Multiobjective Lightweight Optimization Design Method for a Dump Truck Carriage under Multiple Working Conditions

Abstract

To reduce the transportation cost and improve the fuel economy of a dump truck in road transport, vehicle lightweight optimization with the objectives such as the mass, stiffness, and sixth-order frequency of the carriage is adopted in this study. First, the carriage is divided into finite element meshes, and the working conditions of the carriage are analyzed under the conditions of full-load and uniform transportation, 0° lift, and 30° lift. Then, a comprehensive model consisting of full-load uniform transport and 0° lifting dual conditions is established, and 18 plate thicknesses are defined as design variables. The samples derived from the design variables of the carriages via the optimal Latin hypercube method are employed to analyze, in which the design variable that critically impacts the optimization objective is screened out from the sample for research. In addition, the dual-case approximation model constructed by a variety of methods is executed for accuracy comparison. And a moving least squares method combining linear and specific numerical values with the highest accuracy is selected to build a dual-case approximation model for the carriage. Ultimately, on the basis of the constructed double-condition approximation model, a multiobjective genetic algorithm is utilized to optimize the mass, bending stiffness, and sixth-order frequency. Compared with other methods, the multiobjective genetic algorithm can maintain the optimal solution, and the stored optimal solution can also be introduced into other groups to obtain another optimal solution. The simulation results, obtained from the scenarios of the carriage under the conditions of full-load uniform transportation, 0° lift and 30° lift, reveal that, on the basis of keeping the shape and structure of the original carriage unchanged, the mass is reduced by 1.215 t, the overall bending stiffness of the carriage is increased by 16300 N·mm−1, and the sixth-order modal frequency is optimized.

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“…They optimized surface roughness, tool wear, and material-removal rates during DFA machining of Tinalox, Hyperlox and HSN2 coatings. In 2022, Zhang et al [53] applied a combination of linear and numerically specific moving least squares methods to construct an approximate model of a sliding frame. They optimized the design of mining dump truck vehicles for lightweighting with the objectives of compartment mass, stiffness, and sixth-order frequency, which helped improve the economy, dynamics, braking, and safety of the dump trucks.…”