摘要
为提高航空发动机叶片砂带抛光后的表面质量,选取影响抛光质量的主要工艺因素砂带粒度、接触力、砂带线速度、进给速度,以抛光后的叶片表面粗糙度作为评价指标,展开叶片砂带抛光工艺参数优化研究。首先,进行抛光工艺试验,基于试验结果采用BP神经网络法建立表面粗糙度与工艺参数之间的预测模型;其次,根据所建立的预测模型,采用遗传算法和粒子群算法对工艺参数进行优化对比,确定最佳工艺参数;最后,对优化后的工艺参数进行试验验证,试验结果表明:采用优化后的工艺参数进行叶片型面抛光,抛光后叶片表面粗糙度明显减小,表面质量得到了显著提高。
To improve the surface quality of aeroengine blade after polishing with abrasive belt,select process factors：abrasive grain size,contact force,belt speed and feed rate as the major impact on the quality of the polishing,take the blade surface roughness after polishing as the evaluation index,carry out the research of process parameter optimization in belt polishing of blade.Firstly,the prediction model between surface roughness and process parameters is established using the BP neural network.Secondly,the process parameters are optimized and contrasted using genetic algorithm and particle swarm optimization according to the established model.Finally,the optimized process parameters are verified experimentally and the results show that：the surface quality is improved significantly as the blade surface roughness decreases greatly after polishing with the optimized process parameters.
出处
《航空制造技术》
2016年第8期60-65,75,共7页
Aeronautical Manufacturing Technology
关键词
叶片抛光
工艺参数优化
神经网络
遗传算法
粒子群算法
Blade polishing
Process parameters optimization
Neural network
Genetic algorithm
Particle swarm optimization
