A Self-Supervised CAD Sequence Generation Framework for Modeling Process Discovery

3-D modeling technologies play a crucial role in modern manufacturing. 3-D models are often exported as boundary representations for compatibility and data protection, which remove the modeling history and limit editability. To restore modeling sequences from such models, researchers employ neural networks to infer the possible modeling steps. This approach needs a large amount of labeled sequence data, and annotating such data is time-consuming. To address this issue, we propose a self-supervised pretraining method that generates modeling sequences directly from boundary representation models. Training data are first generated using a heuristic modeling sequences generation algorithm. Before training, each B-rep model is preprocessed into a zone graph representation. We then introduce the modeling operation evaluation network, which extracts features and scores each candidate operation to sequentially reconstruct the model. By selecting the most suitable operation at each step, the network progressively reconstructs the modeling sequence. This approach effectively reconstructs modeling sequences, restores the editability of B-rep models, and significantly reduces the reliance on labeled data.