FeatureNet: Machining feature recognition based on 3D Convolution Neural Network. (August 2018)

• Record Type: Journal Article
• Authors: Zhang, Zhibo; Jaiswal, Prakhar; Rai, Rahul
• Is Part Of: Computer aided design. Volume 101(2018)
• Page Start: 12
• Page End: 22
• Publication Date: 2018-08
• Subjects: Machining feature recognition -- Convolution neural network -- Deep learning -- Computer aided process planning (CAPP); Computer-aided design -- Periodicals; Engineering design -- Data processing -- Periodicals; Computer graphics -- Periodicals; Conception technique -- Informatique -- Périodiques; Infographie -- Périodiques; Computer graphics; Engineering design -- Data processing; Periodicals; Electronic journals; 620.00420285
• DOI: 10.1016/j.cad.2018.03.006
• Languages: English
• ISSNs: 0010-4485

Abstract

Abstract: 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. Highlights: 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.