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Volumn 30, Issue 5, 2009, Pages 1518-1523
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Analysis of the effect of reinforcement particles on the compressibility of Al-SiC composite powders using a neural network model
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Author keywords
Al SiC; Compaction; Composite; Compressibility; Densification; Neural network modeling
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Indexed keywords
AL-SIC;
AL-SIC COMPOSITES;
COMPACTING PRESSURES;
COMPACTION PRESSURES;
COMPOSITE;
COMPOSITE POWDERS;
DENSIFICATION;
HIDDEN LAYERS;
NEURAL NETWORK MODELING;
NEURAL NETWORK MODELS;
PARTICLE REARRANGEMENTS;
REINFORCEMENT PARTICLES;
SIC POWDERS;
SIGMOID TRANSFER FUNCTIONS;
UNIAXIAL COMPACTIONS;
ARTIFICIAL INTELLIGENCE;
BACKPROPAGATION;
BACKPROPAGATION ALGORITHMS;
COMPACTION;
COMPRESSIBILITY;
LEARNING ALGORITHMS;
LEARNING SYSTEMS;
NEURAL NETWORKS;
PARTICLE SIZE;
POWDERS;
REINFORCEMENT;
SILICON CARBIDE;
TITRATION;
ALUMINUM;
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EID: 60249088033
PISSN: 02641275
EISSN: None
Source Type: Journal
DOI: 10.1016/j.matdes.2008.07.052 Document Type: Article |
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Times cited : (55)
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References (41)
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