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Tool-wear monitoring during micro-end milling using wavelet packet transform and Fisher’s linear discriminant

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Abstract

Tool wear is one of the most important parameters in micro-end milling, and can be used to monitor the condition of the machine and the tool. A micro-end mill has different characteristics from a macro-scale end mill; in particular, shank run-out (which is negligible in the macro-scale tool due to the low aspect ratio) is significant in micro-end milling, inducing excessive tool wear and reduced tool life and leading to sudden, premature failure. In this paper, a novel tool-wear monitoring method is described for determining the state of a micro-end mill using wavelet packet transforms and Fisher’s linear discriminant. Force and torque signals were measured using a dynamometer and were used to reflect geometric changes in the micro-end mill due to wear. Because of the small signal-to-noise ratio, sensor signals measured during the milling process were periodically averaged, and the resulting single-period signals provided improved efficiency of feature extraction using wavelet packet transforms. The extracted features were classified in the wavelet domain and used to determine the tool state employing a hidden Markov model. The recognition results were compared with those of an energy-based monitoring technique, and we found that our method could determine the tool state more accurately for both normal wear and premature failure of micro-end mills.

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Authors and Affiliations

  1. Department of Mechanical and Aerospace Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, South Korea

    Young-Sun Hong, Jong-Seol Moon & Sung-Hoon Ahn

  2. Institute of Advanced Machines and Design, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, South Korea

    Hae-Sung Yoon, Young-Man Cho & Sung-Hoon Ahn

  3. BK21 Plus Transformative Training Program for Creative Mechanical and Aerospace Engineers, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, South Korea

    Hae-Sung Yoon

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  1. Young-Sun Hong
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Correspondence to Young-Man Cho or Sung-Hoon Ahn.

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Hong, YS., Yoon, HS., Moon, JS. et al. Tool-wear monitoring during micro-end milling using wavelet packet transform and Fisher’s linear discriminant. Int. J. Precis. Eng. Manuf. 17, 845–855 (2016). https://doi.org/10.1007/s12541-016-0103-z

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  • DOI: https://doi.org/10.1007/s12541-016-0103-z

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