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Recent developments in in-situ monitoring and process control in AdditiveManufacturing (AM), also known as 3D-printing, allows the collection of largeamounts of emission data during the build process of the parts beingmanufactured. This data can be used as input into 3D and 2D representations ofthe 3D-printed parts. However the analysis and use, as well as thecharacterization of this data still remains a manual process. The aim of thispaper is to propose an adaptive human-in-the-loop approach using MachineLearning techniques that automatically inspect and annotate the emissions datagenerated during the AM process. More specifically, this paper will look at twoscenarios: firstly, using convolutional neural networks (CNNs) to automaticallyinspect and classify emission data collected by in-situ monitoring andsecondly, applying Active Learning techniques to the developed classificationmodel to construct a human-in-the-loop mechanism in order to accelerate thelabeling process of the emission data. The CNN-based approach relies ontransfer learning and fine-tuning, which makes the approach applicable to otherindustrial image patterns. The adaptive nature of the approach is enabled byuncertainty sampling strategy to automatic selection of samples to be presentedto human experts for annotation.

An adaptive human-in-the-loop approach to emission detection of Additive Manufacturing processes and active learning with computer vision