Automatic Segmentation and Recognition of the Microstructure of High-Strength Low-Alloy Steel.
Saved in:
| Title: | Automatic Segmentation and Recognition of the Microstructure of High-Strength Low-Alloy Steel. |
|---|---|
| Authors: | Wang, Lu1 (AUTHOR), Ren, Ziying2 (AUTHOR), Song, Baoyu1,2 (AUTHOR), Wang, Bing1,2 (AUTHOR), Chen, Qiaochuan1 (AUTHOR), Wang, Jingjing2 (AUTHOR), Zhou, Tianpeng2 (AUTHOR), Han, Yuexing1 (AUTHOR) han_yx@i.shu.edu.cn |
| Source: | Materials (1996-1944). Jun2026, Vol. 19 Issue 12, p2554. 16p. |
| Subjects: | Image segmentation, Microstructure, Artificial neural networks, Crystal grain boundaries, Low alloy steel, Image processing |
| Abstract: | Metallographic microstructure analysis is essential for understanding the evolution of steel microstructures during heat treatment and mechanical processing. However, accurate analysis of optical micrographs remains difficult because of blurred grain boundaries, grayscale inhomogeneity within grains, and irregular grain morphologies. To address these issues, this work proposes an automated metallographic image-processing method based on superpixels, DPSS (dual-phase steel segmentation), with the main contribution focused on microstructure segmentation. First, image contrast and boundary visibility are enhanced by edge detection and sharpening. Then, superpixel segmentation is combined with extracted edge information to improve boundary localization and preserve irregular grain morphology, enabling more complete extraction of grain or particle regions from optical images. The proposed method is validated on optical micrographs of Mn-Si low-alloy steel, and the results show that it provides more accurate and complete segmentation than conventional ImageJ (Version: 1.54f)-based processing. Based on the segmented regions, a lightweight neural network is further used for phase identification. The final classification recognition accuracy can reach 99.91%. This classification result serves to demonstrate that the improved segmentation results can provide more reliable inputs for subsequent microstructure recognition. Overall, the proposed method offers an effective and automated solution for metallographic image segmentation and supports more accurate downstream phase analysis. [ABSTRACT FROM AUTHOR] |
| Copyright of Materials (1996-1944) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
Be the first to leave a comment!
