Generative AI has emerged as a transformative paradigm in engineering design, enabling automated synthesis and reconstruction of complex 3D geometries while preserving feasibility and performance relevance. This paper introduces a domain-specific implicit generative framework for turbine blade geometry using DeepSDF, addressing critical gaps in performance-aware modeling and manufacturable design generation. The proposed method leverages a continuous signed distance function (SDF) representation to reconstruct and generate smooth, watertight geometries with quantified accuracy. It establishes an interpretable, near-Gaussian latent space that aligns with blade-relevant parameters, such as taper and chord ratios, enabling controlled exploration and unconditional synthesis through interpolation and Gaussian sampling. In addition, a compact neural network maps engineering descriptors, such as maximum directional strains, to latent codes, facilitating the generation of performance-informed geometry. The framework achieves high reconstruction fidelity, with surface distance errors concentrated within $1\%$ of the maximum blade dimension, and demonstrates robust generalization to unseen designs. By integrating constraints, objectives, and performance metrics, this approach advances beyond traditional 2D-guided or unconstrained 3D pipelines, offering a practical and interpretable solution for data-driven turbine blade modeling and concept generation.

翻译：生成式人工智能已成为工程设计领域的变革性范式，能够自动合成和重建复杂的三维几何形状，同时保持可行性与性能相关性。本文针对涡轮叶片几何，提出了一种基于DeepSDF的领域专用隐式生成框架，以解决性能感知建模与可制造设计生成方面的关键不足。所提方法利用连续符号距离函数表示，以量化精度重建并生成光滑、水密的几何形状。该方法建立了一个可解释的近似高斯潜在空间，该空间与叶片相关参数（如锥度比和弦长比）对齐，从而支持通过插值和高斯采样进行受控探索与无条件合成。此外，一个紧凑的神经网络将工程描述符（如最大方向应变）映射至潜在编码，促进了性能感知几何的生成。该框架实现了高重建保真度，其表面距离误差集中在最大叶片尺寸的$1\%$以内，并对未见设计展现出强大的泛化能力。通过整合约束、目标与性能指标，该方法超越了传统的二维引导或无约束三维流程，为数据驱动的涡轮叶片建模与概念生成提供了一个实用且可解释的解决方案。