Bridging Design Gaps: A Parametric Data Completion Approach With Graph Guided Diffusion Models

TL;DR

This paper presents a novel graph-guided diffusion model for parametric data completion in engineering design, significantly improving imputation accuracy and diversity over classical methods, and serving as an AI co-pilot for design exploration.

Contribution

The study introduces a graph attention network-based generative imputation model that outperforms existing methods in accuracy and diversity, enabling better design decision-making and creative exploration.

Findings

Outperforms classical imputation methods in accuracy and diversity

Enables exploration of multiple design options for incomplete CAD models

Effectively captures complex parametric interdependencies using graph models

Abstract

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.