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Updated: Sep 26, 2025

Applicability Analysis of Assessment Methods for Morphological Parameters of Corroded Steel Bars

Applicability Analysis of Assessment Methods for Morphological Parameters of Corroded Steel Bars

Published on: November 1, 2018

Machine-learning-based quality-level-estimation system for inspecting steel microstructures.

Hiromi Nishiura¹, Atsushi Miyamoto¹, Akira Ito¹

¹Research and Development Group, Hitachi Ltd., 292 Yoshida-cho, Totsuka-ku, Yokohama-shi, Kanagawa-ken 244-0817, Japan.

Microscopy (Oxford, England)

|April 19, 2022

Summary

This study introduces an automated system for estimating special steel quality using machine learning. The novel approach enhances accuracy by preprocessing images and augmenting data, outperforming human inspectors.

Keywords:

data augmentation machine learning overfitting steel microstructures visual inspection

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Area of Science:

Materials Science
Computer Science
Artificial Intelligence

Background:

Quality control of special steel relies on visual inspection of microstructures from microscopic images.
Sensory-based visual inspection is prone to human judgment variations and training errors, leading to overfitting and reduced generalization performance.

Purpose of the Study:

To develop an automatic quality level estimation system for special steel using machine learning.
To address the challenges of overfitting and performance degradation in visual inspection data.

Main Methods:

Proposed a machine learning system for automatic quality level estimation.
Implemented image preprocessing techniques to extract quality-related features and reduce image variations.
Utilized a data augmentation technique incorporating on-site judgment variations to mitigate overfitting with limited data.

Main Results:

The proposed system achieved a correct-answer rate of 92.5% in quality level estimation.
This rate surpasses the approximate 90% accuracy of human inspectors.
The methods demonstrate significant promise for practical application in special steel quality control.

Conclusions:

The developed automatic quality level estimation system effectively improves accuracy compared to human inspection.
Image preprocessing and data augmentation are crucial for enhancing the generalization performance of machine learning models in this domain.
The system shows potential for reliable and efficient implementation in industrial quality control settings.