Optimization of Surface Roughness When Turning Polyamide using ANN-IHSA Approach

  • Authors

    • Milos Madic Faculty of Mechanical Engineering, University of Niš
    • D. Markovi?
    • M. Radovanovi?
    https://doi.org/10.14419/ijet.v1i4.378

    Received date: August 14, 2012

    Accepted date: September 6, 2012

    Published date: September 10, 2012

  • Abstract

    This study presents an approach by coupling artificial neural network (ANN) and improved harmony search algorithm (IHSA) to determine the optimum cutting parameter settings for minimizing surface roughness when turning of polyamide material. An ANN model surface roughness was developed in terms of cutting speed, feed rate, depth of cut, and tool nose radius using the data from the turning experiment conducted according to Taguchi’s L27 orthogonal array. The optimal cutting parameter settings were determined by applying the IHSA to the developed ANN surface roughness model. The results show that the proposed optimization approach can be efficiently used for optimization of cutting parameter settings when turning polyamides. Although determining ANN and IHSA parameters is quite complex and problem dependent, it can be simplified by using Taguchi’s experimental design as in this study.

    Author Biography

    • Milos Madic, Faculty of Mechanical Engineering, University of Niš
      Department of Production Engineering, Research Assistant

  • References

    1. J. Z. Zhang, J. C. Chen, E. D. Kirby, Surface roughness optimization in an end-milling operation using the Taguchi design method, Journal of materials processing technology, Vol. 184, No. 1-3, (2007), pp. 233–239.
    2. F. Kahraman, A. Sagbas, An investigation of the effect of heat treatment on surface roughness in machining by using statistical analysis, Iranian Journal of Science & Technology, Transaction B: Engineering, Vol. 34, No. B5, (2010), pp. 591-595.
    3. V. N. Gaitonde, S. R. Karnik, M. Francisco, D. J. Paulo, Taguchi approach for achieving better machinability in unreinforced and reinforced polyamides, Journal of Reinforced Plastics and Composites, Vol. 27, (2008), pp. 909-924.
    4. V. G. Dhokia, S. Kumar, P. Vichare, S. T. Newman, R. D. Allen, Surface roughness prediction model for CNC machining of polypropylene, Proceedings of the Institution of Mechanical Engineers Part B-Journal of Engineering Manufacture Vol. 222, No. 2, (2008), pp. 137-153.
    5. F. Čuš, M. Milfelner, Ј. Balič, An intelligent system for monitoring and optimization of ball-end milling process, Journal of Materials Processing Technology, Vol. 175, No. 1-3, (2006), pp. 90–97.
    6. V. N. Gaitonde, F. R. Karnik, M. Francisco, D. J. Paulo, Modeling and analysis of machinability characteristics in PA6 and PA66 GF30 polyamides through artificial neural network, Journal of Thermoplastic Composite Materials, Vol. 23, No. 3, (2010), pp. 313-336.
    7. Z. W. Geem, J. H. Kim, G. V. Loganathan, A new heuristic optimization algorithm: harmony search, Simulation, Vol. 76, No. 2, (2001), pp. 60–68.
    8. O. Zarei, M. Fesanghary, B. Farshi, R. Jalili Saffar, M. R. Razfar, Optimization of multi-pass face-milling via harmony search algorithm, Journal of Materials Processing Technology, Vol. 209, No. 5, (2009), pp. 2386–2392.
    9. D. C. Montgomery, Design and analysis of experiments, New York: John Wiley & Sons Inc., (2001).
    10. M. S. Phadke, Quality engineering using robust design, New Jersey: AT&T Bells Laboratory/Prentice-Hall, (1989).
    11. V. N. Gaitonde, S. R. Karnik, M. Francisco, D. J. Paulo, Study on some aspects of machinability in unreinforced and reinforced polyamides, Journal of Composite Materials, Vol. 43, No. 7, (2009), pp. 725-739.
    12. U. Esme, A. Sagbas, F. Kahraman, Prediction of surface roughness in wire electrical discharge machining using design of experiments and neural networks, Iranian Journal of Science & Technology, Transaction B: Engineering, Vol. 33, No. B3, (2009), pp. 231-240.
    13. M. H. Beale, M. Hagan, H. Demuth, Neural network toolbox user’s guide. The Mathworks Inc., (2010).
    14. S. Sumathi, P. Surekha, Computational intelligence paradigms: theory and applications using MATLAB. Boca Raton: CRC Press, Taylor & Francis Group, (2010).
    15. M. Madić, M. Radovanović, Optimal selection of ANN training and architectural using Taguchi method: a case study, FME Transactions, Vol. 39, No. 2 (2011), pp. 79-86.
    16. I. Mukherjee, P. K. Ray, A review of optimization techniques in metal cutting processes, Computers & Industrial Engineering, Vol. 50, No. 1-2, (2006), pp. 15-34.
    17. D. Zou, L. Gao, J. Wu, S. Li, Y. Li, A novel global harmony search algorithm for reliability problems, Computers & Industrial Engineering, Vol. 58, No.2, (2010), pp. 307–316.
    18. M. Mahdavi, M. Fesanghary, E. Damangir, An improved harmony search algorithm for solving optimization problems, Applied Mathematics and Computation, Vol. 188, No. 2, (2007), pp. 1567-1579.
    19. M. Marković, M. Madić, G. Petrović, Assessing the performance of improved harmony search algorithm (IHSA) for the optimization of unconstrained functions using Taguchi experimental design, Scientific Research and Essays Vol. 7, No. 12, (2012) pp. 1312-1318.

  • Downloads

  • How to Cite

    Madic, M., Markovi?, D., & Radovanovi?, M. (2012). Optimization of Surface Roughness When Turning Polyamide using ANN-IHSA Approach. International Journal of Engineering and Technology, 1(4), 432-443. https://doi.org/10.14419/ijet.v1i4.378