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<p>Cutterhead is the core component of tunnel boring machine (TBM), and its lightweight is of great significance for saving manufacturing cost, reducing energy consumption and operating cost. Taking the cutterhead of TBM as the research object, this paper constructs the lightweight design model considering the randomness of design parameters. The model takes the cutterhead weight as the optimization objective, and the maximum stress, deformation and minimum fatigue life of the cutterhead as the probabilistic constraints. In order to reduce the optimization cost of the lightweight design model, the Latin centroidal Voronoi tessellation (LCVT) sampling method was used for initial sampling to construct the initial kriging model between the structural parameters and the performance responses of the cutterhead, and the local adaptive sampling (LAS) strategy was used to update the kriging model sequentially to ensure the approximation accuracy of key region in lightweight design of cutterhead. Finally, the Monte Carlo simulation-sequential quadratic programming (MCS-SQP) algorithm was used to optimize the structural parameters of the cutterhead. Through the simulation verification of the optimal structural parameters, on the basis of satisfying the probabilistic constraints of stress, deformation and fatigue life, the method proposed in this paper can effectively reduce the weight of the TBM cutterhead, and the weight reduction ratio reaches 9.58 %.</p>

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Lightweight design of TBM cutterhead based on LCVT and local adaptive sampling

  • 期刊论文

  • 发表日期：

    2025年9月4日

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  • Shiyou Chen,
  • Kai Wang,
  • Lei Fan,
  • Yang Wang,
  • Xiaoke Li

摘要

Cutterhead is the core component of tunnel boring machine (TBM), and its lightweight is of great significance for saving manufacturing cost, reducing energy consumption and operating cost. Taking the cutterhead of TBM as the research object, this paper constructs the lightweight design model considering the randomness of design parameters. The model takes the cutterhead weight as the optimization objective, and the maximum stress, deformation and minimum fatigue life of the cutterhead as the probabilistic constraints. In order to reduce the optimization cost of the lightweight design model, the Latin centroidal Voronoi tessellation (LCVT) sampling method was used for initial sampling to construct the initial kriging model between the structural parameters and the performance responses of the cutterhead, and the local adaptive sampling (LAS) strategy was used to update the kriging model sequentially to ensure the approximation accuracy of key region in lightweight design of cutterhead. Finally, the Monte Carlo simulation-sequential quadratic programming (MCS-SQP) algorithm was used to optimize the structural parameters of the cutterhead. Through the simulation verification of the optimal structural parameters, on the basis of satisfying the probabilistic constraints of stress, deformation and fatigue life, the method proposed in this paper can effectively reduce the weight of the TBM cutterhead, and the weight reduction ratio reaches 9.58 %.