标题： TOBACO：通过带限坐标网络进行拓扑优化的成分梯度合金 显示英文标题

标题： TOBACO: Topology Optimization via Band-limited Coordinate Networks for Compositionally Graded Alloys

摘要： 组合梯度合金（CGAs）通过允许成分的空间变化，提供了前所未有的设计灵活性；可以针对局部载荷条件调整材料性能。 这种灵活性使得组件比传统的单一材料组件更坚固、更轻且更具成本效益。 由于增材制造（AM）的最新进展，特别是多材料打印和材料沉积精度的提高，CGAs的制造变得越来越可行。 然而， CGAs的AM需要施加制造约束；特别是对成分最大空间梯度的限制。 本文介绍了一种基于拓扑优化（TO）的框架，用于设计具有可控成分梯度的优化CGA组件。 特别是，我们使用带限坐标神经网络表示受约束的成分分布。 通过调节网络的带宽，我们确保隐式满足梯度限制，消除了对显式约束的需要。 所提出的方法还受益于使用坐标网络的TO固有优势，包括网格无关性、高分辨率设计提取和端到端可微性。 我们的框架的有效性通过各种弹性及热弹性TO实例得到了验证。 显示英文摘要

摘要： Compositionally Graded Alloys (CGAs) offer unprecedented design flexibility by enabling spatial variations in composition; tailoring material properties to local loading conditions. This flexibility leads to components that are stronger, lighter, and more cost-effective than traditional monolithic counterparts. The fabrication of CGAs have become increasingly feasible owing to recent advancements in additive manufacturing (AM), particularly in multi-material printing and improved precision in material deposition. However, AM of CGAs requires imposition of manufacturing constraints; in particular limits on the maximum spatial gradation of composition. This paper introduces a topology optimization (TO) based framework for designing optimized CGA components with controlled compositional gradation. In particular, we represent the constrained composition distribution using a band-limited coordinate neural network. By regulating the network's bandwidth, we ensure implicit compliance with gradation limits, eliminating the need for explicit constraints. The proposed approach also benefits from the inherent advantages of TO using coordinate networks, including mesh independence, high-resolution design extraction, and end-to-end differentiability. The effectiveness of our framework is demonstrated through various elastic and thermo-elastic TO examples.