Identification of Optimum Parameters of Deep Drawing of a Cylindrical Workpiece using Neural Network and Genetic Algorithm

Intelligent deep-drawing is an instrumental research field in sheet metal forming. A set of 28 different experimental data have been employed in this paper, investigating the roles of die radius, punch radius, friction coefficients and drawing ratios for axisymmetric workpieces deep drawing. This paper focuses an evolutionary neural network, specifically, error back propagation in collaboration with genetic algorithm. The neural network encompasses a number of different functional nodes defined through the established principles. The input parameters, i.e., punch radii, die radii, friction coefficients and drawing ratios are set to the network; thereafter, the material outputs at two critical points are accurately calculated. The output of the network is used to establish the best parameters leading to the most uniform thickness in the product via the genetic algorithm. This research achieved satisfactory results based on demonstration of neural networks.

• D. Singh
• R. Yousefi
• M. Boroushaki

• Deep-drawing
• Neural network
• Genetic algorithm
• Sheet metal forming.

[1] K.A. Stelson, "Real time identification of workpiece material
characteristics from measurements during brakeforming", ASME
Journal of Engineering for Industry, 105 (1983) 45-53.
[2] A. Chandra, "Real-time identification and control of springback in sheet
metal forming", ASME Journal of Engineering for Industry, 109 (1987)
265-273.
[3] Manabe, K., Soeda, K., Nagashima, T., and Nishimura, H., "Adaptive
control method of deep drawing using the variable blank holding force
technique", Journal of the Japan Society for Technology of Plasticity.
33(1992) 423-428.
[4] Manabe, K., Yoshihara, S., Yang, M., and Nishimura, H., "Fuzzy
controlled variable blank holding force technique, for circular cup deep
drawing of the aluminium alloy sheet", Proceedings of NAMRC, Paper
No. MF95-121, (1995) 41-46.
[5] R.L. whitely, "The importance of the directionality in drawing quality
steel", Trans. ASM 52 (1960) 154-163.
[6] L.W.Hu, "Studies of plastic flow of anisotropic metals", Journal of
Applied Mechaniccs. 23 (1952) 444-452.
[7] M. Ruminski, J. Luksza, J. Kusiak and M. Packo," Analysis of the
effect of the die shape on the distribution of the mechanical properties
and strain field in the tube sinking process", Journal of Materials
Processing Technology. 80 (1998) 683-689.
[8] C.C. Tai, J.C. Lin, "Optimization of the deep-drawing clearance
design", The International Journal of Advanced Manufacturing
Technology. 14 (1998) 390-398.
[9] H. You-Min, J.-W. Chen, "Influence of the die arc formability in
cylindrical cup-drawing", Journal of Materials Processing Technology.
55 (1995) 360-369.
[10] J. Zhao, F. Wang, "Paramter identification by neural network for
intelligent deep drawing of the axisymmetric workpieces" Journal of
Materials Processing Technology.166 (2005) 387-391.
[11] ZHAO Jun, QIN Si-ji, CAO Hong-qiang, LI Shuo-ben. Analytic
description of the intelligent deep drawing process for axisymmetriccurve
workpiece. Journal of Plasticity Engineering 5 (4) 1998 47-58.