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ml-iter-additive

This software provides the code for an iterative machine learning framework that uses extremely randomized trees (also known as extreme random forests) for predicting temperature profiles in an additive manufacturing process.

Requirements:

  • Scikit-Learn 0.19.1
  • Numpy 1.14
  • Pandas 0.22
  • XGBoost 0.7 or higher

Files

Core Files

  • additive_util.py: Core utility file for this repository (including incorporating neighbor information)

Notebooks

  • predict_cube.ipynb
  • predict_date_cube.ipynb
  • predict_cube_iterative.ipynb

Data

The prepared dataset is available at https://www.dropbox.com/s/cbwyhy18ofw0t7j/data.zip?dl=0

Citation

If you use this code or data, please cite:

A. Paul, M.Mozaffar, Z. Yang, W. Liao, A. Choudhary, J. Cao and A. Agrawal. A real-time iterative approach for temperature profile prediction in additive manufacturing processes. 6th IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2019

Developer Team & Collaborators

The code was developed by the CUCIS group at the Electrical and Computer Engineering Department in Northwestern University.

  1. Arindam Paul (arindam.paul@eecs.northwestern.edu)
  2. Jagat Sesh Challa (jagatsesh@northwestern.edu)
  3. Ankit Agrawal (ankitag@eecs.northwestern.edu)
  4. Wei-keng Liao (wkliao@eecs.northwestern.edu)
  5. Alok Choudhary (choudhar@eecs.northwestern.edu)

The development team would like thank the collaborators Mojtaba Mozaffar and Prof. Jian Cao from Northwestern's Advanced Manufacturing Processes Laboratory.

Questions/Comments

email: arindam.paul@eecs.northwestern.edu or ankitag@eecs.northwestern.edu
Copyright (C) 2019, Northwestern University.
See COPYRIGHT notice in top-level directory.

Funding Support

This work was performed under the following financial assistance award 70NANB19H005 from U.S. Department of Commerce, National Institute of Standards and Technology as part of the Center for Hierarchical Materials Design (CHiMaD). Partial support is also acknowledged from DOE awards DE-SC0014330, DE-SC0019358.

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An iterative machine learning framework for predicting temperature profiles for an additive manufacturing process

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