Intelligent Manufacturing Technology in the Steel Industry of China: A Review

被引:41
作者
Zhou, Dongdong [1 ,2 ]
Xu, Ke [1 ,2 ]
Lv, Zhimin [1 ]
Yang, Jianhong [3 ]
Li, Min [1 ]
He, Fei [1 ]
Xu, Gang [1 ]
机构
[1] Univ Sci & Technol Beijing, Collaborat Innovat Ctr Steel Technol, 30 Xueyuan Rd, Beijing 100083, Peoples R China
[2] Yangjiang Alloy Mat Lab, 1 Luoqin Rd, Jiangcheng Dist 529500, Yangjiang, Peoples R China
[3] Univ Sci & Technol Beijing, Sch Mech Engn, 30 Xueyuan Rd, Beijing 100083, Peoples R China
基金
国家重点研发计划; 中国国家自然科学基金;
关键词
intelligent manufacturing; steel industry; China; typical models; TEMPERATURE DISTRIBUTION; SURFACE; PERFORMANCE; DESIGN; SYSTEM;
D O I
10.3390/s22218194
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Intelligent manufacturing, defined as the integration of manufacturing with modern information technologies such as 5G, digitalization, networking, and intelligence, has grown in popularity as a means of boosting the productivity, intelligence, and flexibility of traditional manufacturing processes. The steel industry is a necessary support for modern life and economic development, and the Chinese steel industry's capacity has expanded to roughly half of global production. However, the Chinese steel industry is now confronted with high labor costs, massive carbon emissions, a low level of intelligence, low production efficiency, and unstable quality control. Therefore, China's steel industry has launched several large-scale intelligent manufacturing initiatives to improve production efficiency, product quality, manual labor intensity, and employee working conditions. Unfortunately, there is no comprehensive overview of intelligent manufacturing in China's steel industry. We began this research by summarizing the construction goals and overall framework for intelligent manufacturing of the steel industry in China. Following that, we offered a brief review of intelligent manufacturing for China's steel industry, as well as descriptions of two typical intelligent manufacturing models. Finally, some major technologies employed for intelligent production in China's steel industry were introduced. This research not only helps to comprehend the development model, essential technologies, and construction techniques of intelligent manufacturing in China's steel industry, but it also provides vital inspiration for the manufacturing industry's digital and intelligence updates and quality improvement.
引用
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页数:20
相关论文
共 109 条
[1]   Research on Intelligent Manufacturing System Architecture and Key Technology of Radar Complete Machine Assembly [J].
Ben, Kecun .
PROCEEDINGS OF THE SEVENTH ASIA INTERNATIONAL SYMPOSIUM ON MECHATRONICS, VOL II, 2020, 589 :30-41
[2]   Building Construction Operation Simulation Based on BIM Technology and Intelligent Robots [J].
Cai, Hanying .
JOURNAL OF INTERCONNECTION NETWORKS, 2022, 22 (03)
[3]   The interplay of circular economy with industry 4.0 enabled smart city drivers of healthcare waste disposal [J].
Chauhan, Ankur ;
Jakhar, Suresh Kumar ;
Chauhan, Chetna .
JOURNAL OF CLEANER PRODUCTION, 2021, 279
[4]  
Chen X., 2020, P 2019 5 INT C ENERG, V461
[5]   Online Detection of Surface Defects Based on Improved YOLOV3 [J].
Chen, Xuechun ;
Lv, Jun ;
Fang, Yulun ;
Du, Shichang .
SENSORS, 2022, 22 (03)
[6]   Manufacturing upgrading in industry 4.0 era [J].
Chen, Yongdang ;
Han, Zhiyou ;
Cao, Kunyu ;
Zheng, Xianrong ;
Xu, Xiaobo .
SYSTEMS RESEARCH AND BEHAVIORAL SCIENCE, 2020, 37 (04) :766-771
[7]   Industry 4.0 applications for sustainable manufacturing: A systematic literature review and a roadmap to sustainable development [J].
Ching, Ng Tan ;
Ghobakhloo, Morteza ;
Iranmanesh, Mohammad ;
Maroufkhani, Parisa ;
Asadi, Shahla .
JOURNAL OF CLEANER PRODUCTION, 2022, 334
[8]   Research on Evaluation of Intelligent Manufacturing Capability and Layout Superiority of Supply Chains by Big Data Analysis [J].
Deng, Kaiwen .
JOURNAL OF GLOBAL INFORMATION MANAGEMENT, 2022, 30 (07)
[9]   Non-Destructive Testing Using Eddy Current Sensors for Defect Detection in Additively Manufactured Titanium and Stainless-Steel Parts [J].
Farag, Heba E. ;
Toyserkani, Ehsan ;
Khamesee, Mir Behrad .
SENSORS, 2022, 22 (14)
[10]   Research on the maturity of big data management capability of intelligent manufacturing enterprise [J].
Ge, Jing ;
Wang, Feng ;
Sun, Hongxia ;
Fu, Liuliu ;
Sun, Mingwei .
SYSTEMS RESEARCH AND BEHAVIORAL SCIENCE, 2020, 37 (04) :646-662