An Investigation on the Effects of Optimum Forming Parameters in Hydromechanical Deep Drawing Process Using the Genetic Algorithm

Document Type: Research Paper

Authors

Department of Mechanical Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran.

Abstract

The present research work is concerned with the effects of optimum process variables in elevated temperature hydro-mechanical deep drawing of 5052 aluminum alloy. Punch-workpiece and die-workpiece friction coefficients together with the initial gap between the blank holder and matrix were considered as the process variables which, in optimization terminology, are called design parameters. Since both the maximum reduction in sheet thickness and the final product uniformity (thickness variation) are important in the hydro-mechanical deep drawing, they are selected as the objective functions for optimization. After conducting 27 finite-element simulations of the operation and validation of the numerical results, a neural network was trained and combined with the genetic algorithm to obtain the optimum design parameters. The outcomes of this investigation have shown that these optimized process variables simultaneously resulted in the best values for both the objective functions, in comparison with all the conducted finite-element analyses.

Keywords

Main Subjects


[1]           M. Koç, 2008, Hydroforming for advanced manufacturing, Elsevier,

[2]           S.-H. Zhang, J. Danckert, Development of hydro-mechanical deep drawing, Journal of Materials Processing Technology, Vol. 83, No. 1, pp. 14-25, 1998.

[3]           L. Lang, J. Danckert, K. B. Nielsen, Investigation into hydrodynamic deep drawing assisted by radial pressure: Part I. Experimental observations of the forming process of aluminum alloy, Journal of Materials Processing Technology, Vol. 148, No. 1, pp. 119-131, 2004.

[4]           R. A. Ayres, Alloying aluminum with magnesium for ductility at warm temperatures (25 to 250 C), Metallurgical Transactions A, Vol. 10, No. 7, pp. 849-854, 1979.

[5]           P. Groche, R. Huber, J. Dörr, D. Schmoeckel, Hydromechanical deep-drawing of aluminium-alloys at elevated temperatures, CIRP Annals-Manufacturing Technology, Vol. 51, No. 1, pp. 215-218, 2002.

[6]           K. Nakamura, Warm deep drawability with hydraulic counter pressure of 1050 aluminum sheets, Japan Institute of Light Metals, Journal, Vol. 47, No. 6, pp. 323-328, 1997.

[7]           H. Choi, M. Koç, J. Ni, A study on warm hydroforming of Al and Mg sheet materials: mechanism and proper temperature conditions, Journal of Manufacturing Science and Engineering, Vol. 130, No. 4, pp. 041007, 2008.

[8]           M. Hosseinpour, A. Gorji, M. Bakhshi, On the experimental and numerical study of formability of Aluminum sheet in warm hydroforming process, Modares Mechanical Engineering, Vol. 15, No. 2, 2015.

[9]           H. Gedikli, Ö. N. Cora, M. Koç, Comparative investigations on numerical modeling for warm hydroforming of AA5754-O aluminum sheet alloy, Materials & Design, Vol. 32, No. 5, pp. 2650-2662, 2011.

[10]         A. Hashemi, M. H. Gollo, S. H. SEYEDKASHI, Process window diagram of conical cups in hydrodynamic deep drawing assisted by radial pressure, Transactions of Nonferrous Metals Society of China, Vol. 25, No. 9, pp. 3064-3071, 2015.

[11]         R. K. Desu, S. K. Singh, A. K. Gupta, Comparative study of warm and hydromechanical deep drawing for low-carbon steel, The International Journal of Advanced Manufacturing Technology, Vol. 85, No. 1-4, pp. 661-672, 2016.

[12]         Q.-F. Chang, D.-Y. Li, Y.-H. Peng, X.-Q. Zeng, Experimental and numerical study of warm deep drawing of AZ31 magnesium alloy sheet, International Journal of Machine Tools and Manufacture, Vol. 47, No. 3, pp. 436-443, 2007.

[13]         S. Mahabunphachai, M. Koç, Investigations on forming of aluminum 5052 and 6061 sheet alloys at warm temperatures, Materials & Design (1980-2015), Vol. 31, No. 5, pp. 2422-2434, 2010.

 [14]         A. Ataee, E. Azarlu, Multi-objective Optimization of web profile of railway wheel using Bi-directional Evolutionary Structural Optimization, Journal of Computational Applied Mechanics, 2017.[15]         J. H. Holland, 1992, Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence, MIT press,

[16]         M. Sharififar, S. Akbari Mousavi, Numerical study and genetic algorithm optimization of hot extrusion process to produce rectangular waveguides, Journal of Computational Applied Mechanics, Vol. 47, No. 2, pp. 129-136, 2016.

[17]         H. Mohammadi, M. Sharififar, A. A. Ataee, Numerical and Experimental Analysis and Optimization of Process Parameters of AA1050 Incremental Sheet Forming, Journal of Computational Applied Mechanics, Vol. 45, No. 1, pp. 35-45, 2014.

[18]         K. Deb, 2001, Multi-objective optimization using evolutionary algorithms, John Wiley & Sons,

[19]         Y. Aue-U-Lan, G. Ngaile, T. Altan, Optimizing tube hydroforming using process simulation and experimental verification, Journal of Materials Processing Technology, Vol. 146, No. 1, pp. 137-143, 2004.