DREAM3D-NX
v7.4.1A material's performance is decided by its microstructure. DREAM3D-NX turns 3D microstructural data into quantitative, simulation-ready models — so you can predict and engineer material behavior computationally instead of relying on slow, costly physical test campaigns. Used by materials researchers across academia, government, and industry worldwide.
From microstructure to engineered performance
Whether you're developing alloys, qualifying additive parts, or running an ICME workflow, DREAM3D-NX connects what you measure to what you simulate.
Predict performance computationally
Link microstructure to properties and explore the design space in silico — before committing to expensive, time-consuming test coupons.
Cut experimental cost
Build statistically representative synthetic microstructures to stand in for physical samples, reducing how many experiments you have to run.
Bridge characterization to simulation
Take raw EBSD and CT data through quantification to FEM-ready meshes in one reproducible pipeline — no ad-hoc scripts or manual format juggling.
Reproducible, automatable workflows
Save, share, and re-run pipelines, and drive the same operations from Python to scale across HPC and larger automated workflows.
What you can do with DREAM3D-NX
Multi-modal import
Bring EBSD, CT, and arbitrary HDF5 data together in one unified model — no custom converters between instruments and formats.
3D reconstruction
Recover full 3D microstructures from 2D section data with statistical fidelity, so you can analyze volumes you never physically sectioned.
Integrated VTK visualization
Inspect and validate results interactively in the visualization pane — catch problems before they reach simulation.
Microstructure statistics
Quantify grain size, shape, orientation, and neighborhood statistics to connect microstructure directly to material behavior.
FEM export
Generate finite-element meshes ready for downstream solvers — feed simulation directly, without manual mesh conversion.
Python API
Automate and scale the same pipeline operations from Python with the simplnx library — integrate into HPC and CI workflows.