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A multiscale topology optimization design framework with data driven surrogate model.

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Summary
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This study introduces a new multiscale framework for designing advanced metamaterials. It enables simultaneous optimization of material properties and structure for better performance in aerospace and biomedical fields.

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Area of Science:

  • Computational Mechanics
  • Materials Science
  • Topology Optimization

Background:

  • Conventional topology optimization struggles with functionally graded hierarchical structures due to scale-decoupling and computational costs.
  • Designing complex, high-performance metamaterials requires advanced computational approaches.

Purpose of the Study:

  • To develop a transformative offline-online multiscale framework for designing functionally graded hierarchical structures.
  • To enable simultaneous optimization of macroscopic topology and microscopic lattice parameters for performance-driven metamaterials.

Main Methods:

  • Utilized Moving Least Squares surrogate models for real-time property mapping via multiscale finite element analysis, bypassing scale separation.
  • Implemented a unified Discrete Material Optimization scheme for concurrent co-optimization of topology and lattice parameters.
  • Validated the framework on geometrically complex benchmarks.

Main Results:

  • Demonstrated superior mechanical rationality with load-path-aligned configuration invariance and adaptive density modulation.
  • Achieved simultaneous control over spatial configuration distribution and anisotropic property gradation.
  • Bridged high-dimensional design freedom with computational tractability.

Conclusions:

  • The developed framework establishes a new paradigm for designing performance-driven metamaterials.
  • Offers manufacturing-ready solutions for aerospace and biomedical applications.
  • Overcomes limitations of conventional topology optimization for hierarchical structures.