Parametric Programming of Multi-Type Holes Using the Improved Genetic Algorithm

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For path planning and program generation of multi-type holes, this paper presents a method of the path planning, by using the improved genetic algorithm based on chaos algorithm, and develops an interactive programming system for multi-type holes. Users can choose regular holes, and input the parameters and the necessary information, according to their needs. Then the system will combine holes automatically, and calculate the position coordinates of each hole. After that the path planning is carried out by using the improved genetic algorithm. Finally, the system carries out the simulation experiment of the path planning, and outputs NC program. Experiment shows the system can realize the path planning of multi-type holes by a combination of regular holes and generate the NC code after path planning. The optimization of path is better and the operation is more convenient than UG and other professional CAM software.

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333-341

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February 2018

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© 2018 Trans Tech Publications Ltd. All Rights Reserved

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[1] Huang Yuqi. Realization of rectangular array hole processing by using subroutine. Application Technology. 4(2012): 179-181.

Google Scholar

[2] Information on http: /www. deltagreentech. com. cn/productlv4-134-164. html.

Google Scholar

[3] Information on http: /www. bj-fanuc. com. cn/HomePage/HoneIndex/index.

Google Scholar

[4] Yu Yingying, Chen yan, Li Taoyin. Improved ant colony-genetic algorithm for solving TSP. Computer Simulation. 30. 11(2013): 317-320.

Google Scholar

[5] Wang Xiaofeng, Si Shoukui, Sun Xijing. The Solution of TSP Problem Combined with chaotic algorithm . National Conference on teaching and application of mathematical modeling. 2005: 91-95.

Google Scholar

[6] Wu Meiping, Zhai Jianjun, Liao Wenhe. Research on NC machine parameter Optimization . China Mechnical Engineer. 15. 3 (2004): 235-237.

Google Scholar

[7] Lin Dongmei, Wang Dong, Zhong Yong. Monte Carlo algorithm for fixing partial edges belonging to TSP global optimal solution. Journal of Chinese Computer Systems. 31. 4(2010): 747-751.

Google Scholar

[8] Zhang Wenxiu, Liang Yi. Mathematical foundation of genetic algorithm. Xi'an Jiao Tong University Press. (2000).

Google Scholar

[9] Yao Junfeng, Mei Chi, Peng Xiaoqi. The application research of the chaos genetic algorithm (CGA) and its evaluation of optimization efficiency. Acta Automatica Sinica 28. 6(2002): 935-942.

Google Scholar