Multiobjective Path Optimization for Arc Welding Robot Based on DMOEA/D-ET Algorithm and Proxy Model

Abstract

In the arc welding process, a reasonable welding path can improve welding efficiency and quality, especially when a large number of welding seams exist. Hence, this article introduces an intelligent path optimization strategy to optimize the sequence of welding seams. The shortest path length, power consumption, and welding deformation are considered optimization objectives, and welding deformation is studied based on the proxy model to improve optimization efficiency. Then, the improved discrete multiobjective decomposition algorithm based on event-triggering strategy (DMOEA/D-ET) is proposed. Here, the grid method and decomposition multiobjective algorithm (MOEA/D) are used to improve search performance. The adaptive neighborhood strategy is used to improve the quality and distribution of noninferior solutions. After the comparison with NSGA-II, NSGA-III, and CGMOPSO algorithms, the DMOEA/D-ET algorithm shows better performance on both convergence and diversity. Finally, the proposed strategy is used for welding robot path planning, and the simulation results show its effectiveness.