We developed a novel framework using Deep 3D Convolutional Neural Networks (3D-CNNs) termed FeatureNet to learn machining features from CAD models of mechanical parts. FeatureNet learns the distribution of complex machining feature shapes across a large 3D model data set 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 synthetically constructed. For more details, please refer to our paper published in Computer-Aided Design journal, Vol. 101, 2018. The FeatureNet database is provided in this repository.

This repository includes a zipped folder consisting of the complete FeatureNet database. The zipped folder consists of 24 sub-folders, each representing a unique machining feature with class label (from 0-23). Every sub-folder has 1000 .STL files, which are randomly generated samples of the corresponding machining feature. The list of machining features and their parameters are provided in the .PDF file included with the repository.

If you use our dataset for your work, please cite:

Zhang, Z., Jaiswal, P., & Rai, R. (2018). FeatureNet: Machining feature recognition based on 3D Convolution Neural Network. Computer-Aided Design, 101, 12-22.

MAD Lab
Department of Mechanical and Aerospace Engineering
University at Buffalo, Buffalo, NY - 14260
http://madlab.eng.buffalo.edu/