Defect Classification in Additive Manufacturing Using CNN-Based Vision
  Processing
Xiao Liu Alessandra Mileo Alan F. Smeaton
The development of computer vision and in-situ monitoring using visual
sensors allows the collection of large datasets from the additive manufacturing
(AM) process. Such datasets could be used with machine learning techniques to
improve the quality of AM. This paper examines two scenarios: first, using
convolutional neural networks (CNNs) to accurately classify defects in an image
dataset from AM and second, applying active learning techniques to the
developed classification model. This allows the construction of a
human-in-the-loop mechanism to reduce the size of the data required to train
and generate training data.