Inspecting Defects of EB-PBF Components with Active Thermography and Deep Learning: A Feasibility Study

Electron Beam Powder Bed Fusion (EB-PBF) is a powerful additive manufacturing technique capable of producing high-performance metal parts with complex geometries. However, inherent process instabilities can lead to defects that compromise part quality and structural integrity. Traditional non-destructive testing (NDT) methods are often costly and time-consuming, and typically require specialized equipment and/or expertise. As a promising alternative, this study explores the feasibility of using active infrared thermography (IRT), coupled with deep learning, for the automated quality assessment of EB-PBF fabricated parts. We present a case study using parts with artificially induced subsurface defects, captured through active thermo-graphic imaging. A comprehensive dataset of thermal images is generated and used to train and evaluate a customized deep learning framework based on the You Only Look Once (YOLO) architecture for automated defect detection and categorization. Our results demonstrate the potential of combining IRT with data-driven analysis to offer a fast, contactless, and scalable solution for inspecting EB-PBF parts, while also highlighting the current limitations and future directions for this emerging approach.