Signal Stream Clustering for Tool-Rotation-Level Tool Condition Monitoring in Milling Process

Si Jie Phua, Xiang Li, Wee Keong Ng, Beng Siong Lim, Weixiang Zhong, and Junhong Zhou
Submission Type: 
Full Paper
AttachmentSizeTimestamp
phmc_09_47.pdf2.65 MBSeptember 17, 2009 - 7:25pm

Researchers in tool condition monitoring often collect large amount of sensor signal data from experiments to study the complex tool condition relationships with signals. In order to provide new light into this process on a real-time basis, it is critical to identify and detect abnormality at the lowest resolution possible so that the wear behavior on each flute within a tool revolution can be clearly shown. A signal stream clustering method is developed to separate numerous tool-revolution signals into similar groups, each representing a specific set of corresponding events. In our experiment, the 1000 tool-revolution signals in force signal stream are grouped into 5 clusters. These clusters in turn provide a visual mean to assess the tool condition at the most detailed level. In addition, the clusters also enable complex tool condition relationships to be established from the signatures of each set of events.

Publication Control Number: 
047
Submission Keywords: 
applications: industrial
applications: manufacturing
CBM
condition monitoring
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