Shayu Song
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Shayu Song has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Guijian Xiao
College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing, China
1 shared publication - Wei Li
College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044, China
1 shared publication - Ying Liu
College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044, China
1 shared publication - Youdong Zhang
College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044, China
1 shared publication
Latest Publications
- Open Access
Prediction of Surface Roughness of Abrasive Belt Grinding of Superalloy Material Based on RLSOM-RBF
It is difficult to accurately predict the surface roughness of belt grinding with superalloy materials due to the uneven material distribution and complex material processing. In this paper, a radial basis neural network is proposed to predict surface roughness. Firstly, the grinding system of the superalloy belt is introduced. The effects of the material removal process and grinding parameters on the surface roughness in belt grinding were analyzed. Secondly, an RBF neural network is trained by reinforcement learning of a self-organizing mapping method. Finally, the prediction accuracy and simulation results of the proposed method and the traditional prediction method are analyzed using the ten-fold cross method. The results show that the relative error of the improved RLSOM-RBF neural network prediction model is 1.72%, and the R-value of the RLSOM-RBF fitting result is 0.996.
Open AccessPrediction of Surface Roughness of Abrasive Belt Grinding of Superalloy Material Based on RLSOM-RBF
It is difficult to accurately predict the surface roughness of belt grinding with superalloy materials due to the uneven material distribution and complex material processing. In this paper, a radial basis neural network is proposed to predict surface roughness. Firstly, the grinding system of the superalloy belt is introduced. The effects of the material removal process and grinding parameters on the surface roughness in belt grinding were analyzed. Secondly, an RBF neural network is trained by reinforcement learning of a self-organizing mapping method. Finally, the prediction accuracy and simulation results of the proposed method and the traditional prediction method are analyzed using the ten-fold cross method. The results show that the relative error of the improved RLSOM-RBF neural network prediction model is 1.72%, and the R-value of the RLSOM-RBF fitting result is 0.996.