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Volumn 25, Issue 1-2, 2005, Pages 101-107

Application of a neural network to predict thickness strains and finite element simulation of hydro-mechanical deep drawing

Author keywords

Artificial neural network; Deep drawing; Finite element analysis; Hydro mechanical; Hydro mechanical deep drawing; LS DYNA 2D

Indexed keywords

BOUNDARY CONDITIONS; COMPUTER SIMULATION; DEEP DRAWING; FINITE ELEMENT METHOD; FRACTURE; FRICTION; STEEL SHEET;

EID: 11144275202     PISSN: 02683768     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00170-003-1842-4     Document Type: Article
Times cited : (22)

References (12)
  • 1
    • 0033743683 scopus 로고    scopus 로고
    • Numerical model for the oil pressure distribution in the hydromechanical deep drawing process
    • Jensen MR, Olovsson L, Danckert J (2000) Numerical model for the oil pressure distribution in the hydromechanical deep drawing process. J Mater Process Technol 103:74-79
    • (2000) J Mater Process Technol , vol.103 , pp. 74-79
    • Jensen, M.R.1    Olovsson, L.2    Danckert, J.3
  • 2
    • 0025513245 scopus 로고
    • Counter-pressure deep drawing and its application in the forming and automobile parts
    • Amino H, Nakamura K, Nakagawa T (1990) Counter-pressure deep drawing and its application in the forming and automobile parts. J Mater Process Technol 23:243-265
    • (1990) J Mater Process Technol , vol.23 , pp. 243-265
    • Amino, H.1    Nakamura, K.2    Nakagawa, T.3
  • 3
    • 3042560408 scopus 로고
    • Pressure assisted deep drawing
    • El-Sebaie MG, Mellor PB (1973) Pressure assisted deep drawing. Ann CIRP 22(1):71-72
    • (1973) Ann CIRP , vol.22 , Issue.1 , pp. 71-72
    • El-Sebaie, M.G.1    Mellor, P.B.2
  • 4
    • 0023271097 scopus 로고
    • Sheet metal forming with hydraulic counter pressure in Japan
    • Nakamura K, Nakagawa T (1987) Sheet metal forming with hydraulic counter pressure in Japan. Ann CIRP 36(1):191-194
    • (1987) Ann CIRP , vol.36 , Issue.1 , pp. 191-194
    • Nakamura, K.1    Nakagawa, T.2
  • 5
    • 0005369619 scopus 로고    scopus 로고
    • Artificial intelligence of process parameters and adaptive control system for deep-drawing process
    • Manabe K, Yang M, Yoshihara S (1998) Artificial intelligence of process parameters and adaptive control system for deep-drawing process. J Mater Process Technol 80-81:421-426
    • (1998) J Mater Process Technol , vol.80-81 , pp. 421-426
    • Manabe, K.1    Yang, M.2    Yoshihara, S.3
  • 6
    • 0037349206 scopus 로고    scopus 로고
    • Using FEM and neural network prediction of hydrodynamic deep drawing of T-piece maximum length
    • Lin JC (2003) Using FEM and neural network prediction of hydrodynamic deep drawing of T-piece maximum length. Finite Elem Anal Des 39:445-456
    • (2003) Finite Elem Anal Des , vol.39 , pp. 445-456
    • Lin, J.C.1
  • 8
    • 0023331258 scopus 로고
    • An introduction to computing with neural nets
    • Lippmann RP (1987) An introduction to computing with neural nets. IEEE ASSP Mag, pp 4-22
    • (1987) IEEE ASSP Mag , pp. 4-22
    • Lippmann, R.P.1
  • 11
    • 0031075659 scopus 로고    scopus 로고
    • Prediction of the limiting draw ratio and the maximum drawing load in the cup drawing
    • Leu D-K (1997) Prediction of the limiting draw ratio and the maximum drawing load in the cup drawing. Int J Mech Sci 37(2):201-213
    • (1997) Int J Mech Sci , vol.37 , Issue.2 , pp. 201-213
    • Leu, D.-K.1
  • 12
    • 0021975579 scopus 로고
    • On the hydrodynamic deep-drawing process
    • Tirosh J, Konvalina P (1985) On the hydrodynamic deep-drawing process. Int J Mech Sci 27:595-607
    • (1985) Int J Mech Sci , vol.27 , pp. 595-607
    • Tirosh, J.1    Konvalina, P.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.