메뉴 건너뛰기




Volumn 177, Issue 1-3, 2006, Pages 323-326

Modelling of the rheological behaviour of aluminium alloys in multistep hot deformation using the multiple regression analysis and artificial neural network techniques

Author keywords

Artificial neural network; Flow modelling; Multiple regression analysis; Multistep deformation test

Indexed keywords

DEFORMATION; HOT WORKING; NEURAL NETWORKS; REGRESSION ANALYSIS; RHEOLOGY; TORSION TESTING;

EID: 33745830846     PISSN: 09240136     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jmatprotec.2006.03.230     Document Type: Article
Times cited : (24)

References (17)
  • 1
    • 0002595058 scopus 로고    scopus 로고
    • Restoration mechanisms and hot rolling of Al alloys
    • Bieler T.R., Lalli L.A., and MacEwen S.R. (Eds)
    • McQueen H.J. Restoration mechanisms and hot rolling of Al alloys. In: Bieler T.R., Lalli L.A., and MacEwen S.R. (Eds). Hot Deformation of Aluminium Alloy vol. 2 (1998) 383-396
    • (1998) Hot Deformation of Aluminium Alloy , vol.2 , pp. 383-396
    • McQueen, H.J.1
  • 2
    • 0033078679 scopus 로고    scopus 로고
    • Using neural networks to predict parameters in the hot working of aluminium alloys
    • Chun M.S., Biglou J., Lenard J.G., and Kim J.G. Using neural networks to predict parameters in the hot working of aluminium alloys. J. Mater. Process. Technol. 86 (1999) 245-251
    • (1999) J. Mater. Process. Technol. , vol.86 , pp. 245-251
    • Chun, M.S.1    Biglou, J.2    Lenard, J.G.3    Kim, J.G.4
  • 3
    • 4944244973 scopus 로고    scopus 로고
    • Prediction of nickel-base superalloys' rheological behaviour under hot forging conditions using artificial neural networks
    • Bariani P.F., Bruschi S., and Dal Negro T. Prediction of nickel-base superalloys' rheological behaviour under hot forging conditions using artificial neural networks. J. Mater. Process. Technol. 152 (2004) 395-400
    • (2004) J. Mater. Process. Technol. , vol.152 , pp. 395-400
    • Bariani, P.F.1    Bruschi, S.2    Dal Negro, T.3
  • 4
    • 12344283988 scopus 로고    scopus 로고
    • Testing and modelling of material response to deformation in bulk metal forming
    • Bariani P.F., Dal Negro T., and Bruschi S. Testing and modelling of material response to deformation in bulk metal forming. Keynote Pap. Ann. CIRP 53 2 (2004) 1-22
    • (2004) Keynote Pap. Ann. CIRP , vol.53 , Issue.2 , pp. 1-22
    • Bariani, P.F.1    Dal Negro, T.2    Bruschi, S.3
  • 5
    • 0029236455 scopus 로고
    • A high temperature constitutive equation for AA 2014 PM alloy and its application to isothermal forging
    • Forcellese A. A high temperature constitutive equation for AA 2014 PM alloy and its application to isothermal forging. Int. J. Mater. Prod. Technol. 10 (1995) 1-15
    • (1995) Int. J. Mater. Prod. Technol. , vol.10 , pp. 1-15
    • Forcellese, A.1
  • 7
    • 0029357321 scopus 로고
    • Analysis of a model to forecast thermal deformation of ball screw feed drive systems
    • Huang S.-C. Analysis of a model to forecast thermal deformation of ball screw feed drive systems. Int. J. Mach. Tools Manuf. 35 (1995) 1099-1104
    • (1995) Int. J. Mach. Tools Manuf. , vol.35 , pp. 1099-1104
    • Huang, S.-C.1
  • 8
    • 79953731126 scopus 로고    scopus 로고
    • Thermal error modelling for real-time error compensation
    • Chen J.S., Yuan J., and Ni J. Thermal error modelling for real-time error compensation. Int. J. Adv. Manufact. Technol. 12 (1996) 266-275
    • (1996) Int. J. Adv. Manufact. Technol. , vol.12 , pp. 266-275
    • Chen, J.S.1    Yuan, J.2    Ni, J.3
  • 9
    • 0029371166 scopus 로고
    • Neural network approach to flow stress evaluation in hot deformation
    • Rao K.P., and Prasad Y.K.D.V. Neural network approach to flow stress evaluation in hot deformation. J. Mater. Process. Technol. 53 (1995) 552-566
    • (1995) J. Mater. Process. Technol. , vol.53 , pp. 552-566
    • Rao, K.P.1    Prasad, Y.K.D.V.2
  • 10
    • 0036604792 scopus 로고    scopus 로고
    • A neural-computation method of predicting the early interaural cross-correlation coefficient (IACCE3) for auditoria
    • Nannariello J., and Fricke F.R. A neural-computation method of predicting the early interaural cross-correlation coefficient (IACCE3) for auditoria. Appl. Acoust. 63 (2001) 627-641
    • (2001) Appl. Acoust. , vol.63 , pp. 627-641
    • Nannariello, J.1    Fricke, F.R.2
  • 11
    • 0034292699 scopus 로고    scopus 로고
    • The application of ANNs in certain-analysis problems
    • Kasperkiewicz J. The application of ANNs in certain-analysis problems. J. Mater. Process. Technol. 106 (2000) 74-79
    • (2000) J. Mater. Process. Technol. , vol.106 , pp. 74-79
    • Kasperkiewicz, J.1
  • 12
    • 0004406228 scopus 로고    scopus 로고
    • Effect of the training set size on springback control by neural network in an air bending process
    • Forcellese A., Gabrielli F., and Ruffini R. Effect of the training set size on springback control by neural network in an air bending process. J. Mater. Process. Technol. 80-81 (1998) 493-500
    • (1998) J. Mater. Process. Technol. , vol.80-81 , pp. 493-500
    • Forcellese, A.1    Gabrielli, F.2    Ruffini, R.3
  • 16
    • 0002094946 scopus 로고
    • Data prediction for a neural network
    • Lawrence J. Data prediction for a neural network. AI Expert 6 (1991) 34-41
    • (1991) AI Expert , vol.6 , pp. 34-41
    • Lawrence, J.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.