About this Abstract |
| Meeting |
2024 TMS Annual Meeting & Exhibition
|
| Symposium
|
Algorithm Development in Materials Science and Engineering
|
| Presentation Title |
Deep Learning Approaches for Time-resolved Laser Absorptance Prediction in Additive Manufacturing |
| Author(s) |
Runbo Jiang, Anthony Rollett |
| On-Site Speaker (Planned) |
Anthony Rollett |
| Abstract Scope |
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 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 vapor depression 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 implicit features of unprocessed 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 regression models. Though with different advantages, both approaches reached a smooth mean absolute error less than 3.3%. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Additive Manufacturing, Computational Materials Science & Engineering, ICME |