Application of neural networks to predict the elevated temperature flow behavior of a low alloy steel

Computational Materials Science

Volumn 43, Issue 4, 2008, Pages 752-758

  Lin, Y C ^a   Zhang, Jun ^b   Zhong, Jue ^a  

^a CENTRAL SOUTH UNIVERSITY   (China)

^b ZHENGZHOU UNIVERSITY   (China)

Author keywords

07.05.Mh; 42CrMo steel; 81.40.Jj; 81.40.Lm; 83.50. v; Artificial neural networks; Back propagation (BP) learning algorithm; Flow stress; Hot compression deformation

Indexed keywords

ARTIFICIAL INTELLIGENCE; CHROMIUM ALLOYS; NEURAL NETWORKS; STEEL;

07.05.MH; 42CRMO STEEL; 81.40.JJ; 81.40.LM; 83.50.-V; ARTIFICIAL NEURAL NETWORKS; BACK PROPAGATION (BP) LEARNING ALGORITHM; COMPRESSIVE DEFORMATION BEHAVIOR; ELEVATED TEMPERATURES; FLOW STRESS; HOT COMPRESSION DEFORMATION; HOT FORMING; LOW ALLOY STEEL; PROCESSING PARAMETERS;

STRAIN RATE;

EID: 52949098334     PISSN: 09270256     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.commatsci.2008.01.039     Document Type: Article

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