Abstract
Engine hood is one of the important parts of the vehicles, which has influences on the lightweight, structural safety, pedestrian protection, and aesthetics. The optimization design of engine hood is a high-dimensional, multi-objective, and mixed-variable optimization problem. In order to reduce the physical test investment in the development and improve the efficiency of optimization, this article proposes a data-driven method for optimal hood design. A newly proposed single-objective optimization algorithm is improved by several strategies for multi-objective constrained problem with mixed variables. Then the hood is optimized through the specially designed machine learning model. Finally, both the hood's weight and pedestrian injury are reduced while maintaining structural stiffness and frequency in the desired range. The comparative study and final hood optimization results prove the effectiveness of the proposed method.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China (#51705312, #11772191).
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Han Li is a Ph.D. candidate at School of Mechanical Engineering, Shanghai Jiao Tong University, P. R. China. His research interests include missing data imputation, applications of industrial data, and heuristic optimization and evolutionary learning.
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Li, H., Liu, Z. & Zhu, P. An improved multi-objective optimization algorithm with mixed variables for automobile engine hood lightweight design. J Mech Sci Technol 35, 2073–2082 (2021). https://doi.org/10.1007/s12206-021-0423-5
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DOI: https://doi.org/10.1007/s12206-021-0423-5