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Computer vision based automatic evaluation method of Y2O3 steel coating performance with SEM image.

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This study presents an automated deep learning method for analyzing steel microstructure using scanning electron microscopy (SEM). The Tang Rui Detect (TRD) model efficiently quantifies features, improving reliability in materials science.

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Area of Science:

  • Materials Science
  • Computer Vision
  • Deep Learning

Background:

  • Manual analysis of steel microstructure via scanning electron microscopy (SEM) is time-consuming and subjective.
  • Current methods lack efficiency and accuracy in quantifying microstructural features.

Purpose of the Study:

  • To develop an automated deep learning-based evaluation method for steel microstructure analysis.
  • To improve the efficiency and reliability of microstructural feature detection and quantification.

Main Methods:

  • Utilized advanced computer vision algorithms for microstructure analysis.
  • Developed a specialized deep learning model, Tang Rui Detect (TRD), for dendritic solidification structures.
  • Implemented an automated system for feature detection and quantification.

Main Results:

  • Achieved efficient and accurate detection and quantification of steel microstructure features.
  • Demonstrated the effectiveness of the Tang Rui Detect (TRD) model for dendritic solidification.
  • Showcased the potential for automating microstructural evaluation and surface modification analysis.

Conclusions:

  • The deep learning-based method significantly enhances the automation and reliability of steel microstructure analysis.
  • This approach simplifies training and loss function design for improved evaluation.
  • The study offers a robust solution for materials science research and industrial applications.