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Experimental Study of the Cutting Force During Laser-Assisted Machining of Fused Silica Based on Artificial Neural Network and Response Surface Methodology

  • Huawei Song
  • Guoqi Ren
  • Jinqi Dan
  • Jialun Li
  • Junfeng Xiao
  • Jianfeng Xu
Original Paper
  • 13 Downloads

Abstract

Laser-assisted machining (LAM) is considered an efficient method for the processing of fused silica. In this study, an analysis model based on artificial neural network (ANN) with Bayesian regularization algorithm (BR) was used to investigate the effects of the machine parameters (rotation speed, feed rate, cutting depth, and pulse duty ratio) on the resultant cutting force during the LAM of fused silica. Its prediction capability was validated experimentally and evaluated quantitatively. The optimal combination of machine parameters corresponding to the minimum resultant cutting force was then studied using the genetic algorithm (GA) coupled with the established ANN model. Moreover, the optimal numerical solution was verified experimentally, and the processing quality under optimal machine parameters was characterized through analyzing the surface morphology and roughness. In addition, the performances of prediction and optimization of ANN model were compared with the model based on response surface methodology (RSM). And the mean absolute error in prediction and the optimal cutting force are reduced by 34.47% and 19.11% respectively, compared to RSM. The results clearly show that the ANN model achieves a better behavior in studying the influence of the machine parameters during the LAM of fused silica.

Keywords

Laser-assisted machining Fused silica ANN RSM 

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Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China (grant no. 51627807). The authors thank the Analytical and Testing Center of Huazhong University of Science and Technology. The authors are also thankful to Ms. Yan Zhu for providing help for the SEM analysis for research work.

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Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  • Huawei Song
    • 1
  • Guoqi Ren
    • 1
  • Jinqi Dan
    • 1
  • Jialun Li
    • 1
  • Junfeng Xiao
    • 1
  • Jianfeng Xu
    • 1
  1. 1.State Key Laboratory of Digital Manufacturing Equipment & Technology, School of Mechanical Science and EngineeringHuazhong University of Science and TechnologyWuhanChina

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