摘要
镍基合金在航空涡轮机、压气机的叶片制造中广泛应用,提升其加工效率与质量是行业热点。为实现航发叶片恒力磨抛的表面粗糙度准确预测,提出一种基于改进麻雀搜索算法(CSSA)优化BP神经网络的预测模型,开展机器人砂带磨抛工艺参数预测研究。选取4个主要影响因素为变量进行正交试验和极差分析;利用BP神经网络建立表面粗糙度预测模型,引入Tent混沌映射和种群多样性变异,进行麻雀搜索算法的改进,再通过CSSA对预测模型进行优化。结果表明:优化后的粗糙度预测误差降低了85.9%,相比优化前提升明显,可准确预测所输入工艺参数对应的粗糙度值。
Nickel-based alloys are widely used in the manufacture of blades for aviation turbines and compressors,and impro⁃ving their processing efficiency and quality is a hot spot in the industry.In order to realize the accurate prediction of the surface rough⁃ness of the constant force grinding and polishing of aero-engine blades,a prediction model based on the chaos sparrow search algo⁃rithm(CSSA)optimization of BP neural network was proposed,and the research on the prediction of the process parameters of the ro⁃bot abrasive belt grinding and polishing was carried out.Four main influencing factors were selected as variables to conduct orthogonal experiments and range analysis.BP neural network was used to establish a surface roughness prediction model,and Tent chaotic map and population diversity variation were introduced to conduct the sparrow search algorithm.After improvement,the prediction model was optimized by CSSA.The results show that the roughness prediction error after optimization is reduced by 85.9%,which is obvi⁃ously improved compared with that before optimization.The roughness value corresponding to the input process parameters can be accu⁃rately predicted.
作者
潘江涛
李波
聂奥
柳光金
PAN Jiangtao;LI Bo;NIE Ao;LIU Guangjin(School of Mechanical Engineering,Hubei University of Arts and Science,Xiangyang Hubei 441053,China;XY-HUST Advanced Manufacturing Engineering Research Institute,Xiangyang Hubei 441106,China)
出处
《机床与液压》
北大核心
2023年第22期80-86,共7页
Machine Tool & Hydraulics
基金
湖北省教育厅优秀中青年科技创新团队计划项目(T201919)。
关键词
机器人磨抛
正交试验
CSSA-BP神经网络
表面粗糙度预测
Robot grinding and polishing
Orthogonal experiment
CSSA-BP neural network
Surface roughness prediction
