摘要
由于当前已有方法未能考虑大型数控铣床转动轴误差测试点的分布问题,导致误差信息获取准确率下降,耗时增加。为了有效解决上述问题,提出一种大型数控铣床转动轴联动误差信息获取方法。将大型数控铣床运动部件的误差元素划分两类,分别对直线运动部件和旋转运动部件的两类误差进行数学建模。同时研究大型数控铣床转动轴误差测试点的分布方案,使用基于随机概率的SOM神经网络分类算法对各个测试点进行优化。以此为基础,组建大型数控铣床转动轴联动误差信息获取模型,有效实现大型数控铣床转动轴联动误差信息获取。仿真实验结果表明,所提方法能够有效提升误差信息获取准确率,减少误差信息获取耗时。
As the current existing methods fail to consider the distribution of error test points on the rotation axis of a large-scale CNC milling machine,the accuracy of obtaining error information decreases and time-consuming increases.In order to effective⁃ly solve the above-mentioned problems,a method for obtaining the linkage error information of the rotation axis of a large-scale CNC milling machine is proposed.The error elements of the moving parts of the large-scale CNC milling machine are divided in⁃to two categories,and the two types of errors of linear motion parts and rotary motion parts are mathematically modeled respec⁃tively.At the same time,the distribution scheme of the test points of the rotation axis error of the large-scale CNC milling ma⁃chine is studied,and each test point is optimized using the SOM neural network classification algorithm based on random proba⁃bility.On this basis,a large-scale CNC milling machine rotation axis linkage error information acquisition model is built to ef⁃fectively achieve the large-scale CNC milling machine rotation axis linkage error information acquisition.Simulation results show that the proposed method can effectively improve the accuracy of error information acquisition and reduce the time-consum⁃ing error information acquisition.
作者
付会凯
吴兰
FU Hui-kai;WU Lan(Mechanical and Electrical Engineering Institute,Xinxiang University,Henan Xinxiang 453003,China;College of Electrical Engineering,Henan University of Technology,Henan Zhengzhou 450007,China)
出处
《机械设计与制造》
北大核心
2021年第11期151-154,共4页
Machinery Design & Manufacture
基金
河南省高等学校重点科研项目—基于机器视觉FPC缺陷智能检测与分析装置的设计与实现(19A510021)
河南省一流本科课程((教高[2020]193号)第287项)
河南省虚拟仿真实验教学项目((教高〔2019〕672号)第100项)。
关键词
大型数控铣床
转动轴
联动误差信息
获取
Large-Scale CNC Milling Machine
Rrotation Axis
Linkage Error Information
Acquisition
