中国激光, 2011, 38 (2): 0203003, 网络出版: 2011-01-14
基于神经网络和粒子群算法的激光熔覆工艺优化
Optimization of Laser Cladding Process Variables Based on Neural Network and Particle Swarm Optimization Algorithms
激光技术 激光熔覆 反向传播神经网络 粒子群算法 工艺优化 laser technique laser cladding back propagation neural network particle swarm optimization process variables optimization
摘要
采用反向传播（BP）神经网络和粒子群算法相结合的方法对激光熔覆过程的工艺参数进行优化。运用BP神经网络建立熔覆带特征(熔覆带高度、熔覆带宽度)与熔覆工艺参数之间的预测模型，利用训练样本对所建立的网络进行训练，形成输入与输出之间的高度映射关系，在此基础上，使用粒子群优化算法对工艺参数进行寻优。试验结果表明，使用该方法优化得到的工艺参数进行加工获得的结果与预期结果有较小的误差，有利于获得预期的熔覆质量。
Abstract
Combination of back propagation(BP)neural network and particle swarm optimization (PSO) algorithms is used to optimize process variables during the laser cladding. BP neural network model is developed to express the relationship between the clad process variables and the clad parameters (the width, height of clad bead), and the samples obtained in experiments are used to train network model to form the perfect map relation between input and output. Then, PSO algorithm is used to grabble the suitable values of the process variables. The experimental clad parameters with the process variable values calculated by this optimization method are coincident well with the expected ones. It is verified experimentally that combination of BP neural network and PSO algorithms can help to obtain the expected laser clad quality.
倪立斌, 刘继常, 伍耀庭, 鄢锉. 基于神经网络和粒子群算法的激光熔覆工艺优化[J]. 中国激光, 2011, 38(2): 0203003. Ni Libin, Liu Jichang, Wu Yaoting, Yan Cuo. Optimization of Laser Cladding Process Variables Based on Neural Network and Particle Swarm Optimization Algorithms[J]. Chinese Journal of Lasers, 2011, 38(2): 0203003.
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