Probability of detection of porosity defects for electron beam powder bed fusion additive manufacturing using total electron emissions

Electron Beam Powder Bed Fusion (EB-PBF) is a promising manufacturing technology, enabling the creation of complex geometries not possible with traditional manufacturing methods. However, like all additive manufacturing processes, the presence of defects within EB-PBF printed parts has limited its adoption in industry. While non-destructive evaluation methods such as x-ray computed tomography are available for qualifying EB-PBF parts, signal attenuation for high-Z or large-scale components motivate the need for in-situ defect monitoring approaches. An emerging approach to in-situ characterization is the detection and interpretation of emitted electrons, which without active signal isolation can be referred to as total electron emissions (TEE). While TEE monitoring in EB-PBF has been studied by various groups, results have typically been presented as qualitative comparisons which highlights the need for a quantitative assessment of the capabilities of the method. The focus of this study is to present a methodology for collecting TEE during the EB-PBF process, to generate images of each layer using collected TEE data, and to quantify the probability of detection of porosity. This methodology was then applied to samples produced using a commercial EB-PBF machine. The TEE data were processed into images and analyzed for defects. The defects were compared to ground truth data and used to generate probability of detection curves, qualifying the a 90/95 for various TEE defect detection sensitivities. These probability of detection curves are compared to the likelihood of false positive curves to further elucidate the capabilities of the detection system. • Defects in EB-PBF can be detected by monitoring total electron emissions. • Probability of detection for defects detected via electron emissions was evaluated. • Increasing the sensitivity of the detection system increases detection probability. • False positive rate was evaluated against probability of detection.