[1] R. Ma, J. Chen, Y. Feng, Z. Zhou, and J. Xie, “ELA-YOLO: An efficient method with linear attention for steel surface defect detection during manufacturing,” Adv. Eng. Inform., vol. 65, p. 103377, 2025.
[2] V. Nath, C. Chattopadhyay, and K. A. Desai, “NSLNet: An improved deep learning model for steel surface defect classification utilizing small training datasets,” Manuf. Lett., vol. 35, pp. 39–42, 2023.
[3] W. Fang, J. X. Huang, T. X. Peng, Y. Long, and F. X. Yin, “Machine learning-based performance predictions for steels considering manufacturing process parameters: A review,” J. Iron Steel Res. Int., vol. 31, no. 7, pp. 1555–1581, 2024.
[4] J. Jakubowski, N. Wojak-Strzelecka, R. P. Ribeiro, S. Pashami, S. Bobek, J. Gama, and G. J. Nalepa, “Artificial intelligence approaches for predictive maintenance in the steel industry: A survey,” arXiv preprint arXiv:2405.12785, 2024.
[5] C. Pepe, G. Farella, G. Bartucci, and S. M. Zanoli, “Recent innovations in computer and automation engineering for performance improvement in the steel industry production chain: A review,” Energies, vol. 18, no. 8, 2025.
[6] D. Zhou, K. Xu, Z. Lv, J. Yang, M. Li, F. He, and G. Xu, “Intelligent manufacturing technology in the steel industry of China: A review,” Sensors, vol. 22, no. 21, p. 8194, 2022.
[7] K. Tsutsui, T. Namba, K. Kihara, J. Hirata, S. Matsuo, and K. Ito, “Current trends on deep learning techniques applied in iron and steel making field: A review,” ISIJ Int., vol. 64, no. 11, pp. 1619–1640, 2024.
[8] Redchuk and F. Walas Mateo, “New business models on artificial intelligence—the case of the optimization of a blast furnace in the steel industry by a machine learning solution,” Appl. Syst. Innov., vol. 5, no. 1, p. 6, 2021.
[9] D. Cemernek, S. Cemernek, H. Gursch, A. Pandeshwar, T. Leitner, M. Berger, G. Klösch, and R. Kern, “Machine learning in continuous casting of steel: A state-of-the-art survey,” J. Intell. Manuf., vol. 33, no. 6, pp. 1561–1579, 2022.
[10] Q. Xie, M. Suvarna, J. Li, X. Zhu, J. Cai, and X. Wang, “Online prediction of mechanical properties of hot rolled steel plate using machine learning,” Mater. Des., vol. 197, p. 109201, 2021.
[11] S. W. Choi, B. G. Seo, and E. B. Lee, “Machine learning-based tap temperature prediction and control for optimized power consumption in stainless electric arc furnaces (EAF) of steel plants,” Sustainability, vol. 15, no. 8, p. 6393, 2023.
[12] M. Khaledi, A. Rashidi Mehrabadi, and M. Mirabi, “Prediction of the corrosion and scaling index of industrial cooling water circulation circuits using artificial intelligence: Case study of circulating water circuits in electric arc furnaces of Khuzestan Steel,” SSRN, 2024.
[13] Y. Zhang, C. J. Zhang, K. Zeng, L. Zhu, and Y. Han, “Research on terminal control model of intelligent mining of flame spectral information of converter mouth in late smelting stage,” Ironmaking Steelmaking, vol. 48, no. 6, pp. 677–684, 2021.
[14] S. Li, S. Wang, W. Li, G. Zhang, Z. Huang, X. Luo, and H. Yu, “Rolled thickness prediction for titanium/steel‐clad plates based on combined method of theoretical and neural network,” Steel Res. Int., vol. 96, no. 3, p. 2400602, 2025.
[15] S. Y. Lee, B. A. Tama, S. J. Moon, and S. Lee, “Steel surface defect diagnostics using deep convolutional neural network and class activation map,” Appl. Sci., vol. 9, no. 24, p. 5449, 2019.
[16] K. Choi, K. Koo, and J. S. Lee, “Development of defect classification algorithm for POSCO rolling strip surface inspection system,” in Proc. 2006 SICE–ICASE Int. Joint Conf., Oct. 2006, pp. 2499–2502. IEEE.
[17] L. North, K. Blackmore, K. Nesbitt, and M. R. Mahoney, “Methods of coke quality prediction: A review,” Fuel, vol. 219, pp. 426–445, 2018.
[18] C. Yang, C. Yang, J. Li, Y. Li, and F. Yan, “Forecasting of iron ore sintering quality index: A latent variable method with deep inner structure,” Comput. Ind., vol. 141, p. 103713, 2022.
[19] S. Liu, X. Liu, Q. Lyu, and F. Li, “Comprehensive system based on a DNN and LSTM for predicting sinter composition,” Appl. Soft Comput., vol. 95, p. 106574, 2020.
[20] F. He and L. Zhang, “Mold breakout prediction in slab continuous casting based on combined method of GA–BP neural network and logic rules,” Int. J. Adv. Manuf. Technol., vol. 95, no. 9, pp. 4081–4089, 2018.
[21] J. Cheng, C. Zhao-Zhen, T. Nai-Biao, Y. Ji-Lin, and Z. Miao-Yong, “Molten steel breakout prediction based on genetic algorithm and BP neural network in continuous casting process,” in Proc. 31st Chin. Control Conf., Jul. 2012, pp. 3402–3406. IEEE.
[22] W. Zhao, F. Chen, H. Huang, D. Li, and W. Cheng, “A new steel defect detection algorithm based on deep learning,” Comput. Intell. Neurosci., vol. 2021, no. 1, p. 5592878, 2021.
[23] B. Si, M. Yasengjiang, and H. Wu, “Deep learning-based defect detection for hot-rolled strip steel,” in J. Phys.: Conf. Ser., vol. 2246, no. 1, p. 012073. IOP Publishing, 2022.
[24] J. Jang, D. Van, H. Jang, D. H. Baik, S. D. Yoo, J. Park, S. Mhin, J. Mazumder, and S. H. Lee, “Residual neural network-based fully convolutional network for microstructure segmentation,” Sci. Technol. Weld. Join., vol. 25, no. 4, pp. 282–289, 2020.
[25] B. Zhu, Z. Chen, F. Hu, X. Dai, L. Wang, and Y. Zhang, “Feature extraction and microstructural classification of hot stamping ultra-high strength steel by machine learning,” JOM, vol. 74, no. 9, pp. 3466–3477, 2022.
[26] “Accelerating phase-field predictions via recurrent neural networks learning the microstructure evolution in latent space,” [Unpublished/Incomplete Citation].
[27] H. Di, X. Ke, Z. Peng, and D. Dongdong, “Surface defect classification of steels with a new semi-supervised learning method,” Opt. Lasers Eng., vol. 117, pp. 40–48, 2019.
[28] Y. Gao, H. Zhang, L. Zhu, F. Xie, and D. Xiao, “ZFD-Net: Zinc flower defect detection model of galvanized steel surface based on improved YOLOv5,” PLoS One, vol. 20, no. 6, p. e0325507, 2025.
[29] B. Kim, J. Kwon, S. Choi, J. Noh, K. Lee, and J. Yang, “Corrosion image monitoring of steel plate by using k-means clustering,” J. Korean Inst. Surf. Eng., vol. 54, no. 5, pp. 278–284, 2021.
[30] R. Zheng, Y. Bao, L. Zhao, and L. Xing, “Prediction of steelmaking process variables using K-medoids and a time-aware LSTM network,” Heliyon, vol. 10, no. 12, 2024.
[31] Y. Ji, S. Liu, M. Zhou, Z. Zhao, X. Guo, and L. Qi, “A machine learning and genetic algorithm-based method for predicting width deviation of hot-rolled strip in steel production systems,” Inf. Sci., vol. 589, pp. 360–375, 2022.
[32] C. Song, J. Cao, L. Wang, J. Xiao, and Q. Zhao, “Transverse thickness profile control of electrical steel in 6-high cold rolling mills based on the GA–PSO hybrid algorithm,” Int. J. Adv. Manuf. Technol., vol. 121, no. 1, pp. 295–308, 2022.
[33] R. Ricardo Rodríguez, I. F. Benítez, G. González Yero, and J. R. Núñez Alvarez, “Multi-agent system for steel manufacturing process,” Int. J. Electr. Comput. Eng. (IJECE), vol. 12, no. 3, pp. 2441–2453, 2022.
[34] K. Kerdprasop, N. Kerdprasop, and P. Chuaybamroong, “Deep learning and machine learning models to predict energy consumption in steel industry,” Int. J. Mach. Learn., vol. 13, pp. 142–145, 2023.
[35] X. Chen, J. Van Hillegersberg, E. Topan, S. Smith, and M. Roberts, “Application of data-driven models to predictive maintenance: Bearing wear prediction at TATA steel,” Expert Syst. Appl., vol. 186, p. 115699, 2021.
[36] U. Samal, “Evolution of machine learning and deep learning in intelligent manufacturing: A bibliometric study,” Int. J. Syst. Assur. Eng. Manag., pp. 1–17, 2025.
[37] X. Chen, J. Van Hillegersberg, E. Topan, S. Smith, and M. Roberts, “Application of data-driven models to predictive maintenance: Bearing wear prediction at TATA steel,” Expert Syst. Appl., vol. 186, p. 115699, 2021.
[38] McKinsey & Company, “How a steel plant in India tapped the value of data and won global acclaim,” 2020.
[39] Tata Steel, Integrated Report 2023–24: Intellectual Capital – Industry 4.0, Tata Steel Official Reports, 2024.
[40] R. Kumar Balaraman, S. Hussain, J. K. Ong, Q. Y. Tan, and N. Raghavan, “Feature-driven density prediction of maraging steel additively manufactured samples using pyrometer sensor and supervised machine learning,” IEEE Access, vol. 12, pp. 172892–172909, 2024.
[41] Khalili-Fard, F. Sabouhi, and A. Bozorgi-Amiri, “Data-driven robust optimization for a sustainable steel supply chain network design: Toward the circular economy,” Comput. Ind. Eng., vol. 195, p. 110408, 2024.
[42] V. Colla, C. Pietrosanti, E. Malfa, and K. Peters, “Environment 4.0: How digitalization and machine learning can improve the environmental footprint of the steel production processes,” Matér. Tech., vol. 108, no. 5–6, p. 507, 2020.
[43] H. Zhao, Y. Chen, B. Dang, and X. Jian, “Research on steel production scheduling optimization based on deep learning,” in Proc. 4th Int. Symp. Artif. Intell. Intell. Manuf. (AIIM), Dec. 2024, pp. 813–816. IEEE.
[44] M. Coniglio, A. Cimino, and V. Corvello, “Artificial intelligence and the future of supply chain management,” in Int. Res. Innov. Forum, Cham: Springer Nature Switzerland, 2024, pp. 43–52.
[45] Ş. Duymaz and A. F. Güneri, “The application of machine learning algorithms in the estimation of production lead times: A case study of a steel construction manufacturing company,” J. Adv. Manuf. Eng. (JAME), vol. 5, no. 1, 2024.
[46] Khalili-Fard, F. Sabouhi, and A. Bozorgi-Amiri, “Data-driven robust optimization for a sustainable steel supply chain network design: Toward the circular economy,” Comput. Ind. Eng., vol. 195, p. 110408, 2024.
[47] T.-L. Nguyen, P.-H. Nguyen, H.-A. Pham, T.-G. Nguyen, D.-T. Nguyen, T.-H. Tran, H.-C. Le, and H.-T. Phung, “A novel integrating data envelopment analysis and spherical fuzzy MCDM approach for sustainable supplier selection in steel industry,” Mathematics, vol. 10, p. 1897, 2022.
)