Abstract
Rational weld seams sequence is important for improving welding productivity and quality. Hence, intelligent path optimization strategy is introduced to obtain optimized weld seams sequence in this article. The path length and total welding deformation are considered for multi-objective path planning. First, the optimization problem description is presented. At the same time, the path length and the total welding deformation of some sequences are calculated. Then, improved multi-objective particle swarm optimization algorithm is studied. In addition, the agent model is obtained based on the sample data and experiment design. At last, the proposed algorithm is applied to optimize the welding path length and total welding deformation. The simulation results show the effectiveness of the optimization strategy.
Highlights
Intelligent manufacturing can realize high efficiency and green manufacturing.1,2As an important unit, robot has been widely used to strengthen manufacturing competitiveness due to its flexible, efficient, and accurate operation
To evaluate the performance of the DDN-multi-objective particle swarm optimization (MOPSO) algorithm, the traveling salesman problems (TSPs) is used for algorithm test
The DDN-MOPSO is compared with other multi-objective optimization algorithm, which are Non-Dominated Sorting Genetic Algorithm (NSGA)-II,[21] NSGA-III,[22] MOPSO,[23] and Clustering Guidance (CG)MOPSO.[18]
Summary
Intelligent manufacturing can realize high efficiency and green manufacturing.1,2As an important unit, robot has been widely used to strengthen manufacturing competitiveness due to its flexible, efficient, and accurate operation. In the study by Wang et al.,[7] the double-global optimal particle swarm optimization (PSO) algorithm was proposed to optimize the spot welding robot path planning. To realize arc welding robot path intelligent optimization in this article, welding deformation calculation and intelligent optimization algorithm are two main contents.
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