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Research on reconstruction and high-precision detection of tool wear edges under complex lighting environmental influences

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Abstract

On account of the complex lighting environmental impact of the machine tool, the tool body and wear boundary cannot be accurately extracted, which brings adverse effects to the wear detection. At the same time, most of the research on the side edge wear detection of end mill has focused on local wear, lacking the judgment and analysis of the overall side edge wear. In view of this situation, a “holistic to local” tool wear detection method based on the reconstruction idea was proposed. Firstly, the wear condition of the tool flank was identified as a whole, and then, the wear area affecting the remaining useful life of the tool was detected. The method mainly consists of three steps: the extraction of tool body, the reconstruction of tool wear edge, and tool wear detection. Aiming at the complex background of the tool image, a semantic segmentation algorithm based on U-net neural network was applied to separate foreground and background and extract the tool body. Secondly, based on pixel accuracy, the tool edge was extracted and the tool wear boundary was reconstructed. Finally, the range of the wear boundary coordinates was obtained, the number of pixels occupied by the maximum wear amount was gained, and the maximum wear of the tool was calculated according to the pixel length equivalent. Comparing the wear amount detected by the algorithm with the actual measured value, the results show that the method has high detection accuracy and the average error is controlled within 5%, which proves the effectiveness of the method and realizes the high-precision detection of the wear state of the tool during machining.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Funding

This project was supported by the National Key Research and Development Project of China (2022YFB4601403) and the National Natural Science Foundation of China (No. 52175336).

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Authors and Affiliations

  1. Centre for Advanced Jet Engineering Technologies (CaJET), School of Mechanical Engineering, Shandong University, Jinan, 250061, China

    Ting Lei, Bin Zou, Wei Chen, Qinbing Zheng, Jinzhao Yang, Lei Li & Jikai Liu

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

    Ting Lei, Bin Zou, Wei Chen, Qinbing Zheng, Jinzhao Yang, Lei Li & Jikai Liu

  3. National Demonstration Center for Experimental Mechanical Engineering Education, Shandong University, Jinan, China

    Ting Lei, Bin Zou, Wei Chen, Qinbing Zheng, Jinzhao Yang, Lei Li & Jikai Liu

  4. Additive Manufacturing Research Center of Shandong University of National Engineering Research Center of Rapid Manufacturing, Shandong University, Jinan, 250061, China

    Ting Lei, Bin Zou, Wei Chen, Qinbing Zheng, Jinzhao Yang, Lei Li & Jikai Liu

Authors
  1. Ting Lei
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  2. Bin Zou
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  3. Wei Chen
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  6. Lei Li
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Contributions

TL (first author): methodology, validation, formal analysis, investigation, writing original draft. BZ (corresponding author): formal analysis, resources, writing—review and editing. WC, QZ, JY: formal analysis. LL, JL: check and verify original draft. The author’s contribution corresponds their order. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Bin Zou.

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This study does not involve human participants and/or animal studies.

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Not applicable. All data in this paper can be published and verified by all authors.

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The authors declare no competing interests.

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I would like to declare on behalf of my co-authors that the work described was original research that has not been published previously and not under consideration for publication elsewhere, in whole or in part.

Highlights

1. A method for detecting side edge wear of end milling cutters in complex lighting environments is proposed.

2. Use U-net based voice segmentation network to achieve tool body extraction.

3. Tool wear boundary reconstruction based on pixel accuracy.

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Cite this article

Lei, T., Zou, B., Chen, W. et al. Research on reconstruction and high-precision detection of tool wear edges under complex lighting environmental influences. Int J Adv Manuf Technol 129, 4529–4540 (2023). https://doi.org/10.1007/s00170-023-12446-2

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  • DOI: https://doi.org/10.1007/s00170-023-12446-2

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