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An online tool wear detection system in dry milling based on machine vision

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Abstract

Tool wear is accelerated with the friction in the tool–workpiece contact during dry cutting. Tool changing early or late will affect the quality of tool and workpiece. An online and machine system vision-based is built to monitor tool condition in real time. MATLAB is used to compile the self-matching algorithm, which considers the features of interested targets on the flank face. Furthermore, a corresponding GUI is designed and encapsulated for both the bottom and flank edges. It is shown that the absolute value of the error on the maximum wear width is not more than 0.007 mm for the bottom edge. For the flank edge, the absolute value of the error is not more than 0.030 mm owing to the local highlighting interference. It is proved that the system can guarantee the quality of tool and workpiece and avoid unnecessary waste significantly. This platform can enhance the utilization of the tool in dry cutting.

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Acknowledgments

The authors want to extend sincere thanks to all the previous researches that contributed to this paper.

Funding

This study is supported by the AVIC Cheng Du Aircraft Industrial (Group) Co. Ltd. (grant no. 2014-063), Key R & D project of Shandong Province (grant no. 2017GGX30141), and difital workshop of aeronautic large-scale complex structural (intelligent manufacturing special support by Ministry of Industry and Information Technology in 2015) (grant no. 40205000150X).

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Authors and Affiliations

  1. School of Mechanical Engineering, Shandong University, Jinan, 250061, China

    Qiulin Hou, Jie Sun & Panling Huang

  2. Key Laboratory of High Efficiency and Clean Mechanical Manufacture of Ministry of Education, Shandong University, Jinan, 250061, China

    Qiulin Hou, Jie Sun & Panling Huang

  3. Tianjin Institute of Navigation Instrument, Tianjin, 300131, China

    Zhenyu Lv

  4. AVIC Cheng Du Aircraft Industrial (Group) Co., Ltd., Chengdu, 610092, China

    Ge Song & Chao Sun

Authors
  1. Qiulin Hou
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  2. Jie Sun
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  3. Zhenyu Lv
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  4. Panling Huang
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  5. Ge Song
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  6. Chao Sun
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Correspondence to Jie Sun.

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Hou, Q., Sun, J., Lv, Z. et al. An online tool wear detection system in dry milling based on machine vision. Int J Adv Manuf Technol 105, 1801–1810 (2019). https://doi.org/10.1007/s00170-019-04367-w

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  • DOI: https://doi.org/10.1007/s00170-019-04367-w

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