  3D Convolutional Neural Networks (3D-CNN) have been used for object
recognition based on the voxelized shape of an object. However, interpreting
the decision making process of these 3D-CNNs is still an infeasible task. In
this paper, we present a unique 3D-CNN based Gradient-weighted Class Activation
Mapping method (3D-GradCAM) for visual explanations of the distinct local
geometric features of interest within an object. To enable efficient learning
of 3D geometries, 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 GradCAM. 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 design for manufacturability decision support system.
