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

TL;DR

This paper presents an adaptive human-in-the-loop machine learning framework for automatic inspection, classification, and annotation of emission data in additive manufacturing, enhancing process monitoring efficiency.

Contribution

It introduces a CNN-based classification model combined with active learning for emission data analysis in AM, enabling automated and adaptive data annotation.

Findings

CNN-based model achieves high classification accuracy.

Active learning reduces manual labeling effort.

Transfer learning makes the approach adaptable to other industrial images.

Abstract

Recent developments in in-situ monitoring and process control in Additive Manufacturing (AM), also known as 3D-printing, allows the collection of large amounts of emission data during the build process of the parts being manufactured. This data can be used as input into 3D and 2D representations of the 3D-printed parts. However the analysis and use, as well as the characterization of this data still remains a manual process. The aim of this paper is to propose an adaptive human-in-the-loop approach using Machine Learning techniques that automatically inspect and annotate the emissions data generated during the AM process. More specifically, this paper will look at two scenarios: firstly, using convolutional neural networks (CNNs) to automatically inspect and classify emission data collected by in-situ monitoring and secondly, applying Active Learning techniques to the developed classification model to construct a human-in-the-loop mechanism in order to accelerate the labeling process of the emission data. The CNN-based approach relies on transfer learning and fine-tuning, which makes the approach applicable to other industrial image patterns. The adaptive nature of the approach is enabled by uncertainty sampling strategy to automatic selection of samples to be presented to human experts for annotation.