Prediction of tribological behavior of aluminum-copper based composite using artificial neural network

Journal of Alloys and Compounds

Volumn 470, Issue 1-2, 2009, Pages 584-588

  Hayajneh, Mohammed ^a   Hassan, Adel Mahamood ^a   Alrashdan, Abdalla ^a   Mayyas, Ahmad Turki ^a  

^a JORDAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Jordan)

Author keywords

Artificial neural network (ANN); Metal matrix composite; Metals and alloys; Wear

Indexed keywords

ALLOYING ELEMENTS; ALLOYS; ALUMINA; ALUMINUM; ARTIFICIAL INTELLIGENCE; ATMOSPHERIC HUMIDITY; BACKPROPAGATION; CARBON FIBER REINFORCED PLASTICS; COMPOSITE MICROMECHANICS; COPPER; DISKS (STRUCTURAL COMPONENTS); LASER BEAM WELDING; METALLIC COMPOUNDS; METALLIC MATRIX COMPOSITES; METALLURGY; METALS; REINFORCEMENT; SILICON ALLOYS; SILICON CARBIDE;

AL-CU ALLOYS; ALUMINUM COPPERS; ARTIFICIAL NEURAL NETWORK (ANN); AVERAGE VALUES; DRY SLIDING WEAR TESTS; FEED FORWARD BACK PROPAGATION; METAL MATRIX COMPOSITE; METALS AND ALLOYS; MG METAL MATRIXES; NORMAL LOADS; PIN-ON-DISK APPARATUS; REINFORCEMENT PARTICLES; RELATIVE ERRORS; RELATIVE HUMIDITIES; ROTATIONAL SPEED; SILICON CARBIDE COMPOSITES; TRIBOLOGICAL BEHAVIORS; WEAR; WEAR LOSS;

NEURAL NETWORKS;

EID: 59249086891     PISSN: 09258388     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jallcom.2008.03.035     Document Type: Article

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