Regenerative Topology Optimization of Fine Lattice Structures

Area of Science:

• Computational Engineering
• Materials Science
• Additive Manufacturing

Background:

• Topology optimization is a key method for designing lightweight, high-performance structures.
• Existing methods may not efficiently handle complex dynamic load cases or large-scale lattice generation.

Purpose of the Study:

• To present a novel generative approach for 3D lattice structures optimized for mass and deflection.
• To enable efficient optimization under dynamic load conditions using parallel processing.
• To create intricate lattice designs suitable for additive manufacturing.

Main Methods:

• A generative approach inspired by topology optimization principles.
• Iterative determination and erosion of unnecessary struts based on stress analysis.
• Random generation of new struts and simultaneous simulation for dynamic load cases.
• Massively parallel graphics processing unit (GPU) implementation for efficient computation.

Main Results:

• Generated lattice structures exhibit high strength-to-weight ratios comparable to state-of-the-art methods.
• The method produces qualitatively different, intricate lattice designs.
• Demonstrated efficient scaling on parallel GPUs for large-scale lattice optimization.
• Validated algorithm versions across various load cases for high-resolution object generation.

Conclusions:

• The proposed generative approach offers a powerful new tool for designing optimized 3D lattice structures.
• The method is highly parallelizable and efficient, particularly for dynamic load cases.
• The resulting intricate microstructures are well-suited for additive manufacturing applications.