AMMap

Additive Manufacturing Mapping of Compositional Spaces with Thermodynamic, Analytical, and Artificial Intelligence Models

AMMap tool utilizes novel nimplex high-performance Nim library for generation of simplex graphs, developed in our recent paper (10.48550/arXiv.2402.03528), to describe the complex space that represents the design space possible for alloys made with additive manufacturing.

AMMap implements callables for several different CALPHAD based methods out-of-the-box and is set to grow rapidly in the near future. These include thermodynamic equlibrium calculations with pycalphad, Scheil-Gulliver solidification with scheil, and 5 different models for predicting cracking susceptibility. These methods are discussed in this publication on Design methodology for functionally graded materials: Framework for considering cracking.

Results coming from these methods are used to establish feasible subspace, which is then used to find optimal paths in the space using any path planning tool with Python or CLI interface.

For user convenience, cloud-based GitHub Codespaces can be used for all Jupyter notebook exercises. However, one should note Codespaces lack the computational strength to perform Scheil-Gulliver sufficiently fast; thus we recommend running them on an HPC node, submitting for external evaluation with a tool like papermill, or persisting the result (see scheilmap.json) to then be analysed in Codespaces.

Capabilities

Note Full technical discussions will be in upcoming manuscript. Section below is a short highlights of tool ability.

1. Simplex/Compositional Graph Generation to allow for compositionally complex materials with high order of possible combinations to be fully considered
2. Stitching of elemental spaces from different thermodynamic databases into singular traversable graph to allow for path planning across multiple composition regions from incompatible databases due to model differences
3. Material Information Generation Thermodynamic (Equilibrium and Scheil-Gulliver) information about the material at any given graph point calculated to determine phase composition to avoid undesired phase formation in path planning. Thermodynamic information can then be used to determine the hot-cracking susceptibility of a point to further inform material design. Visual representations of capabilities for example systems can be seen at end of README below
4. Infeasibility Gliding Detects infeasible regions and avoids uneccessary calculations of interior points to reduce total computational cost
5. Path Planning Compatibility Deployment of any graph algorithms on created design space and highly compatible with backends of choice. Currently finds the shortest path which can be stretched. Found path can then be simplified to change from point-to-point path to generalized format

Path planning across elemental spaces from seperate thermodynamic databases demonstrated in the path planning from SS304L to pure Ti

Planned

1. YAML format input for human readable definition of design space, constraints, and objectives
2. Expanded Property Models More material properties that can be mapped onto compositional graphs are planned, e.g. elastic modulii
3. Output of callables for Machine Learning
4. Path Planning Updates to minimize the number of turns or more generally hyperplanes.
5. Build Planning Algorithm Collaboration to create algorithm to convert given path into physical AM build path for fabrication

Installation

Conda Environment and Installs

It is recommended to use new environements for all python projects, this can be done as follows:

conda create -n AMMAP python=3.11 liblapack jupyter numpy pandas plotly scikit-learn

conda activate AMMAP

Or, if your environment already exists, simply:

conda install -y python=3.11 liblapack jupyter numpy pandas plotly scikit-learn

Clone repository

Clone the github repository in order to have the jupyter notebook and callables

git clone https://github.com/PhasesResearchLab/AMMap.git

nimplex

The primary installation requirement is nimplex, which requires the small and easy-to-install Nim (Installation Instructions) compiler (assuming you already have a C compiler), which can be done with a single command on most Unix (Linux/MacOS) systems:

• using conda cross-platform package manager:

  conda install -c conda-forge nim

• on MacOS, assuming you have Homebrew installed:

  brew install nim

• with your Linux distribution's package manager (note that it may be an outdated nim version, impacting performance), for instance on Ubuntu/Debian Linux:

  apt-get install nim

Then, you can use the boundeled Nimble tool (pip-like package manager for Nim) to install two top-level dependencies: arraymancer, which is a powerful N-dimensional array library, and nimpy which helps with the Python bindings. You can do it with a single command:

nimble install -y arraymancer nimpy

Now, you can update the nimplex submodule repository and compile it for AMMap. You want to do so in a way that creates its Python bindings. You will need slightly different flags depending on your system configuration, but for Unix (Linux/MacOS) you can do so with commands below, after making sure you are in the root AMMap directory:

git submodule update --init --recursive
nim c --d:release --threads:on --app:lib --out:nimplex.so nimplex/src/nimplex.nim
nim c --d:release --threads:on --app:lib --out:utils/plotting.so nimplex/src/nimplex/utils/plotting.nim

For Windows and other platforms, you should consult nimpy instructions.

CALPHAD Tools

When you are done, you should also install pycalphad and a forked version of a python package for scheil found here

pip install git+https://github.com/HUISUN24/scheil.git
pip install pycalphad

Optional Pathfinding used in Example

pip install pqam-rmsadtandoc2023 pathfinding

Other useful packages

To run Jupyter notebooks from the command line, especially useful on an HPC, you should install papermill:

pip install papermill

Example Outputs

Equilibrium

Cracking Criteria

Path Planning with Infeasibility Gliding