Methodology of preform design considering workability in metal forming by the artificial neural network and Taguchi method

Journal of Materials Processing Technology

Volumn 80-81, Issue , 1998, Pages 487-492

  Ko, D C ^a   Kim, D H ^b   Kim, B M ^b   Choi, J C ^b  

^a Yangsan College   (South Korea)

^b Pusan National University   (South Korea)

Author keywords

Artificial neural network; Ductile fracture; Preform design; Taguchi method

Indexed keywords

EID: 0001127525     PISSN: 09240136     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0924-0136(98)00152-6     Document Type: Article

References (8)

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3. S. Roy, S. Ghosh, R. Shivipuri, A New Approach to Optimal Design of Multi-stage Metal Forming Processes with Micro Genetic Algorithms, Int. J. Mach. Tool Manuf. 37 (1) (1997) 29-44.
4. D.E. Rumlhart, G.E. Hinton, R.J. Wiliams, Learning Internal Representations by Error Propagation, Parallel Distributed Processing, Cambridge, MA: MIT Press, 1986.
5. P.J. Ross, Taguchi Techniques for Quality Engineering: Loss Function, Orthogonal Experiments, Parameter and Tolerance Design, New York: McGraw-Hill, 1988.
6. S. Kobayashi, S.I. Oh, T. Altan, Metal Forming and the Finite Element Method, London: Oxford University Press, 1989.
7. S.I. Oh, C.C. Chen, S. Kobayashi, Ductile fracture in axisymmetric extrusion and drawing: part 2-workability in extrusion and drawing, J. Eng. Ind. 101 (1979) 36-44.
8. A.K Hingwe, R.C. Greczanik, M. Knoerr, Prediction of Internal Defects by Finite Element analysis, Proceedings of the 9th International Cold Forging Congress, Solihull, UK, 1995, pp. 209-216.