While topology optimization is promising for additive manufacturing structures, challenges arise in designing multi-material assemblies. The size often surpasses additive manufacturing build volumes, hindering successful manufacturing. Additionally, intricate topology-optimized structures complicate the assembly and decomposition of multiple material components. Addressing the aforementioned issues can be achieved by incorporating dimensional and assembly constraints into the optimization process. So far, these constraints have only been studied and implemented separately, leading to suboptimal solutions. Simply applying these two constraints together can also lead to excessive computational complexity. This paper introduces a multi-material topology optimization framework that considers both dimensional and assembly constraints. We propose an assembly direction-aligned method for dimensional constraints to reduce computational costs and an adaptive weighting factor for assembly constraints to enhance numerical stability. Validation through numerical examples and successful fabrication and assembly of a 3D-printed prototype underscore the framework’s efficacy in ensuring the manufacturability and assemblability of structures designed via topology optimization.

中文翻译：

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考虑最大构建体积和装配过程的增材制造多材料拓扑优化

虽然拓扑优化对于增材制造结构很有希望，但设计多材料组件时却出现了挑战。尺寸通常超过增材制造的构建体积，阻碍了成功的制造。此外，复杂的拓扑优化结构使多种材料组件的组装和分解变得复杂。通过将尺寸和装配约束纳入优化过程可以解决上述问题。到目前为止，这些约束只是单独研究和实施，导致解决方案不是最优的。简单地将这两个约束应用在一起也会导致计算复杂性过高。本文介绍了一种同时考虑尺寸和装配约束的多材料拓扑优化框架。我们提出了一种用于尺寸约束的装配方向对齐方法，以减少计算成本，并提出了一种用于装配约束的自适应加权因子，以增强数值稳定性。通过数值示例以及 3D 打印原型的成功制造和组装进行的验证强调了该框架在确保通过拓扑优化设计的结构的可制造性和可组装性方面的功效。