Cutting tool wear is a very complex process. Various factors have a direct or indirect effect on cutting tool wear, resulting in uncertainty, so it is difficult for experimental data and result to have good stability. However, Vibration analysis is a very important means for condition monitoring and fault diagnosis. This paper aims to study the methods of tool vibration signal processing, pattern recognition and trend prediction. Collected on tool vibration signal at different times, wavelet noise reduction is used to pretreat the vibration signals. Then, for the self-similar vibration signals, we propose the fractional Brownian motion (FBM) theory with long-range dependence (LRD). Combined with Wigner-Ville spectrum, characteristic parameter can be extracted, so the cutting tool wear state can be determined according to fractal dimension and average slope of the fitting curve of the logarithm power spectrum. Finally, we use FBM model to predict the trend of tool vibration signals. Experiments show that the methods have a good effect on tool wear state recognition and trend prediction.
| Published in | International Journal of Mechanical Engineering and Applications (Volume 3, Issue 1) |
| DOI | 10.11648/j.ijmea.20150301.11 |
| Page(s) | 1-5 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2015. Published by Science Publishing Group |
Tool Wear, Fractal Dimension, Wigner-Ville Spectrum, FBM Model, Trend Prediction
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APA Style
Liang Jian-kai, Song Wan-qing, Li Qing. (2015). Research on Cutting Tool Wear Based on Fractional Brownian Motion. International Journal of Mechanical Engineering and Applications, 3(1), 1-5. https://doi.org/10.11648/j.ijmea.20150301.11
ACS Style
Liang Jian-kai; Song Wan-qing; Li Qing. Research on Cutting Tool Wear Based on Fractional Brownian Motion. Int. J. Mech. Eng. Appl. 2015, 3(1), 1-5. doi: 10.11648/j.ijmea.20150301.11
AMA Style
Liang Jian-kai, Song Wan-qing, Li Qing. Research on Cutting Tool Wear Based on Fractional Brownian Motion. Int J Mech Eng Appl. 2015;3(1):1-5. doi: 10.11648/j.ijmea.20150301.11
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Download@article{10.11648/j.ijmea.20150301.11,
author = {Liang Jian-kai and Song Wan-qing and Li Qing},
title = {Research on Cutting Tool Wear Based on Fractional Brownian Motion},
journal = {International Journal of Mechanical Engineering and Applications},
volume = {3},
number = {1},
pages = {1-5},
doi = {10.11648/j.ijmea.20150301.11},
url = {https://doi.org/10.11648/j.ijmea.20150301.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijmea.20150301.11},
abstract = {Cutting tool wear is a very complex process. Various factors have a direct or indirect effect on cutting tool wear, resulting in uncertainty, so it is difficult for experimental data and result to have good stability. However, Vibration analysis is a very important means for condition monitoring and fault diagnosis. This paper aims to study the methods of tool vibration signal processing, pattern recognition and trend prediction. Collected on tool vibration signal at different times, wavelet noise reduction is used to pretreat the vibration signals. Then, for the self-similar vibration signals, we propose the fractional Brownian motion (FBM) theory with long-range dependence (LRD). Combined with Wigner-Ville spectrum, characteristic parameter can be extracted, so the cutting tool wear state can be determined according to fractal dimension and average slope of the fitting curve of the logarithm power spectrum. Finally, we use FBM model to predict the trend of tool vibration signals. Experiments show that the methods have a good effect on tool wear state recognition and trend prediction.},
year = {2015}
}
TY - JOUR T1 - Research on Cutting Tool Wear Based on Fractional Brownian Motion AU - Liang Jian-kai AU - Song Wan-qing AU - Li Qing Y1 - 2015/02/01 PY - 2015 N1 - https://doi.org/10.11648/j.ijmea.20150301.11 DO - 10.11648/j.ijmea.20150301.11 T2 - International Journal of Mechanical Engineering and Applications JF - International Journal of Mechanical Engineering and Applications JO - International Journal of Mechanical Engineering and Applications SP - 1 EP - 5 PB - Science Publishing Group SN - 2330-0248 UR - https://doi.org/10.11648/j.ijmea.20150301.11 AB - Cutting tool wear is a very complex process. Various factors have a direct or indirect effect on cutting tool wear, resulting in uncertainty, so it is difficult for experimental data and result to have good stability. However, Vibration analysis is a very important means for condition monitoring and fault diagnosis. This paper aims to study the methods of tool vibration signal processing, pattern recognition and trend prediction. Collected on tool vibration signal at different times, wavelet noise reduction is used to pretreat the vibration signals. Then, for the self-similar vibration signals, we propose the fractional Brownian motion (FBM) theory with long-range dependence (LRD). Combined with Wigner-Ville spectrum, characteristic parameter can be extracted, so the cutting tool wear state can be determined according to fractal dimension and average slope of the fitting curve of the logarithm power spectrum. Finally, we use FBM model to predict the trend of tool vibration signals. Experiments show that the methods have a good effect on tool wear state recognition and trend prediction. VL - 3 IS - 1 ER -
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