An efficient optimization method for periodic lattice cellular structure design based on the K-fold SVR model


The design optimization of periodic lattice cellular structure relying exclusively on the computational simulation model is a time-consuming, even computationally prohibitive process. To relieve the computational burden, an efficient optimization method for periodic lattice cellular structure design based on the K-fold support vector regression model (K-SVR) is proposed in this paper. First, based on the loading experiments, the most promising unit cell of periodic lattice cellular structure is selected from five typical unit cells. Second, an initial SVR model is constructed to replace the simulation model of the periodic lattice cellular structure, and the K-fold cross-validation approach is used to extract the error information from the SVR model at the sample points. According to the error information, the sample points are sorted and classified into several sub-sets. Then, a global K-SVR model is re-constructed by aggregating each SVR model under each sub-set. Third, considering that there exists prediction errors between the K-SVR model and the simulation model, which may lead to infeasible optimal solutions, an uncertainty quantification approach is developed to ensure the feasibility of the optimal solution for the periodic lattice cellular structure design. Finally, the effectiveness and merits of the proposed approach are demonstrated on the design optimization of the A-pillar and seat-bottom frame.

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This work has been supported by the China Postdoctoral Science Foundation under Grant No.2020M682396, and the National Defense Innovation Program under Grant No. 18-163-00-TS-004-033-01.

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Correspondence to Peng Jin.

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Zhang, L., Hu, J., Meng, X. et al. An efficient optimization method for periodic lattice cellular structure design based on the K-fold SVR model. Engineering with Computers (2021).

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  • Periodic lattice cellular structure
  • Support vector regression model
  • Additive manufacturing
  • Uncertainty quantification
  • Design optimization