  The rapid development in additive manufacturing (AM), also known as 3D
printing, has brought about potential risk and security issues along with
significant benefits. In order to enhance the security level of the 3D printing
process, the present research aims to detect and recognize illegal components
using deep learning. In this work, we collected a dataset of 61,340 2D images
(28x28 for each image) of 10 classes including guns and other non-gun objects,
corresponding to the projection results of the original 3D models. To validate
the dataset, we train a convolutional neural network (CNN) model for gun
classification which can achieve 98.16% classification accuracy.
