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Prediction Model of Material Removal for Polishing Single Crystal Silicon by Cluster Magnetorheological Finishing with Dynamic Magnetic Fields Based on BP Neural Network

Received: 11 September 2020     Accepted: 27 September 2020     Published: 13 October 2020
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Abstract

Magnetorheological Finishing (MRF) is a new optical surface processing method, which has the advantages that good polishing effect, no subsurface damage, and suitable for complex surface processing. However, the interaction mechanism between the MRF pad and the workpiece is very complicated, so that the existing MRF material removal theoretical model is not accurate enough to establish the relationship between polishing parameters and material removal. In order to improve the processing efficiency and explore the material removal mechanism, a cluster magnetorheological finishing (CMRF) with dynamic magnetic fields method was proposed. Studying CMRF with dynamic magnetic fields material removal model is helpful to explain the removal mechanism more deeply, and improve the processing efficiency. In this study, the CMRF method was used to conduct a multi-factor orthogonal test on 2-inch single crystal silicon wafers. Based on the empirical Preston equation, the relationship between the machining gap and the polishing pressure was explained. Orthogonal experiments were done for a series of speeds, and obtaining the order of the influence of various factors on the average surface roughness Ra of the workpiece was: workpiece rotation speed > polishing disk speed > magnetic poles rotation speed > oscillating speed; the material removal rate (MRR) was: polishing disk speed > workpiece rotation speed > magnetic poles rotation speed > oscillating speed. Then combining with the orthogonal experimental data, and taking the surface roughness Ra and MRR as evaluation criteria, using Adam (Adaptive momentum) optimization algorithm to build a prediction model of Ra and MRR for polishing single crystal silicon by CMRF with dynamic magnetic fields based on BP neural network. For the prediction result, Ra of the maximum error was 7.05%, the minimum was 0.31%; MRR of the maximum error was 10.22%, the minimum was 1.32%. Therefore, the feasibility of this model for predicting the results of CMRF was verified, and it laid a good foundation for the development of CMRF technology and its industrial application.

Published in Engineering Science (Volume 5, Issue 3)
DOI 10.11648/j.es.20200503.13
Page(s) 38-44
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2020. Published by Science Publishing Group

Keywords

Cluster Magnetorheological Finishing, Dynamic Magnetic Fields, Preston Equation, BP Neural Network, Prediction Model

References
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[4] Catrin, R., Neauport, J., Taroux, D., Cormont, P., Maunier, C., & Lambert, S. (2014). Magnetorheological finishing for removing surface and subsurface defects of fused silica optics. Optical Engineering, 53 (9), 092010.
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Cite This Article
  • APA Style

    Qixiang Zhang, Mingliang Guo, Jiayun Deng, Jisheng Pan. (2020). Prediction Model of Material Removal for Polishing Single Crystal Silicon by Cluster Magnetorheological Finishing with Dynamic Magnetic Fields Based on BP Neural Network. Engineering Science, 5(3), 38-44. https://doi.org/10.11648/j.es.20200503.13

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    ACS Style

    Qixiang Zhang; Mingliang Guo; Jiayun Deng; Jisheng Pan. Prediction Model of Material Removal for Polishing Single Crystal Silicon by Cluster Magnetorheological Finishing with Dynamic Magnetic Fields Based on BP Neural Network. Eng. Sci. 2020, 5(3), 38-44. doi: 10.11648/j.es.20200503.13

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    AMA Style

    Qixiang Zhang, Mingliang Guo, Jiayun Deng, Jisheng Pan. Prediction Model of Material Removal for Polishing Single Crystal Silicon by Cluster Magnetorheological Finishing with Dynamic Magnetic Fields Based on BP Neural Network. Eng Sci. 2020;5(3):38-44. doi: 10.11648/j.es.20200503.13

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  • @article{10.11648/j.es.20200503.13,
      author = {Qixiang Zhang and Mingliang Guo and Jiayun Deng and Jisheng Pan},
      title = {Prediction Model of Material Removal for Polishing Single Crystal Silicon by Cluster Magnetorheological Finishing with Dynamic Magnetic Fields Based on BP Neural Network},
      journal = {Engineering Science},
      volume = {5},
      number = {3},
      pages = {38-44},
      doi = {10.11648/j.es.20200503.13},
      url = {https://doi.org/10.11648/j.es.20200503.13},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.es.20200503.13},
      abstract = {Magnetorheological Finishing (MRF) is a new optical surface processing method, which has the advantages that good polishing effect, no subsurface damage, and suitable for complex surface processing. However, the interaction mechanism between the MRF pad and the workpiece is very complicated, so that the existing MRF material removal theoretical model is not accurate enough to establish the relationship between polishing parameters and material removal. In order to improve the processing efficiency and explore the material removal mechanism, a cluster magnetorheological finishing (CMRF) with dynamic magnetic fields method was proposed. Studying CMRF with dynamic magnetic fields material removal model is helpful to explain the removal mechanism more deeply, and improve the processing efficiency. In this study, the CMRF method was used to conduct a multi-factor orthogonal test on 2-inch single crystal silicon wafers. Based on the empirical Preston equation, the relationship between the machining gap and the polishing pressure was explained. Orthogonal experiments were done for a series of speeds, and obtaining the order of the influence of various factors on the average surface roughness Ra of the workpiece was: workpiece rotation speed > polishing disk speed > magnetic poles rotation speed > oscillating speed; the material removal rate (MRR) was: polishing disk speed > workpiece rotation speed > magnetic poles rotation speed > oscillating speed. Then combining with the orthogonal experimental data, and taking the surface roughness Ra and MRR as evaluation criteria, using Adam (Adaptive momentum) optimization algorithm to build a prediction model of Ra and MRR for polishing single crystal silicon by CMRF with dynamic magnetic fields based on BP neural network. For the prediction result, Ra of the maximum error was 7.05%, the minimum was 0.31%; MRR of the maximum error was 10.22%, the minimum was 1.32%. Therefore, the feasibility of this model for predicting the results of CMRF was verified, and it laid a good foundation for the development of CMRF technology and its industrial application.},
     year = {2020}
    }
    

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  • TY  - JOUR
    T1  - Prediction Model of Material Removal for Polishing Single Crystal Silicon by Cluster Magnetorheological Finishing with Dynamic Magnetic Fields Based on BP Neural Network
    AU  - Qixiang Zhang
    AU  - Mingliang Guo
    AU  - Jiayun Deng
    AU  - Jisheng Pan
    Y1  - 2020/10/13
    PY  - 2020
    N1  - https://doi.org/10.11648/j.es.20200503.13
    DO  - 10.11648/j.es.20200503.13
    T2  - Engineering Science
    JF  - Engineering Science
    JO  - Engineering Science
    SP  - 38
    EP  - 44
    PB  - Science Publishing Group
    SN  - 2578-9279
    UR  - https://doi.org/10.11648/j.es.20200503.13
    AB  - Magnetorheological Finishing (MRF) is a new optical surface processing method, which has the advantages that good polishing effect, no subsurface damage, and suitable for complex surface processing. However, the interaction mechanism between the MRF pad and the workpiece is very complicated, so that the existing MRF material removal theoretical model is not accurate enough to establish the relationship between polishing parameters and material removal. In order to improve the processing efficiency and explore the material removal mechanism, a cluster magnetorheological finishing (CMRF) with dynamic magnetic fields method was proposed. Studying CMRF with dynamic magnetic fields material removal model is helpful to explain the removal mechanism more deeply, and improve the processing efficiency. In this study, the CMRF method was used to conduct a multi-factor orthogonal test on 2-inch single crystal silicon wafers. Based on the empirical Preston equation, the relationship between the machining gap and the polishing pressure was explained. Orthogonal experiments were done for a series of speeds, and obtaining the order of the influence of various factors on the average surface roughness Ra of the workpiece was: workpiece rotation speed > polishing disk speed > magnetic poles rotation speed > oscillating speed; the material removal rate (MRR) was: polishing disk speed > workpiece rotation speed > magnetic poles rotation speed > oscillating speed. Then combining with the orthogonal experimental data, and taking the surface roughness Ra and MRR as evaluation criteria, using Adam (Adaptive momentum) optimization algorithm to build a prediction model of Ra and MRR for polishing single crystal silicon by CMRF with dynamic magnetic fields based on BP neural network. For the prediction result, Ra of the maximum error was 7.05%, the minimum was 0.31%; MRR of the maximum error was 10.22%, the minimum was 1.32%. Therefore, the feasibility of this model for predicting the results of CMRF was verified, and it laid a good foundation for the development of CMRF technology and its industrial application.
    VL  - 5
    IS  - 3
    ER  - 

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Author Information
  • School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou, China

  • School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou, China

  • School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou, China

  • School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou, China

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