基于神经网络的激光熔覆高度预测

Laser Cladding Height Prediction Based on Neural Network

激光成形过程中,对熔覆高度进行实时检测,从而实现熔覆高度闭环控制是成形高质量零件的保证。激光成形过程是一个多参数耦合的非线性过程,大量激光参数对成形熔覆表面质量具有重要影响。在分析激光参数对熔覆高度影响的基础上,建立利用激光工艺参数预测熔覆高度的误差反向传播(Backpropagation,BP)神经网络模型,完成了网络算法设计。通过激光成形试验采集样本,利用训练样本对所建立的网络进行训练,完成网络输入输出高度映射关系,并利用测试样本对所训练的网络进行检验。仿真试验表明,神经网络熔覆高度预测模型具有很高的精度,验证了该预测模型在理论和实践上的可行性与有效性。神经网络熔覆高度预测模型为实现激光加工过程熔覆高度实时预测与闭环控制打下基础,对提高成形产品质量具有重要意义。

Real-time detection and closed-loop control of laser cladding height is necessary for forming high quality parts. Technological parameters are coupled and the forming process is a non-linear process. A large number of laser parameters affect the quality of the laser cladding surface. Based on the analysis of the influence of laser parameters on cladding height, the BP （Back propagation） neural network prediction model of cladding height is build. The neural network arithmetic is designed and the samples are acquired by laser forming experiment. The training samples are used to train the network to accomplish the mapping relation between input and output of the network. The test samples are used to verify the performance of the trained network. Simulation results indicate that the prediction model has sufficient accuracy. The BP neural network prediction model of cladding height is feasible and valid in theory and in practice. The laser cladding height BP neural network prediction model lays the foundation for real-time height prediction and closed-loop control in laser forming process, and it has great significance for improving the quality of formed parts.