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Drilling Process of GFRP Composites: Modeling and Optimization Using Hybrid ANN

Author

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  • Mohamed S. Abd-Elwahed

    (Mechanical Engineering Department, King Abdulaziz University, Jeddah 22254, Saudi Arabia)

Abstract

This paper aims to optimize the machining parameters of the drilling process of woven-glass-fiber reinforced epoxy (WGFRE) composites. It will focus on modeling and optimizing drill spindle speed and feed with different laminate thicknesses, with respect to torque and delamination factor. The response surface analysis and artificial neural networks are utilized to model and evaluate the effect of control parameters and their interaction on the drilling process outcomes. The particle swarm optimization algorithm is used to improve the ANN training, to increase its performance in prediction. The optimization method of desirability, based on RSM, is applied to validate the optimal combination of control factors, in the space of the study. The influences of the control parameters on the drilling process outcomes are discussed in detail. The optimal machining parameters were 0.025 mm/r and 1600 rpm for feed and spindle speed, respectively, with a GFRE laminate of 5.4 mm thickness. The RSM and ANN–PSO models applied to predict the drilling-process parameters showed a very high agreement with the experimental data.

Suggested Citation

  • Mohamed S. Abd-Elwahed, 2022. "Drilling Process of GFRP Composites: Modeling and Optimization Using Hybrid ANN," Sustainability, MDPI, vol. 14(11), pages 1-15, May.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:11:p:6599-:d:826203
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    Citations

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    Cited by:

    1. Siyi Ding & Xiaohu Zheng & Mingyu Wu & Qirui Yang, 2022. "A Novel Sustainable Processing Mode for Burr Classified Prediction of Weak Rigid Drilling Process Using a Fusion Modeling Method," Sustainability, MDPI, vol. 14(12), pages 1-21, June.
    2. Xiaojia Ji & Xuanyi Lu & Chunhong Guo & Weiwei Pei & Hui Xu, 2022. "Predictions of Geological Interface Using Relevant Vector Machine with Borehole Data," Sustainability, MDPI, vol. 14(16), pages 1-17, August.

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