BRepMAE: Self-Supervised Masked BRep Autoencoders for Machining Feature Recognition

TL;DR

BRepMAE introduces a self-supervised masked autoencoder framework that effectively learns representations from unlabeled CAD models for machining feature recognition, significantly reducing the need for labeled data.

Contribution

The paper presents a novel masked graph autoencoder for CAD models that improves machining feature recognition, especially with limited labeled data.

Findings

High recognition accuracy with only 0.1% of training data.

Significant improvement over existing methods with limited data.

Effective self-supervised pre-training on large unlabeled CAD datasets.

Abstract

We propose a masked self-supervised learning framework, called BRepMAE, for automatically extracting a valuable representation of the input computer-aided design (CAD) model to recognize its machining features. Representation learning is conducted on a large-scale, unlabeled CAD model dataset using the geometric Attributed Adjacency Graph (gAAG) representation, derived from the boundary representation (BRep). The self-supervised network is a masked graph autoencoder (MAE) that focuses on reconstructing geometries and attributes of BRep facets, rather than graph structures. After pre-training, we fine-tune a network that contains both the encoder and a task-specific classification network for machining feature recognition (MFR). In the experiments, our fine-tuned network achieves high recognition rates with only a small amount of data (e.g., 0.1% of the training data), significantly enhancing its practicality in real-world (or private) scenarios where only limited data is available. Compared with other MFR methods, our fine-tuned network achieves a significant improvement in recognition rate with the same amount of training data, especially when the number of training samples is limited.