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Prediction of laser cutting parameters for polymethylmethacrylate sheets using random vector functional link network integrated with equilibrium optimizer

Author

Listed:
  • Ammar H. Elsheikh

    (Tanta University)

  • Taher A. Shehabeldeen

    (Huazhong University of Science and Technology
    Kafrelsheikh University)

  • Jianxin Zhou

    (Huazhong University of Science and Technology)

  • Ezzat Showaib

    (Tanta University)

  • Mohamed Abd Elaziz

    (Zagazig University)

Abstract

In this paper, an enhanced random vector functional link network (RVFL) algorithm was employed to predict kerf quality indices during CO2 laser cutting of polymethylmethacrylate (PMMA) sheets. In the proposed model, the equilibrium optimizer (EO) is used to augment the prediction capability of RVFL via selecting the optimal values of RVFL parameters. The predicting model includes four input variables: gas pressure, sheet thickness, laser power, and cutting speed, and five kerf quality indices: rough zone ratio, widths of up and down heat affected zones, maximum surface roughness, and kerf taper angle. The experiments were designed using Taguchi L18 orthogonal array. The kerf surface contains three main zones: rough, transient, and smooth zones. The results of conventional RVFL as well as modified RVFL-EO algorithms were compared with experimental ones. Seven statistical criteria were used to assess the performance of the proposed algorithms. The results indicate that the RVFL-EO model has the predicting ability to estimate the laser-cutting characteristics of PMMA sheet.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:joinma:v:32:y:2021:i:5:d:10.1007_s10845-020-01617-7
    DOI: 10.1007/s10845-020-01617-7
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    References listed on IDEAS

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    1. Jagadish & Sumit Bhowmik & Amitava Ray, 2019. "Prediction of surface roughness quality of green abrasive water jet machining: a soft computing approach," Journal of Intelligent Manufacturing, Springer, vol. 30(8), pages 2965-2979, December.
    2. Sudipto Chaki & Ravi N. Bathe & Sujit Ghosal & G. Padmanabham, 2018. "Multi-objective optimisation of pulsed Nd:YAG laser cutting process using integrated ANN–NSGAII model," Journal of Intelligent Manufacturing, Springer, vol. 29(1), pages 175-190, January.
    3. Zhiwen Huang & Jianmin Zhu & Jingtao Lei & Xiaoru Li & Fengqing Tian, 2020. "Tool wear predicting based on multi-domain feature fusion by deep convolutional neural network in milling operations," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 953-966, April.
    4. Carlos Gonzalez-Val & Adrian Pallas & Veronica Panadeiro & Alvaro Rodriguez, 2020. "A convolutional approach to quality monitoring for laser manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 789-795, March.
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    Cited by:

    1. 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.
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    3. Seyed Hamidreza Hazaveh & Ali Bayandour & Azam Khalili & Ali Barkhordary & Ali Farzamnia & Ervin Gubin Moung, 2023. "Impulsive Noise Suppression Methods Based on Time Adaptive Self-Organizing Map," Energies, MDPI, vol. 16(4), pages 1-15, February.

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