About this Abstract |
| Meeting |
2026 TMS Annual Meeting & Exhibition
|
| Symposium
|
Advancing the Frontier of Powder Materials Processing and Sintering: A MPMD/EPD Symposium in Honor of Eugene Olevsky
|
| Presentation Title |
Machine Learning-Enabled Characterization of 3D Particle Contacts from 2D Microstructural Images in Powder-Based Metal Additive Manufacturing |
| Author(s) |
Nicholas Satterlee, Elisa Torresani, Runjian Jiang, Marc P.F.H.L. van Maris, Diletta Giuntini, Eugene Olevsky, John S. Kang |
| On-Site Speaker (Planned) |
John S. Kang |
| Abstract Scope |
Accurate microstructural characterization is essential for understanding and optimizing the performance of metallic components produced by powder-based additive manufacturing (PBAM). While two-dimensional (2D) cross-sectional imaging is widely used due to its accessibility, conventional image processing methods are labor-intensive and insufficient for reliably capturing three-dimensional (3D) particle contact features. This study introduces a machine learning (ML) framework for predicting 3D particle contact areas (PCAs) directly from 2D microstructural images, eliminating the need for complete 3D x-ray computed tomography (CT) datasets. The framework leverages contact line statistics extracted from 2D images to predict 3D contact areas. Validation using 316L stainless steel PBAM samples demonstrates that the model achieves an average accuracy of 98.5%. By providing a scalable, cost-effective, and accurate method for PCA characterization, this work advances the quantitative analysis of process-induced PCA anisotropy in PBAM and supports improved process optimization and material design. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Additive Manufacturing, Characterization, Machine Learning |