Tool Condition Monitoring in Micro-End Milling using wavelets

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dc.contributor.author N K Dubey
dc.contributor.author A Roushan
dc.contributor.author U S Rao
dc.contributor.author K Sandeep
dc.contributor.author K Patra
dc.date.accessioned 2019-08-16T06:21:23Z
dc.date.available 2019-08-16T06:21:23Z
dc.date.issued 2017-11-29
dc.identifier.issn 17578981
dc.identifier.uri http://localhost:8080/xmlui/handle/123456789/364
dc.description.abstract In this work, Tool Condition Monitoring (TCM) strategy is developed for micro-end milling of titanium alloy and mild steel work-pieces. Full immersion slot milling experiments are conducted using a solid tungsten carbide end mill for more than 1900 s to have reasonable amount of tool wear. During the micro-end milling process, cutting force and vibration signals are acquired using Kistler piezo-electric 3-component force dynamometer (9256C2) and accelerometer (NI cDAQ-9188) respectively. The force components and the vibration signals are processed using Discrete Wavelet Transformation (DWT) in both time and frequency window. 5-level wavelet packet decomposition using Db-8 wavelet is carried out and the detailed coefficients D1 to D5 for each of the signals are obtained. The results of the wavelet transformation are correlated with the tool wear. In case of vibration signals, de-noising is done for higher frequency components (D1) and force signals were de-noised for lower frequency components (D5). Increasing value of MAD (Mean Absolute Deviation) of the detail coefficients for successive channels depicted tool wear. The predictions of the tool wear are confirmed from the actual wear observed in the SEM of the worn tool. en_US
dc.language.iso en en_US
dc.publisher Institute of Physics Publishing en_US
dc.subject tool condition monitoring; wavelet transform; DWT; tool wear; en_US
dc.title Tool Condition Monitoring in Micro-End Milling using wavelets en_US
dc.type Article en_US


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