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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

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

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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.