Skip to main content
Log in

Uncertainty Optimization Design of a Vehicle Body Structure Considering Random Deviations

  • Published:
Automotive Innovation Aims and scope Submit manuscript

Abstract

In 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\( \sigma \) 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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Lin, X.H., Feng, Y.X., Tan, J.R., et al.: Robust optimization design method for quality characteristics of mechanical products based on quantifier constraints. J. Mech. Eng. 49(15), 169–179 (2013)

    Article  Google Scholar 

  2. Du, X.P., Guo, J., Beeram, H.: Sequential optimization and reliability assessment for multidisciplinary systems design. Struct. Multidiscip. Optim. 35(2), 117–130 (2008)

    Article  MathSciNet  Google Scholar 

  3. Liu, Z.F., Atamturktur, S., Juang, H.C.: Reliability based multi-objective robust design optimization of steel moment resisting frame considering spatial variability of connection parameters. Eng. Struct. 76, 393–403 (2014)

    Article  Google Scholar 

  4. Cui, J., Zhang, W.G., Chang, W.B., et al.: Optimization design of collision safety robustness based on double response surface model. J. Mech. Eng. 47(24), 97–103 (2011)

    Article  Google Scholar 

  5. Zhang, Y., Li, G.Y., Zhong, Z.H.: Research on application of reliability-based multidisciplinary design optimization in lightweight design of thin-walled beams. China Mech. Eng. 20(15), 1885–1889 (2009)

    Google Scholar 

  6. Qiu, R.B., Chen, Y., Lei, F., et al.: Research on lightweight of b-pillar for car side impact body based on 6σ robust design. Mech. Strength 38(03), 502–508 (2016)

    Google Scholar 

  7. Xie, R., Lan, F.C., Chen, J.Q., et al.: Multi-objective optimization method of lightweight body structure to meet reliability requirements. Chin. J. Mech. Eng. 47(4), 117–124 (2011)

    Article  Google Scholar 

  8. Chen, G.D., Han, X.: Multi-objective optimization method based on agent model and its application in vehicle body design. J. Mech. Eng. 50(09), 70 (2014)

    Google Scholar 

  9. Chan, C.L., Low, B.K.: Probabilistic analysis of laterally loaded piles using response surface and neural network approaches. Comput. Geotech. 43, 101–110 (2012)

    Article  Google Scholar 

  10. Goel, T., Stander, N.: Comparing three error criteria for selecting radial basis function network topology. Comput. Methods Appl. Mech. Eng. 198(27–29), 2137–2150 (2009)

    Article  MathSciNet  Google Scholar 

  11. Biancardi, M., Villani, G.: Robust Monte Carlo method for R&D real options valuation. Comput. Econ. 49(3), 481–498 (2017)

    Article  Google Scholar 

  12. Chen, C., Yu, D.K.: Lightweight design of medium passenger car frame based on relative sensitivity analysis. Automot. Technol. 06, 27–30 (2014)

    Google Scholar 

  13. Lai, Y.Y.: Isight parametric optimization theory and detailed examples. Beijing University of Aeronautics and Astronautics Press, Beijing (2012)

    Google Scholar 

  14. Ekbal, A., Saha, S.: Combining feature selection and classifier ensemble using a multiobjective simulated annealing approach: application to named entity recognition. Soft. Comput. 17(1), 1–16 (2013)

    Article  Google Scholar 

Download references

Acknowledgements

This study was supported by the National Natural Science Foundation of China (51775193) and the Science and Technology Planning Project of Guangdong Province, China (2016A050503021, 2015B0101137002, and 2017B010119001).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jianhua Zhou.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhou, J., Lan, F., Chen, J. et al. Uncertainty Optimization Design of a Vehicle Body Structure Considering Random Deviations. Automot. Innov. 1, 342–351 (2018). https://doi.org/10.1007/s42154-018-0041-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42154-018-0041-9

Keywords

Navigation