  3D convolutional neural networks (3D-CNN) have been used for object
recognition based on the voxelized shape of an object. In this paper, we
present a 3D-CNN based method to learn distinct local geometric features of
interest within an object. In this context, the voxelized representation may
not be sufficient to capture the distinguishing information about such local
features. To enable efficient learning, we augment the voxel data with surface
normals of the object boundary. We then train a 3D-CNN with this augmented data
and identify the local features critical for decision-making using 3D
gradient-weighted class activation maps. An application of this feature
identification framework is to recognize difficult-to-manufacture drilled hole
features in a complex CAD geometry. The framework can be extended to identify
difficult-to-manufacture features at multiple spatial scales leading to a
real-time decision support system for design for manufacturability.
