HG-CAD: Hierarchical Graph Learning for Material Prediction and Recommendation in CAD

Abstract

To enable intelligent CAD design tools, we introduce a machine learning architecture, namely HG-CAD, that supports the automated material prediction and recommendation of assembly bodies through joint learning of body and assembly-level features using a hierarchical graph representation. Specifically, we formulate the material prediction and recommendation process as a node-level classification task over a novel hierarchical graph representation of CAD models, with a low-level graph capturing the body geometry, a high-level graph representing the assembly topology, and a batch-level mask randomization enabling contextual awareness. This enables our network to aggregate geometric and topological features from both the body and assembly levels, leading to superior performance. Qualitative and quantitative evaluation of the proposed architecture on the Fusion 360 Gallery Assembly Dataset demonstrates the feasibility of our approach, outperforming both computer vision and human baselines, while showing promise in application scenarios. The proposed HG-CAD architecture that unifies the processing, encoding, and joint learning of multi-modal CAD features can scale to large repositories, incorporating designers' knowledge into the learning process. These capabilities allow the architecture to serve as a recommendation system for design automation and a baseline for future work.