  Supervised classification methods have been widely utilized for the quality
assurance of the advanced manufacturing process, such as additive manufacturing
(AM) for anomaly (defects) detection. However, since abnormal states (with
defects) occur much less frequently than normal ones (without defects) in a
manufacturing process, the number of sensor data samples collected from a
normal state is usually much more than that from an abnormal state. This issue
causes imbalanced training data for classification analysis, thus deteriorating
the performance of detecting abnormal states in the process. It is beneficial
to generate effective artificial sample data for the abnormal states to make a
more balanced training set. To achieve this goal, this paper proposes a novel
data augmentation method based on a generative adversarial network (GAN) using
additive manufacturing process image sensor data. The novelty of our approach
is that a standard GAN and classifier are jointly optimized with techniques to
stabilize the learning process of standard GAN. The diverse and high-quality
generated samples provide balanced training data to the classifier. The
iterative optimization between GAN and classifier provides the high-performance
classifier. The effectiveness of the proposed method is validated by both
open-source data and real-world case studies in polymer and metal AM processes.
