Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30184
Identification of Optimum Parameters of Deep Drawing of a Cylindrical Workpiece using Neural Network and Genetic Algorithm

Authors: D. Singh, R. Yousefi, M. Boroushaki

Abstract:

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.

Keywords: Deep-drawing, Neural network, Genetic algorithm, Sheet metal forming.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1056334

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1757

References:


[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.