Academic Journal of Engineering and Technology Science, 2025, 8(4); doi: 10.25236/AJETS.2025.080415.
Weiluo Wang1, Zi Wang2
1College of Mechanical Engineering, Chongqing University of Technology, Chongqing, China, 400054
2School of Smart City and Transportation, Southwest Jiaotong University, Chengdu, China, 611756
The existing parameter optimization methods tend to fall into local optimization, and it is difficult to balance the contradiction between machining time and deformation, resulting in low machining efficiency and unstable surface quality. To address the above problems, a multi-objective optimization method combining the black hole algorithm and continuous ant colony algorithm (BH-ACOR) is proposed. In terms of the algorithm, the black hole mechanism is introduced to, divide the solution file into "black holes" (the first k superior solutions) and "planets" (the last m inferior solutions), the exploration ability of the solution space is improved through the global search of the planets to the black holes and the local perturbation strategy of the black holes. A normalized weighted objective function is defined to balance the time efficiency and the weighting requirements of aerospace blade milling. Finally, the algorithm is simulated using MATLAB, compare with the traditional methods. The BH-ACOR algorithm reduces the machining time by 17.3%, reduces the amount of deformation by 15.6%, and improves the convergence speed by 22%, which verifies its effectiveness and engineering applicability in complex multi-objective optimization.
BH-ACOR, Aerospace Blade, ACOR
Weiluo Wang, Zi Wang. Parameters Optimization Method of Milling Aerospace Blade Milling Based on Black Hole Continuous Ant Colony Algorithm. Academic Journal of Engineering and Technology Science (2025), Vol. 8, Issue 4: 110-120. https://doi.org/10.25236/AJETS.2025.080415.
[1] DORIGO M, GAMBARDELLA L M. Ant colony system: a cooperative learning approach to the traveling salesman problem [J]. IEEE Transactions on Evolutionary Computation, 1997,1(1):53-66.
[2] JIN TT, WU S F, LI S, et al. Research on improved ant colony algorithm for lightweight of main girders[J]. Machine Design and Manufacturing Engineering, 2019, 48(1):9-13. (in Chinese).
[3] DAI Y T, ZHAO X R, LIU L Q. Parameter identification of ship longitudinal motions using continuous ant colony algorithm with period searching[J]. Journal of ShipMechanics, 2010,14(8):872-878. (in Chinese).
[4] CAO T K, LIU Y J, XU Y T. Cutting performance of tool with continuous lubrication at tool-chip interface[J]. International Journal of Precision Engineering and Manufacturing-Green Technology, 2020, 7(2):347-359.
[5] LAN Z Q, LIANG Q Y. Analysis and Control of Machining Deformation Factors of Aero- engine Blades [J]. Scientific and Technological Innovation, 2019(4).
[6] SONG DD, XUE F, WU D D, et al. Iso-parametric Path-planning Method of Twin-tool Milling for Turbine Blades [J]. The International Journal of Advanced Manufacturing Technology,2018, 98(9): 3179—3189.
[7] K UMAR R, BILGA P S, SINGH S. Multi objective optimization using different methods of assigning weights to energy consumption responses, surface roughness and material removal rate during rough turning operation[J]. Journal of Cleaner Production,2017,164:45-57.
[8] Wang Dong, Xi Xinfu, Ge Xiaoyi, Sun Yize. Optimization Design of Latch Needle Mechanism in Warp Knitting Machine Using Improved Ant Colony Algorithm[J]. Machinery Design & Manufacture. 2023, 11(6).
[9] LI T, KONG LL, ZHANG H C, et al. Recent research and development of typical cutting machine tool's energy consumption model[J]. Journal of MechanicalEngineering,2014,50(7):102-111.
[10] Chen Hongsong, Dong Dingqian, Huang Bing, Liao Yinghua, Xu Yun, Zhang Wenke, Zhang Jiawei. Optimization of Milling Process Parameters for Aviation Blades Based on Genetic Algorithm[J]. Tool Engineering, 2021, 55(09): 68-73.