基于粒子群优化算法的圆锥刀侧铣刀轴轨迹规划

发布时间：2018-03-18 22:23

本文选题：圆锥刀　切入点：侧铣　出处：《计算机集成制造系统》2016年10期 　论文类型：期刊论文

【摘要】：针对圆锥刀侧铣加工非可展直纹面的刀轴轨迹规划问题,提出一种基于粒子群优化算法的解决方案。将刀轴上一点到圆锥面的距离定义为该点对应的当量半径,将刀轴上一点到非可展直纹面的距离与当量半径之差定义为径距偏差,在此基础上提出单个刀位实现最优的判定条件,即刀轴位姿应满足刀轴上各点径距偏差的平方和为最小。以初始刀位为基准,通过3维小量平动局部调整刀轴两端点的位置,将调整后的刀轴位姿描述成6个平动参数的函数,从而将刀具包络面向非可展直纹面的逼近问题分解为每个刀位下位姿参数的寻优问题。提出基于粒子群优化方法的求解策略,并给出具体的实现步骤。实例计算表明,该方法可使单刀位的优化实现过程简单、结果稳定,显著降低刀轴优化后的曲面包络误差,可为非可展直纹面的侧铣加工提供一定的理论依据。
[Abstract]:In order to solve the problem of tool path planning for machining non-developable straight surface, a solution based on particle swarm optimization (PSO) algorithm is proposed. The distance from one point on the cutter axis to the cone surface is defined as the equivalent radius of the point. The difference between the distance from one point on the cutter axis to the non-developable straight surface and the equivalent radius is defined as the deviation of the diameter distance. On this basis, a single tool position is proposed to realize the optimal judgment condition. That is, the position and pose of the cutter shaft should meet the minimum square sum of the deviation of each point on the cutter shaft. Based on the initial tool position, the position of the two ends of the cutter shaft is adjusted by a small amount of three dimensional translational motion, and the adjusted position of the cutter shaft position is described as a function of six translational parameters. Thus, the approach problem of tool envelope facing non-developable straight line surface is decomposed into the optimization problem of the pose parameters of each tool position. A solution strategy based on particle swarm optimization method is proposed, and the concrete implementation steps are given. This method can make the optimization process of single tool position simple, the result is stable, and the surface envelope error of the optimized tool shaft can be significantly reduced, which can provide a certain theoretical basis for the side milling of non-developable straight surface.
【作者单位】：大连交通大学机械工程学院;
【基金】：国家自然科学基金资助项目(51405058)~~
【分类号】：TG54

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