Optimization of flow forming process parameters of al-8014 using genetic algorithm

  • Authors

    • T Mahender CMR Institute of Technology
    • Bellam Venkatesh CMR Institute of Technology
    2018-06-01
    https://doi.org/10.14419/ijet.v7i2.10852
  • Flow Forming, Surface Roughness, Optimization, RSM, Genetic Algorithm.

  • Flow forming is an advanced version of the metal spinning process in which the metal is formed into the desired shape without formation of a chip. There are many flow forming process parameters that will influence the surface roughness of the material. In the current study, three process parameters are considered in the flow forming of AA8014. The process parameters are speed of the mandrel, longitudinal feed and the flow rate of the coolant and the response is surface roughness. Design of experiments based response surface method (RSM) is used to study the effect of process parameters on the response. A quadratic mathematical model was developed by RSM is used for optimization of surface roughness by using an evolutionary technique Genetic Algorithm. The surface roughness obtained at optimum process parameters from Genetic Algorithm is in good agreement with the experimental results.

     

     

  • References

    1. [1] M. J. Davidson, K. Balasubramanian, and G. R. N. Tagore, “Experimental investigation on flow-forming of AA6061 alloy-A Taguchi approach,†J. Mater. Process. Technol., vol. 200, no. 1–3, pp. 283–287, 2008. https://doi.org/10.1016/j.jmatprotec.2007.09.026.

      [2] M. Joseph Davidson, K. Balasubramanian, and G. R. N. Tagore, “Surface roughness prediction of flow-formed AA6061 alloy by design of experiments,†J. Mater. Process. Technol., vol. 202, no. 1–3, pp. 41–46, 2008. https://doi.org/10.1016/j.jmatprotec.2007.08.065.

      [3] C. Paper and A. Kumar, “Processing of Seamless Tubes of Aluminum Alloy Aa2219 by Flow Forming,†no. December, 2009.

      [4] G. Venkateshwarlu, K. R. Kumar, and T. A. Janardhanreddy, “Experimental Study of Flow forming Process Parameters on Thickness variation of Aluminum Alloy AA6061 Tubes,†vol. 2, no. 10, pp. 33–40, 2016.

      [5] M. Srinivasulu, M. Komaraiah, and C. S. K. P. Rao, “Experimental studies on the characteristics of AA6082 flow formed tubes,†J. Mech. Eng. Res., vol. 4, no. October, pp. 192–198, 2012.

      [6] X. Liu, C. Liu, L. Zhao, H. Huang, X. Zhao, and X. Sun, “The finite element method appling in the flow forming,†CIS 2009 - 2009 Int. Conf. Comput. Intell. Secur, vol. 1, pp. 556–558, 2009. https://doi.org/10.1109/CIS.2009.202.

      [7] H. R. Molladavoudi and F. Djavanroodi, “Experimental study of thickness reduction effects on mechanical properties and spinning accuracy of aluminum 7075-O, during flow forming,†Int. J. Adv. Manuf. Technol., vol. 52, no. 9–12, pp. 949–957, 2011. https://doi.org/10.1007/s00170-010-2782-4.

      [8] K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II,†IEEE Trans. Evol. Comput, vol. 6, no. 2, pp. 182–197, 2002. https://doi.org/10.1109/4235.996017.

      [9] M. S. Mohebbi and A. Akbarzadeh, “Experimental study and FEM analysis of redundant strains in flow forming of tubes,†J. Mater. Process. Technol., vol. 210, no. 2, pp. 389–395, 2010. https://doi.org/10.1016/j.jmatprotec.2009.09.028.

      [10] K. Deb, Optimization for engineering design:Algorithms and examples. Prentice Hall of India, 2005.

      [11] O. F. Mechanical, “Ijmet © I a E M E a Genetic Algorithm Approach To the Optimization of,†pp. 459–470, 2012.

      [12] D. Marini, D. Cunningham, P. Xirouchakis, and J. R. Corney, “Flow forming: A review of research methodologies, prediction models and their applications,†Int. J. Mech. Eng. Technol., vol. 7, no. 5, pp. 285–315, 2016.

      [13] M. Sivanandini, S. S. Dhami, and B. S. Pabla, “Flow Forming Of Tubes-A Review,†Int. J., vol. 3, no. 5, pp. 1–11, 2012.

      [14] D. Venkatesan, K. Kannan, and R. Saravanan, “A genetic algorithm-based artificial neural network model for the optimization of machining processes,†Neural Comput. Appl., vol. 18, no. 2, pp. 135–140, 2009. https://doi.org/10.1007/s00521-007-0166-y.

      [15] G. H. Gowd and A. G. Krishna, “Empirical Modeling of Bead Geometry and Optimization in Laser Beam Welding,†vol. 4, no. III, pp. 427–454, 2011.

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    Mahender, T., & Venkatesh, B. (2018). Optimization of flow forming process parameters of al-8014 using genetic algorithm. International Journal of Engineering & Technology, 7(2), 868-873. https://doi.org/10.14419/ijet.v7i2.10852