Please use this identifier to cite or link to this item: https://doi.org/10.1109/EFTA.2007.4416766
DC FieldValue
dc.titleModel-based monitoring and failure detection methodology for ball-nose end milling
dc.contributor.authorHuang, S.
dc.contributor.authorGoh, K.M.
dc.contributor.authorChuan, K.
dc.contributor.authorWong, Y.S.
dc.contributor.authorHong, G.S.
dc.date.accessioned2014-04-24T10:17:06Z
dc.date.available2014-04-24T10:17:06Z
dc.date.issued2007
dc.identifier.citationHuang, S.,Goh, K.M.,Chuan, K.,Wong, Y.S.,Hong, G.S. (2007). Model-based monitoring and failure detection methodology for ball-nose end milling. IEEE International Conference on Emerging Technologies and Factory Automation, ETFA : 155-160. ScholarBank@NUS Repository. <a href="https://doi.org/10.1109/EFTA.2007.4416766" target="_blank">https://doi.org/10.1109/EFTA.2007.4416766</a>
dc.identifier.isbn1424408261
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/51625
dc.description.abstractThis paper presents a model-based monitoring and failure detection approach in ball-nose end milling process. A mechanistic force model has been established for high speed milling on hardened stavax steel with 6mm micro-grain tungsten carbide 2 flute ball-nose end mill. The threshold curve can be obtained off-line based on the process model as the cutting engagement conditions along the tool path are determined at the simulation stage. The measured cutting forces are monitored on-line to detect the faults by comparing them with the threshold curve at machining stage. If a fault is detected at certain position along the tool path, an intelligent predictive method developed in [1] is utilized to predict whether this fault will result in catastrophic failure. Experimental results are provided to demonstrate the feasibility of this approach. © 2007 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/EFTA.2007.4416766
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentMECHANICAL ENGINEERING
dc.description.doi10.1109/EFTA.2007.4416766
dc.description.sourcetitleIEEE International Conference on Emerging Technologies and Factory Automation, ETFA
dc.description.page155-160
dc.description.coden85ROA
dc.identifier.isiutNOT_IN_WOS
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