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The wear detection of mill-grinding tool based on acoustic emission sensor

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Abstract

The monitoring of tool wear plays an important role in improving the processing efficiency and reducing the production cost of enterprises. This paper is focused on the detection of electroplated diamond mill-grinding tools by using the acoustic emission sensor. The wear stages of mill-grinding tools are divided into three parts, namely initial wear stage, normal wear stage, and severe wear stage. The characteristic parameter method and the waveform analysis method are applied to analyze the acoustic emission signals. The wear characteristics of the tool and workpiece in different wear stages are observed and analyzed. The results indicate that the acoustic emission waveform is relatively stable in the initial wear stage, and the continuous acoustic emission signal is dominated. Moreover, the diamond abrasive grains are mainly worn and slightly broken in the normal wear stage, and there are some pits on the machined workpiece surface after the initial wear stage. In the severe wear stage, most of the abrasive grains are broken or broken in a large area, and there are burst acoustic emission signals in the waveform.

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The authors confirm that all data and materials reported in this paper during the current study are available.

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Funding

This study was supported by the Science and Technology Planning Project of Fujian Province (2019H0015) and Subsidized Project for Postgraduates’ Innovative Fund in Scientific Research of Huaqiao University (18014080007).

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Authors and Affiliations

  1. College of Mechanical Engineering and Automation, Huaqiao University, Xiamen, 361021, China

    Wuzhen Huang, Yuan Li, Xian Wu & Jianyun Shen

Authors
  1. Wuzhen Huang
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  2. Yuan Li
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  3. Xian Wu
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  4. Jianyun Shen
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Contributions

The authors who contributed to this work have been listed in the author information. Wuzhen Huang: methodology, conceptualization, data curation, formal analysis, visualization, writing—original draft. Yuan Li: funding acquisition, investigation, methodology, project administration, resources, writing—review and editing. Xian Wu: investigation, validation, writing—review and editing. Jianyun Shen: conceptualization, supervision, investigation, resources.

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Correspondence to Yuan Li.

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Huang, W., Li, Y., Wu, X. et al. The wear detection of mill-grinding tool based on acoustic emission sensor. Int J Adv Manuf Technol 124, 4121–4130 (2023). https://doi.org/10.1007/s00170-022-09058-7

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  • DOI: https://doi.org/10.1007/s00170-022-09058-7

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