摘要
提出一种基于粒子群优化(particle swarm optimization,PSO)算法的数控机床几何误差补偿方法,通过对工件NC代码进行优化来提高数控机床精度。首先,通过对测量误差数据进行拟合,建立每个轴的基本误差项的优化多项式,根据误差定义将多项式常数项设为0,通过F检验来确定最佳多项式拟合阶数。其次,利用机床的正和逆运动学建立刀具位姿与NC代码之间的双向转换,通过SmartCNC500_DRTD五轴机床的后置处理器生成NC代码,将理想NC代码的刀具位姿引入几何误差模型建立数学表达式。最后,提出一种基于PSO算法的NC代码优化方法,将粒子定义为刀具位姿,初始粒子围绕理想刀具位姿生成,改进粒子移动方法以避免局部最优。工件切削实验表明该方法能够得到较优的NC代码,提高了机床的加工精度,验证了本文方法的可行性和有效性。
In this paper,a geometric error compensation method for CNC machine tools based on particle swarm optimization(PSO)algorithm is proposed.The accuracy of CNC machine tools is improved by optimizing the NC code of the workpiece.Firstly,by fitting the measurement error data,the optimal polynomial of the basic error term of each axis is established.The polynomial constant term is set to 0 according to the error definition,and the best polynomial fitting order is determined by the F test.Secondly,using the forward and inverse kinematics of the machine tool to establish the bidirectional conversion between the tool pose and the NC code,the NC code is generated by the post processor of the SmartCNC500_DRTD five-axis machine tool,and the tool pose of the ideal NC code is introduced into the geometric error model to establish the mathematics expression.Finally,an optimization method based on PSO algorithm is proposed.The particle is defined as the tool pose,the initial particle is generated around the ideal tool pose,and the particle moving method is improved to avoid local optimum.The workpiece cutting experiment shows that the method can obtain better NC code,improve the machining accuracy of the machine tool,and verify the feasibility and effectiveness of the method.
作者
余文利
邓小雷
谢长雄
王建臣
YU Wenli;DENG Xiaolei;XIE Changxiong;WANG Jianchen(College of Mechanical and Electrical Engineering,Quzhou College of Technology,Quzhou 324000,CHN;Key Laboratory of Air-driven Equipment Technology of Zhejiang Province,Quzhou University,Quzhou 324000,CHN;Zhejiang Yonglida CNC Technology Co.,Ltd.,Quzhou 324000,CHN)
出处
《制造技术与机床》
北大核心
2020年第8期134-141,共8页
Manufacturing Technology & Machine Tool
基金
国家自然科学基金(51605253)
浙江省博士后择优资助项目(zj20180077)。
关键词
五轴数控机床
几何误差
参数化模型
粒子群优化
误差补偿
five-axis machine tools
geometric errors
parametric models
particle swarm optimization
error compensation
