Baldwin Martha, Meisel Nicholas A, McComb Christopher
Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA.
School of Engineering Design and Innovation, The Pennsylvania State University, University Park, Pennsylvania, USA.
3D Print Addit Manuf. 2025 Feb 13;12(1):23-35. doi: 10.1089/3dp.2023.0316. eCollection 2025 Feb.
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 macroscale 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.
增材制造通过提高部件强度和减少材料需求，彻底改变了结构优化。实现这些改进的一种方法是应用多晶格结构，其中宏观性能依赖于细观结构晶格单元的详细设计。目前设计此类结构的许多方法使用数据驱动设计来生成多晶格过渡区域，利用仅由细观结构几何形状提供信息的机器学习模型。然而，将力学性能集成到用于训练此类机器学习模型的数据集中是否比仅使用几何数据更有益，仍不清楚。为了解决这个问题，这项工作实现并评估了一种用于生成多晶格过渡区域的混合几何/属性变分自编码器（VAE）。在我们的研究中，我们发现混合VAE在通过过渡区域保持刚度连续性方面表现出增强的性能，表明它们适用于需要平滑力学性能的设计任务。
