基于LSO-BP模型的铝合金薄壁零件铣削力预测  

Prediction of milling force of thin-walled aluminium alloy parts based on LSO-BP model

作　　者：沈浩[1] 岳顺龙 王全 SHEN Hao;YUE Shun-long;WANG Quan(School of Mechanical and Electrical Engineering,Lanzhou Univ.of Tech.,Lanzhou 730050,China)

机构地区：[1]兰州理工大学机电工程学院,甘肃兰州730050

出　　处：《兰州理工大学学报》2024年第5期39-43,共5页Journal of Lanzhou University of Technology

摘　　要：铣削力是铝合金薄壁零件铣削加工中很重要的过程参量,其信息的准确反馈对减小工件变形有着十分重要的作用,因而需要实现铣削力的精准预测.首先,通过铝合金薄壁零件铣削加工的仿真实验获得铣削力数据;其次,针对传统BP神经网络的弊端利用狮群算法进行改进,并将铣削力数据导入改进后网络进行训练,从而建立LSO-BP预测模型;最后,分别利用LSO-BP模型、PSO-BP模型和传统BP神经网络模型预测铣削力.均方根误差、平均相对误差和相关系数等评价指标的对比结果表明,LSO-BP模型预测铣削力的性能明显优于PSO-BP模型和传统BP神经网络模型.The milling force is an important process parameter in the milling of thin-walled aluminium alloy parts,with accurate feedback playing a significant role in reducing the deformation of the work pieces.In order to achieve the accurate prediction of milling force,firstly,the milling force data was obtained through simulation tests for the milling of thin-walled aluminum alloy parts.Secondly,to address the drawbacks of the traditional BP neural network,the Lion Swarm algorithm was used to improve it.The milling force data was imported into the improved network for training,to establish the LSO-BP prediction model.Finally,the LSO-BP model,PSO-BP model,and the traditional BP neural network model were used to predict the milling force respectively.The comparison results of evaluation indexes such as root mean square error,average relative error,and correlation coefficient show that the LSO-BP model significantly outperforms both the PSO-BP model and the traditional BP neural network model in predicting the milling force.

关 键 词：薄壁零件 铣削力 神经网络 预测模型 

分 类 号：TH16[机械工程—机械制造及自动化]