摘要
针对自由曲面在机检测过程中存在检测效率低、路径规划时间长等问题,提出一种改进的电鳗觅食优化算法(IEEFO),对在机检测路径进行规划。在自由曲面上提取检测点坐标,使用改进电鳗觅食优化算法对在机检测路径进行规划,使用佳点集初始化策略来初始化种群,提高了初始解的遍历性,使用及时回馈融合信息机制确保信息的流通和更新,有助于快速接近全局最优。采用精英高斯变异策略,提高全局搜索能力,避免陷入局部最优解。最后,将该算法分别进行仿真和在机检测实验,将其与鲸鱼优化算法(WOA)、灰狼优化算(GWO)和未优化的电鳗觅食优化算法(EEFO)进行对比。结果表明:仿真实验中,IEEFO算法规划的路径长度更短,收敛速度更快且精度较高;在机检测实验中,IEEFO将检测时间缩短至655 s,与未规划路径及WOA、GWO、EEFO算法规划路径相比,长度分别减少780.66、253.11、74.43、103.00 mm,检测效率分别提高了44.96%、15.81%、8.52%和9.78%。改进的电鳗觅食优化算法可以有效提高自由曲面工件在机检测的效率。
Aiming at the problems of low inspection efficiency and long path planning time of free-form surface in the process of on-machine inspection,an improved electric eel foraging optimization algorithm(IEEFO)was proposed to plan the inspection path on the machine.The coordinates of inspection points were extracted from the free-form surface,and the improved electric eel foraging optimization algorithm was used to plan the inspection path on the machine.By using the best point set to initialize the population,the ergodic of the initial solution was improved,and the timely feedback fusion information mechanism was employed to ensure the flow and update of information,which was conducive to quickly approaching the global optimal.The elite Gaussian mutation strategy was adopted to improve the global search ability and avoid falling into the local optimal solution.Finally,the algorithm was respectively simulated and tested on the machine,and compared with the whale optimization algorithm(WOA),the grey wolf optimization algorithm(GWO),and the electric eel foraging optimization algorithm(EEFO).The results show that in the simulation experiment,the path length of the IEEFO algorithm is shorter,the convergence speed is faster and the accuracy is higher.In the on-machine detection experiment,using IEEFO,the detection time is shortened to 655 s;compared with the unplanned path and the detection paths of the WOA,GWO and EEFO algorithms,the path lengths are reduced by 780.66 mm,253.11 mm,74.43 mm,and 103.00 mm respectively,the detection efficiencies are increased by 44.96%,15.81%,8.52%and 9.78%respectively.The IEEFO algorithm can effectively enhance the efficiency of on-machine detection of free-form surface workpieces.
作者
刘亚鹏
陈岳坪
马洁高
LIU Yapeng;CHEN Yueping;MA Jiegao(School of Mechanical and Automotive Engineering,Guangxi University of Science and Technology,Liuzhou Guangxi 545616,China;Dongfeng Liuzhou Automobile Co.,Ltd.,Liuzhou Guangxi 545005,China)
出处
《机床与液压》
北大核心
2025年第21期134-139,共6页
Machine Tool & Hydraulics
基金
广西自然科学基金项目(2025GXNSFHA069171)
2016年广西高校高水平创新团队及卓越学者计划项目(桂教人[2016]42号)
广西科技大学创新团队支持计划项目(科大科研发[2017]64号)
关键词
自由曲面
在机检测
路径规划
电鳗觅食优化算法
free-form surface
on-machine inspection
path planning
electric eel foraging optimization algorithm
