DocumentCode :
2572992
Title :
Model-based monitoring and failure detection methodology for ball-nose end milling
Author :
Huang, Sheng ; Goh, Kiah Mok ; Shaw, Kah Chuan ; Wong, Yoke San ; Hong, Geok Soon
Author_Institution :
Singapore Inst. of Manuf. Technol., Singapore
fYear :
2007
fDate :
25-28 Sept. 2007
Firstpage :
155
Lastpage :
160
Abstract :
This 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 6 mm 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 is utilized to predict whether this fault will result in catastrophic failure. Experimental results are provided to demonstrate the feasibility of this approach.
Keywords :
computerised monitoring; cutting; fault diagnosis; hardening; milling; production engineering computing; ball-nose end milling; cutting engagement conditions; failure detection methodology; intelligent predictive method; mechanistic force model; micrograin tungsten carbide flute ball-nose; model-based monitoring; online monitoring; stavax steel hardening; Acoustic sensors; Condition monitoring; Engines; Fault detection; Force sensors; Machining; Metalworking machines; Milling machines; Predictive models; Solid modeling;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Emerging Technologies and Factory Automation, 2007. ETFA. IEEE Conference on
Conference_Location :
Patras
Print_ISBN :
978-1-4244-0825-2
Electronic_ISBN :
978-1-4244-0826-9
Type :
conf
DOI :
10.1109/EFTA.2007.4416766
Filename :
4416766
Link To Document :
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