如黄油般顺滑：评估属性增强潜空间中的多晶格过渡
Smooth Like Butter: Evaluating Multi-Lattice Transitions in Property-Augmented Latent Spaces
AI总结：
针对多晶格结构设计中现有机器学习模型仅用几何数据的问题，本研究提出几何/属性混合VAE生成过渡区域，实验证实其能更好维持刚度连续性，适用于力学性能平滑的设计需求。
摘要：
增材制造通过提升构件强度、降低材料用量，彻底革新了结构优化领域。实现这类性能提升的一种途径是采用多晶格结构，其宏观尺度性能取决于细观结构晶格单元的精细化设计。当前设计此类结构的诸多方法采用数据驱动设计来生成多晶格过渡区域，所使用的机器学习模型仅以细观结构的几何信息作为输入。然而，目前尚不清楚，在用于训练这类机器学习模型的数据集中整合力学性能，是否会比仅使用几何数据带来更优的效果。为解决这一问题，本研究实现并评估了一种几何/属性混合变分自编码器（Variational Autoencoder，VAE），用于生成多晶格过渡区域。研究发现，混合VAE在维持过渡区域刚度连续性方面表现出更优的性能，表明其适用于要求力学性能平滑过渡的设计任务。
英文摘要：
Additive manufacturing has revolutionized structural optimization by enhancing component strength and reducing material requirements. One approach used to achieve these improvements is the application of multi-lattice structures, where the macro-scale performance relies on the detailed design of mesostructural lattice elements. Many current approaches to designing such structures use data-driven design to generate multi-lattice transition regions, making use of machine learning models that are informed solely by the geometry of the mesostructures. However, it remains unclear if the integration of mechanical properties into the dataset used to train such machine learning models would be beneficial beyond using geometric data alone. To address this issue, this work implements and evaluates a hybrid geometry/property Variational Autoencoder (VAE) for generating multi-lattice transition regions. In our study, we found that hybrid VAEs demonstrate enhanced performance in maintaining stiffness continuity through transition regions, indicating their suitability for design tasks requiring smooth mechanical properties.
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