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Estimation of Average Grain Size from Microstructure Image Using a Convolutional Neural Network.

Jun-Ho Jung¹, Seok-Jae Lee², Hee-Soo Kim¹

¹Department of Advanced Materials Engineering, Chosun University, 309 Pilmun-daero, Dong-gu, Gwangju 61452, Korea.

Materials (Basel, Switzerland)

|October 14, 2022

Summary

This summary is machine-generated.

This study uses a convolutional neural network to accurately estimate average grain size from microstructure images. Machine learning offers a faster, simpler alternative to traditional methods for material analysis.

Keywords:

convolutional neural network grain size image recognition machine learning microstructure

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Area of Science:

Materials Science
Computational Materials Science
Artificial Intelligence in Materials Science

Background:

Accurate measurement of average grain size in microstructures is crucial for material property prediction.
Traditional methods for microstructure analysis, such as manual measurement or image analysis software, can be time-consuming and subjective.
Machine learning presents a promising avenue for automating and accelerating microstructure analysis.

Purpose of the Study:

To develop and validate a convolutional neural network (CNN) model for the quantitative evaluation of average grain size from microstructure images.
To compare the efficiency and accuracy of machine learning-based analysis with traditional methods.
To investigate the internal workings of the CNN to understand how it interprets microstructure features for grain size estimation.

Main Methods:

Microstructure images were generated using phase-field simulations.
A convolutional neural network (CNN) model was employed for regression analysis, directly predicting average grain size from images.
The CNN's understanding of microstructure images was analyzed by examining its mid-layer representations.

Main Results:

The CNN model demonstrated high accuracy in estimating average grain size within the training data range.
The network effectively estimated the average grain sizes of experimental images featuring distinct grain boundaries.
Analysis of the network's internal layers suggested it recognized grain boundary curvatures to infer average grain size.

Conclusions:

Convolutional neural networks can accurately and efficiently determine average grain size from microstructure images.
Machine learning regression models offer a viable and potentially superior alternative to conventional microstructure analysis techniques.
The CNN's ability to learn complex features like grain boundary curvature highlights its potential for advanced material characterization.