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Study of high-frequency sound signals for tool wear monitoring in micromilling

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Abstract

This study analyzed the sound signals obtained from the micromilling process for microtool wear monitoring. Various spans of spectral features were created by analyzing sound signals on tool wear monitoring in microcutting. The selection algorithm based on class mean scattering criteria and the hidden Markov model (HMM) model was developed to verify the effect of various feature selection algorithms on the system performance. The effect of the feature bandwidth size, the size of observation sequence, and choice of the hidden states for HMM parameters were also studied. The results indicate that the normalized sound signals obtained from the single microphone with a frequency range between 20 and 80 kHz demonstrated the potential to provide a solution to monitor micromills with the proper selection of feature bandwidth and other parameters.

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Authors and Affiliations

  1. Department of Mechanical Engineering, National Chung Hsing University, Taichung, Taiwan, 402

    Ming-Chyuan Lu & Bing-Syun Wan

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  1. Ming-Chyuan Lu
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  2. Bing-Syun Wan
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Correspondence to Ming-Chyuan Lu.

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Lu, MC., Wan, BS. Study of high-frequency sound signals for tool wear monitoring in micromilling. Int J Adv Manuf Technol 66, 1785–1792 (2013). https://doi.org/10.1007/s00170-012-4458-8

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  • DOI: https://doi.org/10.1007/s00170-012-4458-8

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