Neural network approach for the selection of processing parameters of aluminium-iron composite preforms during cold upsetting

Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture

Volumn 224, Issue 3, 2010, Pages 459-472

  Poshal, G ^a   Ganesan, P ^a  

^a JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY   (India)

Author keywords

Aluminium iron composites; Neural network; Upsetting

Indexed keywords

ALUMINIUM-IRON COMPOSITES; COLD UPSETTING; COMPACT LOAD; DIE FORGING; EXPERIMENTAL DATA; EXPERIMENTAL SETUP; FATIGUE STRENGTH; FORMABILITY INDEX; IRON CONTENT; METAL POWDER; POWDER FORGING; PROCESS SETTINGS; PROCESSING PARAMETERS; TRAINING DATA;

ALUMINUM; ALUMINUM POWDER METALLURGY; ASPECT RATIO; DENSITY (SPECIFIC GRAVITY); FORGING MACHINES; FORGINGS; FORMABILITY; IMPACT STRENGTH; IRON RESEARCH; MECHANICAL PROPERTIES; METALLURGY; NANOCRYSTALLINE MATERIALS; POWDER METAL PRODUCTS; PREFORMING; SINTERING; TENSILE STRENGTH; UPSETTING (FORMING);

NEURAL NETWORKS;

EID: 77950353439     PISSN: 09544054     EISSN: None     Source Type: Journal    
DOI: 10.1243/09544054JEM1654     Document Type: Article

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