【人工智能在钢铁冶金及材料中的应用】专刊-2023年5期
【人工智能在钢铁冶金及材料中的应用】专刊-2023年5期
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《钢铁研究学报》中、英文版官方公众号。推送最新出版的论文和写作技巧，为材料冶金领域的各位学者提供交流学习平台。
钢铁工业是国民经济的基础工业，目前，通过工业大数据、人工智能为代表的新一代信息技术，实现冶金过程的智能化，推进智能制造，助推中国传统制造业的转型升级，有着重要的经济和社会意义。
为了宣传钢铁冶金及材料领域人工智能技术的发展及大规模应用，Journal of Iron and Steel Research International（《钢铁研究学报》英文版）特邀请瑞典皇家工学院牟望重研究员、东北大学孙杰教授、北京科技大学吴宏辉教授、苏州大学章顺虎副教授担任客座主编，组织“人工智能在钢铁冶金及材料中的应用”主题专刊。
01
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引用本文 ：
Wangzhong Mu, Jie Sun, Hong-hui Wu & Shun-hu Zhang. Special issue on application of AI in steelmaking and ferrous materials. J. Iron Steel Res. Int. (2023).
https://doi.org/10.1007/s42243-023-00970-0
02
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引用本文 ：
Shu-yi Zhou & Xiao-yan Liu. A simple image-based method for online moisture content estimation of iron ore green pellets. J. Iron Steel Res. Int. (2023).
https://doi.org/10.1007/s42243-023-00968-8
03
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引用本文 ：
Shu-han Liu, Wen-qiang Sun, Wei-dong Li & Bing-zhen Jin. Prediction of blast furnace gas generation based on data quality improvement strategy. J. Iron Steel Res. Int. (2023).
https://doi.org/10.1007/s42243-023-00944-2
04
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引用本文 ：
Jia-wei Guo, Dong-ping Zhan, Guo-cai Xu, Nai-hui Yang, Bo Wang, Ming-xin Wang & Geng-wei You. An online BOF terminal temperature control model based on big data learning. J. Iron Steel Res. Int. (2023).
https://doi.org/10.1007/s42243-023-00952-2
05
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Yu-xiao Liu, Yan-wu Dong, Zhou-hua Jiang, Yu-shuo Li, Wei Zha, Yao-xin Du & Shu-yang Du. XGBoost-based model for predicting hydrogen content in electroslag remelting. J. Iron Steel Res. Int. (2023).
https://doi.org/10.1007/s42243-023-00962-0
06
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引用本文 ：
Yang Han, Ze-qian Cui, Li-jing Wang, Jie Li, Ai-min Yang & Yu-zhu Zhang. Cascade model for continuous prediction of silicon content of molten iron with coupled state variable nodes. J. Iron Steel Res. Int. (2023).
https://doi.org/10.1007/s42243-022-00906-0
07
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引用本文 ：
Ran Liu, Zhi-feng Zhang, Xin Li, Xiao-jie Liu, Hong-yang Li, Xiang-ping Bu, Jun Zhao & Qing Lyu. Hot metal quality monitoring system based on big data and machine learning. J. Iron Steel Res. Int. (2023).
https://doi.org/10.1007/s42243-023-00934-4
08
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引用本文 ：
Guang-da Bao, Ting Wu, Duo-gang Wang, Xiao-bin Zhou & Hai-chuan Wang. Multi-model coupling-based dynamic control system of ladle slag in argon blowing refining process. J. Iron Steel Res. Int. (2023).
https://doi.org/10.1007/s42243-023-00929-1
09
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引用本文 ：
Gong-hao Lian, Qi-hao Sun, Xiao-ming Liu, Wei-miao Kong, Ming Lv, Jian-jun Qi, Yong Liu, Ben-ming Yuan & Qiang Wang. Automatic recognition and intelligent analysis of central shrinkage defects of continuous casting billets based on deep learning. J. Iron Steel Res. Int. (2023).
https://doi.org/10.1007/s42243-023-00937-1
10
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引用本文 ：
Long Zhang, Sai-fei Yan, Jun Hong, Qian Xie, Fei Zhou & Song-lin Ran. An improved defect recognition framework for casting based on DETR algorithm. J. Iron Steel Res. Int. (2023).
https://doi.org/10.1007/s42243-023-00920-w
11
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引用本文 ：
Le-bao Song, Dong Xu, Peng-fei Liu, Jin-hang Zhou, Hui-qing Yan, Jing-dong Li, Hai-nan He, Hai-jun Yu, Xiao-chen Wang & Quan Yang. A novel mechanism fusion data control method for slab camber in hot rolling. J. Iron Steel Res. Int. (2023).
https://doi.org/10.1007/s42243-023-00935-3
12
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引用本文 ：
Qian-qian Dong, Qing-ting Qian, Min Li & Gang Xu. Monitoring and diagnosis of complex production process based on free energy of Gaussian–Bernoulli restricted Boltzmann machine. J. Iron Steel Res. Int. (2022).
https://doi.org/10.1007/s42243-022-00867-4
13
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引用本文：
Jia-qiang Chen, Shu-zong Chen, Chang-chun Hua, Cheng Jia & Cheng Qian. Extended-state-observer-based robust torsional vibration suppression for rolling mill main drive system with input saturation. J. Iron Steel Res. Int. (2023).
https://doi.org/10.1007/s42243-023-00933-5
14
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引用本文：
Yang-huan Xu, Dong-cheng Wang, Bo-wei Duan & Hong-min Liu. Data-driven flatness intelligent representation method of cold rolled strip. J. Iron Steel Res. Int. (2023).
https://doi.org/10.1007/s42243-023-00956-y
15
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引用本文：
Yun-jian Hu, Jie Sun, Huai-tao Shi, Qing-long Wang & Jian-zhao Cao. Roll gap prediction in acceleration and deceleration process of cold rolling based on a data-driven method. J. Iron Steel Res. Int. (2023).
https://doi.org/10.1007/s42243-023-00950-4
16
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引用本文：
Li Wang, Song-lin He, Zhi-ting Zhao & Xian-du Zhang. Prediction of hot-rolled strip crown based on Boruta and extremely randomized trees algorithms. J. Iron Steel Res. Int. (2023).
https://doi.org/10.1007/s42243-023-00964-y
17
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引用本文：
Xiao-ya Huang, Biao Zhang, Qiang Tian, Hong-hui Wu, Bin Gan, Zhong-nan Bi, Wei-hua Xue, Asad Ullah & Hao Wang. Machine learning study on time–temperature–transformation diagram of carbon and low-alloy steel. J. Iron Steel Res. Int. (2023).
https://doi.org/10.1007/s42243-023-00932-6
18
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引用本文：
Xing-qi Jia, Feng-hua Lu, Kai Yang, Shi-long Liu, Chun Yu, Wei Li & Xue-jun Jin. An optimization of harmonic structure nickel-saving cryogenic steel via combinatorial high-throughput experiment. J. Iron Steel Res. Int. (2023).
https://doi.org/10.1007/s42243-023-00945-1
19
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引用本文：
Xuan-dong Wang, Nan Li, Hang Su & Hui-min Meng. Prior austenite grain boundary recognition in martensite microstructure based on deep learning. J. Iron Steel Res. Int. (2023).
https://doi.org/10.1007/s42243-023-00947-z
Guest Editor
Wangzhong Mu
Royal Institute of Technology (KTH), Sweden
The guest editor of this special issue is currently a Digital Futures Tenure Faculty Member at Royal Institute of Technology (KTH), Sweden. He obtained his Docent qualification (2022) and Ph.D. degree (2015) from KTH, and bachelor & master degree from Northeastern University (China). Besides, he has also worked at McMaster University (Canada), Tohoku University (Japan), and Ferritico AB (Sweden) after Ph.D. His research experience and interest focus on sustainable metallurgy, steelmaking (in particular non-metallic inclusion control), intelligent material design, and multiscale characterization, etc. He has published over 60 academic papers in SCI-indexed journals and presented over 50 times at international conferences, including 15 times as keynote/invited speakers. He serves as the active organizers and advisor members of several international metallurgical conferences including TMS, CSSCR, IPES, etc. Meanwhile, he serves as the PI for over ten international and national levels research grants from, e.g., EU EIT RawMateiral, VINNOVA (Swedish Innovation Agency), SSF (Swedish Foundation for Strategic Research), etc.
Jie Sun
Northeastern University, China
The guest editor of this special issue is currently a professor at the State Key Laboratory of Rolling and Automation, Northeastern University. He received the B.E. degree in material forming and control engineering and the Ph.D. degree in material forming engineering from Northeastern University, Shenyang, China, in 2005 and 2011, respectively. His current research interests include modeling and control for complex industrial processes, control system design for the rolling process, and intelligent control theory and application. He has published 1 book and 65 academic papers collected by SCI database and has obtained 26 Chinese patents.
Hong-hui Wu
University of Science and Technology Beijing, China
The guest editor of this special issue is currently a professor at the Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, China. He graduated from the Hong Kong University of Science and Technology and is the recipient of the National Outstanding Youth Fund, the National Key Research and Development Program for Young Scientists. With a research focus on the application of artificial intelligence techniques to advanced metallic materials, he has published over 120 papers in SCI-indexed journals, which have received more than 5000 citations.
Shun-hu Zhang
Soochow University, China
The guest editor of this special issue is currently a professor at Shagang School of Iron and Steel, Soochow University, China. He received the Bachelor’s degree from Anhui University of Technology in Material Forming and Control Engineering in 2008. Then, majored in Material Processing Engineering, he received the Master’s and Doctoral degrees from Northeastern University in July, 2013. In the same year, the dissertation was appraised as the Excellent Doctoral Dissertation. His research direction is to investigate the optimization and control of rolling process in the field of material forming. He has taken in charge of nine scientific research projects including the Outstanding Youth Fund of Jiangsu Province and four National Natural Science Foundation of China. Also, he has published 58 SCI/EI papers on several important scientific journals, including 5 papers in Appl. Math. Model., 6 papers in Int. J. Mech. Sci., 4 papers in Meccanica, etc. as the first or corresponding author.
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About JISRI
《 钢 铁研究学报（英文版） 》（ Journal of Iron and Steel Research International ）创刊于1994年，由中国钢铁工业协会主管、中国钢研科技集团有限公司主办的学术期刊，主要刊载 冶金与金属加工工艺及金属基材料科学与技术 ，包括钢铁冶金工艺流程优化、冶金资源综合利用、节能环保；金属基材料的制备、表征、组织结构和性能等。科睿唯安JCR2021影响因子： 1.619 , 即时影响因子2.238，中科院期刊分区进入 材料科学2区 ， 由Springer出版集团全球独家出版，被 SCI、EI 等数据库收录。连续 11 年入选“ 最具国际影响力期 刊 ”。
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