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High-efficiency abrasive water jet milling of aspheric RB-SiC surface based on BP neural network depth control models

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Abstract

For large processing allowance of large diameter RB-SiC mirror blanks and low efficiency of grinding, an abrasive water jet milling is used to quickly remove the processing allowance. In this article, a single kerf profile processed by abrasive water jet milling was effectively fitted to the Gaussian curve. By superimposing Gaussian curves linearly, the surface waviness of superimposed curve was gradually reduced as step-over distance decreased. The surface waviness induced by abrasive water jet milling can be effectively reduced when step-over distance is controlled to less than 1.8σ. BP neural network models between step-over distance, traverse speed, and milling depth were established. The prediction error of milling depth can be controlled at about 5% of the total depth, with a maximum error less than 7%. The aspherical RB-SiC surface was generated by abrasive water jet milling with a processing path composed of 20 spiral segments. Different milling depths were obtained by setting different levels of traverse speed and step-over distance for each spiral segment. The processed aspherical surface was highly fitted to the design aspherical surface with a maximum error about 10% of the total depth. The error curves float at the zero line, and the error curves were controlled at 20% of the total depth. By this method, the processing allowance of large diameter RB-SiC mirror blanks can be effectively reduced.

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Funding

This work was supported by the National Natural Science Foundation of China (No. 52075302), the National Key R&D Program of China (No. 2021YFB3203100), and the Shenzhen Science and Technology Program (No. GJHZ20210705142537003).

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Authors and Affiliations

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

    Hongxing Deng, Peng Yao, Shimeng Yu, Hongtao Zhu & Dun Liu

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

    Hongxing Deng, Peng Yao, Shimeng Yu, Hongtao Zhu & Dun Liu

  3. Shenzhen Research Institute of Shandong University, Shenzhen, 518057, China

    Peng Yao

  4. Fine Mechanics & Physics, Changchun Institute of Optics, Chinese Academy of Sciences, Changchun, 130033, China

    Kuo Hai

  5. School of Mechanical Engineering, Yanshan University, Qinhuangdao, 066004, Hebei, China

    Chuanzhen Huang

Authors
  1. Hongxing Deng
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  2. Peng Yao
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  3. Kuo Hai
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  4. Shimeng Yu
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  5. Chuanzhen Huang
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  6. Hongtao Zhu
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  7. Dun Liu
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Contributions

Hongxing Deng: conceptualization, methodology, validation, formal analysis, investigation, writing—original draft. Peng Yao: resources, writing—review and editing, funding acquisition, data curation. Kuo Hai: resources, writing—review and editing. Shimeng Yu: resources and review. Chuanzhen Huang: resources, project administration. Hongtao Zhu: formal analysis, writing—review and editing. Dun Liu: resources, data curation.

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Correspondence to Peng Yao or Kuo Hai.

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Deng, H., Yao, P., Hai, K. et al. High-efficiency abrasive water jet milling of aspheric RB-SiC surface based on BP neural network depth control models. Int J Adv Manuf Technol 126, 3133–3148 (2023). https://doi.org/10.1007/s00170-023-11275-7

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  • DOI: https://doi.org/10.1007/s00170-023-11275-7

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