Prediction of the hot deformation behavior for Aermet100 steel using an artificial neural network

Computational Materials Science

Volumn 48, Issue 3, 2010, Pages 626-632

  Ji, Guoliang ^a,b   Li, Fuguo ^a   Li, Qinghua ^a   Li, Huiqu ^c   Li, Zhi ^c  

^a NORTHWESTERN POLYTECHNICAL UNIVERSITY   (China)

^b HENAN POLYTECHNIC UNIVERSITY   (China)

^c BEIJING INSTITUTE OF AERONAUTICAL MATERIALS   (China)

Author keywords

Aermet100 steel; Artificial neural network; Hot deformation; Learning algorithm

Indexed keywords

ARTIFICIAL NEURAL NETWORK; ARTIFICIAL NEURAL NETWORK MODELS; EXPERIMENTAL DATA; FEEDFORWARD BACKPROPAGATION; FLOW STRESS; HOT COMPRESSION TESTS; HOT DEFORMATION; HOT DEFORMATION BEHAVIORS; LEVENBERG-MARQUARDT LEARNING ALGORITHMS; NOISE SENSITIVITY; NOISE TOLERANCE; STATISTICAL INDICES; STRAIN RANGES; TEMPERATURE RANGE; ULTRA HIGH STRENGTH STEEL;

APPROXIMATION THEORY; BACKPROPAGATION ALGORITHMS; COMPRESSION TESTING; DAMAGE DETECTION; DATA FLOW ANALYSIS; DEFORMATION; EXTRAPOLATION; FORECASTING; HIGH STRENGTH STEEL; LEARNING ALGORITHMS; STRAIN RATE;

NEURAL NETWORKS;

EID: 77950948552     PISSN: 09270256     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.commatsci.2010.02.031     Document Type: Article

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