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Shape regulation of tapered microchannels in silica glass ablated by femtosecond laser with theoretical modeling and machine learning

Author

Listed:
  • Kai Liao

    (Xi’an Jiaotong University
    Xi’an Jiaotong University)

  • Wenjun Wang

    (Xi’an Jiaotong University
    Xi’an Jiaotong University)

  • Xuesong Mei

    (Xi’an Jiaotong University
    Xi’an Jiaotong University)

  • Wenwen Tian

    (Xi’an Jiaotong University
    Xi’an Jiaotong University)

  • Hai Yuan

    (Xi’an Microelectronics Technology Institute)

  • Mingqiong Wang

    (Xi’an Microelectronics Technology Institute)

  • Bozhe Wang

    (Xi’an Microelectronics Technology Institute)

Abstract

Femtosecond laser processing is widely used in the micromachining of hard and brittle materials. Preparation of tapered microchannels with customizable cross-sections in silica glass using ultrafast lasers is of great significance in the field of microfluidic applications. In this paper, the width and depth of tapered microchannel in silica glass are predicted by combining theoretical modeling and machine learning. The functional relationship between laser processing parameters and microchannel width is obtained by theoretical modeling and introducing correction coefficients. The estimated model width is highly consistent with the experimental results. To solve the complex nonlinear mapping relationship between microchannel depth and processing parameters, a machine learning method based on a backpropagation neural network algorithm is proposed. By reasonably selecting model parameters, accurate prediction of microchannel depth is achieved with the corresponding average relative prediction error of 5.174%. Based on the proposed method, an effective parameter optimization strategy for achieving microchannels of specific sizes is developed. This method provides a new scheme for size prediction and controllable fabrication of silica glass microchannels with a femtosecond laser. Moreover, the proposed approach significantly reduces the time and cost of trial and error during actual processing and product development.

Suggested Citation

  • Kai Liao & Wenjun Wang & Xuesong Mei & Wenwen Tian & Hai Yuan & Mingqiong Wang & Bozhe Wang, 2023. "Shape regulation of tapered microchannels in silica glass ablated by femtosecond laser with theoretical modeling and machine learning," Journal of Intelligent Manufacturing, Springer, vol. 34(7), pages 2907-2924, October.
  • Handle: RePEc:spr:joinma:v:34:y:2023:i:7:d:10.1007_s10845-022-01950-z
    DOI: 10.1007/s10845-022-01950-z
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    References listed on IDEAS

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    1. Maciej Grzenda & Andres Bustillo, 2019. "Semi-supervised roughness prediction with partly unlabeled vibration data streams," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 933-945, February.
    2. Ammar H. Elsheikh & Taher A. Shehabeldeen & Jianxin Zhou & Ezzat Showaib & Mohamed Abd Elaziz, 2021. "Prediction of laser cutting parameters for polymethylmethacrylate sheets using random vector functional link network integrated with equilibrium optimizer," Journal of Intelligent Manufacturing, Springer, vol. 32(5), pages 1377-1388, June.
    3. Michael D. T. McDonnell & Daniel Arnaldo & Etienne Pelletier & James A. Grant-Jacob & Matthew Praeger & Dimitris Karnakis & Robert W. Eason & Ben Mills, 2021. "Machine learning for multi-dimensional optimisation and predictive visualisation of laser machining," Journal of Intelligent Manufacturing, Springer, vol. 32(5), pages 1471-1483, June.
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