Deep learning approaches for instantaneous laser absorptance prediction in additive manufacturing
Source abstract
Abstract
Abstract 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 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 vapor depression images and corresponding laser absorptance. In this work, we propose two different approaches to predict instantaneous laser absorptance. The end-to-end approach uses deep convolutional neural networks to learn implicit features of X-ray images automatically 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. While having distinct advantages, both approaches achieved a consistently low mean absolute error of less than 3.3%.
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Research context
OpenAlex topics: Additive Manufacturing Materials and Processes · Welding Techniques and Residual Stresses · Advanced X-ray and CT Imaging
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (69)
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