Machine ensemble approach for simultaneous detection of transient and gradual abnormalities in end milling using multisensor fusion

Journal of Materials Processing Technology

Volumn 209, Issue 10, 2009, Pages 4728-4738

  Binsaeid, Sultan ^a   Asfour, Shihab ^a   Cho, Sohyung ^b   Onar, Arzu ^c  

^a UNIVERSITY OF MIAMI   (United States)

^b SOUTHERN ILLINOIS UNIVERSITY EDWARDSVILLE   (United States)

^c ST JUDE CHILDREN S RESEARCH HOSPITAL   (United States)

Author keywords

Feature selection; Machine ensemble; Multisensor fusion; Tool condition monitoring

Indexed keywords

AUTOMATED MANUFACTURING ENVIRONMENTS; END MILLINGS; END MILLS; ENSEMBLE METHODS; FEATURE SELECTION; FEATURE SUBSETS; FLANK WEARS; FREQUENCY DOMAINS; MACHINE ENSEMBLE; MACHINE-LEARNING; MACHINING EXPERIMENTS; MACHINING PARAMETERS; MAJORITY VOTING; MULTI LAYERS; MULTI-SENSOR FUSION TECHNIQUES; MULTIPLE SENSORS; MULTISENSOR FUSION; SIMULTANEOUS DETECTIONS; SPINDLE POWER; STACKED GENERALIZATIONS; TOOL CONDITION MONITORING; TOOL CONDITIONS;

CARBIDES; COMPOSITE MICROMECHANICS; CUTTING; CUTTING TOOLS; FEATURE EXTRACTION; MACHINE TOOLS; MACHINING; SENSORS; SIGNAL PROCESSING; SUPPORT VECTOR MACHINES; TOOL STEEL;

CONDITION MONITORING;

EID: 67349144405     PISSN: 09240136     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jmatprotec.2008.11.038     Document Type: Article

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