一种改进的Douglas-Peucker数控加工轨迹压缩方法  

Improved Douglas-Peucker CNC Machining Trajectory Compression Method

作　　者：王品[1,3] 王婧如 张丽鹏 王森 荆东东[1,2] WANG Pin;WANG Jingru;ZHANG Lipeng;WANG Sen;JING Dongdong(Shenyang Institute of Computing Technology,Chinese Academy of Sciences,Shenyang 110168,China;University of Chinese Academy of Sciences,Beijing 100049,China;Shenyang Zhongke CNC Technology Co.,Ltd,Shenyang 110168,China)

机构地区：[1]中国科学院沈阳计算技术研究所,沈阳110168 [2]中国科学院大学,北京100049 [3]沈阳中科数控技术股份有限公司,沈阳110168

出　　处：《小型微型计算机系统》2025年第1期64-71,共8页Journal of Chinese Computer Systems

基　　金：国产高档数控系统智能化技术研究开发及应用验证项目(2018ZX04035-001)资助。

摘　　要：数控加工程序通常由计算机辅助制造系统生成,以微小直线段的形式“以直代曲”来指导数控机床进行直线插补运动.随着工艺复杂度和精度要求的提高,数控加工程序的数据量急剧增加,不仅增加了数据存储和传输的难度,而且会引起机床执行过程中速度的频繁调整.针对以上问题,提出了一种融合深度学习的改进Douglas-Peucker三维数控加工轨迹压缩方法,该方法通过引入曲率和距离容差度的超参数考虑了加工轨迹中数据点序列的几何特性,并通过深度神经网络模型动态地优化算法中的超参数,从而实现更高的压缩效率.此外,算法中利用了KD树结构优化误差计算,确保压缩后的数据能够在给定的公差范围内精确呈现原始数据的特性.实验表明,该算法可大幅减少数据量,并确保压缩后的数据准确呈现原始数据的特性.Numerical control machining programs are typically generated by computer-aided manufacturing systems,guiding numerical control machine tools in linear interpolation motion by approximating curves with tiny linear segments.With the rising complexity and precision demands of the process,the volume of data in numerical control machining programs has seen a drastic increase.This not only complicates data storage and transmission but also leads to frequent speed adjustments during the operation of the machine tool.In response to these challenges,an improved Douglas-Peucker three-dimensional numerical control machining trajectory compression method that integrates deep learning has been proposed.This method takes into account the geometric characteristics of the data point sequence in the machining trajectory by introducing hyperparameters of curvature and distance tolerance.It dynamically optimizes the hyperparameters within the algorithm using a deep neural network model,thereby achieving a higher compression efficiency.Additionally,the algorithm employs a KD tree structure to refine error calculations,ensuring that the compressed data can precisely represent the characteristics of the original data within a given tolerance range.Experiments have demonstrated that this algorithm can significantly reduce the volume of data and guarantee that the compressed data accurately reflects the attributes of the original data.

关 键 词：DOUGLAS-PEUCKER算法 轨迹压缩 轮廓误差 深度神经网络 参数优化 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]