Structural Optimization And Code Design Of Thin-walled Automotive Body Frame With Complex Cross Section

Recent years,the competition of automotive industry is fierce and the enterprises are seeking to shorten the design cycle of automotive products.In the conceptual design stage,in order to improve the design quality,reduce the research and development cost and shorten the design cycle,the thin-walled frame model is applied to design the automotive body.The model is hinged by thin-walled beams.Therefore,the cross-sectional shapes of thin-walled beams need to be optimized to improve the performance of automobile structure.Firstly,this paper compares the different automotive optimization model and determines the frame model is suitable.secondly,this paper describes the stiffness calculation method and manufacturing process of thin-walled beam,and derives the stamping cost formulation.Thirdly,this paper innovatively proposes a cross-sectional shape optimization method to acquire a low cost,high stiffness,manufacturable and lightweight TWBs.The cross-sectional area is taken as the objective function to establish an optimization formulation with multiple constraints such as cross-sectional stiffness,manufacturability and cost of TWBs.The constraints are introduced to the single objective genetic algorithm according to the principle of non-dominance.MATLAB software is applied to design the code,and the effectiveness of the proposed method is verified by an example of double cell cross-sectional shape.Besides,the simplified automobile body frame,which consists of thin-walled beams,can effectively predict the global performances including weight,rigidity and frequency.Therefore,to improve these global performances,this paper innovatively proposes a cross-sectional shape optimization method considering weight,rigidity and frequency,simultaneously.CarFrame software are applied to calculate the weight,stiffness and frequency of the frame model of different cross-sectional shape.The obtained data are fitted by artificial neural network and the MATLAB code can be obtained to effectively predict the performance of the body frame with different cross-sectional shapes.The non-dominated sorting genetic algorithm are introduced to obtain the pareto front satisfying constraints and the reliable basis is provided for optimal scheme selection of the concept design.The effectiveness of the proposed method is verified by optimizing the cross-sectional shape for A-pillar of SUV automobile body frame.