Research Progress on High-efficiency Intensive Production and Lean Quality Control for Wide Hot-rolled Steel Strip

doi:10.3901/JME.260173

Abstract

Abstract: Wide hot-rolled strip is a core raw material for modern manufacturing，and its efficient large-scale production combined with refined quality management is essential for industrial upgrading. However，the hot rolling process is characterized by strongly coupled multivariable factors，along with limited material information sensing，fragmented data systems，and insufficient automation in critical stages. These challenges hinder the coordinated optimization of product quality，rolling stability，and manufacturing cost. This research systematically reviews recent technological advances，with a focus on progress in the following key areas. In the field of multidimensional material information sensing for hot continuous rolling，robust sensing systems based on machine learning and deep learning have addressed the difficulty of feature extraction under high-temperature and high-noise conditions，substantially improving information acquisition in complex conditions. In the area of multi-zone operational interconnection and edge–end collaboration，the integration of heterogeneous data from multiple sources and the use of dynamic resource allocation mechanisms have eliminated information discontinuities among storage yards，rolling lines，and roll grinding systems，enabling coordinated optimization across regions. In centralized control of multiple rolling lines and regions in hot continuous rolling，intelligent rolling technologies oriented toward reduced manual intervention have been developed by leveraging advanced inspection systems and high-accuracy hybrid modeling，thereby enhancing the compactness and responsiveness of the production process. In cross-business coordination for hot continuous rolling，lean management platforms integrating online prediction，real-time monitoring，and anomaly diagnosis have been established within a deeply integrated cyber–physical framework，driving transformation in production management across multiple business domains. Finally，the paper summarizes current developments and outlines future directions for efficient large-scale production and refined quality management of wide hot-rolled strip.

Key words: hot-rolled steel strip, lean quality control, intensive production, multi-zone centralized control, multi-service coordination

CLC Number:

TG156

SHAO Jian, HE Anrui, YANG Quan. Research Progress on High-efficiency Intensive Production and Lean Quality Control for Wide Hot-rolled Steel Strip[J]. Journal of Mechanical Engineering, 2025, 62(6): 1-28.

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

URL: http://www.cjmenet.com.cn/EN/10.3901/JME.260173

http://www.cjmenet.com.cn/EN/Y2025/V62/I6/1

References

[1] 王国栋. 近年我国轧制技术的发展、现状和前景[J]. 轧钢，2017，34(1)：1-8. WANG Guodong. Development，current situation and prospect of Chinese steel rolling technology in recent years[J]. Steel Rolling，2017，34(1)：1-8.
[2] 康永林. “十三五”中国轧钢技术进步及展望[J]. 钢铁，2021，56(10)：1-15. KANG Yonglin. China steel rolling technology progress in the 13th five-year plan and prospection[J]. Iron and Steel，2021，56(10)：1-15.
[3] 何安瑞，刘超，邵健. 热轧带钢高效与智能轧制新技术研究进展及应用[J]. 轧钢，2024，41(05)：38-50. HE Anrui，LIU Chao，SHAO Jian. Research progress and application of new advanced and intelligent rolling technologies for hot rolled strip[J]. Steel Rolling，2024，41(5)：38-50.
[4] 邵健，何安瑞，董光德，等. 基于工业互联的钢铁全流程质量管控系统[J]. 冶金自动化，2020，44(1)：8-16，43. SHAO Jian，HE Anrui，DONG Guangde，et al. Whole process quality management and control system of iron and steel based on industrial interconnection[J]. Metallurgical Industry Automation，2020，44(1)：8-16，43.
[5] 邵健，何安瑞. 热轧工艺过程和质量管控平台的研发和应用[J]. 轧钢，2018，35(2)：6-11. SHAO Jian，HE Anrui. Development and application of technics process and quality management platform in hot rolling[J]. Steel rolling，2018，35(2)：6-11.
[6] 张殿华，彭文，孙杰，等. 板带轧制过程中的智能化关键技术[J]. 钢铁研究学报，2019，31(2)：174-179. ZHANG Dianhua，PENG Wen，SUN Jie，et al. Key intelligent technologies of steel strip rolling process[J]. Journal of Iron and Steel Research，2019，31(2)：174-179.
[7] 孙友昭，王晓晨，杨荃，等. 轧钢产线智能装备的研发与应用[J]. 轧钢，2024，41(4)：1-8. SUN Youzhao，WANG Xiaochen，YANG Quan，et al. Research andd application of intelligent equipment for rolling lines[J]. Steel Rolling，2024，41(4)：1-8.
[8] 邵健，何安瑞，陈雨来，等. 热轧智能工厂构架设计与实践：有形与无形的统一[J]. 中国冶金，2022，32(1)：1-10. SHAO Jian，HE Anrui，CHEN Yulai，et al. Framework design and practice of hot rolling intelligent plant：Unity of tangible and intangible[J]. China Metallurgy，2022，32(1)：1-10.
[9] 张健民，单旭沂. 热轧产线智能制造技术应用研究——宝钢1580热轧示范产线[J]. 中国机械工程，2020，31(2)：246-251. ZHANG Jianmin，SHAN Xuxi. Application of intelligent manufacturing technology in hot rolling production line[J]. China Mechanical Engineering，2020，31(2)：246-251.
[10] HUSSAIN T，HONG J，SEOK J. A hybrid deep learning and machine learning-based approach to classify defects in hot rolled steel strips for smart manufacturing[J]. Computer，Materials and Continua，2024，80(2)：2099-2119.
[11] HELIFA B，OULHADJ A，BENBELGHIT A，et al. Detection and measurement of surface cracks in ferromagnetic materials using eddy current testing[J]. NDT & E International，2006，39(5)：384-390.
[12] VASCOTTO M. High speed surface defect identification on steel strip[J]. MPT Metallurgical Plant and Technology International，1996，4：70-73.
[13] TANG Y G，ZHANG X M，LI X L，et al. Application of a new image segmentation method to detection of defects in castings[J]. International Journal of Advanced Manufacturing Technology，2009，43(5)：431-439.
[14] PRIEWALD R H，MAGELE C，LEDGER R D，et al. Fast magnetic flux leakage signal inversion for the reconstruction of arbitrary defect profiles in steel using finite elements[J]. IEEE Transactions on Magnetics，2013，49(1)：506-516.
[15] MORDIA R，VERMA A K. Visual techniques for defects detection in steel products：A comparative study[J]. Engineering Failure Analysis，2022，134：106047.
[16] MA J，TONG X，HOU Y，et al. Defects in metal-forming：Formation mechanism，prediction and avoidance[J]. International Journal of Machine Tools and Manufacture，2025，207：104268.
[17] 龚聪，徐杜. 光源强度变化对图像检测精度的影响及其解决方法[J]. 科学技术与工程，2014，14(13)：236-239. GONG Cong，XU Du. Impact and solution of light source intensity changes to image measuring precision[J]. Science Technology and Engineering，2014，14(13)：236-239.
[18] 徐科，徐金梧. 基于图象处理的冷轧带钢表面缺陷在线检测技术[J]. 钢铁，2002(12)：61-64. XU Ke，XU Jinwu. On-line inspection of surface defects of cold rolled strips based on image processing[J]. Iron and Steel，2002(12)：61-64.
[19] 徐科，宋敏，杨朝霖，等. 隐马尔可夫树模型在带钢表面缺陷在线检测中的应用[J]. 机械工程学报，2013，49(22)：34-40. XU Ke，SONG Min，YANG Chaolin，et al. Application of hidden markov tree model to on-line detection of surface defects for steel strips [J]. Journal of Mechanical Engineering，2013，49(22)：34-40.
[20] 李毅仁，李子正，邝霜，等. 高品质板带形-性-表综合控制技术的发展[J]. 轧钢，2025，42(5)：3-14，35. LI Yiren，LI Zizeng，KUANG Shuang，et al. Development of integrated control technology for shape-property- surface quality of high-quality strip[J]. Steel Rolling，2025，42(5)：3-14，35.
[21] CHOI K，KOO K，LEE J S. Development of defect classification algorithm for POSCO rolling strip surface inspection system[C]//IEEE International Joint Conference on SICE-ICASE，2006：2499-2502.
[22] RINN R，BECKER M，FOEHR F，et al. Steel mill defect detection and classification at 3000 ft/min using mainstream technology[C]//Proceedings of Real-Time Imaging III，1998，3303：20-26.
[23] CERACKI P，REIZIG H J，RUDOLPHI U，et al. On-line surface inspection of hot rolled strip[J]. MPT Metallurgical Plant and Technology International，2000，23(4)：66-68.
[24] 徐科，周鹏，杨朝霖. 基于光度立体学的金属板带表面微小缺陷在线检测方法[J]. 机械工程学报，2013，49(4)：25-29. XU Ke，ZHOU Peng，YANG Chaolin. On-line detection technique of tiny surface defects for metal plates and strip based on photometric stereo[J]. Journal of Mechanical Engineering，2013，49(4)：25-29.
[25] 徐科，杨朝霖，周鹏，等. 基于线型激光的连铸板坯表面裂纹在线检测技术[J].北京科技大学学报，2009，31(12)：1620-1624. XU Ke，YANG Chaolin，ZHOU Peng，et al. On-line detection technique of surface cracks for continuous casting slabs based on linear lasers[J]. Journal of University of Science and Technology Beijing，2009，31(12)：1620-1624.
[26] CALEB P，STEUER M. Classification of surface defects on hot rolled steel using adaptive learning methods[J]. IEEE Knowledge-Based Intelligent Engineering Systems and Allied Technologies，2000，1：103-108.
[27] HUANG Y，QIU X，WANG S，et al. A compact convolutional neural network for surface defect inspection[J]. Sensors，2020，20(7)：1-19.
[28] YANG L，HUANG X，REN Y，et al. Steel plate surface defect detection based on dataset enhancement and lightweight convolution neural network[J]. Machines，2022，10(7)：523-539.
[29] IBRAHIM A A M S，TAPAMO J R. Steel surface defect detection and classification using bag of visual words with BRISK[C]//Congress on Smart Computing Technologies. Singapore：Springer Nature Singapore，2022：235-246.
[30] 陈兆宇，荆丰伟，李杰，等. 基于改进降噪自编码器半监督学习模型的热轧带钢水梁印识别算法[J].工程科学学报，2022，44(8)：1338-1348. CHEN Zhaoyu，JING Fengwei，LI Jie，et al. Recognition algorithm of hot-rolled strip steel water beam mark based on a semi-supervised learning model of an improved denoising autoencoder[J]. Chinese Journal of Engineering，2022，44(8)：1338-1348.
[31] ZHAO W，CHEN F，HUANG H，et al. A new steel defect detection algorithm based on deep learning[J]. Computational Intelligence and Neuroscience，2021，1：5592878.
[32] HE Y，SONG K，MENG Q，et al. An end-to-end steel surface defect detection approach via fusing multiple hierarchical features[J]. IEEE Transactions on Instrumentation and Measurement，2019，69(4)：1493-1504.
[33] SU J，LUO Q，WANG Y，et al. FusionKD：Fusion knowledge distillation of vision-language foundation model for strip steel surface defect detection[J]. Information Fusion，2026，128：103940.
[34] TAKADA H，TOMURA Y，ARATANI M，et al. On-line detection system for internal flaws in as-hot-rolled steel strip using ultrasonic probe array[J]. Materials Transactions，2011，52(3)：531-538.
[35] 石桂芬，何永辉，张清. 漏磁法检测薄带钢内部缺陷的研究进展[J]. 世界钢铁，2013，13(4)：58-62. SHI Guifen，HE Yonghui，ZHANG Qing. Research progress on magnetic flux leakage method for internal flaw detection of thin steel strip[J]. World Steel，2013，13(4)：58-62.
[36] 郝柏桥，范玉刚，宋执环. 基于深度迁移学习的脉冲涡流热成像裂纹缺陷检测[J]. 光学学报，2023，43(4)：146-154. HAO Baiqiao，FAN Yugang，SONG Zhihuan. Deep transfer learning-based pulsed eddy current thermography for crack defect detection[J]. Acta Optica Sinica，2023，43(4)：146-154.
[37] MORI S，SUEN C Y，YAMAMTOT K. Historical review of OCR research and development[J]. Proceedings of the IEEE，1992，80(7)：1029-1058.
[38] RANI S，DAHIYA P K. A review of recognition technique used automatic liccense plate recognition system[J]. International Journal of Computer Applications，2015，121(17)：6-9.
[39] MOHAMAD M，NASIEN D，HASSAN H，et al. A review on feature extraction and feature selection for handwritten character recognition[J]. International Journal of Advanced Computer Science and Applications，2015，6(2)：204-212.
[40] 黄瀚敏，汪先矩，易正俊，等. 一种基于特征提取的手写字符识别技术[J]. 重庆大学学报(自然科学版)，2000(1)：66-69. HUANG Hanmin，WANG Xianju，YI Zhengjun，et al. A character recognition based on feature extraction[J]. Journal of Chongqing University (Natural Science Edition)，2000(1)：66-69.
[41] 王海涛，黄文杰，朱永凯，等. 基于聚类分析与神经网络的车牌字符识别[J]. 数据采集与处理，2008(2)：238-242. WANG Haitao，HUANG Wenjie，ZHU Yongkai，et al. License plate recognition based on clustering analysis and neural network[J]. Journal of Data Acquistion & Processing，2008(2)：238-242.
[42] SANG J L，WOOKYONG K，GYOGWON K，et al. Recognition of slab identification numbers using a fully convolutional network[J]. ISIJ International，2018，58(4)：696-703.
[43] GE J，LIU L，SUN J，et al. Automatic recognition of hot spray marking dot-matrix characters for steel-slab industry[J]. Journal of Intelligent Manufacturing，2023，34(3)：869-884.
[44] 谢俊. 复杂场景下的钢坯喷印编号定位及识别技术研究[D]. 长沙：湖南大学，2019. XIE Jun. Research on steel billet spray printing numbering，positioning，and recognition technology in complex scenarios[D]. Changsha：Hunan University，2019.
[45] 葛晓军. 钢厂智能标号系统重字符定位与识别技术研究[D]. 长沙：湖南大学，2020. GE Xiaojun. Research on heavy character positioning and recognition technology for intelligent marking system in steel plant [D]. Changsha：Hunan University，2020.
[46] 郑天意. 工业场景下钢卷标号识别方法研究[D]. 长沙：湖南大学，2022. ZHENG Tianyi. Research on steel coil label recognition method in industrial scenario[D]. Changsha：Hunan University，2022.
[47] 徐冬，代振洋，刘洋，等. 轧辊交叉对中间坯镰刀弯生成过程的影响[J]. 工程科学学报，2018，40(8)：954-960. XU Dong，DAI Zhenyang，LIU Yang，et al. Influence of crossed roller on generating camber in hot rough rolling[J]. Chinese Journal of Engineering，2018，40(8)：954-960.
[48] 陈江宁. 现代宽厚板特殊检测仪表的应用与展望[J]. 自动化仪表，2001，22(1)：1-5，8. CHEN Jiangning. The application and prospects of modern special detecting instrument for the heavy plate[J]. Process Automation Instrumentation，2001，22(1)：1-5，8.
[49] 李毅杰，王力飞，孙一康，等. 线阵CCD用于热轧带钢头部形状检测[J]. 北京科技大学学报，1994(3)：280-282，288. LI Yijie，WANG Lifei，SUN Yikang，et al. Detecting the shape of hot strip head area with linear CCD[J]. Journal of University of Science and Technology Beijing，1994(3)：280-282，288.
[50] 徐冬，杨荃，王晓晨，等. 基于机器视觉的热轧中间坯镰刀弯在线检测系统[J]. 中南大学学报(自然科学版)，2018，49(7)：1657-1666. XU Dong，YANG Quan，WANG Xiaochen，et al. Vision-based camber on-line measurement system in hot rough rolling[J]. Journal of Central South University (Science and Technology)，2018，49(7)：1657-1666.
[51] KAMPMEIJER L，HOL C，DE R J，et al. Strip tracking measurement and control in hot strip rolling[J]. MetallurgiaItaliana，2014，106(3)：29-34.
[52] MONTAGUE R J，WATTAN J，BROWN K J. A machine vision measurement of slab camber in hot strip rolling[J]. Journal of Materials Processing Technology，2005，168(1)：172-180.
[53] GE S，PENG Y，SUN J L，et al. Online visual detection system for head warping and lower buckling of hot-rolled rough slab[J]. Sensors，2025，25(6)：1662.
[54] 刘迎港. 热轧板坯头尾翘扣高度机器视觉检测与自动控制技术开发[D]. 秦皇岛：燕山大学，2023. LIU Yinggang. Development of machine vision detection and automatic control technology for head and tail buckling height of hot rolled slabs[D]. Qinhuangdao：Yanshan University，2023.
[55] 史志晖. 基于机器学习的热轧带钢的镰刀弯检测算法研究[D]. 包头：内蒙古科技大学，2022. SHI Zhihui. Research on the algorithm for detecting camber in hot rolled strip steel based on machine learning[D]. Baotou：Inner Mongolia University of Science and Technology，2022.
[56] HE W Z，SUN W，WANG X C，et al. Hot rolling finishing mill interstand strip image classification based on simple convolutional neural network[C]//2024 IEEE International Conference on Advanced Information，Mechanical Engineering，Robotics and automation，2024：75-79.
[57] 罗张. 基于GPU的并行带钢边缘检测系统的设计与实现[D]. 武汉：华中科技大学，2015. LUO Zhang. Design and implementation of a parallel strip steel edge detection system based on GPU[D]. Wuhan：Huazhong University of Science and Technology，2015.
[58] HAN F X，LIANG Y H，JING R L，et al. Real-time surface defect detection with compound scaling dynamic neural networks[C]//2024 IEEE International Conference on High Performance Computing and Communications，2024，737-744.
[59] ZHANG H K，MIAO Q Q，LI S Q，et al. An efficient and real-time steel surface defect detection method based on single-stage detection algorithm[J]. Multimedia Tools and Applications，2024，83：90595-90617.
[60] OKSUZ K，CAM B C，KALKAN S，et al. Imbalance problems in object detection：A review[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence，2021，43(10)：3388-3415.
[61] 窦智，高浩然，刘国奇，等. 轻量化YOLOv8的小样本钢板缺陷检测算法[J]. 计算机工程与应用，2024，60(9)：90-100. DOU Zhi，GAO Haoran，LIU Guoqi，et al. Small sample steel plate defect detection algorithm of lightweight YOLOv8[J]. Computer Engineering and Applications，2024，60(9)：90-100.
[62] 曾治霖，瞿昊，杜正春. 基于深度学习和生成对抗网络的发动机缸体表面缺陷检测方法[J]. 机械工程学报，2025，61(2)：46-55. ZENG Zhilin，QU Hao，DU Zhengchun. An engine cylinder surface defect detection algorithm based on the YOLOv5 network and Pix2pix model[J]. Journal of Mechanical Engineering，2025，61(2)：46-55.
[63] 李可，祁阳，宿磊，等. 基于改进ACGAN的钢表面缺陷视觉检测方法[J]. 机械工程学报，2022，58(24)：32-40. LI Ke，QI Yang，SU Lei，et al. Visual inspection of steel surface defects based on improved auxiliary classification generation adversarial network[J]. Journal of Mechanical Engineering，2022，58(24)：32-40.
[64] GOODFELLOW I，POUGET-ABADIE J，MIRZA M，et al. Generative adversarial networks[J]. Communications of the ACM，2020，63(11)：139-144.
[65] RADFORD A，METZ L，CHINTALA S，et al. Unsuppervised representation learning with deep convolutional generative adversarial networks[J]. 2016，1511：06434.
[66] ARJOVSKY M，CHINTALA S，BOTTOU L. Wasserstein generative adversarial networks[C]//Proceedings of the 34th International Conference on Machine Learning. Albion，NewYork，USA：MLR Press，2017：214-223.
[67] 师红宇，王嘉鑫，李怡. 基于改进ACGAN算法的带钢小样本数据增强方法[J]. 计算机集成制造系统，2025，31(1)：211-218. SHI Hongyu，WANG Jiaxin，LI Yi. Small sample data enhancement method for strip steel based on improved ACGAN algorithm[J]. Computer Integrated Manufacturing Systems，2025，31(1)：211-218.
[68] 陈海永，徐森，刘坤，等. 基于谱残差视觉显著性的带钢表面缺陷检测[J]. 光学精密工程，2016，24(10)：2572-2580. CHEN Haiyong，XU Sen，LIU Kun，et al. Surface defect detection of steel strip based on spectral residual visual saliency[J]. Optics and Precision Engineering，2016，24(10)：2572-2580.
[69] DAMACHARLA P，ACHUTH M，RINGENBERG J，et al. TLU-Net:a deep learning approach for automatic steel surface defect detection[C]//2021 International Conference on Applied Artificial Intelligence (ICAPAI)，2021：1-6.
[70] SONG G，SONG K，YAN Y. EDRNet：encoder-decoder residual network for salient object detection of strip steel surface defects[J]. IEEE Transactions on Instrumentation and Measurement，2020，69(12)：9709-9719.
[71] DONG H，SONG K，HE Y，et al. PGA-Net：pyramid feature fusion and global context attention network for automated surface defect detection[J]. IEEE Transactions on Industrial Informatics，2020，16(12)：7448-7458.
[72] 杨水山，何永辉，赵万生. Boosting优化决策树的带钢表面缺陷识别技术[J]. 红外与激光工程，2010，39(5)：954-958. YANG Shuishan，HE Yonghui，ZHAO Wansheng. Strip steel surface defect recognition based on Boosting optimized decision tree[J]. Infrared and Laser Engineering，2010：39(5)：954-958.
[73] 代小红，陈华江，朱超平. 一种基于改进Faster RCNN的金属材料工件表面缺陷检测与实现研究[J]. 表面技术，2020，49(10)：362-371. DAI Xiaohong，CHEN Huajiang，ZHU Chaoping. Surface defect detection and realization of metal workpiece based on improved faster RCNN[J]. Surface Technology，2020，49(10)：362-371.
[74] 崔金星，邓烁，彭艳，等. 工业数据驱动的轧机振动预测和工艺优化[J]. 振动、测试与诊断，2022，42(1)：110-116，198. CUI Jinxing，DENG Shuo，PENG Yan，et al. Rolling mill vibration prediction and process optimization driven by industrial data[J]. Journal of Vibration，Measurement& Diagnosis，2022，42(1)：110-116，198.
[75] BABAEE E，ANUAR N B，WAHAB A W，et al. An overview of audio event detection methods from feature extraction to classification[J]. Applied Artificial Intelligence，2017，31：9-10.
[76] 刘书超，王国栋，孙杰，等. 数据驱动的转炉智能吹炼控制系统的开发与应用[J]. 钢铁，2023，58(9)：92-103. LIU Shuchao，WANG Guodong，SUN Jie，et al. Development and application of digital-driven converter intelligent blowing control system[J]. Iron and Steel，2023，58(9)：92-103.
[77] REYES N，LATIFI S. Audio enhancement and synthesis using generative adversarial networks：A survey[J]. International Journal of Computer Application，2019，182(35)：27-31.
[78] GUL S，KHAN M. A survey of audio enhancement algorithms for music，speech，bioacoustics，biomedical，industrial，and environmental sounds by image U-Net[J]. IEEE Access，2023，11：144456-144483.
[79] 李长海，孙彦广. 基于信息物理深度融合的钢铁流程动态调度技术[J]. 中国冶金，2023，33(12)：90-96. LI Changhai，SUN Yanguang. Steel processes dynamic scheduling technology based on deep cyber physical fusion[J]. Chinese Metallurgy，2023，33(12)：90-96.
[80] 吴鸣. 罩式炉钢卷堆垛优化模型研究[J]. 工业炉，2015，37(5)：10-15. WU Ming. Research on optimization mathematics model of bell-type furnace for steel coil stacking[J]. Industrial Furnace，2015，37(5)：10-15.
[81] 王新东，倪振兴，刘福龙，等. 唐钢新区基于数字孪生技术的全流程智能化工厂设计与实践[J]. 冶金自动化，2023，47(1)：112-121. WANG Xindong，NI Zhenxing，LIU Fulong，et al. Design and practice of whole process intelligent plants based on digital twin technology in tangsteel new area[J]. Metallurgical Industry Automation，2023，47(1)：112-121.
[82] 李耀华，徐乐江，胡国奋，等. 基于混沌遗传算法的板坯入库决策优化方法[J]. 系统仿真学报，2005，17(11)：2620-2623. LI Yaohua，XU Lejiang，HU Guofen，et al. Optimization method of slab location decision model based on chaos gentic algorithm[J]. Journal of System Simulation，2005，17(11)：2620-2623.
[83] 时辰. 热轧板坯入库物流空间调度问题的建模与求解[D]. 沈阳：东北大学，2014. SHI Chen. Modeling and solution of logistics space scheduling problem for hot rolled plate warehousing[D]. Shenyang：Northeastern University，2014
[84] SINGH K，SRINIVAS A，TIWARI M. Modelling the slab stack shuffling problem in developing steel rolling schedules and its solution using improved parallel genetic algorithms[J]. International Journal of Production Economics，2004，91(2)：135-147.
[85] KIM K. Evaluation of the number of rehandles in container yards[J]. Computer & Industrial Engineering，1997，32(4)：701-711.
[86] 胡学雄. 冷热轧磨辊间磨床乳化液集中处理技术[J]. 轧钢，2019，36(6)：63-65. HU Xuexiong. Technology of emulsion centralized treatment of roll grinding shop at hot and cold rolling plant[J]. Steel Rolling，2019，36(6)：63-65.
[87] 祁明杰，王鹏冲，肖永基. 磨辊间系统数据故障的一种处理方法[J]. 冶金自动化，2024，48(S1)：396-398. QI Mingjie，WANG Pengchong，XIAO Yongji. A handling method for system data faults between grinding rollers[J]. Metallurgical Industry Automation，2024，48(S1)：396-398.
[88] 李立刚，孙文权，李贤春，等. 磨辊间智能管控一体化关键技术[J]. 冶金自动化，2019，43(6)：53-57. LI Ligang，SUN Wenquan，LI Xianchun，et al. Key technologies of intelligent management and integration of roll-shops[J]. Metallurgical Industry Automation，2019，43(6)：53-57.
[89] 孙文权，张喜榜，周文彬，等. 智能磨辊间关键技术的开发与应用[J]. 冶金自动化，2021，45(2)：9-15. SUN Wenquan，ZHANG Xibang，ZHOU Wenbin，et al. Developme nt and application of key technologies in intelligent roll shop[J]. Metallurgical Industry Automation，2021，45(2)：9-15.
[90] BOUSDEKIS A，LEPENIOTI K，NTALAPERAS D，et al. A RAMI 4.0 view of predictive maintenance：Software architecture，platform and case study in steel industry[J]. Advanced Information Systems Engineering Workshops，2019：95-106.
[91] Primetals Technologies. Through-process quality control (TPQC)：Latest developments，benefits，and successes[EB/OL].[2021-06-03]. https://www.primetals.com/en/portfolio/solutions/automation-and-digitalization/through-process-quality-control/.html.
[92] JFE Steel Corporation. System for detecting signs of equipment anomalies using data science Technology (J-dscom™) [EB/OL]. [2024-06-01]. https://www.jfe-steel.co.jp/en/products/solution/j-dscom/index.html.
[93] 丁敬国，金利，孙丽荣，等. 板带热轧过程智能化建模方法的研究现状与展望[J]. 冶金自动化，2022，46(6)：25-37. DING Jingguo，JIN Li，SUN Lirong，et al. Research status and prospect of intelligent modeling method for hot strip rolling process[J]. Metallurgical Industry Automation，2022，46(6)：25-37.
[94] 张殿华，孙杰，丁敬国，等. 基于CPS架构的板带热轧智能化控制[J]. 轧钢，2021，38(2)：1-9. ZHANG Dianhua，SUN Jie，DING Jingguo，et al. Intelligent control of hot strip rolling based on CPS architecture[J]. Steel Rolling，2021，38(2)：1-9.
[95] DING Chengyan，SUN Jie，LI Xiaojian，et al. Intelligent diagnosis for hot-rolled strip crown with unbalanced data using a hybrid multi-stage ensemble model[J]. Journal of Central South University，2024，31(3)：762-782.
[96] 丁婧伊，金嘉晖，杨丰赫，等. 基于云边协作的工业互联网排产方法：以钢铁热轧生产为例[J]. 电子学报，2024，52(9)：2988-2999. DING Qianyi，JIN Jiahui，YANG Fenghe，et al. Industrial internet scheduling method based on cloud-edge collaboration：A case study of steel hot rolling[J]. Acta Electronica Sinca，2024，52(9)：2988-2999.
[97] 李文成，陈文照，高燕. 钢铁企业MES系统中的数据存储技术及其应用[J]. 信息通信，2013(4)：179-180. LI Wencheng，CHEN Wenzhao，GAO Yan. Data storage technology and its application in the MES system of steel enterprises[J]. Infromation & Communications，2013(4)：179-180.
[98] 张伟，黄慈，张宇，等. 钢铁企业生产过程数据集成平台研究与开发[J]. 重庆理工大学学报(自然科学)，2020，34(8)：184-189. ZHANG Wei，HUANG Ci，ZHANG Yu，et al. Research and development of data Integration platform for iron and steel production process[J]. Journal of Chongqing University of Technology，2020，34(8)：184-189.
[99] 曹培，刘安平，李路. 基于工业互联网平台的数字钢卷设计与实现[J]. 冶金自动化，2022，46(S1)：15-18. CAO Pei，LIU Anping，LI Lu. Design and implementation of digital steel coils based on industrial internet platform[J]. Metallurgical Industry Automation，2022，46(S1)：15-18.
[100] 董广，郑英杰，张平，等. 鞍钢热轧厂数字化车间建设探索与实践[C]//中国金属学会.第十四届中国钢铁年会论文集—14.冶金自动化与智能化，2023：95-105. DONG Guang，ZHENG Yingjie，ZHANG Ping，et al. Exploration and practice on the construction of digital workshop in angang hot rolling mill[C]//Proceedings of the 14th China Iron and Steel Conference of the Chinese Society for Metals - 14. Metallurgical Automation and Intelligence，2023：95-105.