3DThesis
3DThesis is a C++ application that quickly simulates transient temperature fields in an additive manufacturing component. It uses an analytical solution for a moving Gaussian heat source on a fixed grid, which allows independent calculations of points in space-time. 3DThesis is ideal for rapid analysis of melt pool characteristics and solidification conditions because it enables efficient simulations by focusing only on points near the melt pool at each timestep.
Uses:
- Thermal predictions
- Solidification conditions
Applications and Approaches:
- AM
- Processing
Support and Funding:
US DOE EERE Advanced Materials & Manufacturing Technology Office
Contact:
Adamantine
Adamantine is a C++ thermomechanical code for additive manufacturing that’s built on deal.II, ArborX, Trilinos, and Kokkos. It simulates the thermomechanical evolution of a component during the manufacturing process with accurate representations of physical states: solid, liquid, and powder. Adamantine integrates experimental data through the ensemble Kalman filter to enhance simulation accuracy.
Uses:
- Thermomechanical prediction
Applications and Approaches:
- AM
- Processing
- Data assimilation
- Thermomechanics
- HPC
- GPU
Support and Funding:
Joint US DOE Office of Science and NNSA Exascale Computing Project
Contact:
Stephen Dewitt, Bruno Turcksin
AdditiveFOAM
AdditiveFOAM is a computational framework for simulating transport phenomena in additive manufacturing (AM) processes. Built on OpenFOAM, a leading open-source computational fluid dynamics software, AdditiveFOAM uses advanced finite volume methods to solve complex multiphysics problems. This tool can simulate explicit part geometries and scan paths, and it supports coupling with ExaCA to enable process-structure predictions. These capabilities make it a powerful tool for addressing processing challenges in AM.
Uses:
- Thermofluid prediction
- Solidification conditions
Applications and Approaches:
- AM
- Processing
- Heat transfer
- High performance computing
Support and Funding:
US DOE EERE Advanced Materials & Manufacturing Technologies Office
Joint US DOE Office of Science and NNSA Exascale Computing Project
Contact:
CabanaPD
CabanaPD is a C++ application for simulating powder-based manufacturing processes. CabanaPD supports simulation of both powder filling and consolidation for solid-state and powder bed predictions. To leverage both central processing unit (CPU) and graphics processing unit (GPU) architectures, CabanaPD uses the Cabana library, built on Kokkos and MPI. The scalable and performance portable code enables large-scale powder simulation on high-performance computing (HPC) supercomputers and local workstations.
Uses:
- Solid-state processing (e.g. PM-HIP)
- Powder dynamics (e.g. powder filling or powder bed)
Applications and Approaches:
- Powder-based manufacturing
- Processing
- GPU
- HPC
Support and Funding:
Joint US DOE EERE Advanced Materials & Manufacturing Technologies Office and Office of Science Advanced Scientific Computing Research Program
Joint US DOE Office of Science and NNSA Exascale Computing Project
Contact:
Equilipy
Equilipy is a Python package for computing large batches of thermodynamic quantities. This tool can be used to calculate the conditions for multiphase, multicomponent equilibria. For example, general N-component phase equilibria can be easily generated at a given set of compositions, temperatures, and pressures. In addition to leveraging the Gibbs energy functions to calculate phase equilibria, a method known as the CALPHAD approach, Equilipy incorporates a new Gibbs energy minimization algorithm to ensure efficient computation. The software package can run on both workstations and large computing systems.
Uses:
- Alloy design
Applications and Approaches:
- Thermodynamics
- HPC
Support and Funding:
US DOE EERE Office of Sustainable Transportation
Vehicle Technologies Office
Contact:
ExaCA
ExaCA is a C++ application designed to predict as-solidified grain structures from input time-temperature history data. Built with message passing interface (MPI) and Kokkos, ExaCA supports scalable, performance-portable simulations across many central processing unit (CPU) and graphics processing unit (GPU) architectures. To achieve this, the tool uses approximations for heterogeneous nucleation, the solidification velocity-undercooling relationship, and dendrite geometry in cubic crystals. ExaCA's ability to couple with various process models and leverage GPUs gives it the ability to handle up to billions of computational cells efficiently, which makes it a powerful tool for large-scale microstructure simulations.
Uses:
- Texture prediction
- Grain size and shape distribution prediction
Applications and Approaches:
- AM
- Microstructure
- HPC
- GPU
Support and Funding:
US DOE EERE Advanced Materials & Manufacturing Technologies Office
Joint US DOE Office of Science and NNSA Exascale Computing Project
Contact:
Finch
Finch is a C++ software tool that simulates heat transfer and melt pool dynamics in additive manufacturing. The tool efficiently solves the heat transfer equation during AM processing, while emphasizing computational scalability and performance. Built on the Cabana library with Kokkos and MPI, Finch runs on various hardware and couples directly with ExaCA for microstructure simulations that provide valuable insights into the relationship between processing conditions and microstructure evolution.
Uses:
- Thermal predictions
- Solidification conditions
Applications and Approaches:
- AM
- Processing
- Heat transfer
- HPC
- GPU
Support and Funding:
US DOE EERE Advanced Materials & Manufacturing Technology Office
Joint US DOE Office of Science and NNSA Exascale Computing Project
Contact:
Mist
Mist is a Python tool designed for storing, sharing, and using information about materials in models and simulations. The tool reduces barriers in workflows that involve multiple modeling tools, whether by substituting similar tools (e.g., different AM thermal simulation codes) or linking sequential models (e.g., AM thermal simulation to microstructure prediction to strength prediction). Mist facilitates smoother integration and data management across diverse modeling processes.
Uses:
- Material properties
Applications and Approaches:
- AM
- Database
Support and Funding:
US DOE EERE Advanced Materials & Manufacturing Technology Office
Contact:
Myna
Myna is a Python framework that connects real-world process data to simulation tools for additive manufacturing. By automating the configuration and execution of simulations using process data, Myna makes it easier to run simulations that accurately represent real conditions. The simulations and the as-built part are registered to the same coordinate system. Current data sources include the MDF's Peregrine tool, and supported modeling tools include AdditiveFOAM, ExaCA, 3DThesis, and Mist.
Uses:
- Automated workflows
- Registered simulations
Applications and Approaches:
- Workflow
- HPC
Support and Funding:
US DOE EERE Advanced Materials & Manufacturing Technology Office
Contact:
Ramen
Ramen is a Python library that offers inexpensive analytic and semi-analytic models for process-structure-property calculations of alloys. It includes plotting tools for creating process maps and uses Mist as the underlying data structure for input management, which makes it efficient for quick analyses and visualization.
Uses:
- Analytical models for materials behavior
Applications and Approaches:
- AM
- Microstructure
- Properties
Support and Funding:
US DOE EERE Advanced Materials & Manufacturing Technology Office
Contact:
Simurgh
Simurgh is an X-ray computed tomography (XCT) reconstruction software powered by artificial intelligence (AI). It outperforms traditional XCT scans by creating high-resolution digital models from fast and sparse data acquisitions. Simurgh also significantly reduces the operational costs of XCT scans, which makes advanced non-destructive testing more affordable and less labor-intensive.
Uses:
- Process optimization
- Materials development
- Failure analysis
- Quality assurance
Applications and Approaches:
- Data
- Characterization
- XCT
- AI
Support and Funding:
US DOE EERE Advanced Materials & Manufacturing Technologies Office
Contact:
To learn more about licensing this technology, email partnerships@ornl.gov or call 865-574-1051.