Improved many-objective particle swarm optimization based welding sequence optimization research
Lei Dong, Shimin Gu, Jianwei Dong, Qiukai Ji, Jinfeng Liu
TL;DR
This paper introduces a new optimization method for welding sequences in shipbuilding to reduce deformation and stress.
Contribution
An improved many-objective particle swarm optimization algorithm (IMaOPSO) is proposed for welding sequence optimization.
Findings
IMaOPSO outperforms NSGA-II, SPEA2, and SMPSO in convergence speed and stability.
The optimal welding sequence reduces deformation by 32.6% to 62.2% compared to other methods.
The method integrates process and geometric constraints for better welding quality.
Abstract
Welding sequence optimization (WSO) for ship components is a complex, multi-objective, and nonlinear challenge. Traditional methods relying heavily on engineer experience often lead to inadequate decisions, resulting in excessive deformation, residual stress, and even cracking. To address this, we propose a systematic WSO method for ship structural parts that integrates both process and geometric constraints. The optimization objectives are formally defined through objective functions quantifying structural deformation and residual stress. For solving this high-dimensional problem, an Improved Many-Objective Particle Swarm Optimization (IMaOPSO) algorithm is developed. IMaOPSO enhances the classical PSO by incorporating an adaptive fuzzy dominance relation to improve selection pressure and a perturbation term guided by elite solutions to maintain population diversity. This ensures rapid…
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Taxonomy
TopicsAdvanced Multi-Objective Optimization Algorithms · Welding Techniques and Residual Stresses · Topology Optimization in Engineering