speed milling,” Computer-Aided Design, vol. 35, pp. 395–401, 2003.CrossRefGoogle Scholar
  6. 6.
    V. Tandon, H. El-Mounayri, and H. Kishawy, “NC end milling optimization using evolutionary computation,” International Journal of Machine Tools and Manufacture, vol. 42, pp. 595–605, 2002.CrossRefGoogle Scholar
  7. 7.
    S. S. Rao, “Mechanical vibrations.” Upper Saddle River, New Jersey: Pearson Education, 2004, pp. 236.Google Scholar
  8. 8.
    S. Smith and J. Tlusty, “An overview of modelling and simulation of the milling process,” Journal of Engineering for Industry, vol. 113, pp. 169–175, 1991.CrossRefGoogle Scholar
  9. 9.
    E. Goldberg David, Genetic algorithms in search, optimization, and machine learning. Reading: Addison-Wesley Pub. Co, 1989.MATHGoogle Scholar
  10. 10.
    J. E. Baker, “Reducing bias and inefficiency in the selection algorithm,” Proceedings of the second international conference on Genetic Algorithms, pp. 14–21, 1987.Google Scholar
  11. 11.
    Z. Michalewicz, “Genetic algorithms + data structures = evolution programs.” Berlin: Springer-Verlag, 1992, pp. 165–192.Google Scholar
  12. 12.
    M. Mitchell, An introduction to genetic algorithms. Cambridge, London: MIT Press, 1996.Google Scholar

Copyright information

© Springer-Verlag London 2004

Authors and Affiliations

  • M. K. A. Mohd Ariffin
    • 1
  • N. D. Sims
    • 1
  • K. Worden
    • 1
  1. 1.Department of Mechanical Engineering, Faculty of EngineeringUniversity of Sheffield S1 3JDUK

Personalised recommendations