Application of back-propagation neural network technique to high-energy planetary ball milling process for synthesizing nanocomposite WC-MgO powders

Materials and Design

Volumn 30, Issue 8, 2009, Pages 2867-2874

  Ma, J ^a   Zhu, S G ^a   Wu, C X ^a   Zhang, M L ^a  

^a DONGHUA UNIVERSITY   (China)

Author keywords

Artificial neural networks; Mechanical alloying; Nano composites

Indexed keywords

ARTIFICIAL NEURAL NETWORKS; AS-MILLED POWDERS; BACK-PROPAGATION NEURAL NETWORKS; BALL MILLING PARAMETERS; BALL MILLING PROCESS; BALL-TO-POWDER WEIGHT RATIO; BP NETWORKS; HIGH ENERGY; INPUT PARAMETER; MEDIAN PARTICLE SIZE; MGO POWDERS; MILLING BALLS; MILLING PARAMETERS; MORPHOLOGICAL CHARACTERISTIC; PHYSICAL MODEL; PLANETARY BALL MILLING; PROPERTIES OF COMPOSITES; STATISTICAL APPROACH;

BACKPROPAGATION; CRYSTALLITE SIZE; MECHANICAL ALLOYING; METALLURGY; MILLING (MACHINING); MILLING MACHINES; MODELS; NANOCOMPOSITES; NEURAL NETWORKS; POWDERS; SPHERES; TUNGSTEN CARBIDE;

BALL MILLING;

EID: 67349173212     PISSN: 02641275     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.matdes.2009.01.016     Document Type: Article

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