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High Throughput Quantitative Metallography for Complex Microstructures Using Deep Learning: A Case Study in Ultrahigh

Brian L DeCost1, Bo Lei2, Toby Francis2

  • 1Material Measurement Laboratory,National Institute of Standards and Technology,Gaithersburg MD, 20899,USA.

Microscopy and Microanalysis : the Official Journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada
|March 15, 2019
PubMed
Summary

Deep learning models automate microstructure segmentation in ultrahigh carbon steel, replacing manual analysis. This enables accurate cementite particle and denuded zone measurements from complex micrographs.

Keywords:
deep learningmicrostructuresegmentationsteel

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Area of Science:

  • Materials Science
  • Computational Materials Science
  • Artificial Intelligence

Background:

  • Microstructure characterization is crucial for understanding material properties.
  • Manual segmentation of complex microstructures is time-consuming and subjective.
  • Automated methods are needed to improve efficiency and objectivity.

Purpose of the Study:

  • To develop and apply a deep convolutional neural network (CNN) segmentation model for automated microstructure analysis.
  • To segment cementite particles and denuded zones in ultrahigh carbon steel microstructures.
  • To enable quantitative analysis of microstructure features from complex micrographs.

Main Methods:

  • A deep convolutional neural network segmentation model was employed.
  • The model was trained and validated on an ultrahigh carbon steel microstructure dataset.
  • Two segmentation tasks were performed: cementite particle segmentation and multi-constituent microstructure segmentation.
  • Combined models were used to derive particle size and denuded zone width distributions.

Main Results:

  • Successfully automated the segmentation of cementite particles in a spheroidized matrix.
  • Enabled segmentation of complex microstructures including grain boundary carbide, spheroidized matrix, denuded zones, and Widmanstätten cementite.
  • Demonstrated the ability to obtain empirical cementite particle size and denuded zone width distributions.
  • The annotated dataset is publicly available.

Conclusions:

  • Deep learning models offer a powerful tool for automated microstructure segmentation.
  • The developed models can accurately quantify features in complex steel microstructures.
  • Automated segmentation facilitates more objective and efficient materials characterization.