激光修锐砂轮工艺参量的预测和优化算法  

Prediction and optimization algorithm of process parameters for laser dressing grinding wheels

作　　者：周聪[1] 张玲[1] 陈根余[1,2] 邓辉[1] 蔡颂[1] 

机构地区：[1]湖南大学激光研究所,湖南大学410082 [2]湖南大学汽车车身先进设计制造国家重点实验室,长沙410082

出　　处：《激光技术》2015年第3期320-324,共5页Laser Technology

基　　金：国家科技重大专项课题资助项目(2012ZX04003-101)

摘　　要：为了找到一种适用于激光修锐砂轮工艺参量预测和优化的方法,采用神经网络和粒子群算法,建立了激光修锐砂轮工艺参量优化模型。首先构建了工艺参量与工件表面粗糙度之间映射关系的神经网络模型,然后基于预测模型采用粒子群算法实现工艺参量优化,最后采用粒子群算法优化获取的5组工艺参量进行了激光修锐试验。结果表明,样本值与神经网络仿真输出值的相对误差小于3%,试验值与期望值的相对误差控制在6%以内。综合说明该优化模型具备良好的优化能力。In order to find a method of prediction and optimization of laser dressing grinding wheel,an optimization model of process parameters for laser dressing grinding wheels was established based on the neural network and particle swarm optimization. Firstly,the neural network model mapping the relationship between the process parameters and the specimen surface roughness was constructed. Then,the process parameters were optimized by means of the particle swarm optimization algorithm based on the predication model. Finally,laser dressing experiments were carried out based on 5groups of parameters optimized by the particle swarm algorithm. Experimental results show that the relative error between the sample value and output value from neural network is less than 3% and the relative error between the test value and the expected value is lower than 6%. In conclusion,the model has good ability of optimization.

关 键 词：激光技术 激光修锐 神经网络 粒子群算法 工艺参量优化 

分 类 号：TN249[电子电信—物理电子学]