A real-time iterative machine learning approach for temperature profile prediction in additive manufacturing processes

Arindam Paul, Mojtaba Mozaffar, Zijiang Yang, Wei Keng Liao, Alok Choudhary, Jian Cao, Ankit Agrawal

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Additive Manufacturing (AM) is a manufacturing paradigm that builds three-dimensional objects from a computer-aided design model by successively adding material layer by layer. AM has become very popular in the past decade due to its utility for fast prototyping such as 3D printing as well as manufacturing functional parts with complex geometries using processes such as laser metal deposition that would be difficult to create using traditional machining. As the process for creating an intricate part for an expensive metal such as Titanium is prohibitive with respect to cost, computational models are used to simulate the behavior of AM processes before the experimental run. However, as the simulations are computationally costly and time-consuming for predicting multiscale multi-physics phenomena in AM, physics-informed data-driven machine-learning systems for predicting the behavior of AM processes are immensely beneficial. Such models accelerate not only multiscale simulation tools but also empower real-time control systems using in-situ data. In this paper, we design and develop essential components of a scientific framework for developing a data-driven model-based real-time control system. Finite element methods are employed for solving time-dependent heat equations and developing the database. The proposed framework uses extremely randomized trees - an ensemble of bagged decision trees as the regression algorithm iteratively using temperatures of prior voxels and laser information as inputs to predict temperatures of subsequent voxels. The models achieve mean absolute percentage errors below 1% for predicting temperature profiles for AM processes. The code is made available for the research community at https://github.com/paularindam/ml-iter-additive.

Original languageEnglish (US)
Title of host publicationProceedings - 2019 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2019
EditorsLisa Singh, Richard De Veaux, George Karypis, Francesco Bonchi, Jennifer Hill
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages541-550
Number of pages10
ISBN (Electronic)9781728144931
DOIs
StatePublished - Oct 2019
Externally publishedYes
Event6th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2019 - Washington, United States
Duration: Oct 5 2019Oct 8 2019

Publication series

NameProceedings - 2019 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2019

Conference

Conference6th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2019
CountryUnited States
CityWashington
Period10/5/1910/8/19

Keywords

  • Additive manufacturing
  • Ensemble learning
  • Spatiotemporal modeling

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Information Systems and Management

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  • Cite this

    Paul, A., Mozaffar, M., Yang, Z., Liao, W. K., Choudhary, A., Cao, J., & Agrawal, A. (2019). A real-time iterative machine learning approach for temperature profile prediction in additive manufacturing processes. In L. Singh, R. De Veaux, G. Karypis, F. Bonchi, & J. Hill (Eds.), Proceedings - 2019 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2019 (pp. 541-550). [8964151] (Proceedings - 2019 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DSAA.2019.00069