Lattice Structure Optimization for Additive Manufacturing: Manufacturability-Driven Design and Pareto Front Construction

2026-09-27

AI Summary

This study addresses the challenges of inefficient Pareto front construction and unmet manufacturing constraints in multi-physics lattice design by proposing a manufacturing-constraint-driven joint optimization method. The approach integrates inverse homogenization topology optimization with a density generation network to learn latent representations, thereby expanding the feasible solution space. Furthermore, differentiable manufacturing constraints and a progressive mechanism are innovatively embedded to achieve synergistic optimization of physical performance and manufacturability. Experimental results demonstrate that, compared to random initialization, the proposed method increases the success rate to 92.6% and achieves a Pareto front hypervolume of 0.0787, significantly improving the search efficiency within the design space.

Abstract

Lattice metamaterials support lightweight, multifunctional structures, while additive manufacturing (AM) enables complex geometries. Yet multiphysics lattice design faces two challenges: efficiently constructing well-covered multi-objective Pareto fronts under limited budgets, and satisfying manufacturing constraints such as overhangs, enclosed cavities, and restricted powder-removal channels. We propose a manufacturing-constraint-driven method for lattice optimization and Pareto-front construction. Differentiable manufacturing constraints are embedded in inverse-homogenization topology optimization, enabling joint optimization of physical performance and manufacturability. A progressive Pareto-front mechanism uses a density-generation network to learn latent representations of high-quality lattices, interpolates neighboring nondominated representations, and decodes them into initial density fields for subsequent optimization. Newly found nondominated solutions update the network and sample set, progressively expanding the manufacturable set. On 3D periodic unit cells, with 1000 optimization runs, network initialization achieves a 92.60% success rate and 916 manufacturable samples, versus 78.30% and 776 for random initialization. Its Pareto front reaches a hypervolume of 0.0787, compared with 0.0675 for random initialization. The results show that the method efficiently constructs broadly covered manufacturable Pareto fronts for multiple physical properties.

Problem

• Lattice structure optimization
• Additive manufacturing
• Multi-objective Pareto front
• Manufacturability constraints
• Multiphysics design

Innovation

• Lattice Structure Optimization
• Additive Manufacturing
• Inverse Homogenization Topology Optimization
• Differentiable Manufacturing Constraints
• Progressive Pareto Front