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

Qixiang Zhang; Mingliang Guo; Jiayun Deng; Jisheng Pan

doi:doi:10.11648/j.es.20200503.13

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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

Qixiang Zhang, Mingliang Guo, Jiayun Deng, Jisheng Pan

Published in Engineering Science (Volume 5, Issue 3)

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), 2024. Published by Science Publishing Group

Keywords

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

References

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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

Qixiang Zhang

School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou, China
Mingliang Guo

School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou, China
Jiayun Deng

School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou, China
Jisheng Pan

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

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Plain Text BibTeX RIS

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

Copy | Download

@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}
}

Copy | Download

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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