钢箱梁喷砂除锈并联机器人喷枪轨迹智能优化  

Intelligent Optimization of Spray Gun Trajectory of Parallel Robot for Sand Blasting and Rust Removal of Steel Box Girder

作　　者：高仕琳 高国琴[1] 方志明[1] GAO Shi-lin;GAO Guo-qin;FANG Zhi-ming(School of Electrical&Information Engineering,Jiangsu University,Zhenjiang 212013,China)

机构地区：[1]江苏大学电气信息工程学院,江苏镇江212013

出　　处：《软件导刊》2021年第8期6-11,共6页Software Guide

基　　金：国家自然科学基金项目(51375210);镇江市重点研发计划项目(GZ2018004)。

摘　　要：研究一种钢箱梁喷砂除锈并联机器人喷枪轨迹的智能优化方法。首先,针对并联机器人喷砂除锈作业建立表面粗糙度预测模型,基于该模型设定能保证表面粗糙度符合工艺要求的约束条件,建立以喷砂除锈效率为优化目标的喷枪轨迹参数优化模型,以完成各片面喷枪轨迹的优化;然后,采用遗传蚁群算法对各片面喷枪轨迹进行组合优化;最后,通过仿真实验验证该方法的有效性。通过建立表面粗糙度预测模型能有效解决目前钢箱梁表面粗糙度难以实时检测的问题,通过智能优化获得的喷枪轨迹既能保证表面粗糙度符合工艺要求,又能提高喷砂除锈效率。An intelligent optimization method for spray gun trajectory of parallel robot for sand blasting and rust removal of steel box girder is studied.Firstly,a prediction model of surface roughness is established for the sandblasting operation of the parallel robot.Furthermore,based on the prediction model,the constraints that can ensure the surface roughness conforms to the process requirements are set up,and the optimization model of gun trajectory parameter with the efficiency of sand blasting and rust removal as the optimization target is established to complete the optimization of the trajectory of each one-sided gun.Then,the genetic ant colony algorithm is used to optimize the trajectory of each interfacial spray gun.Finally,the effectiveness of the optimization method is verified by simulation.This paper not only solves the problem that it is difficult to detect the surface roughness of steel box girder in real time by establishing the prediction model of surface roughness,but also ensures that the surface roughness meets the process requirements and improves the efficiency of sand blasting and rust removal.

关 键 词：表面粗糙度预测模型 喷砂除锈并联机器人 轨迹参数优化 组合优化 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]