Rigid-flexible Coupling Analysis And Optimization Of Steering Linkages For Heavy Truck

The steering system of heavy truck has complex structures and is ofen used in complicated and changing conditions. The dual-front axle steering system, for instance, contains numerous components which occur deformation under loading process. However, most researches about the dual-front axle steering system were based on the the rigid model while neglecting to consider the deformation of steering linkages. Starting off with steering linkages flexibility, in this thesis, the subject about rigid-flexible coupling analysis and optimization of steering linkages for heavy truck is put forword.Primarily, a rigid model of dual-front axle steering system was established in ADAMS, and implementation approaches of stand still steering simulation and parallel wheel travel simulation were respectively explained. Then finite element modal analysis of steering linkages was done base on Hyper Works which generated corresponding modal neutral files. By replacing each steering linkage of rigid model with relevant modal neutral file, a rigid-flexible coupling steering system was built. Through the stand still steering simulation and parallel wheel travel simulation, the effects of steering links flexibility on kinematic characteristics and dynamic performance were studied.Secondly, a virtual model of whole dual-front axle truck was set up and actual field test was carried out in order to ensure the correctness of this model. By reference to the national standard on the handling and stability testing, multiple simulation tests were performed. Meanwhile, the differences of simulation results between rigid model and rigid-flexible model were discusseed.Finally, as to the performance weakness of heavy truck built in this thesis, experiment designs with the rigid-flexible coupling steering system were conducted and the approximate models were acquired. The optimization issue was solved using a GA-PSO algorithm. The optimal results indicated that the optimization method adopting approximate model was effective and feasible.