Image2Feature: A Framework for Local Machining Feature Recognition in 3D CAD Models via Learning-Based Object Detection from 2D Images

초록

As digital transformation in the manufacturing industry accelerates, there is growing interest in design and production automation based on three-dimensional (3D) Computer-Aided Design (CAD) models. In this context, Machining Feature Recognition (MFR), which automatically extracts machining information from CAD models, has emerged as a key enabling technology. However, existing Boundary Representation (B-rep)-based methods rely heavily on topological structures, making them prone to failure when recognizing complex or intersecting features. To overcome this limitation, this paper proposes Image2Feature, a framework that combines image-based object detection with B-rep-based geometric validation. The proposed method captures projection and cross-sectional images from 3D CAD models along multiple axes and detects local machining features such as chamfers, fillets, holes, and tapers using a learning-based object detection model. The detection results are mapped to 3D B-rep faces through ray casting, and feature-specific geometric validation rules are applied to produce the final recognition results. In the post-processing stage, composite features such as counterbores, countersinks, and coaxial holes are further identified from the recognized faces. Experiments on 15 test models achieved a mean score of 91.3%. Furthermore, comparative evaluation against AAGNet, BRepNet, and UV-Net was conducted on the MFCAD++ and Fusion 360 Gallery datasets. The proposed method is applicable not only to process planning automation, but also to reverse engineering, design quality inspection, and 3D model retrieval and classification.

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