  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.
