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The development of computer vision and in-situ monitoring using visualsensors allows the collection of large datasets from the additive manufacturing(AM) process. Such datasets could be used with machine learning techniques toimprove the quality of AM. This paper examines two scenarios: first, usingconvolutional neural networks (CNNs) to accurately classify defects in an imagedataset from AM and second, applying active learning techniques to thedeveloped classification model. This allows the construction of ahuman-in-the-loop mechanism to reduce the size of the data required to trainand generate training data.

Defect Classification in Additive Manufacturing Using CNN-Based Vision Processing