Multi-objective Optimization Of Bridge Crane Girder Based On DOE And Wolf Pack Algorithm

Many domestic scholars have carried out lightweight design or dynamic optimization of overhead crane,but most of them only study one of them without comprehensive consideration.In view of the above problems,this paper takes lightweight and improving its dynamic performance as the objective to optimize the design.On the premise of satisfying the practical application of bridge crane design,it can reduce self-weight and obtain better dynamic performance.In this paper,the main girder of a bridge crane is studied,and the box girder of crane is designed by using DOE-Design of Experiments analysis method,finite element dynamics theory and intelligent search algorithm.The goal of lightweight and dynamic performance optimization of box girder is achieved.The main contents of this dissertation are as follows:(1)Through the research of multi-objective optimization design method of crane box girder and the research status,background and process of wolf pack optimization algorithm,the main research direction and content of this paper are established.(2)A parametric model of the box girder is constructed in ANSYS Workbench 14.5.The static analysis of the finite element method verifies the rationality of the model and extracts the relevant parameters.On this basis,the dynamic analysis is carried out to extract the frequency parameters and the related vibration modes which have a great impact on the vibration of the box girder.(3)Based on DOE analysis method,the main parameters are selected as design variables through the analysis of stress,strain,mass and second-order modal natural frequencies of the main girder.With the second-order modal natural frequencies as one of the optimization objectives,a network model between the design variables and the second-order modal natural frequencies is constructed by using the neural network algorithm.(4)Wolf pack algorithm(WPA)is studied.The multi-objective wolf pack algorithm is introduced into this paper.Based on the local extremum and low optimization accuracy of the algorithm,an improved multi-objective wolf pack algorithm is proposed.By choosing the appropriate parameters of the algorithm through real-time monitoring of the optimization process,the improved wolf pack algorithm and BP neural network algorithm are used to synergistically optimize the main beam structure model.(5)From the solution concentration obtained after the collaborative optimization,the optimum scheme is selected by using the entropy method to establish the mathematical model of the main girder,and the static and dynamic verification of the improved bridge-starting girder is carried out.It is concluded that the improved bridge-starting box girder results are feasible and superior.