Multi-objective Optimization Design Of Commercial Vehicle Cab Based On NSGA-Ⅲ Algorithm

In the context of the "double carbon" strategy and the needs of the times,automotive lightweight technology is particularly important in energy saving and emission reduction.The cab is an important part of a commercial vehicle and its weight reduction plays a key role in the lightweighting of the vehicle structure.The commercial vehicle structure is poorly lightened,which makes fuel consumption increase and causes environmental pollution.The cab is the key research object of commercial vehicle lightweighting,the increase in the number of optimization targets of traditional optimization algorithms causes the global search ability and solution accuracy to be reduced in the process of multiobjective optimization design of cab.The screening of lightweight design solutions is interfered by human subjective consciousness,which makes the cab structure optimization efficiency low and lightweight effect bad.Therefore,the paper takes a commercial vehicle cab of an enterprise as the research object and uses NSGA-Ⅲ algorithm combined with entropy-weight-improved TOPSIS method for multi-objective optimization design of the cab based on implicit parametric modeling technology.The main research contents are as follows:(1)The implicit parametric modeling technique was used to establish the implicit parametric model of the cab and generated the parametric finite element model of the cab.By comparing the analysis results of cab modal simulation and modal test,the accuracy of the model was verified to meet the actual needs of enterprises.Simulation analysis and evaluation of torsional stiffness and bending stiffness of the cab were also carried out for the model,which laid the foundation for the subsequent optimization design of the cab structure.(2)Stiffness,modal and mass sensitivity analyses were performed on the structural parameters of the cab based on parametric finite element model of the cab.Considering the comprehensive influence of cab component thickness and beam cross-sectional shape parameters,the key parameters were screened by combining relative sensitivity analysis and structural sensitivity analysis.Twenty-four thickness and six beam cross-sectional shape variables were determined as design variables for multi-objective optimization of the cab in the end.(3)The sample points obtained by the optimal Latin hypercube experimental design method were used to construct a response surface approximation model and the modal confidence factor was combined to improve the fitting accuracy of the model.With the minimum mass and maximum stiffness as the optimization objectives,the NSGA-Ⅲ algorithm was used for the multi-objective optimization design of the cab.The reverse generation distance and the number of Pareto optimal solutions were used as the performance evaluation indexes of this algorithm,which was used as the basis for comparison with the optimization results of NSGA-II algorithm.It shows that the NSGAIII algorithm is able to provide more optimal solutions to meet expectations.(4)The relative closeness of all non-dominated Pareto solutions generated by the NSGA-Ⅲ algorithm was calculated and ranked by using the entropy-weight-improved TOPSIS method.The best decision solution for cab lightweighting was determined by comparing the decision solutions with the TOPSIS method.The final results show that the cab mass is reduced by 24.7 kg,with a reduction rate of 8.0% and a significant lightweight effect.While the torsional stiffness is increased by 5.3% and the bending stiffness is increased by 4.5% under the premise of improving the cab modalities.The research results have certain engineering application value for the multi-objective optimization design of commercial vehicle cabs.