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Characterization and reconstruction of 3D stochastic microstructures via supervised learning.

R Bostanabad1, W Chen1, D W Apley2

  • 1Department of Mechanical Engineering, Northwestern University, Evanston, Illinois, U.S.A.

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|July 6, 2016
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This study advances computational methods for reconstructing 3D stochastic microstructures. The enhanced approach accurately preserves spatial dependencies, aiding material science research.

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3DCharacterization and reconstructionstatistical equivalencystochastic microstructuresupervised learning

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Area of Science:

  • Materials Science
  • Computational Modeling
  • Image Analysis

Background:

  • Understanding the processing-structure-property chain in materials requires accurate characterization of stochastic microstructures.
  • Existing methods for microstructure reconstruction face limitations in handling 3D complexity and computational efficiency.

Purpose of the Study:

  • To extend a supervised learning approach for characterizing and reconstructing 3D stochastic microstructures.
  • To enhance model performance by integrating user-defined predictors.
  • To develop a computationally efficient reduced model for microstructure reconstruction.

Main Methods:

  • Digitizing microstructure images to create training datasets for supervised learning models.
  • Extending the approach to handle three-dimensional (3D) data.
  • Incorporating user-defined predictors to improve model accuracy.
  • Developing a reduced model to optimize computational performance.

Main Results:

  • The extended approach successfully characterizes and reconstructs 3D stochastic microstructures.
  • Incorporating user-defined predictors improved the model's performance.
  • The reduced model achieved comparable effectiveness to the full model.
  • Spatial dependencies in the microstructures were well-preserved in the reconstructed samples.

Conclusions:

  • The developed computational approach offers an efficient and accurate method for reconstructing 3D stochastic microstructures.
  • This advancement facilitates a deeper understanding of material structure-property relationships.
  • The method provides a valuable tool for materials design and simulation.