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Machine vision online detection for abrasive protrusion height on the surface of electroplated diamond wire saw

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Abstract

Electroplated diamond wire saw is the main tool for slicing semiconductor materials such as silicon. The quality and efficiency of slicing would be directly affected by the manufacturing quality of the electroplated diamond wire saw. The abrasive protrusion height of the electroplated diamond wire saw is one of the important indexes to evaluate its manufacturing quality. However, only the offline method can be used to detect the protrusion height of the abrasives, which makes it impossible to realize the online control and optimization of the manufacturing process parameters. If the offline detection results are not qualified, the electroplated diamond wire saw with a length of tens of kilometers would be scrapped, which is easy to cause a waste of resources. The above problems could be solved by adding an online detection and feedback adjustment module in the electroplated diamond wire saw manufacturing processes. To this end, the influence of abrasive distribution position on its protrusion height imaging distortion is analyzed. A distortion correction criterion for the abrasive protrusion height is established. Connected domain labeling and Hough transform are used to extract information from electroplated diamond wire saw images. A method based on machine vision is proposed, which can realize the online detection of the protrusion height of abrasives on the surface of the electroplated diamond wire saw. Experiment results show that the detection accuracy of this method is better than that of the offline method. Based on the hardware conditions in this paper, the average sampling interval of this method is 0.15 s, which could meet the online detection requirements for the abrasive protrusion height of the electroplated diamond wire saw.

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Funding

This work is supported by the National Natural Science Foundation of China (52175418, 51775317) and the Key Research and Development Program of Shandong Province, China (2022CXGC010201, 2019JZZY020209).

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Authors and Affiliations

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

    Yukang Zhao, Peiqi Ge, Wenbo Bi, Jintao Zheng & Jialei Lan

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

    Peiqi Ge & Wenbo Bi

Authors
  1. Yukang Zhao
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  2. Peiqi Ge
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  3. Wenbo Bi
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  4. Jintao Zheng
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Contributions

Yukang Zhao: writing — original draft, writing — review & editing, investigation, methodology. Peiqi Ge: conceptualization, writing — review & editing, funding acquisition, project administration, supervision. Wenbo Bi: writing — review & editing. Jintao Zheng: Writing — review & editing. Jialei Lan: Validation.

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Correspondence to Peiqi Ge.

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Zhao, Y., Ge, P., Bi, W. et al. Machine vision online detection for abrasive protrusion height on the surface of electroplated diamond wire saw. Int J Adv Manuf Technol 121, 7923–7932 (2022). https://doi.org/10.1007/s00170-022-09901-x

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