Liu Ying, Song Shayu, Zhang Youdong, Li Wei, Xiao Guijian
College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044, China.
Materials (Basel). 2021 Sep 30;14(19):5701. doi: 10.3390/ma14195701.
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.
由于材料分布不均和材料加工复杂，准确预测高温合金材料带式磨削的表面粗糙度具有一定难度。本文提出一种径向基神经网络来预测表面粗糙度。首先，介绍了高温合金带式磨削系统，分析了材料去除过程和磨削参数对带式磨削表面粗糙度的影响。其次，通过自组织映射方法的强化学习对径向基神经网络进行训练。最后，采用十折交叉法分析了所提方法与传统预测方法的预测精度和仿真结果。结果表明，改进后的RLSOM-RBF神经网络预测模型的相对误差为1.72%，RLSOM-RBF拟合结果的R值为0.996。
