Data-driven engineering design is constrained by the lack of large-scale 3D datasets that pair geometry with physics-based performance labels. In particular, existing 3D data augmentation techniques have limitations in preserving subtle and diverse geometric variations, and it remains difficult to automate the subsequent simulation-labeling process, where boundary conditions vary depending on the generated geometry. We present DeepJEB++, a foundation-model-driven data-augmentation framework that expands a small seed set of jet engine brackets into a large, simulation-labeled 3D dataset under constrained resources. Our key idea is to augment in the data-rich 2D latent space, then transfer to 3D. In Stage 1, we fine-tune a pretrained 2D latent diffusion model on multi-view renders and synthesize novel views by latent interpolation, retaining manufacturable designs through a vision-language-model (VLM) quality filter. In Stage 2, the validated images are lifted to 3D meshes by a domain-adapted generative foundation model. In Stage 3, an automated pipeline recognizes the load and bolt interfaces on each mesh and assigns finite-element labels -- mass, stress, and displacement -- without manual intervention. We assess augmentation quality along three intrinsic axes: manufacturability, label fidelity against the SimJEB ground truth, and distributional consistency. Starting from fewer than 400 seed designs, DeepJEB++ yields 15,360 simulation-labeled 3D brackets -- a 40x expansion -- using a single GPU per stage. The dataset will be made publicly available to support reproducible engineering-AI research.

翻译：数据驱动的工程设计受限于缺乏兼具几何结构与物理性能标注的大规模三维数据集。现有三维数据增广技术在保留细微多样几何变化方面存在局限性，且后续仿真标注过程自动化困难——边界条件需随生成几何结构动态调整。本文提出DeepJEB++框架，该基础模型驱动的数据增广方法能在有限资源条件下，将小型种子集（喷气发动机支架）扩展为包含仿真标注的大规模三维数据集。核心思路在于：在数据富集的二维潜空间进行增广，再迁移至三维空间。第一阶段，对预训练的二维潜扩散模型进行多视角渲染微调，通过潜空间插值合成新视角图像，并借助视觉语言模型（VLM）质量过滤器保留可制造设计；第二阶段，利用领域自适应生成基础模型将验证后的图像提升为三维网格；第三阶段，通过自动化流水线识别各网格的载荷与螺栓接口，无需人工干预即可完成有限元标签（质量、应力、位移）的分配。我们从三个内在维度评估增广质量：可制造性、标签保真度（以SimJEB真值为基准）及分布一致性。DeepJEB++仅需每阶段单GPU支持，即可从不足400个种子设计扩展至15,360个带仿真标签的三维支架（40倍扩增）。该数据集将公开发布，以支持可复现的工程人工智能研究。