螺杆转子盘铣刀铣削表面粗糙度预测

螺杆转子主要应用在压缩机、螺杆泵等设备中，其表面质量对使用性能及工作寿命起到关键性作用。工艺参数为影响螺杆转子表面粗糙度的主要因素之一。为了探究工艺参数对螺旋曲面铣削表面质量的影响规律，设计转子铣削实验，获取预测及实验对比样本。利用改进的北方苍鹰搜索算法(INGO)对BP神经网络的初始权值和阈值进行优化，以便提高铣削后的多头螺杆转子表面粗糙度的预测精度。通过实验结果验证所提出算法的预测精度。结果表明，提出的预测模型在平均训练精度及预测精度等方面均优于GRU神经网络及CNN-GRU神经网络模型，其中平均训练精度及预测精度分别约为94.502%和95.523%。故提出的算法具有较高的预测精度，可为合理选择螺杆转子铣削加工的工艺参数提供理论依据。

Prediction of surface roughness of screw rotor disc milling cutter

Screw rotors are mainly used in compressors, screw pumps and other equipment, and their surface quality plays a key role in service performance and service life. Process parameters are one of the main factors affecting the surface roughness of screw rotors. In order to explore the influence of process parameters on the surface quality of helical surface milling, a rotor milling experiment was designed to obtain prediction and experimental comparison samples. The improved northern goshawk search algorithm (INGO) is used to optimize the initial weights and thresholds of the BP neural network, so as to improve the prediction accuracy of the surface roughness of the milled multi-head screw rotor. Experimental results verify the prediction accuracy of the proposed algorithm. The results show that the proposed prediction model outperforms GRU neural network and CNN-GRU neural network models in terms of average training accuracy and prediction accuracy. The average training accuracy and prediction accuracy are about 94.502% and 95.523% respectively. Therefore, the proposed algorithm has high prediction accuracy and can provide a theoretical basis for reasonable selection of processing parameters of screw rotor milling.