摘要
在传统速度规划方法中,轴向加速度约束通常是定值约束,不能充分发挥机床轴向加减速性能,影响加工效率.对此提出了一种新的考虑轴向加速度动态约束的进给速度规划算法.首先,对NURBS曲线路径进行弧长自适应二分离散处理,获得弧长参数和采样点.之后,构建切向速度、加速度、加加速度约束和轴向加速度动态约束的优化模型,对采样点的多约束模型进行两次线性规划求解,得到采样点的优化进给速度.通过两次线性规划和轴向转矩参考处理,计算各轴在不同速度下的加速度上下极限动态约束,在提高加工效率同时没有增加搜索最优值的约束条件,具有较高计算效率.最后,对采样点进行样条拟合得到进给速度规划曲线.与传统定值约束规划对比实验的结果表明,所提算法牺牲最大轮廓误差2.63%,使加工时间降低了16.34%,提高加工效率的同时并没有牺牲较大精度,证明了算法的可行性和有效性.
In conventional feedrate planning,the axial acceleration constraint is usually a fixed value constraint,which cannot efficiently use the acceleration capacity of the axes of the machine tools and affects the machining effeciency.A new feedrate planning algorithm considering the dynamic constraint of axial acceleration is proposed.First,the non-uniform rational B-splines curve path is processed by adaptive dichotomous discretization to acquire the arc length parameters and sampling points.Then the optimizing model is constructed with the tangential velocity,acceleration,jerk,and dynamic axial acceleration constraints.A two-stage linear programing is applied to the multiconstraint optimizing model on the sampling points to find the points’optimal velocity.The dynamic constraints of the upper and lower limits of the acceleration of each axis at different velocities are obtained through the two-stage linear programing and the reference axial torque,which improves the processing efficiency without increasing the constraint condition of searching the optimal value,thus ensuring high computation efficiency.Finally,the sample points are fitted with a spline to obtain the feedrate planning curve.In a comparative experiment with the conventional fixed constraint planning,the proposed algorithm sacrifices 2.63%of the maximum contour error,reduces the processing time by 16.34%,and improves the processing efficiency without sacrificing much precision,which proves the feasibility and effectiveness of the algorithm.
作者
董靖川
郭健鑫
刘喆
谭志兰
王太勇
Dong Jingchuan;Guo Jianxin;Liu Zhe;Tan Zhilan;Wang Taiyong(School of Mechanical Engineering,Tianjin University,Tianjin 300354,China)
出处
《天津大学学报(自然科学与工程技术版)》
EI
CAS
CSCD
北大核心
2021年第9期890-898,共9页
Journal of Tianjin University:Science and Technology
基金
国家自然科学基金资助项目(51605328).