Prediction of static recrystallization in a multi-pass hot deformed low-alloy steel using artificial neural network

Journal of Materials Processing Technology

Volumn 209, Issue 9, 2009, Pages 4611-4616

  Lin, Y C ^a   Liu, Ge ^a   Chen, Ming Song ^a   Zhong, Jue ^a  

^a BEIJING UNIVERSITY OF POSTS AND TELECOMMUNICATIONS   (China)

Author keywords

42CrMo steel; Artificial neural networks; Hot compression deformation; Static recrystallization

Indexed keywords

42CRMO STEEL; ARTIFICIAL NEURAL NETWORK MODELS; ARTIFICIAL NEURAL NETWORKS; AUSTENITE GRAIN SIZES; DEFORMATION DEGREES; DEFORMATION TEMPERATURES; DEGREE OF DEFORMATIONS; FLOW STRESS; GOOD CORRELATIONS; HOT COMPRESSION DEFORMATION; HOT COMPRESSION TESTS; INITIAL GRAIN SIZES; LOW-ALLOY STEELS; MULTI PASS; STATIC RECRYSTALLIZATION;

AUSTENITE; BACKPROPAGATION; CHROMIUM ALLOYS; COMPRESSION TESTING; DEFORMATION; GRAIN SIZE AND SHAPE; PLASTIC FLOW; RECRYSTALLIZATION (METALLURGY); STEEL; STRAIN RATE;

NEURAL NETWORKS;

EID: 64249111906     PISSN: 09240136     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jmatprotec.2008.10.020     Document Type: Article

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