A Graph Neural Network-Based Recommendation System for Product Manufacturing Information in 3D-CAD Models

Product Manufacturing Information is the key element in the digital transformation of manufacturing. It facilitates the transition from 2D drawing-based workflows to 3D model-based workflows, unlocking downstream automating tasks in the CAx-process chain. However, the current methods of enhancing 3D-CAD models with PMI, such as manual creation or rule-based approaches, are labor-intensive, error-prone, and inefficient, particularly when applied to numerous similar models. To address these challenges, this work proposes the development of a graph-based deep learning system to automate the PMI creation in 3D-CAD models. A new graph representation for 3D-CAD models incorporating PMI is presented. A data generation pipeline and two homogeneous graph neural network-based methods, node classification and link prediction, are then proposed to predict the existence of PMI for a given 3D-CAD model. Results demonstrate the performance of node classification for predicting PMI on individual faces of unseen 3D-CAD models while link prediction shows the capability of predicting PMI between two faces of a 3D-CAD model. These findings form the groundwork for building a fully automated recommendation system for PMI in real-world industry applications.