Shan Kun, Zhang Yashuang, Lan Yingduo, Jiang Kaimeng, Xiao Guijian, Li Benkai
AECC Shenyang Liming Aero-Engine Co., Ltd., No. 6 Dongta Street, Dadong District, Shenyang 110862, China.
College of Mechanical and Vehicle Engineering, Chongqing University, No. 174 Shazheng Street, Shapingba District, Chongqing 400444, China.
Materials (Basel). 2023 Nov 18;16(22):7224. doi: 10.3390/ma16227224.
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 R value of 0.919.
钛合金因其出色的强度和耐腐蚀性，已成为各行各业不可或缺的材料。然而，由于钛合金显著的材料特性，磨削加工极具挑战性。因此，建立一个理论粗糙度预测模型至关重要，该模型可用于实时调整加工参数。为预测钛合金磨削表面粗糙度，本研究开发了一种基于粒子群优化与灰狼优化方法相结合的改进径向基函数神经网络模型（GWO-PSO-RBF）。结果表明，本研究开发的改进神经网络在所有预测参数方面均优于经典模型，模型拟合R值为0.919。
