基于改进BP神经网络的螺杆转子铣削表面粗糙度预测

以提高螺杆转子等具有螺旋曲面零件铣削表面质量为目的。根据螺杆转子加工特点，针对主轴转速、进给倍率和间歇进给量进行单因素轮换铣削加工实验。采用改进粒子群算法确定BP神经网络初始权值和阈值的最优值，采用训练后的改进BP神经网络算法对铣削后的螺杆转子表面粗糙度进行预测，并与传统BP神经网络进行对比。结果表明，传统BP神经网络对表面粗糙度的训练精度最低，改进算法中粒子群迭代2 000次的平均相对误差最小，为1.21%。利用模型进行工艺参数对表面粗糙度影响规律的预测，可以看出，其他工艺参数不变的前提下，随着主轴转速的升高，表面粗糙度呈现降低趋势；随间歇进给量的增大，表面粗糙度先降低后升高；表面粗糙度随进给倍率的增加，呈现先降低后升高的趋势。结论：改进神经网络算法可以准确预测铣削后的螺杆转子表面粗糙度，为螺杆转子铣削加工中的工艺参数选择提供理论指导。

Roughness prediction of spiral surface milling based on improved BP neural network

In order to improve the milling surface quality of screw rotor and other parts with spiral surface. According to the machining characteristics of screw rotor, the single factor rotation milling experiment is carried out according to the spindle speed, feed rate and intermittent feed. The improved particle swarm optimization algorithm is used to determine the optimal value of the initial weight and threshold of BP neural network. The trained improved BP neural network algorithm is used to predict the surface roughness of the milled screw rotor, and compared with the traditional BP neural network. The results show that the training accuracy of traditional BP neural network for surface roughness is the lowest, and the average relative error of 2000 iterations of particle swarm optimization in the improved algorithm is the lowest, which is 1. 21%. Using the model to predict the influence law of process parameters on surface roughness, it can be seen that under the premise of other process parameters unchanged, the surface roughness shows a decreasing trend with the increase of spindle speed; With the increase of intermittent feed rate, the surface roughness first decreases then increases; With the increase of feed rate, the surface roughness decreases first then increases. Conclusion: The improved neural network algorithm can accurately predict the surface roughness of spiral surface after milling, and provide theoretical guidance for the selection of process parameters in screw rotor milling.