About this Abstract |
| Meeting |
2026 TMS Annual Meeting & Exhibition
|
| Symposium
|
Additive Manufacturing: Advanced Characterization With Synchrotron, Neutron, and In Situ Laboratory-scale Techniques IV
|
| Presentation Title |
Deconvoluting Thermomechanical Effects in X-ray Diffraction Data using Machine Learning |
| Author(s) |
Rachel Lim, Shun-Li Shang, Chihpin Andrew Chuang, Thien Phan, Zi-Kui Liu, Darren C. Pagan |
| On-Site Speaker (Planned) |
Darren C. Pagan |
| Abstract Scope |
X-ray diffraction is ideal for probing sub-surface state during complex or rapid thermomechanical loading of crystalline materials, such as during additive manufacturing. However, challenges arise as the size of diffraction volumes increases due to spatial broadening and inability to deconvolute the effects of different lattice deformation mechanisms. Here, we present a novel approach using combinations of physics-based modeling and machine learning to deconvolve thermal and mechanical elastic strains for diffraction data analysis. The approach is applied to extract the evolution of thermomechanical state during laser melting of an Inconel 625 wall specimen. A combination of heat transfer and fluid flow, elastoplasticity, and X-ray diffraction simulations are used to generate training data for machine-learning models that map diffracted intensity distributions to underlying thermomechanical strain fields. The trained models are found to be capable of deconvoluting the effects of thermal and mechanical strains, in addition to providing information about underlying strain distributions. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Additive Manufacturing, Machine Learning, Characterization |