TY - JOUR
T1 - Multi-objective optimization method for automatic drilling and riveting sequence planning
AU - Xiao, Hong
AU - Li, Yuan
AU - Zhang, Kaifu
AU - Yu, Jianfeng
AU - Liu, Zhenxing
AU - Su, Jianbin
PY - 2010/12
Y1 - 2010/12
N2 - 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).
AB - 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).
KW - ant colony optimization
KW - automatic drilling and riveting
KW - multi-objective optimization
KW - Pareto-optimal solutions
KW - riveting sequence
UR - http://www.scopus.com/inward/record.url?scp=78650834570&partnerID=8YFLogxK
U2 - 10.1016/S1000-9361(09)60276-4
DO - 10.1016/S1000-9361(09)60276-4
M3 - 文章
AN - SCOPUS:78650834570
SN - 1000-9361
VL - 23
SP - 734
EP - 742
JO - Chinese Journal of Aeronautics
JF - Chinese Journal of Aeronautics
IS - 6
ER -