Optimized design and reliability study of the gantry crane main girder
SUN Xuan
WANG Jiabing
HU Xianxuan
ZHANG Lingyun
JU Chengwei
Abstract:[Objective]To address the problems of insufficient consideration of uncertain factors and low design accuracy in the traditional design of gantry crane main girders,research on lightweight design and multi-objective reliability optimization of main girder webs was conducted to improve the rationality and safety of structural design for heavy lifting equipment.[Methods]Firstly,a parametric finite element model of web thickness was established with SolidWorks software and Ansys software for a gantry crane main girder with a span of 10 m and a load capacity of 800 t,which provided numerical support for optimization analysis.Secondly,an implicit limit state function of the structure was constructed by combining the Latin hypercube sampling method and back propagation(BP)neural network technology,and the structural reliability index was calculated using the Monte Carlo simulation method.Thirdly,multi-objective reliability design optimization was carried out with the non-dominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ),taking the minimum main girder mass,minimum maximum total deformation and maximum reliability index as optimization objectives.Finally,the simulation accuracy of the finite element model was verified through a scaled model lifting test and acoustic emission sensing detection.[Results]The results show that 70 groups of Pareto optimal solution sets are obtained through multi-objective optimization.For the comprehensively selected optimal scheme,the mass of the main girder structure is reduced by 24.56%and the structural reliability is improved by 9.7%under the premise that deformation and stress meet safety requirements.An efficient reliability optimization system for main girders can be constructed with the proposed method,and reference can be provided for the structural safety design of heavy lifting equipment.
Keywords:Gantry crane main girderMulti-objective optimizationBP neural networkReliabilityLightweighting
Publication Date:2026-08-15
Online Publishing Date:2026-09-12(First online date of this platform, not the publication date of the document)
Pages:8( 83-90 )
