Application of constitutive and neural network models for prediction of high temperature flow behavior of Al/Mg based nanocomposite

Transactions of Nonferrous Metals Society of China (English Edition)

Volumn 23, Issue 6, 2013, Pages 1737-1750

  Senthilkumar, V ^a   Balaji, A ^a   Arulkirubakaran, D ^a  

^a NATIONAL INSTITUTE OF TECHNOLOGY   (India)

Author keywords

Arrhenius (AR) hyperbolic model; Flow stress; Hot compression; Johnson cook (JC) model; Modified Zerilli Armstrong (ZA) model; Nanocomposite

Indexed keywords

ALUMINUM; COMPRESSION TESTING; CONSTITUTIVE MODELS; HOT PRESSING; MEAN SQUARE ERROR; NANOCOMPOSITES; NEURAL NETWORKS; PLASTIC FLOW; STRAIN RATE;

ARTIFICIAL NEURAL NETWORK MODELS; HIGH-TEMPERATURE FLOWS; HYPERBOLIC MODELS; JOHNSON-COOKMODEL; REGRESSION COEFFICIENT; ROOT MEAN SQUARE ERRORS; STATISTICAL PARAMETERS; ZERILLI-ARMSTRONG;

DEEP NEURAL NETWORKS;

EID: 84889690159     PISSN: 10036326     EISSN: None     Source Type: Journal    
DOI: 10.1016/S1003-6326(13)62656-4     Document Type: Article

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