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Tool wear monitoring and prediction based on sound signal

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Abstract

Milling plays a core role in the manufacturing industry. If a milling tool suffers severe wear or breakage during the manufacturing process, it requires immediate attention to prevent precision errors and poor surface quality. Previous studies applied multiple sensors, such as force sensors, microphones, and acoustic emission sensors, to extract the features related to tool wear. However, owing to the complex mechanism causing tool wear, the results of these studies were not only quantitatively different but also varied with respect to cutting conditions. Therefore, this research discussed the relationship between sound signal and tool wear under multiple cutting conditions. Collinearity diagnostics and a stepwise regression procedure were used to optimize the time–frequency statistic features generated from the wavelet packet decomposition. The regression and an artificial neural network model were developed to predict the degree of tool wear. The proposed method provided solid statistical support for the feature selection process, and the results showed that it will maintain prediction accuracy regardless of the different cutting conditions. Further, the method achieved better accuracy than the commonly used root mean square value.

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Funding

This work was financially supported by the “Center for Cyber-physical System Innovation” from The Featured Area Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan. Part of the funding also came from the Ministry of Science and Technology (MOST) in Taiwan under Grant no. MOST 107-2218-E-011-002-.

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Authors and Affiliations

  1. Department of Mechanical Engineering, National Taiwan University of Science and Technology, No. 43, Sec. 4, Keelung Rd., Da’an Dist., Taipei, 106, Taiwan

    Meng-Kun Liu, Yi-Heng Tseng & Minh-Quang Tran

  2. Center for Cyber-physical System Innovation, National Taiwan University of Science and Technology, Taipei, 10607, Taiwan

    Meng-Kun Liu

Authors
  1. Meng-Kun Liu
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  2. Yi-Heng Tseng
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  3. Minh-Quang Tran
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Correspondence to Meng-Kun Liu.

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Appendix

Appendix

Fig. 16
figure 16

Picture of fresh tool

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Fig. 17
figure 17

Evolution of frank wear in test 2

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Liu, MK., Tseng, YH. & Tran, MQ. Tool wear monitoring and prediction based on sound signal. Int J Adv Manuf Technol 103, 3361–3373 (2019). https://doi.org/10.1007/s00170-019-03686-2

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