论文部分内容阅读
激光铣削是一种重要的加工技术,为了提高激光铣削预测精度,更好控制激光铣削的质量,提出了粒子群算法优化神经网络的激光铣削质量预测模型(PSO-BPNN)。首先收集激光铣削质量的相关参数数据,归一化处理参数值,然后根据参数值构建神经网络的激光铣削质量预测模型,并采用粒子群算法对神经网络的权值与阈值进行优化,最后以某种材料的激光铣削质量为例对模型性能进行分析。相对于其它激光铣削预测模型,PSO-BPNN可以降低激光铣削质量的预测误差,获得了更高的激光铣削质量预测精度。
Laser milling is an important processing technology. In order to improve the prediction accuracy of laser milling and control the quality of laser milling, a laser particle swarm optimization algorithm based on Particle Swarm Optimization (PSO-BPNN) is proposed. Firstly, the parameters of laser milling quality are collected, and the parameter values are normalized. Then the quality prediction model of the neural network is constructed based on the parameter values, and the weights and thresholds of the neural network are optimized by particle swarm optimization. Finally, For example, the quality of laser milling materials is analyzed. Compared with other laser milling prediction models, PSO-BPNN can reduce the prediction error of laser milling quality and obtain higher laser milling quality prediction accuracy.