A Feasibility Study on Metallurgical Slag Classification by Microstructure Recognition

Abstract:

Property of metallurgical slag generated in smelting or refining process of ferrous production can be determined by its microstructure which depends on chemical composition and production process. This study proposes a deep learning method which is a subfield of artificial intelligence for autonomous slag classification by microstructure recognition. This present work focus on the implementation of a convolutional neural network (CNN) to classify four types of slags that the variance microstructure resulted from the difference of their formation condition. Both secondary electron (SE) and backscattered electron (BSE) image type captured by scanning electron microscope (SEM) are used as dataset. ResNet50, InceptionV3 and DenseNet201 network architectures are selected in this study to evaluate their classification performance. In addition, data augmentation manipulated by the software is randomly flipped both horizontally and vertically to avoid overfitting from a limited number of training images. The results showed that the best approach to classification accuracy is reached 98.89% by CNN. Therefore, it can be concluded that CNN is excellent potential method for autonomous slag microstructure classification systems.

• Periodical: Materials Science Forum (Volume 1009)
• Pages: 107-113
• DOI: https://doi.org/10.4028/www.scientific.net/MSF.1009.107
• Online since: August 2020
• Authors: Jirapracha Thampiriyanon, Kitti Laungsakulthai, Piamsak Laokhen, Siam Thongnak, Sakhob Khumkoa*
• Keywords: Artificial Intelligent, Convolutional Neural Network, Deep Learning, Metallurgical Slag, Microstructural Analysis

References

[1] D.R. Gaskell, in: Method for Phase Diagram Determination, Elsevier Science (2007), pp.442-458.

[2] S.K. Park, I.C. Kwon, S.J. Lee, I.K. Huh and N.C. Cho. Microstructural analysis of slags using Raman micro spectroscope, Journal of Conservation Science, Vol. 35, no. 2 (2019), pp.145-152.

DOI: 10.12654/jcs.2019.35.2.04

[3] I. Joncsky. Microstructures of Metallurgical slags, Archives of Metallurgy and Materials, Vol. 61 (2016), pp.61-66.

[4] T. Mueller, A. G. Kusne and R. Ramprasad. Machine Learning in materials science: Recent progress and emerging applications, Reviews in Computational Chemistry, Vol. 29 (2016).

DOI: 10.1002/9781119148739.ch4

[5] A. Chowdhury, E. Kautz, B. Yener and D. Lewis. Image driven machine learning methods for microstructure recognition, Computational Materials Science, Vol. 123 (2016), pp.176-187.

DOI: 10.1016/j.commatsci.2016.05.034

[6] P. Kim. Deep Learning: With Machine Learning, Neural Networks and Artificial Intelligence, A press (2017).

[7] F. Chollet "Xception. Deep Learning with Depthwise Separable Convolutions, 2017 IEEE Conference on Computer Vison and Pattern Recognition (CVPR), Honolulu, HI (2017), pp.1800-1807.

DOI: 10.1109/cvpr.2017.195

[8] K. He, X. Zhang, S. Ren and J. Sun. Deep Residual Learning for Image Recognition, 2016 IEEE Conference on Computer Vison and Pattern Recognition (CVPR), Las Vegas, NV (2016), pp.770-778.

DOI: 10.1109/cvpr.2016.90

[9] C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens and Z. Wojna. Rethinking the Inception Architecture for Computer Vision, 2016 IEEE Conference on Computer Vison and Pattern Recognition (CVPR), Las Vegas, NV (2016), pp.2818-2826.

DOI: 10.1109/cvpr.2016.308

[10] G. Huang, Z. Liu, L. v. d. Maaten and K.Q. Weinberger. Densely Connected Convolutional Network, 2017 IEEE Conference on Computer Vison and Pattern Recognition (CVPR), Honolulu, HI (2017), pp.2261-2269.

DOI: 10.1109/cvpr.2017.243

[11] M. Bartlomiej, K. Grzegorz, K. Jan and P. Ulrich. Autonomous Interpretation of the Microstructure of Steels and Special Alloys, Materials Science Forum, Vol. 949 (2019), pp.24-31.

[12] S.M. Azimi, D. Britz, M. Engstler, M. Fritz and F. Mücklich. Advanced Steel Microstructural Classification by Deep Learning Method, Scientific Reports, Vol. 8 (2018).

DOI: 10.1038/s41598-018-20037-5

[13] C. Vrancken, P. Longhurst and S. Wagland. Deep learning in material recovery: Development of method to create training database, Expert Systems with Applications, Vol. 125 (2019), pp.268-280.

DOI: 10.1016/j.eswa.2019.01.077

[14] C. Shorten and T.M. Khoshgoftaar. A survey on Image Data Augmentation for Deep Learning, Journal of Big Data, Vol. 6 no.60 (2019).

DOI: 10.1186/s40537-019-0197-0