粒子群优化BP神经网络的激光铣削质量预测模型

为了有效地控制激光铣削层质量，建立了激光铣削层质量（铣削层宽度、铣削层深度）与铣削层参数（激光功率、扫描速度和离焦量）的BP神经网络预测模型。采用粒子群算法优化了BP神经网络的权值和阈值，构建了基于粒子群神经网络的质量预测模型。所提出的PSO-BP算法解决了一般BP算法迭代速度慢，且易出现局部最优的问题，并以Al2O3陶瓷激光铣削质量预测为例，进行算法实现。仿真结果表明:提出的PSO-BP算法迭代次数大大减少，且预测误差明显减少。所构建的质量预测模型具有较高的预测精度和实用价值。

Laser milling quality prediction model of BP neural network by PSO

In order to effectively control the quality of laser milling layer, BP neural network model was established between the laser milling quality of layer(the milling layer width, milling depth) and the milling layer parameters(laser power, scanning speed and defocus amount). Using particle swarm optimization (PSO)BP neural network weights and thresholds, quality prediction model based on particle swarm neural network was built. The proposed PSO-BP algorithm solve the problem that the general BP algorithm iteration speed was slow, and prone to local optimum. Al2O3 ceramics laser milling quality prediction model was taben to realize the algorithm.The simulation results show that the number of iterations of proposed PSO-BP algorithm, and the prediction error are greatly reduced. The built quality prediction model has high prediction accuracy and practical value.