This study introduces a generative imputation model leveraging graph attention networks and tabular diffusion models for completing missing parametric data in engineering designs. This model functions as an AI design co-pilot, providing multiple design options for incomplete designs, which we demonstrate using the bicycle design CAD dataset. Through comparative evaluations, we demonstrate that our model significantly outperforms existing classical methods, such as MissForest, hotDeck, PPCA, and tabular generative method TabCSDI in both the accuracy and diversity of imputation options. Generative modeling also enables a broader exploration of design possibilities, thereby enhancing design decision-making by allowing engineers to explore a variety of design completions. The graph model combines GNNs with the structural information contained in assembly graphs, enabling the model to understand and predict the complex interdependencies between different design parameters. The graph model helps accurately capture and impute complex parametric interdependencies from an assembly graph, which is key for design problems. By learning from an existing dataset of designs, the imputation capability allows the model to act as an intelligent assistant that autocompletes CAD designs based on user-defined partial parametric design, effectively bridging the gap between ideation and realization. The proposed work provides a pathway to not only facilitate informed design decisions but also promote creative exploration in design.

翻译：本研究提出一种利用图注意力网络与表格扩散模型的生成式填补模型，用于补全工程设计中的缺失参数数据。该模型作为人工智能设计协同助手，可为不完整设计提供多种设计方案，我们以自行车设计CAD数据集为例进行了验证。通过对比评估，我们证明该模型在填补选项的准确性与多样性方面均显著优于现有经典方法，如MissForest、hotDeck、PPCA以及表格生成方法TabCSDI。生成式建模还能支持更广泛的设计可能性探索，通过允许工程师研究多种设计补全方案来提升设计决策水平。该图模型将图神经网络与装配图蕴含的结构信息相结合，使模型能够理解并预测不同设计参数间复杂的相互依赖关系。图模型有助于从装配图中准确捕捉并填补复杂的参数关联，这对设计问题至关重要。通过学习现有设计数据集，模型的填补能力使其能够作为智能助手，根据用户定义的部分参数设计自动补全CAD设计，有效弥合概念构思与实现之间的鸿沟。本研究不仅为促进理性设计决策提供了路径，更有助于推动设计领域的创造性探索。