The quantification of absorbed light is essential for understanding laser-material interactions and melt pool dynamics in order to minimize defects in additively manufactured metal components. The geometry of a vapor depression, also known as a keyhole, in melt pools formed during laser melting is closely related to laser energy absorption. This relationship has been observed by the state-of-the-art in situ high speed synchrotron x-ray visualization and integrating sphere radiometry. These two techniques create a temporally resolved dataset consisting of keyhole images and corresponding laser absorptance. In this work, we propose two different approaches to predict laser absorptance. The end-to-end approach uses deep convolutional neural networks to learn features of unprocessed x-ray images automatically without human supervision and predict the laser energy absorptance. The two-stage approach uses a semantic segmentation model to engineer geometric features and predict absorptance using classical regression models. Though with different advantages, both approaches reached a smooth mean absolute error less than 3.4%.
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