Application of classical clustering methods for online tool condition monitoring in high speed milling processes

Proceedings of the 2012 7th IEEE Conference on Industrial Electronics and Applications, ICIEA 2012

Volumn , Issue , 2012, Pages 1249-1254

  Torabi, Amin J ^a   Er, Meng Joo ^a   Li, Xiang ^b   Lim, Beng Siong ^b   Zhai, Lianyin ^a   San, Linn ^a   Gan, Oon Peen ^b   Ching, Chuen Teck ^b  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^b SINGAPORE INSTITUTE OF MANUFACTURING TECHNOLOGY   (Singapore)

Author keywords

Ball Nose End Milling; Clustering; Modeling; Signal Processing; Wavelet Analysis

Indexed keywords

AI TECHNIQUES; ARTIFICIAL INTELLIGENT; CLUSTERING; CLUSTERING METHODS; CONDITION MONITORING SYSTEMS; END-MILLING; END-MILLING PROCESS; FUZZY C-MEANS CLUSTERING METHOD; HIGH SPEED MILLING; IN-PROCESS; MILLING PROCESS; MODELING TECHNIQUE; ON-LINE FAULT DIAGNOSIS; ON-LINE TOOLS; ONLINE CONDITION MONITORING; REFERENCE MODELS; SENSOR SIGNALS; TIME FREQUENCY ANALYSIS; TOOL CONDITION MONITORING; TOOL WEAR; VIBRATION SIGNAL; WAVELET FEATURES;

ARTIFICIAL INTELLIGENCE; BALL MILLING; CLUSTER ANALYSIS; CONDITION MONITORING; CUTTING; FEATURE EXTRACTION; INDUSTRIAL ELECTRONICS; MACHINE TOOLS; MODELS; SIGNAL PROCESSING; SURFACE ROUGHNESS; WAVELET ANALYSIS;

MILLING (MACHINING);

EID: 84871679777     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICIEA.2012.6360914     Document Type: Conference Paper

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