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Automatic steel labeling on certain microstructural constituents with image processing and machine learning tools.

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|June 25, 2019
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Summary
This summary is machine-generated.

This study shows machine learning effectively categorizes steel microstructures from optical images. Random forest classification offers the best performance for automated steel microstructure analysis.

Keywords:
10 Engineering and structural materials106 Metallic materials404 Materials informatics / Genomics505 Optical / Molecular spectroscopyMetallurgymachine learningmicrostructuresoptical microscopypattern recognition

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Area of Science:

  • Materials Science
  • Metallurgy
  • Computer Science

Background:

  • Accurate steel microstructure classification is crucial for material property prediction.
  • Automated analysis methods are needed to handle the complexity of diverse steel microstructures.
  • Previous methods lacked a comprehensive approach for various microstructure types.

Purpose of the Study:

  • To develop and evaluate a machine learning-based classifier for steel microstructures.
  • To identify the most effective machine learning algorithm for this classification task.
  • To provide a complete solution for automatic steel microstructure quantification.

Main Methods:

  • Image pre-processing of optical microscopy images.
  • Statistical analysis and machine learning techniques for classification.
  • Evaluation of various classifiers, including random forest.

Main Results:

  • Steel microstructures (ferrite/pearlite, ferrite/pearlite/bainite, bainite/martensite) were effectively categorized.
  • Random forest classifier demonstrated superior performance and usability compared to other methods.
  • The developed classifier achieved reasonable class-labeling accuracy.

Conclusions:

  • Machine learning, particularly random forest, provides an effective tool for steel microstructure classification.
  • The combination of categorization and pattern recognition offers a total solution for automated microstructure analysis.
  • This approach facilitates the selection of appropriate pattern recognition methods for diverse steel types.