FeatureNet: Machining feature recognition based on 3D Convolution Neural Network

• Published in: Computer aided design Vol. 101; pp. 12 - 22
• Main Authors: Zhang, Zhibo, Jaiswal, Prakhar, Rai, Rahul
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Ltd 01.08.2018; Elsevier BV
• Subjects: Artificial neural networks; Automation; CAD; Computer aided design; Computer aided process planning (CAPP); Convolution; Convolution neural network; Critical components; Deep learning; Digital computers; Feature recognition; Machining; Machining feature recognition; Manufacturing; Mathematical models; Neural networks; Process planning; Recognition; Solid modelling; Studies; Three dimensional models; Deep learning; Machining feature recognition; Convolution neural network; Computer aided process planning (CAPP)
• ISSN: 0010-4485; 1879-2685
• DOI: 10.1016/j.cad.2018.03.006

Automated machining feature recognition, a sub-discipline of solid modeling, has been an active research area for last three decades and is a critical component in digital manufacturing thread for detecting manufacturing information from computer aided design (CAD) models. In this paper, a novel framework using Deep 3D Convolutional Neural Networks (3D-CNNs) termed FeatureNet to learn machining features from CAD models of mechanical parts is presented. FeatureNet learns the distribution of complex manufacturing feature shapes across a large 3D model dataset and discovers distinguishing features that help in recognition process automatically. To train FeatureNet, a large-scale mechanical part datasets of 3D CAD models with labeled machining features is automatically constructed. The proposed framework can recognize manufacturing features from the low-level geometric data such as voxels with a very high accuracy. The developed framework can also recognize planar intersecting features in the 3D CAD models. Extensive numerical experiments show that FeatureNet enables significant improvements over the state-of-the-arts manufacturing feature detection techniques. The developed data-driven framework can easily be extended to identify a large variety of machining features leading to a sound foundation for real-time computer aided process planning (CAPP) systems. •A novel deep 3D CNN framework to learn machining features from CAD models.•A large-scale labeled manufacturing features dataset with 3D CAD models.•Significant improvements over the state-of-the-arts manufacturing feature detection.