Deep learning approaches for time-resolved laser absorptivity prediction

Published

January 5, 2024

Author(s)

Runbo Jiang, John Smith, Yu-Tsen Yi, Tao Sun, Brian Simonds, Anthony D. Rollett

Abstract

The quantification of the amount of absorbed light is essential for understanding laser-material interactions and melt pool dynamics in order to minimize defects in additive 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 absorptivity. 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 the corresponding laser absorptivity. In this work, we propose two different pipelines to predict laser absorptivity. 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 absorptivity. The two-stage approach uses a semantic segmentation model to engineer geometric features and predict absorption using classical regression models. Though with different advantages, both approaches reached a smooth L1 loss less than 6%.

Citation

npj Computational Materials

Pub Type

Journals

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Keywords

Convolutional neural networks, image segmentation, laser absorptivity, synchrotron x-ray imaging, laser powder bed fusion

Citation

Jiang, R. , Smith, J. , Yi, Y. , Sun, T. , Simonds, B. and Rollett, A. (2024), Deep learning approaches for time-resolved laser absorptivity prediction, npj Computational Materials, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=936533 (Accessed October 28, 2025)