Optimization of Radial Basis Function neural network employed for prediction of surface roughness in hard turning process using Taguchi's orthogonal arrays

Expert Systems with Applications

Volumn 39, Issue 9, 2012, Pages 7776-7787

  Pontes, Fabrício José ^b   Paiva, Anderson Paulo De ^a   Balestrassi, Pedro Paulo ^a   Ferreira, João Roberto ^a   Silva, Messias Borges Da ^b  

^a FEDERAL UNIVERSITY OF ITAJUBÁ   (Brazil)

^b SÃO PAULO STATE UNIVERSITY UNESP   (Brazil)

Author keywords

Hard turning; RBF neural networks; Surface roughness; Taguchi methods

Indexed keywords

DESIGN FACTORS; DESIGN OF EXPERIMENTS (DOE); DESIGN PARAMETERS; DIFFERENT SIZES; HARD TURNING; HARD TURNING PROCESS; HARDENED STEEL; NETWORK DESIGN; ORTHOGONAL ARRAY; RADIAL BASE FUNCTION; RADIAL BASIS FUNCTION NEURAL NETWORKS; RADIAL FUNCTIONS; RBF NEURAL NETWORK; SPREAD FACTORS; TAGUCHI; TRAINING SETS; TRIAL-AND-ERROR APPROACH; TURNING PROCESS;

ALGORITHMS; DESIGN; DESIGN OF EXPERIMENTS; EXPERIMENTS; FORECASTING; NETWORK PERFORMANCE; NEURAL NETWORKS; RADIAL BASIS FUNCTION NETWORKS; SURFACE ROUGHNESS; TAGUCHI METHODS; TURNING;

ORTHOGONAL FUNCTIONS;

EID: 84858332972     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2012.01.058     Document Type: Article

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