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Wear Signal Measurement of Micro-End Mill Using Wavelet Analysis

[+] Author Affiliations
Jeong-Bin Park, Yeong-Seon Hong, Jin-Woong Kim, Jun-Cheol Yeo, Kyung-Tae Lee, Young-Man Cho, Sung-Hoon Ahn

Seoul National University, Seoul, South Korea

Jae-Seuk Park

NEO Technical System Co., Ltd., Ansung, Gyeonggi, South Korea

Paper No. MSEC_ICMP2008-72121, pp. 215-220; 6 pages
doi:10.1115/MSEC_ICMP2008-72121
From:
  • ASME 2008 International Manufacturing Science and Engineering Conference collocated with the 3rd JSME/ASME International Conference on Materials and Processing
  • ASME 2008 International Manufacturing Science and Engineering Conference, Volume 2
  • Evanston, Illinois, USA, October 7–10, 2008
  • Conference Sponsors: Manufacturing Engineering Division
  • ISBN: 978-0-7918-4852-4 | eISBN: 978-0-7918-3836-6
  • Copyright © 2008 by ASME

abstract

Tool breakage poses a major problem for mechanical micro-tools. Although the prediction of tool wear is important in the scheduling of tool change and maintaining productivity, the life of micro-tools is generally predicted statistically based on data from experimental machining trials. In this study, force signals were observed during machining using a micro-end mill. The relationship between these signals and the wear of the micro-end mill was investigated using wavelet analysis. Printed Circuit Board (PCB) substrate materials were used as a workpiece, and cutting forces were measured with a dynamometer. To determine the difference between worn and unused tool conditions, the continuous wavelet transform was applied to the force signals that were measured by the dynamometer. The signals from the worn and unused tools had a different dominant frequency or scale. As the machining cutting length increased, the variation in dominant frequency could be identified by wavelet analysis.

Copyright © 2008 by ASME
Topics: Wear , Signals , Wavelets

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