一种复杂图形加工工艺路径优化方法研究

潘盛湖; 张小军

doi:10.13433/j.cnki.1003-8728.20220113

一种复杂图形加工工艺路径优化方法研究

doi: 10.13433/j.cnki.1003-8728.20220113

西南石油大学机电工程学院，成都　610500

基金项目: 四川省教育厅项目（16ZA0070）

详细信息

作者简介:
潘盛湖（1982−），副教授，硕士，研究方向为机电一体化技术、石油机械智能化，psh2000psh@126.com

中图分类号: TH164
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- 被引次数: 0
出版历程
- 收稿日期: 2021-09-10
- 刊出日期: 2023-09-30

Study on Path Optimization Method of Complex Graphics Processing

Department of Mechatronics Engineering, Southwest Petroleum University, Chengdu 610500, China

摘要

摘要: 针对当前复杂图形加工中存在加工轨迹之间空行程多，导致加工过程耗时很长等缺陷，提出一种简单、易于实现的加工轨迹切换优化方法。分析了复杂图形加工轨迹切换控制方法，给出了缩短总空行程的优化思想。基于双向蚁群算法原理，推导了双向最大最小蚁群算法（Bidirectional max-min ant colony system，BMMAS），结合加工轨迹之间轨迹切换特点，对复杂图形加工工艺路径进行规划设计，给出了算法实现流程及加工工艺路径优化的实现要点。最后利用该方法对平面复杂图形进行了优化加工实验。实验结果表明，该加工方法计算简单，加工效率高，空行程路径长度较其它方法短，加工平稳。研究结果对相似复杂图形加工具有参考价值。
- 复杂图形 /
- 加工工艺路径 /
- 优化 /
- 最大最小蚁群算法
Abstract: In view of the defects of the current complex graphics processing, such as too much empty travel between machining paths, resulting in a long time-consuming machining process, a simple and easy to realize machining path switching optimization method was proposed. The switching control method of complex graphics machining path was analyzed, and the optimization idea of shortening the total empty stroke was given. A bidirectional max min ant colony system (BMMAS) was derived based on the principle of bidirectional ant colony algorithm. Combined with the characteristics of trajectory switching between machining trajectories, the machining process path of complex graphics was planned and designed, and the implementation process of the algorithm and the key points of machining process path optimization was given. Finally, the method was used to optimized the machining of plane complex graphics. The experimental results showed that this machining method has the advantages of simple calculation, high machining efficiency, less empty stroke path length and stable machining. The research results have reference value for similar complex graphics processing.
- complex graphics /
- machining process path /
- optimization /
- max-min ant system

HTML全文

图 1 优化路径对比

Figure 1. Path optimization comparison

下载: 全尺寸图片幻灯片

图 2 特征点提取方法

Figure 2. Feature point extraction

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图 3 零件切割顺序

Figure 3. Part cutting sequence

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图 4 候选特征点集合确定准则

Figure 4. Criteria for determining candidate feature point sets

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图 5 原理示意图

Figure 5. Schematic diagram of the principle

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图 6 零件排样

Figure 6. Parts layout

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图 7 运行结果

Figure 7. Operational results

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图 8 优化率直方图

Figure 8. Histogram of optimization rates

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表 1 数据对比

Table 1. Data comparison

优化方法	路径长度/mm	运行时间/ms
横向扫描法	23758.042	1677
纵向扫描法	18 926.951	1864
双向蚁群算法	12624.387	2801
BMMAS算法	10796.142	2679
无优化	24577.578	953

下载: 导出CSV

参考文献(15)

[1]	OYSU C, BINGUL Z. Application of heuristic and hybrid-GASA algorithms to tool-path optimization problem for minimizing airtime during machining[J]. Engineering Applications of Artificial Intelligence, 2009, 22(3): 389-396. doi: 10.1016/j.engappai.2008.10.005
[2]	杨建军, 刘保业, 鞠录岩. 激光切割路径优化的双重编码改进遗传算法[J]. 解放军理工大学学报(自然科学版), 2012, 13(6): 684-687. YANG J J, LIU B Y, JU L Y. Dual coding improved genetic algorithm for optimization of laser cutting path[J]. Journal of PLA University of Science and Technology (Natural Science Edition), 2012, 13(6): 684-687. (in Chinese)
[3]	刘山和, 钱晓明, 楼佩煌, 等. 基于遗传蚁群混合算法的激光切割机路径优化[J]. 机械制造与自动化, 2016, 45(6): 92-95. LIU S H, QIAN X M, LOU P H, et al. Path pptimization of laser cutting based on combination of genetic algorithm and ant algorithm[J]. Machine Building & Automation, 2016, 45(6): 92-95. (in Chinese)
[4]	LAN J, LIN B, HUANG T, et al. Path planning for support heads in mirror-milling machining system[J]. The International Journal of Advanced Manufacturing Technology, 2017, 91(1-4): 617-628. doi: 10.1007/s00170-016-9725-7
[5]	侯普良, 刘建群, 高伟强. 基于改进蚁群算法的激光切割加工路径优化研究[J]. 机电工程, 2019, 36(6): 653-657. HOU P L, LIU J Q, GAO W Q. Optimization of laser cutting path based on improved ant colony algorithm[J]. Journal of Mechanical & Electrical Engineering, 2019, 36(6): 653-657. (in Chinese)
[6]	李世红, 袁跃兰, 刘绅绅, 等. 基于蚁群算法的激光切割工艺路径优化[J]. 锻压技术, 2019, 44(4): 69-72. LI S H, YUAN Y L, LIU S S, et al. Optimization on laser cutting process path based on ant colony algorithm[J]. Forging & Stamping Technology, 2019, 44(4): 69-72. (in Chinese)
[7]	王娜, 王海艳, 姜云春. 激光切割工艺路径的双向蚁群算法优化[J]. 锻压技术, 2020, 45(11): 30-35. WANG N, WANG H Y, JIANG Y C. Optimization on laser cutting process path based on bidirectional ant colony algorithm[J]. Forging & Stamping Technology, 2020, 45(11): 30-35. (in Chinese)
[8]	李妮妮, 陈章位, 陈世泽. 基于局部搜索和遗传算法的激光切割路径优化[J]. 计算机工程与应用, 2010, 46(2): 234-236. LI N N, CHEN Z W, CHEN S Z. Optimization of laser cutting path based on local search and genetic algorithm[J]. Computer Engineering and Applications, 2010, 46(2): 234-236. (in Chinese)
[9]	赵曦, 叶和平. 广义旅行商问题及其求解[J]. 东莞理工学院学报, 2007, 14(5): 75-80. ZHAO X, YE H P. Generalized traveling salesman problem and its solution[J]. Journal of Dongguan University of Technology, 2007, 14(5): 75-80. (in Chinese)
[10]	陶维青, 肖松庆, 李林, 等. 基于双精英蚁群算法的配电网故障区段定位[J]. 合肥工业大学学报(自然科学版), 2020, 43(12): 1626-1632. TAO W Q, XIAO S Q, LI L, et al. Fault location of distribution network based on double elite ACO[J]. Journal of Hefei University of Technology (Natural Science), 2020, 43(12): 1626-1632. (in Chinese)
[11]	汪贵庆, 袁杰, 沈庆宏. 基于精英蚁群算法的交通最优路径研究[J]. 南京大学学报(自然科学版), 2019, 55(5): 709-717. WANG G Q, YUAN J, SHEN Q H. Research on traffic optimal path based on elitist ant system[J]. Journal of Nanjing University (Natural Science), 2019, 55(5): 709-717. (in Chinese)
[12]	STÜTZLE T, HOOS H H. MAX-MIN ant system[J]. Future Generation Computer Systems, 2000, 16(8): 889-914. doi: 10.1016/S0167-739X(00)00043-1
[13]	LI X, WANG L. Application of improved ant colony optimization in mobile robot trajectory planning[J]. Mathematical Biosciences and Engineering, 2020, 17(6): 6756-6774. doi: 10.3934/mbe.2020352
[14]	刘丽珏, 罗舒宁, 高琰, 等. 基于回溯蚁群-粒子群混合算法的多点路径规划[J]. 通信学报, 2019, 40(2): 102-110. LIU L J, LUO S N, GAO Y, et al. Multi-point path planning based on the algorithm of colony-particle swarm optimization[J]. Journal on Communications, 2019, 40(2): 102-110. (in Chinese)
[15]	朱宏伟, 游晓明, 刘升. 协同过滤策略的异构双种群蚁群算法[J]. 计算机科学与探索, 2019, 13(10): 1754-1767. ZHU H W, YOU X M, LIU S. Heterogeneous dual population ant colony algorithm based on cooperative filtering strategy[J]. Journal of Frontiers of Computer Science and Technology, 2019, 13(10): 1754-1767. (in Chinese)