There are numerous riveting points on the large-sized aircraft panel, irregular row of riveting points on delta wing. It is essential to plan the riveting sequence reasonably to improve the efficiency and accuracy of ...There are numerous riveting points on the large-sized aircraft panel, irregular row of riveting points on delta wing. It is essential to plan the riveting sequence reasonably to improve the efficiency and accuracy of automatic drilling and riveting. Therefore, this article presents a new multi-objective optimization method based on ant colony optimization （ACO）. Multi-objective optimization model of automatic drilling and riveting sequence planning is built by expressing the efficiency and accuracy of riveting as functions of the points＇ coordinates. In order to search the sequences efficiently and improve the quality of the sequences, a new local pheromone updating rule is applied when the ants search sequences. Pareto dominance is incorporated into the proposed ACO to find out the non-dominated sequences. This method is tested on a hyperbolicity panel model of ARJ21 and the result shows its feasibility and superiority compared with particle swarm optimization （PSO） and genetic algorithm （GA）.展开更多
基金National Natural Science Foundation of China (50805119)Aeronautical Science Foundation of China (2009ZE53)
文摘There are numerous riveting points on the large-sized aircraft panel, irregular row of riveting points on delta wing. It is essential to plan the riveting sequence reasonably to improve the efficiency and accuracy of automatic drilling and riveting. Therefore, this article presents a new multi-objective optimization method based on ant colony optimization （ACO）. Multi-objective optimization model of automatic drilling and riveting sequence planning is built by expressing the efficiency and accuracy of riveting as functions of the points＇ coordinates. In order to search the sequences efficiently and improve the quality of the sequences, a new local pheromone updating rule is applied when the ants search sequences. Pareto dominance is incorporated into the proposed ACO to find out the non-dominated sequences. This method is tested on a hyperbolicity panel model of ARJ21 and the result shows its feasibility and superiority compared with particle swarm optimization （PSO） and genetic algorithm （GA）.
