Optimized design of truck cab lightweighting based on sensitivity hierarchical comparative analysis method

Abstract

The relative sensitivity analysis method is important in the field of vehicle lightweighting. Combined with optimization algorithms, experiment of design (DOE), etc., it can efficiently explore the impact of unit mass of components on performance and search for components with lightweight space. However, this method does not take into account the size level of each component and the order of magnitude differences in sensitivity under different operating conditions. Therefore, this paper proposed a sensitivity hierarchical comparative analysis method, on the basis of which the thicknesses of 10 groups of components were screened out as design variables by considering the lightweighting effect,cab performance, and passive safety. Through the optimal Latin hypercube method, 70 groups of sample points were extracted to carry out the experimental design, the Kriging surrogate model was established and the NSGA-II genetic algorithm was used to obtain the Pareto optimal solution set, and ultimately a weight reduction of 13.13 kg was realized under the premise that the entire performance of the cab improved.