Avoiding neural network fine tuning by using ensemble learning: Application to ball-end milling operations

International Journal of Advanced Manufacturing Technology

Volumn 57, Issue 5-8, 2011, Pages 521-532

  Bustillo, Andres ^a   Díez Pastor, José Francisco ^a   Quintana, Guillem ^b   García Osorio, César ^a  

^a UNIVERSITY OF BURGOS   (Spain)

^b ASCAMM Centre Tecnologic   (Spain)

Author keywords

Ball end mill; Cutting parameters; Data mining; Ensemble learning; Surface roughness

Indexed keywords

AEROSPACE AND AUTOMOTIVE INDUSTRIES; AISI H13; ARTIFICIAL NEURAL NETWORK APPROACH; BALL END MILLING; BALL-END MILL; CUTTING CONDITIONS; CUTTING PARAMETERS; DATA SETS; DYNAMIC FACTORS; ENSEMBLE LEARNING; FINE TUNING; FINISHING OPERATION; HIGH SPEED MILLING; INDUSTRIAL PROBLEM; MOULDS AND DIES; NEURAL NETWORK PARAMETERS; PREDICTION MODEL; QUENCHED STEEL; TIME-CONSUMING TASKS;

BALL MILLING; BALL MILLS; DATA MINING; FORECASTING; MATHEMATICAL MODELS; MILLING (MACHINING); MILLING MACHINES; MOLDS; NEURAL NETWORKS;

SURFACE ROUGHNESS;

EID: 83555163643     PISSN: 02683768     EISSN: 14333015     Source Type: Journal    
DOI: 10.1007/s00170-011-3300-z     Document Type: Article

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