Online tool wear prediction in drilling operations using selective artificial neural network ensemble model

Neural Computing and Applications

Volumn 20, Issue 4, 2011, Pages 473-485

  Yu, Jianbo ^a,b  

^a SHANGHAI UNIVERSITY   (China)

^b XI'AN JIAOTONG UNIVERSITY   (China)

Author keywords

Drilling; Flank wear; Selective artificial neural network ensemble; Sensor signal; Tool wear

Indexed keywords

ARTIFICIAL NEURAL NETWORK ENSEMBLES; ARTIFICIAL NEURAL NETWORK MODELS; DRILLING OPERATION; FLANK WEAR; GENERALIZATION PERFORMANCE; HEURISTIC APPROACH; INDUSTRY AUTOMATION; MULTIPLE SENSORS; ON-LINE TOOLS; POTENTIAL APPLICATIONS; PRODUCT QUALITY; SELECTIVE ARTIFICIAL NEURAL NETWORK ENSEMBLE; SENSOR SIGNAL; TOOL CONDITION MONITORING; TOOL WEAR;

CONDITION MONITORING; EQUIPMENT; FORECASTING; HEURISTIC METHODS; PERSONNEL TRAINING; SENSORS; WEAR OF MATERIALS;

NEURAL NETWORKS;

EID: 79955806087     PISSN: 09410643     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00521-011-0539-0     Document Type: Article

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