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JSDP 2025, 22(1): 71-82 |
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Firuz Mahjanabadi Z, Jafari P, Rezaei M. Automated Classification of Steel Phases in Scanning Electron Microscope Images. JSDP 2025; 22 (1) :71-82
URL: http://jsdp.rcisp.ac.ir/article-1-1400-en.html
URL: http://jsdp.rcisp.ac.ir/article-1-1400-en.html
Assistant Professor of Department of Electrical and Electronics Engineering, Faculty of Electrical and Computer Engineering, University of Sistan and Baluchestan, Zahedan, Iran
Abstract: (180 Views)
The properties of steels are intrinsically dependent on their microstructural components, known as phases, which form during the manufacturing process. Different steel phases can be observed in microscopic images of steel surfaces. Automatic detection and classification of these phases from images can significantly enhance the understanding of steel properties with improved speed and accuracy. This paper introduces, for the first time, an intelligent and automated method for classifying steel phases from microscopic images. This process requires defining and extracting suitable texture features unique to these images and segmenting the images into highly irregular regions based on the extracted features. To achieve this, the input image is initially divided into blocks, and texture features are extracted independently for each block. The dimensionality of these features is then reduced using Principal Component Analysis, and the refined features are subsequently fed into a Softmax neural network for classification.
The implementation results indicate that the proposed method achieves an accuracy of over 99% in distinguishing between two phases: acicular ferrite and granular ferrite. Furthermore, it attains an accuracy exceeding 86% when classifying three phases: granular ferrite, acicular ferrite, and Widmanstätten ferrite. This suggests that the widely used and conventional k-means clustering method, as a traditional machine learning approach, is incapable of effectively distinguishing microscopic steel phase blocks using extracted texture features. Notably, as of the writing of this paper, no prior research has been conducted on the automatic classification of different ferrite phases, making this study a novel contribution to the field.
In this research, an automated classification algorithm for ferrite phase structures in SEM images of steel is proposed using texture feature extraction methods and machine learning models. The dataset comprises images of 1024×768 resolution, which were divided into 128×128 blocks, with classification performed independently for each block. Due to the limited number of blocks available for training machine learning models, data augmentation techniques such as rotation and scaling were applied to increase the dataset size. Various image processing methods were used to extract 128 texture features. These extracted features were then used to classify different ferrite phases using two machine learning models: k-means clustering and the Softmax neural network. Additionally, PCA was employed to reduce feature dimensionality, which positively impacted the classification of granular and acicular ferrite. While k-means clustering, as a conventional and widely used machine learning method, failed to achieve satisfactory classification accuracy, the proposed approach using a smooth maximum neural network demonstrated exceptional performance. Despite the complex and irregular nature of ferrite shapes, the selected features and the proposed algorithm successfully achieved over 99% accuracy for two-phase classification and over 86% accuracy for three-phase classification.
The implementation results indicate that the proposed method achieves an accuracy of over 99% in distinguishing between two phases: acicular ferrite and granular ferrite. Furthermore, it attains an accuracy exceeding 86% when classifying three phases: granular ferrite, acicular ferrite, and Widmanstätten ferrite. This suggests that the widely used and conventional k-means clustering method, as a traditional machine learning approach, is incapable of effectively distinguishing microscopic steel phase blocks using extracted texture features. Notably, as of the writing of this paper, no prior research has been conducted on the automatic classification of different ferrite phases, making this study a novel contribution to the field.
In this research, an automated classification algorithm for ferrite phase structures in SEM images of steel is proposed using texture feature extraction methods and machine learning models. The dataset comprises images of 1024×768 resolution, which were divided into 128×128 blocks, with classification performed independently for each block. Due to the limited number of blocks available for training machine learning models, data augmentation techniques such as rotation and scaling were applied to increase the dataset size. Various image processing methods were used to extract 128 texture features. These extracted features were then used to classify different ferrite phases using two machine learning models: k-means clustering and the Softmax neural network. Additionally, PCA was employed to reduce feature dimensionality, which positively impacted the classification of granular and acicular ferrite. While k-means clustering, as a conventional and widely used machine learning method, failed to achieve satisfactory classification accuracy, the proposed approach using a smooth maximum neural network demonstrated exceptional performance. Despite the complex and irregular nature of ferrite shapes, the selected features and the proposed algorithm successfully achieved over 99% accuracy for two-phase classification and over 86% accuracy for three-phase classification.
Keywords: Scanning Electron Microscope, Automated Classification, Steel Phases, Neural Networks, K-Means Algorithm.
Type of Study: Research |
Subject:
Paper
Received: 2023/09/26 | Accepted: 2025/03/8 | Published: 2025/06/21 | ePublished: 2025/06/21
Received: 2023/09/26 | Accepted: 2025/03/8 | Published: 2025/06/21 | ePublished: 2025/06/21
References
1. Kwok, T. W. J., and D. Dye, "A review of the processing, microstructure and property relationships in medium Mn steels", International Materials Reviews, vol. 68, no.8, p. 1098-1134, 2023. [DOI:10.1080/09506608.2023.2199617]
2. Uehata, Nao, et al, "Optical microscopy-based damage quantification: an example of cryogenic deformation of a dual-phase steel", isij international, vol. 58, no.1, p. 179-185, 2018. [DOI:10.2355/isijinternational.ISIJINT-2017-468]
3. G. Krauss, "Steels: processing, structure, and performance", Asm International, 2015. [DOI:10.31399/asm.tb.spsp2.9781627082655]
4. E. Pereloma and D. V. Edmonds, "Phase transformations in steels: Diffusionless transformations, high strength steels", modelling and advanced analytical techniques, Elsevier, 2012. [DOI:10.1533/9780857096104]
5. Muñoz-Rodenas, Jorge, et al, "Effectiveness of machine-learning and deep-learning strategies for the classification of heat treatments applied to low-carbon steels based on microstructural analysis", Applied Sciences, vol. 13, no. 6, p. 3479, 2023. [DOI:10.3390/app13063479]
6. J. I. Goldstein, D. E. Newbury, J. R. Michael, N. W. Ritchie, J. H. J. Scott, and D. C. Joy, "Scanning electron microscopy and X-ray microanalysis", springer, 2017. [DOI:10.1007/978-1-4939-6676-9]
7. C. Mignot, "Color (and 3D) for scanning electron microscopy", ed: Oxford University Press, 2018. [DOI:10.1017/S1551929518000482]
8. Hadjipanayis, George C., and Richard W. Siegel, eds. Nanophase materials: Synthesis-properties-applications, Vol. 260. Springer Science & Business Media, 2012.
9. F.-Y. Zhu, Q.-Q. Wang, X.-S. Zhang, W. Hu, X. Zhao, and H.-X. Zhang, "3D nanostructure reconstruction based on the SEM imaging principle, and applications", Nanotechnology, vol. 25, no. 18, p. 185705, 2014. [DOI:10.1088/0957-4484/25/18/185705] [PMID]
10. D. Saladra and M. Kopernik, "Qualitative and quantitative interpretation of SEM image using digital image processing", Journal of microscopy, vol. 264, no. 1, p. 102-124, 2016. [DOI:10.1111/jmi.12431] [PMID]
11. Y. Pourasad, "Detection and classification of breast masses using mammographically image processing," Razi Journal of Medical Sciences, vol. 27, no. 4, pp. 60-73, 2020.
12. S. Siddesha, S. Niranjan, and V. M. Aradhya, "Texture based classification of arecanut," in 2015 International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), IEEE, pp. 688-692, 2015. [DOI:10.1109/ICATCCT.2015.7456971]
13. J. F. Ramirez-Villegas, E. Lam-Espinosa, and D. F. Ramirez-Moreno, "Microcalcification detection in mammograms using difference of Gaussians filters and a hybrid feedforward-Kohonen neural network," in 2009 XXII Brazilian Symposium on Computer Graphics and Image Processing, IEEE, pp. 186-193, 2009. [DOI:10.1109/SIBGRAPI.2009.25]
14. Y. Dong et al., "Locally directional and extremal pattern for texture classification," IEEE Access, vol. 7, pp. 87931-87942, 2019. [DOI:10.1109/ACCESS.2019.2924985]
15. ا. مصطفی، ع، احمدیان، م. ج. ابولحسنی و م. گیتی، «كلاسهبندی بافت تصاویر سونوگرافی بیماریهای منتشر كبدی با استفاده از تبدیل موجك»، مجلة فیزیك پزشكی ایران، 1385، 7 (2)، 67-76.
15. M. Akbar, A. R. Ahmadian, M. J. Abolhasani and M. Giti, "Tissue classification of ultrasound images of diffuse liver diseases using wavelet transform", vol. 2, no. 7, pp. 67-76, 2006.
16. R. Xu and D. Wunsch, Clustering, John Wiley & Sons, 2008. [DOI:10.1002/9780470382776]
17. وحیدی فردوسی صدیقه، امیرخانی حسین، «ترکیب وزندار خوشهبندیها با هدف افزایش صحّت خوشهبندی نهایی»، پردازش علائم و دادهها، ۱۳۹۹; ۱۷ (۲) :۱۰۰-۸۵.
17. Vahidi Ferdosi S, Amirkhani H. "Weighted Ensemble Clustering for Increasing the Accuracy of the Final Clustering", JSDP; 17 (2):100-85, 2020. [DOI:10.29252/jsdp.17.2.100]
18. I. Goodfellow, Y. Bengio, and A. Courville, Deep learning, MIT press, 2016.
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