Skip to main content

A framework for emulating additive manufacturing processes.

Project description

Documentation Status PyPI version

Development Instructions: here & Documentation: here

Overview

FAME is a simulation tool designed to model the laser powder bed fusion (LPBF) additive manufacturing process using the Finite Volume Method (FVM). This tool helps in understanding the thermal and mechanical behavior of materials during the LPBF process.

Features

  • Thermal Simulation: Models the heat distribution and cooling rates.
  • Mechanical Simulation: Analyzes stress and deformation.
  • Material Properties: Supports various materials with customizable properties.
  • User-Friendly Interface: Easy to set up and run simulations.

Installation

To install FAME you need to have anaconda installed then clone the repository and install the required dependencies:

git clone https://github.com/neoceph/FAME.git
cd FAME
conda env create -f environment_linux.yaml

Usage

To run a simulation, use the following command:

fame --input your_config_file.yaml

Replace your_config_file.yaml with your specific configuration file.

Configuration

The configuration file should include parameters such as:

  • Laser power
  • Scan speed
  • Layer thickness
  • Material properties

Example configuration:

simulation:
  domain:
    size: 
      x: [0, 0.5]
    divisions:
      x: [5]
    area: 10e-3
  
  material:
    name: "Aluminum"
    properties:  # Ensure all properties are nested under "properties"
      density:
        baseValue: 2700
        method: "constant"
        referenceTemperature: 298.15
      specificHeat:
        baseValue: 900
        method: "constant"
        referenceTemperature: 298.15
      thermalConductivity:
        baseValue: 1000
        method: "constant"
        referenceTemperature: 298.15

  boundaryConditions:
    parameters:
      temperature:
        variableType: "scalar"
        convectionCoefficient: 15
        emmissivity: 0.85
        ambientTemperature: 298
    x:
      0:  
        - type: "temperature"
          value: 100
      0.5:
        - type: "temperature"
          value: 500

  solver:
    method: "bicgstab"
    tolerance: 1e-8
    maxIterations: 1000
    preconditioner: "jacobi"

  timeControl:
    steadyState: true  # Indicates that this is a steady-state problem

  visualization:
    path: "./results"
    variableName: "temperature_cell"

Contributing

Contributions are welcome! Please fork the repository and submit a pull request.

License

This project is licensed under the MIT License.

Contact

For any questions or issues, please contact aamin1@udayton.edu.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

fame_ud-0.0.5.tar.gz (50.5 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

fame_ud-0.0.5-py3-none-any.whl (19.5 kB view details)

Uploaded Python 3

File details

Details for the file fame_ud-0.0.5.tar.gz.

File metadata

  • Download URL: fame_ud-0.0.5.tar.gz
  • Upload date:
  • Size: 50.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.0.1 CPython/3.13.1

File hashes

Hashes for fame_ud-0.0.5.tar.gz
Algorithm Hash digest
SHA256 6db1c45f936cfb689adafe28987ec3372b758943977a43d5eb32232cbfc888d1
MD5 5500b159819b01112e54c59fd187a392
BLAKE2b-256 9a4f2d114058df0ad93114e95501c9ed2bcc5ca52dcb214e2b3648418fc252e0

See more details on using hashes here.

File details

Details for the file fame_ud-0.0.5-py3-none-any.whl.

File metadata

  • Download URL: fame_ud-0.0.5-py3-none-any.whl
  • Upload date:
  • Size: 19.5 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.0.1 CPython/3.13.1

File hashes

Hashes for fame_ud-0.0.5-py3-none-any.whl
Algorithm Hash digest
SHA256 03ec09308dc835e4ea9461b5dc686f03bd698f1d0b803997f3f08135ed7e9336
MD5 6d7e6cb064f0859e35b6a51e15633065
BLAKE2b-256 f1669ce7c33972567528afaae2d2c5cabbe2ab14e64d48beabb62301d2a456bd

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page