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Process-Structure Linkages Using a Data Science Approach: Application to Simulated Additive Manufacturing Data.

Evdokia Popova1, Theron M Rodgers2, Xinyi Gong3

  • 11Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA.

Integrating Materials and Manufacturing Innovation
|January 25, 2020
PubMed
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This summary is machine-generated.

A new data science workflow models microstructure evolution by linking processing parameters to final structures. This approach, applied to additive manufacturing, creates reduced-order models for microstructure prediction.

Area of Science:

  • Materials Science
  • Data Science
  • Computational Modeling

Background:

  • Microstructure evolution in materials is highly dependent on processing parameters.
  • Additive manufacturing processes create complex microstructures sensitive to variations in processing conditions.
  • Developing predictive models for microstructure is crucial for optimizing material properties.

Purpose of the Study:

  • To develop and demonstrate a novel data science workflow for extracting process-structure linkages.
  • To create a reduced-order model for microstructure evolution.
  • To correlate processing parameters with final microstructure in additive manufacturing.

Main Methods:

  • A four-step data science workflow: data pre-processing, microstructure quantification, dimensionality reduction, and linkage extraction/validation.
Keywords:
Additive manufacturingMicrostructure quantificationMonte Carlo simulationPSP linkagesWorkflows

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  • Application of the workflow to synthetic microstructures generated via Potts-kinetic Monte Carlo (kMC) simulations.
  • Development of a low-dimensional, data-driven model.

    • Main Results:

    • Successfully established a data-driven workflow to model microstructure evolution.
    • Created a low-dimensional model correlating process parameters with final microstructure.
    • Demonstrated the workflow's applicability to additive manufacturing.

    Conclusions:

    • The developed data science workflow effectively extracts process-structure linkages for microstructure evolution.
    • The modular nature of the workflow promotes community dissemination and curation.
    • This data-driven approach provides a valuable tool for understanding and predicting microstructures in additive manufacturing.