Research on Multi-Objective Optimization Method for Hydroforming Loading Path of Centralizer
Zaixiang Zheng, Zhengjian Pan, Hui Tan, Feng Wang, Jing Xu, Yiyang Gu, Guoheng Li
TL;DR
This study explores how to optimize the pressure and feed paths during centralizer hydroforming to achieve uniform wall thickness and avoid defects.
Contribution
The paper introduces a multi-objective optimization framework combining several algorithms with LS-DYNA for hydroforming process design.
Findings
NSGA-II, NCGA, and AMGA successfully generated optimized loading paths.
NSGA-II and AMGA produced larger sets of higher-quality Pareto solutions.
MOPSO showed premature convergence and yielded inferior results.
Abstract
During centralizer hydroforming, internal pressure and axial feed critically influence the forming outcome. Insufficient feed causes excessive thinning and cracking, while excessive feed causes thickening and wrinkling. Achieving uniform wall thickness necessitates careful design of the pressure and feed curves. Using max/min wall thickness as objectives and key control points on these curves as variables, the study integrated Non-dominated Sorting Genetic Algorithm (NSGA-II), Multi-Objective Particle Swarm Optimization (MOPSO), Neighborhood Cultivation Genetic Algorithm (NCGA), and Archive-based Micro Genetic Algorithm (AMGA) with LS-DYNA to automatically optimize loading paths. The results demonstrate the following: ① NSGA-II, NCGA, and AMGA successfully generated optimized paths; ② NSGA-II and AMGA produced larger sets of higher-quality Pareto solutions; ③ AMGA required more…
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Taxonomy
TopicsMetal Forming Simulation Techniques · Advanced Multi-Objective Optimization Algorithms · Metallurgy and Material Forming