In this study, a mixed meta-modeling-based optimization method has been proposed and applied to a commercial vehicle for crashworthiness design subjected to the frontal crash. A full-scale finite element model of the commercial vehicle has been built and validated by a crash test. The front frame parts have been separated to build a sub-model for crashworthiness optimization. Sensitivity analysis has been performed to find the design factors contributing most to crash performance by using design of experiments. With the reduced dimensions of design space, meta-models of crashworthiness criteria (i.e. specific energy absorption, peak crush force, and peak crush acceleration) have been built by using polynomial response surface and radial basis function networks, respectively. The meta-models with higher global fidelity in design space have been adopted to formulate the multi-objective optimization problem of crashworthiness design, which has then been solved by using Non-dominated Sorting Genetic Algorithm-II. The obtained Pareto front has been discussed and validated with that achieved by Strength Pareto Evolutionary Algorithm 2. The normalized optimal solution from the Pareto front has resulted in 11.15% increase in specific energy absorption and 13.2% decrease in peak crush force for the frame and has led to an obvious improvement in occupant protection and energy absorption for the whole vehicle, verifying that the proposed method is effective for vehicle crashworthiness optimization.

<div class="section abstract"><div class="htmlview paragraph">The front end structure is an important role in protecting the vehicle and passengers from harm during the collision. Increasing its protective capacity can be achieved by increasing the thickness or replacing high-strength materials. Most of the current research is analyzed separately from these two aspects. This paper proposes a multi-objective optimization method based on weighting factor analysis, which combines material and thickness selection. Firstly, the optimized components are determined based on the 100% frontal collision simulation results. Secondly, six thicknesses and two materials of the front part of the vehicle body are selected as design variables to construct an orthogonal test design. In this paper, a weight-based multi-factor optimization method is used to numerically analyze the response results obtained by orthogonal experiments. Analyze the impact of each factor on the optimization goal to select the most reliable optimization. This optimization method can select the best material and component thickness combination scheme. The results show that the mass of the selected parts are reduced by 16.5%; the total energy absorption is increased by 5.2%; the intrusion in the dash is reduced by 8.9%; and the peak acceleration of the B-pillar is reduced by 39.2%.The material-structure integration optimization method is an effective method to solve the contradiction between lightweight and crashworthiness.</div></div>

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