Uncertainty Optimization Design of a Vehicle Body Structure Considering Random Deviations

AbstractIn vehicle body manufacturing, there are small differences between the actual value and design value of, for example, plate thickness and material characteristics. This is caused by the processing technology, environment and other uncertain factors. Therefore, the performance of the vehicle body processed according to the deterministic optimization solution fluctuates. The fluctuations may make structural performance fail to meet the design requirements. Thus, in this study, an optimization design is executed with 6$$ \sigma $$σ robustness criteria and a Monte Carlo simulation single-loop optimization strategy based on the radial basis function neural network approximate model considering deviations in plate thickness, elastic modulus, and welding spot diameter, which is called the uncertainty optimization design method. As an example, considering the bending stiffness, torsion stiffness, and first-order frequency as constraints, the method is applied to the lightweight design of a car body structure, and the reliability of deterministic optimization design and uncertainty optimization design is compared. The results demonstrate that the uncertainty optimization design solution is effective and feasible without lowering the static stiffness and modal performance, and the weight is reduced.