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考虑基于蚁群算法的最优刀具进给位置和路径实现多型腔航空结构件的高效铣削加工

Towards Efficient Milling of Multi-Cavity Aeronautical Structural Parts Considering ACO-Based Optimal Tool Feed Position and Path.

作者信息

Xin Yupeng, Li Yuanheng, Li Wenhui, Wang Gangfeng

机构信息

College of Mechanical and Vehicle Engineering, Taiyuan University of Technology, Taiyuan 030024, China.

College of Aeronautics and Astronautics, Taiyuan University of Technology, Taiyuan 030024, China.

出版信息

Micromachines (Basel). 2021 Jan 16;12(1):88. doi: 10.3390/mi12010088.

Abstract

Cavities are typical features in aeronautical structural parts and molds. For high-speed milling of multi-cavity parts, a reasonable processing sequence planning can significantly affect the machining accuracy and efficiency. This paper proposes an improved continuous peripheral milling method for multi-cavity based on ant colony optimization algorithm (ACO). Firstly, by analyzing the mathematical model of cavity corner milling process, the geometric center of the corner is selected as the initial tool feed position. Subsequently, the tool path is globally optimized through ant colony dissemination and pheromone perception for path solution of multi-cavity milling. With the advantages of ant colony parallel search and pheromone positive feedback, the searching efficiency of the global shortest processing path is effectively improved. Finally, the milling programming of an aeronautical structural part is taken as a sample to verify the effectiveness of the proposed methodology. Compared with zigzag milling and genetic algorithm (GA)-based peripheral milling modes in the computer aided manufacturing (CAM) software, the results show that the ACO-based methodology can shorten the milling time of a sample part by more than 13%.

摘要

型腔是航空结构件和模具中的典型特征。对于多型腔零件的高速铣削,合理的加工顺序规划会显著影响加工精度和效率。本文提出一种基于蚁群优化算法(ACO)的改进型多型腔连续周铣方法。首先,通过分析型腔拐角铣削过程的数学模型,选择拐角的几何中心作为刀具初始进给位置。随后,通过蚁群传播和信息素感知对刀具路径进行全局优化,以求解多型腔铣削的路径。凭借蚁群并行搜索和信息素正反馈的优势,有效提高了全局最短加工路径的搜索效率。最后,以某航空结构件的铣削编程为例,验证所提方法的有效性。与计算机辅助制造(CAM)软件中的之字形铣削和基于遗传算法(GA)的周铣模式相比,结果表明基于ACO的方法可使样件的铣削时间缩短超过13%。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/99f4/7830258/1afffbc3f824/micromachines-12-00088-g007.jpg

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