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Deep learning approach for chemistry and processing history prediction from materials microstructure.

Amir Abbas Kazemzadeh Farizhandi1, Omar Betancourt2, Mahmood Mamivand3

  • 1Computer Science Department, Boise State University, Boise, ID, 83702, USA.

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This study introduces a deep learning framework to predict material chemistry and processing history from microstructure images. The model accurately predicts composition and heat treatment temperature for Fe-Cr-Co alloys.

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Area of Science:

  • Materials Science
  • Computational Materials Science
  • Artificial Intelligence

Background:

  • Predicting material composition and processing history from microstructure morphology is crucial for heterogeneous materials.
  • Traditional simulation methods like phase-field are computationally intensive for inverse prediction (morphology to history).
  • Deep learning offers a potential solution for efficient inverse prediction tasks in materials science.

Purpose of the Study:

  • To develop and validate a deep learning framework for predicting chemical composition and processing history from material microstructure morphology.
  • To assess the effectiveness of deep learning models, including transfer learning with EfficientNet, for microstructure feature extraction.
  • To benchmark the model's predictive accuracy on a real alloy system (Fe-Cr-Co).

Main Methods:

  • A deep learning framework was proposed, utilizing convolutional neural networks (CNNs) to analyze microstructure images.
  • A mixed dataset including Fe distribution morphology (images) and concentration data was used for training.
  • The model predicted spinodal temperature and initial chemical composition, with comparisons made between custom CNNs and pretrained EfficientNet layers.

Main Results:

  • A shallow, trained network effectively predicted the chemical composition of Fe-Cr-Co alloys.
  • Accurate prediction of processing temperature required more complex feature extraction from the microstructure.
  • The model demonstrated good agreement with ground truth for chemistry and heat treatment temperature on a real Fe-Cr-Co transmission electron microscopy micrograph.

Conclusions:

  • Deep learning provides an efficient approach for predicting material chemistry from microstructure morphology.
  • Advanced feature extraction techniques are necessary for precise prediction of processing parameters like temperature.
  • The developed framework shows promise for analyzing real alloy systems and accelerating materials discovery.