7th International Congress on 3D Materials Science (3DMS 2025)
Xnovo is attending 3DMS 2025, so come meet us at our sponsor booth.
3DMS 2025 is part of the TMS Specialty Congress 2025 and is featured along with two other co-located events:
- The 3rd World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2025)
- The 8th World Congress on Integrated Computational Materials Engineering (ICME 2025)
Visit the TMS Specialty Congress 2025, 15-19 June, Anaheim Marriott, Anaheim CA – see the full program here
You can learn about our technology through the talks:
Advancing 3D Grain Mapping Accessibility for the Materials Science Community: Insights into Recent Developments of Data Acquisition, Reconstruction and Analysis
Erik Lauridsen, Xnovo
3D Data Processing, Software, and Reconstruction Algorithms II: Tuesday 17 June 9:30-9:50 AM
Polycrystalline Effects on Early Strain Heterogeneity Leading to Fracture for an Aluminium Alloy Under Plane Strain Tension: 3D Correlative X-Ray Tomography and Crystal Plasticity Simulations
Henry Proudhon, Mines Paris
Time Resolved 3D Characterization I: Tuesday 17 June 9:50-10:10 AM
Laboratory-Based 3D X-Ray Scattering Tensor Tomography
Erik Lauridsen, Xnovo
Emerging 3D Characterization Techniques and Instrumentation II: Tuesday 17 June 4:10-4:30 PM
3D Large Volume Non-Destructive Grain Structure Characterization in Metallic Alloys Using Lab-Based Diffraction Contrast Tomography (LabDCT)
Kaushik Yanamandra, Carl Zeiss X-ray Microscopy
Emerging 3D Characterization Techniques and Instrumentation III: Thursday 19 June 8:50-9:10 AM
The Interest of Lab-Based DCT for the 3D Characterization of Monocrystalline Nickel-Based Alloys
Alexiane Arnaud, Safran Group
3D Characterization and Modelling in Advanced Manufacturing: Thursday 19 June 8:50-9:10 AM
And the posters:
Grain Structure Evolution During Heat Treatment of a Semisolid Al-Cu Alloy Studied With Lab-Based Diffraction Contrast Tomography
Jette Oddershede, Xnovo
A Novel Method for Microstructure Prediction of Austenitic Stainless Steel Based on Machine Learning
Yuqing Du, Shanghai Jiao Tong University
