Materials (Nov 2023)

Surface Roughness Prediction of Titanium Alloy during Abrasive Belt Grinding Based on an Improved Radial Basis Function (RBF) Neural Network

  • Kun Shan,
  • Yashuang Zhang,
  • Yingduo Lan,
  • Kaimeng Jiang,
  • Guijian Xiao,
  • Benkai Li

DOI
https://doi.org/10.3390/ma16227224
Journal volume & issue
Vol. 16, no. 22
p. 7224

Abstract

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Titanium alloys have become an indispensable material for all walks of life because of their excellent strength and corrosion resistance. However, grinding titanium alloy is exceedingly challenging due to its pronounced material characteristics. Therefore, it is crucial to create a theoretical roughness prediction model, serving to modify the machining parameters in real time. To forecast the surface roughness of titanium alloy grinding, an improved radial basis function neural network model based on particle swarm optimization combined with the grey wolf optimization method (GWO-PSO-RBF) was developed in this study. The results demonstrate that the improved neural network developed in this research outperforms the classical models in terms of all prediction parameters, with a model-fitting R2 value of 0.919.

Keywords