激光三维铣削是在单层激光铣削基础上，逐层加工形成的，因此，单层铣削深度将决定整个三维铣削层质量。针对激光铣削中影响因素多且存在交互因素而无法用线性关系式表达的现象，基于人工神经网络技术，借助MATLAB平台，建立了工艺参数到铣削深度和铣削深度到工艺参数的双向前馈型反向传播(BP)神经网络模型，利用激光铣削试验获得样本数据对网络进行训练和测试。结果表明，利用带有隐含层的BP前馈型神经网络能够较好地预测和优化加工参数，对于减少激光三维铣削试验次数具有很好的帮助。

Three-dimensional laser milling is based on the single layer milling. So, the depth of single milling determines the quality of the three-dimensional laser milling. There are many mutual factors affecting the laser milling, which can not be expressed as a linear relationship. Based on the artificial neural networks, the bidirectional feed-forward back propagation (BP) neural network models of milling width and depth are set up with Matlab platform. The neural network models are tested using samples obtained from the laser milling experiments. The results show the feed-forward BP neural network with hidden layer can predict and optimize the process parameters. Which is helpful to reduce the times of experiments.