Scholarlink

<p>Quasi-zero-stiffness (QZS) metastructures are promising for low-frequency vibration isolation in precision instruments and aerospace applications, but their inherent nonlinearities make tailoring performance with conventional methods difficult. This paper proposes a data-driven inverse design framework combining a backpropagation neural network with a genetic algorithm. A dataset of 1000 finite element simulations was generated by varying the geometric parameters of the metastructure. The neural network was trained to map these parameters to tangent stiffness and load capacity, and was then coupled with the genetic algorithm to inversely derive geometric configurations that meet target specifications. The neural network achieved excellent predictive accuracy (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\({\text{R}}^{2}\)</EquationSource> <EquationSource Format="MATHML"><math> <msup> <mrow> <mtext>R</mtext> </mrow> <mn>2</mn> </msup> </math></EquationSource> </InlineEquation> = 0.99). For a target of 0.55 N load capacity and 0.01 N/mm tangent stiffness, experimental validation yielded 0.53 N and 0.009 N/mm, while simulations predicted 0.57 N and 0.013 N/mm. The maximum load deviation was 0.02 N (3.6% relative error). Sweep vibration tests showed an initial isolation frequency of 4.27 Hz and an isolation efficiency of 90% at 44.92 Hz. This machine-learning-based inverse design enables precise customization of QZS metastructures, offering an efficient alternative to trial-and-error methods.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Data-driven inverse design of metastructures for tailored quasi-zero-stiffness performance

  • 期刊论文

  • 发表日期：

    2026年3月19日

查看全文
  • Yunfei Lan,
  • Ye Sun,
  • Xuzhe Qiu,
  • Yingying Sun,
  • Yang Xia,
  • Hanxing Zhu,
  • Mohammed Rafiq Abdul Kadir,
  • Yongtao Lyu

摘要

Quasi-zero-stiffness (QZS) metastructures are promising for low-frequency vibration isolation in precision instruments and aerospace applications, but their inherent nonlinearities make tailoring performance with conventional methods difficult. This paper proposes a data-driven inverse design framework combining a backpropagation neural network with a genetic algorithm. A dataset of 1000 finite element simulations was generated by varying the geometric parameters of the metastructure. The neural network was trained to map these parameters to tangent stiffness and load capacity, and was then coupled with the genetic algorithm to inversely derive geometric configurations that meet target specifications. The neural network achieved excellent predictive accuracy ( \({\text{R}}^{2}\) R 2 = 0.99). For a target of 0.55 N load capacity and 0.01 N/mm tangent stiffness, experimental validation yielded 0.53 N and 0.009 N/mm, while simulations predicted 0.57 N and 0.013 N/mm. The maximum load deviation was 0.02 N (3.6% relative error). Sweep vibration tests showed an initial isolation frequency of 4.27 Hz and an isolation efficiency of 90% at 44.92 Hz. This machine-learning-based inverse design enables precise customization of QZS metastructures, offering an efficient alternative to trial-and-error methods.