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Volume monitoring of the milling tool tip wear and breakage based on multi-focus image three-dimensional reconstruction

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Abstract

In precision machining, the milling tool’ geometry has a great influence on the milled surface quality. The research on milling tool state monitoring was mainly based on one-dimensional signals and two-dimensional images, which could indirectly obtain the tool state and wear area, but it could not provide the volume of milling tool wear and breakage area, thereby making it difficult to achieve quantitative analysis tool wear. This paper proposed a three-dimensional (3D) reconstruction method of the milling tool tip, it could build a 3D model of the milling tool tip, and then the volume of the wear and breakage region of the milling tool tip was extracted by the 3D model. Firstly, the focusing degree of image sequence’s pixels was calculated based on the non-subsampled discrete shearlet transform (NSST) and Laplace algorithm, and the 3D reconstruction of the milling tool tip was completed according to the shape-from-focus (SFF) principle; secondly, the depth values were optimized by fitting the focusing degree curve of pixels in the image sequence with Gaussian function; finally, the volume of the 3D point cloud of the milling tool tip was calculated by the Simpson double numerical integration method, and the material loss in the damaged region could be obtained. In the 3D reconstruction experiment of the milling tool tip, comparing the different focus degree evaluation operators of SFF, the proposed 3D reconstruction method has the least noise and the best performance in the root-mean-square error, correlation, and smoothness indexes.

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Data availability

The datasets used or analyzed during the currentstudy are available from the corresponding author on reasonable request.

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Acknowledgements

This work was funded by Special Projects in Key Fields of General Universities in Guangdong Province (No. 2022ZDZX3070) and the Science and Technology Planning Project of Shenzhen Municipality, China (Grant No. JCYJ20190808113413430). The authors are also grateful to the colleagues for their essential contribution to the work.

Funding

The work is financially supported by Special Projects in Key Fields of General Universities in Guangdong Province (No. 2022ZDZX3070) and the Science and Technology Planning Project of Shenzhen Municipality, China (Grant No. JCYJ20190808113413430).

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Author notes

  1. Yeping Peng, Shucong Qin, Tao Wang, Yixi Hu and Shiping Nie contributed equally to this work.

Authors and Affiliations

  1. Guangdong Key Laboratory of Electromagnetic Control and Intelligent Robots, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, 518060, China

    Yeping Peng, Shucong Qin & Yixi Hu

  2. Institute of Intelligent Manufacturing Technology, Shenzhen Polytechnic, Shenzhen, 518055, China

    Tao Wang & Shiping Nie

Authors
  1. Yeping Peng
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  2. Shucong Qin
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  3. Tao Wang
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  4. Yixi Hu
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Contributions

All authors participated in the work of the paper. Yeping Peng: writing—review and editing, methodology; Shucong Qin: writing—original draft, methodology, data curation resource; Tao Wang: project administration, conceptualization, funding acquisition; Yixi Hu: investigation; Shiping Nie: material preparation. All authors read and approved the final manuscript.

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Correspondence to Tao Wang.

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Peng, Y., Qin, S., Wang, T. et al. Volume monitoring of the milling tool tip wear and breakage based on multi-focus image three-dimensional reconstruction. Int J Adv Manuf Technol 126, 3383–3400 (2023). https://doi.org/10.1007/s00170-023-11335-y

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