增材制造晶格结构优化：可制造性驱动设计与帕累托前沿构建
Lattice Structure Optimization for Additive Manufacturing: Manufacturability-Driven Design and Pareto Front Construction
- Shandong University（山东大学）
- Microsoft Research Asia（微软亚洲研究院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对增材制造晶格优化中多目标帕累托前沿构建与制造约束的挑战，提出制造约束驱动的优化方法，结合可微约束与渐进式网络，显著提升可制造性和前沿覆盖。
AI中文摘要：
晶格超材料支持轻量化、多功能结构，而增材制造（AM）能够实现复杂几何形状。然而，多物理场晶格设计面临两个挑战：在有限预算下高效构建覆盖良好的多目标帕累托前沿，以及满足制造约束，如悬垂、封闭空腔和受限的粉末去除通道。我们提出了一种制造约束驱动的晶格优化与帕累托前沿构建方法。可微的制造约束被嵌入到逆均匀化拓扑优化中，从而实现对物理性能和可制造性的联合优化。一种渐进式帕累托前沿机制使用密度生成网络来学习高质量晶格的潜在表示，对相邻的非支配表示进行插值，并将其解码为用于后续优化的初始密度场。新发现的非支配解更新网络和样本集，逐步扩展可制造集。在三维周期性单胞上，经过1000次优化运行，网络初始化实现了92.60%的成功率和916个可制造样本，而随机初始化分别为78.30%和776个。其帕累托前沿的超体积达到0.0787，而随机初始化为0.0675。结果表明，该方法能够高效地为多种物理属性构建覆盖广泛的、可制造的帕累托前沿。
英文摘要：
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.
