Modelling and predicting surface roughness in turning operations using hybrid differential evolution and the group method of data handling networks

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

Volumn 222, Issue 7, 2008, Pages 785-795

  Onwubolu, G C ^a  

^a UNIVERSITY OF THE SOUTH PACIFIC   (Fiji)

Author keywords

DE; GMDH; Hybrid systems; Inductive modelling and prediction; Surface roughness; Turning operations

Indexed keywords

ALGORITHMS; DATA HANDLING; DIGITAL ARITHMETIC; FORECASTING; FRICTION; HEURISTIC PROGRAMMING; MANUFACTURE; METAL ANALYSIS; NEURAL NETWORKS; POPULATION STATISTICS; SPEED; STANDARDS; STEEL; STEEL METALLOGRAPHY; STEEL POWDER METALLURGY; SURFACE PROPERTIES; SURFACE ROUGHNESS; TOOL STEEL; TURNING;

ADVANCED MANUFACTURING; CASE STUDIES; CUTTING CONDITIONS; CUTTING FORCES; DE; DEPTH OF CUT; DIFFERENTIAL EVOLUTION; GMDH; GROUP METHOD OF DATA HANDLING; HIGH-SPEED STEEL; HYBRID DIFFERENTIAL EVOLUTION; HYBRID MODELLING; HYBRID SYSTEMS; INDUCTIVE MODELLING AND PREDICTION; INPUT VARIABLES; MACHINING OPERATIONS; MILD STEELS; POLYNOMIAL NEURAL NETWORK; POPULATION-BASED ALGORITHMS; PRODUCTION COSTS; SELF-ORGANIZING; TURNING OPERATIONS; WORK PIECES;

EVOLUTIONARY ALGORITHMS;

EID: 49349084486     PISSN: 09544054     EISSN: None     Source Type: Journal    
DOI: 10.1243/09544054JEM1079     Document Type: Article

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