|About this Abstract
||7th World Congress on Integrated Computational Materials Engineering (ICME 2023)
||Vapor Depression Segmentation and Absorptivity Prediction from Synchrotron X-ray Images Using Deep Neural Networks
||Runbo Jiang, John Smith, Yu-Tsen Yi, Brian Simonds, Tao Sun, Anthony Rollett
|On-Site Speaker (Planned)
The quantification of the amount of absorbed light is essential for understanding laser-material interactions and melt pool dynamics in additive manufacturing process. The geometry of a vapor depression, also known as a keyhole, in melt pools formed during laser melting is closely related to laser 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 interpret an unprocessed x-ray image and predict the amount of light absorbed. The two-stage approach uses a fine-tuned image segmentation model to extract geometric features and predict absorption using regression. Though with different advantages and limitations, both approaches reached an MAE less than 6.9%.
||Planned: Other (describe below)