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25 September 2025, Volume 49 Issue 5
    

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  • SONG Jianhai, YANG Hairong, WU Yiping, WANG Shiwei
    Metallurgical Industry Automation. 2025, 49(5): 1-10. https://doi.org/10.3969/j.issn.1000-7059.2025.05.20250265
    Abstract ( ) Download PDF ( )   Knowledge map   Save
    With the advancement of artificial intelligence (AI) technology and the increasing demand for deeper digital-physical integration, the traditional L1-L5 five-layer system in the steel industry can no longer meet the requirements of digital transformation. This paper begins by analyzing the connotation and characteristics of digital transformation in group-type steel enterprises.It elaborates on the digital system architecture of such enterprises driven by new-generation information technologies, as well as the three foundational pillars essential for digital transformation and smart factory construction: the industrial internet platform, big data center, and steel AI engine platform. Practical applications in smart governance, smart manufacturing, and smart services are illustrated through specific scenarios. Finally, using Baowu Steel Group, a typical group-type steel enterprise, as an example, the paper explains the evolution path of digital transformation—based on the integration of digital and realworld processes—in response to changes in steel manufacturing models and advancements in AI technology. This path progresses from specialized management at the individual enterprise level to crossdomain integrated operations, from AI-steel integration enabling fullprocess intelligence, and from singleentity management to ecosystem collaboration across the entire value chain. The digital transformation path and system architecture proposed in this paper provide significant reference value for promoting the digital transformation of group-type steel enterprises in China.

  • BAI Xiansong, YAN Xueyong, MA Jinhui, XIAO Xiong, SHAO Jian, ZHANG Xuejun, CHEN Dan
    Metallurgical Industry Automation. 2025, 49(5): 11-24. https://doi.org/10.3969/j.issn.1000-7059.2025.05.20250250
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    In response to the systemic challenges faced in the “multi-variety,small-batch” production model for special seamless steel pipes—including complex quality inspection dimensions,the absence of full-process single-unit traceability,heavy reliance on manual experience for production scheduling,and inadequate closed-loop quality control—this paper proposes and implements a “four-in-one” digital and intelligent factory solution encompassing “inspection-tracking-scheduling-control.” This solution establishes a quality inspection system based on deep learning and multi-modal visual fusion,achieving high-precision online measurement of pipe defects and dimensions. It pioneers a single-pipe tracking method that integrates “video AI+event logic”, overcoming the industry-wide challenge of accurately mapping data to individual pipes throughout the entire process. A dynamic scheduling optimization model,based on a hybrid genetic and simulated annealing algorithm,was developed,significantly enhancing scheduling efficiency and resource utilization in complex order environments. Furthermore,a collaborative quality management system based on the plan-do-check-act (PDCA) cycle was established,enabling systematic and proactive quality management. Application results demonstrate that the system achieves a defect detection rate of 99.95%,a material tracking accuracy of 9998%,an increase in scheduling efficiency of over 60%,and a 50% reduction in product non-conformance rates. This work provides a valuable and exemplary model for the digital transformation of China’s special steel industry.
  • QI Jianguo, DAI Xiande, XU Quan, SUN Minghua
    Metallurgical Industry Automation. 2025, 49(5): 25-36. https://doi.org/10.3969/j.issn.1000-7059.2025.05.20250230
    Abstract ( ) Download PDF ( )   Knowledge map   Save
    Against the backdrop of intensifying global market competition, fluctuations in energy and raw material prices, and the implementation of carbon tariff policies, Xingcheng Special Steel has addressed issues such as insufficient enterprise customization capabilities, a lack of systematic product quality evaluation, high energy consumption due to “black-box” operations in blast furnace ironmaking, poor production coordination in the steel rolling process, and the absence of systematic energy management. Leveraging digital technologies such as the industrial internet, artificial intelligence, big data, and simulation, and drawing on the concept of lighthouse factory promoted by the world Economic Forum, Xingcheng Special Steel has deployed over 40 use cases of the fourth industrial revolution. This has led to remarkable improvements, including a 35.3% increase in customized orders, a 47.3% reduction in nonconforming product rates, and a 10.5% decrease in energy consumption per ton of steel. Through typical use case practices such as manufacturing process customization design supported by big data analysis, transparency in blast furnace black-box operations through multimodal simulation, steel quality enhancement via intelligent closed-loop control, efficient steel rolling process enabled by AI, and optimization of energy, water, and carbon resources driven by advanced analytics, the company has achieved significant success in improving production efficiency, reducing costs, innovating product development, enhancing product quality, and boosting customer satisfaction. These efforts provide a valuable reference for the digital transformation of the steel industry.

  • JI Shumei, HUANG Jing, LU Shuhong
    Metallurgical Industry Automation. 2025, 49(5): 37-47. https://doi.org/10.3969/j.issn.1000-7059.2025.05.20250175
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    With the advancement of technology and economic development, the steel industry is facing new challenges and opportunities. Intelligent factories are a new type of industrial life form that deeply integrates digital technology and manufacturing industry. They have become the core force driving the upgrading of manufacturing industry during the critical period of industrial intelligence transformation and upgrading. This article takes Hebei Iron and Steel Group Shijiazhuang Iron and Steel Co., Ltd. (hereinafter referred to as “Shisteel”) as an example, focusing on the construction and operation of a life form intelligent factory, and systematically studying the transformation and upgrading path of special steel enterprises driven by the deep integration of digital technology and process innovation. Through the integration of industrial Internet, big data, artificial intelligence and other cutting-edge technologies, the cluster deployment of intelligent equipment and the implementation of intelligent manufacturing in the whole process, the intelligent high-quality development of production lines will be pushed to a new height, that giving life a strong physique and intelligent thinking. The fact shows that the construction of intelligent factories in the form of living organisms has achieved significant results in production efficiency, quality and cost control, and green development for Shigang, providing a new model for the high-quality development of the steel industry that can be referenced.

  • TANG Wei, LI Jiafu, GE Xiaobo, HU Qingmang, WANG Hong
    Metallurgical Industry Automation. 2025, 49(5): 48-56. https://doi.org/10.3969/j.issn.1000-7059.2025.05.20250170
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    In response to challenges such as overcapacity in the steel industry, the “dual carbon” goals, the upgrading of customer customized demands, and the technological transformation of Industry 4.0, Xichang Vanadium and Titanium Steel has initiated the construction of a smart factory for high-quality vanadium-titanium steel. The aim is to achieve cost reduction, efficiency improvement, flexible production, and sustainable development through digital transformation. Addressing the problems of data isolation horizontally and attenuation vertically in the traditional pyramid architecture of information systems, Xichang Vanadium and Titanium Steel has carried out top-level architecture design, adjusted business, technical, data, and application architectures, and explored the migration of existing applications to a cloud-edge collaborative industrial Internet platform to achieve deep integration of systems and data. Focusing on scenarios such as “digital design of processes, dynamic optimization of processes, intelligent online detection, online operation monitoring, intelligent warehousing, real-time monitoring and emergency response of safety risks, environmental pollution monitoring and control, and supply chain optimization”, data empowerment has been carried out. A domestic largest-scale integrated control center for the iron-making area of vanadium-titanium magnetite smelting has been built, and a three-in-one digital system for consistent quality management has been constructed. Centering on the customer, the entire supply chain data and collaborative optimization from production to sales, distribution, and transportation have been integrated, significantly enhancing product quality and enterprise competitiveness.

  • CAO Shuwei, XU Yi, CHEN Rongjun, ZHANG Liangbin
    Metallurgical Industry Automation. 2025, 49(5): 57-66. https://doi.org/10.3969/j.issn.1000-7059.2025.05.20250241
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    Aiming at the problems of low production efficiency and poor information collaboration in the traditional steel rolling industry, this study carries out the construction practice of a digital rolling plant based on the industrial Internet platform. Through a series of measures, including constructing a centralized control center, upgrading digital production lines, promoting full-process digitalization of production operations, realizing intelligent decision-making for production control, establishing a visual performance dialogue mechanism, strengthening precise energy consumption control for processes, implementing digital management of the entire equipment life cycle, and improving the digital monitoring system for safety and environmental protection, the factory’s intelligent system is comprehensively improved. During the practice, the data barriers in all production links are effectively broken, and the deep sharing and integration of full-process data flow and information flow are achieved. This successfully promotes the transformation of the enterprise from the traditional heavy industry model to a new innovative development model with significantly improved automation, informatization, and scientific management levels. Specifically, product energy consumption is reduced by 12.7%, the yield rate and qualification rate are increased by 0.1% and 0.7% respectively, and production efficiency is improved by 18%. This study provides practical experience and theoretical basis for the digital upgrading of the steel rolling industry.

  • ZHANG Zhijie, WANG Xiaochen, DENG Zibo, LONG Jintao, ZHANG Yi
    Metallurgical Industry Automation. 2025, 49(5): 67-75. https://doi.org/10.3969/j.issn.1000-7059.2025.05.20250134
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    This paper focuses on the intelligent flexible production smart factory,exploring the practical paths and methods of building wire production line transformation towards intelligence and digitalization. By analyzing the production characteristics of the wire industry and the pain points in intelligent and digital transformation,this study integrates advanced technologies such as 5G,digital twins, and industrial internet to construct an architecture and implementation plan for a flexible production smart factory based on the industrial internet.It summarizes innovative models of smart factories in flexible production, efficiency improvement, and quality optimization,providing replicable experience for peers in the industry. The research shows that building a smart factory can significantly enhance the flexibility and intelligence of wire production,achieving multiple goals including quality improvement,efficiency optimization, and cost reduction.The value of this research lies in its implementation from research to production practice, all elements involved in the intelligent factory of excellence, such as intelligent decision-making digital twin, human-machine collaboration, production and operation management, supply chain coordination, R&D and design functions are observable and available, and equipment intelligence, industrial internet technology, management, etc. represent the latest achievements in the industry, and as one of the 19 steel enterprises selected as the first batch of 235 national pilot enterprises, is a model factory for the industry to learn from.

  • YU Zhigang, SUN Yanguang, LI Guoqiang, ZHANG Jianxiong, GAO Shuang
    Metallurgical Industry Automation. 2025, 49(5): 76-86. https://doi.org/10.3969/j.issn.1000-7059.2025.05.20250260
    Abstract ( ) Download PDF ( )   Knowledge map   Save
    In modern industrial production, precise and efficient steel grade matching plays a pivotal role in material selection, scheduling, and process optimization. However, confronted with the escalating demands for complex multi-dimensional, fuzzy matching, similarity ranking, and weighted queries, traditional relational database-based query methods exhibit inherent limitations, resulting in low efficiency and insufficient flexibility. To address these challenges, this paper proposes an innovative framework for steel grade matching based on vector embedding and high-performance vector databases. The method first standardizes diverse and heterogeneous attributes of steel grades, such as chemical composition, mechanical properties, and heat treatment status, transforming them into unified-dimensional vector representations (embeddings). Subsequently, these vectors are stored in a high-performance vector database employing a hierarchical navigable small world (HNSW) graph index, enabling rapid and accurate steel grade querying and matching through Approximate Nearest Neighbor (ANN) search techniques. This paper elaborates on how various practical matching scenarios—including target value matching, range matching, multi-dimensional weighted matching, and similarity-based lookup from existing steel grades—are unified and implemented as retrieval problems within the vector space. Experimental results clearly demonstrate that when handling steel grade data at the scale of hundreds of thousands, the proposed vector-based method achieves millisecond-level query responses, showcasing significant efficiency gains and a breakthrough in capabilities for fuzzy and complex query scenarios. This research significantly enhances the efficiency, accuracy, and scenario compatibility of steel grade matching, offering a novel technical paradigm and implementation path for data-driven intelligent material management in industrial applications.

  • SUN Ruyu , QI Zheng, ZHANG Yungui, XU Haotian, LIN Qiuyin
    Metallurgical Industry Automation. 2025, 49(5): 87-96. https://doi.org/10.3969/j.issn.1000-7059.2025.05.20250198
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    The intelligent upgrading of metallurgical industry standard interpretation faces multi-dimensional technical challenges, primarily characterized by the multi-version iterative nature of standard texts and strong semantic coupling between provisions. To address the limitations of retrieval-augmented generation (RAG) technology in processing standards, specifically knowledge fragmentation and weakened semantic relevance, this study proposes a hierarchical semantic recursive RAG. Firstly, a multi-level semantic parsing model based on document structural features was established, which achieving hierarchical decomposition and structured semantic representation of standard texts. Secondly, a recursive semantic aggregation algorithm was designed to enhance contextual semantic coherence through bottom-up multi-granularity summarization technology. Finally, a dynamic correlation discovery module was developed, leveraging large language models’ named entity recognition and semantic reasoning capabilities for deep exploration of cross-level constraint relationships. Experimental evaluations demonstrate that compared with baseline RAG methods, HSR-RAG achieves a 14.46% improvement in average F1-score on BERTScore metrics for QA results in professional test sets. In multi-hop QA scenarios, the framework reaches an average ROUGE-L score of 43.69%. This research provides a novel technological pathway for intelligent interpretation of metallurgical industry standards, offering significant practical value for promoting digital transformation in the sector.

  • HUANG Yiyu, KUANG Shilong, XIA Shiqian, WANG Shuofeng
    Metallurgical Industry Automation. 2025, 49(5): 97-108. https://doi.org/10.3969/j.issn.1000-7059.2025.05.20250256
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    Implementing a full-process material tracking system for high-speed wire rod (HSWR) production can significantly enhance an enterprise’s control over the production process and product quality, facilitating its transition towards specialty steel manufacturing. Current material tracking in continuous casting, reheating, rolling, and finishing areas faces challenges including reliance on manual input, difficulty in achieving individual coil and per-billet tracking throughout the entire process, and insufficient accuracy and stability. The less than 100% accuracy of machine vision technologies used for identifying steel heat numbers and C-hook numbers contributes to the instability of HSWR material tracking in industrial practice. This paper proposes methods to improve environmental perception accuracy, such as constructing state machines, utilizing multi-system data comparison, and adding verification bits, thereby enhancing the stability of material tracking. For instance, the verification bit method applied to C-hook identification improved system stability by a factor of over 600. By establishing and linking the correspondences between “steel heat number→cast billet number→wire rod material number→C-hook number”, the system moves beyond single-process tracking to achieve a stable and reliable full-process material tracking solution. Practical applications confirm the effectiveness of the proposed methods and system, which effectively support quality traceability and cross-process coordination.
  • LI Xiaogang, WANG Yanwei, LI Yanan
    Metallurgical Industry Automation. 2025, 49(5): 109-116. https://doi.org/10.3969/j.issn.1000-7059.2025.05.20250218
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    In order to ensure the manufacturing quality and yield of high-end products, a data-driven quality insight analysis system based on HBIS digital industrial internet platform was constructed. The system integrates machine learning algorithms and image processing technology through multi-source data collection and intelligent processing, achieving quality data tracking of the entire iron production process. The system has established a linkage mechanism of online monitoring-intelligent diagnosis-closed-loop control, which optimizes process parameters, traces defects, and predicts abnormal working conditions for key processes such as steelmaking, hot rolling, and cold rolling, forming a digital control loop covering quality index prediction and process diagnosis. Since the application of the system, through the analysis of quality data throughout the entire process, the product qualification rate has been improved, the narrow window control level of indicators has been enhanced, and the quality assurance needs of high-end manufacturing have been effectively supported.
  • QU Tai’an, LIU Changpeng, LIU Yang, ZHU Jiahui, LIANG Yue
    Metallurgical Industry Automation. 2025, 49(5): 117-129. https://doi.org/10.3969/j.issn.1000-7059.2025.05.20250180
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    In the iron and steel industry, traditional PID control of blast furnace hot stoves faces challenges such as nonlinear hysteresis and poor multi-condition adaptability, which is difficult to meet the dual requirements of energy efficiency optimization and environmental compliance. A fusion control method based on AI agents was proposed, and a three-in-one intelligent control system of “prediction-knowledge-optimization” was constructed. Through the double-layer coupling of the LSTM algorithm and the knowledge embedding technology, the mean absolute error (MAE) of the dome temperature prediction is achieved at 5.3 ℃, which is 58% higher than that of the traditional ARIMA model. By establishing a four-dimensional knowledge representation system and combining it with the Rete rule engine, a response of 9.8 s for abnormal working conditions is achieved. By developing the DeepSeek lightweight decision-making engine, multi-objective optimization was achieved with 3.2 iterations of convergence, reducing the unit consumption of hot air per ton of iron by 14.8% and achieving a quarterly energy-saving benefit of 9 million yuan. Industrial validation demonstrates that this system significantly enhances thermal efficiency and environmental performance in 3200 m3blast furnace applications, providing theoretical and engineering references for the intelligent upgrading of industrial furnaces.
  • WANG Shulei, XU Yang, SHEN Zhiyue, ZHANG Xiaohua, FAN Xiaoshuai
    Metallurgical Industry Automation. 2025, 49(5): 130-138. https://doi.org/10.3969/j.issn.1000-7059.2025.05.20250258
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    In order to realize the rapid construction of the knowledge graph for the convertible steelmaking process scene while balancing its accuracy and practicality, this paper proposes a knowledge graph construction mode that integrates top-down and bottom-up approaches. On the one hand, guided by the principle of event evolution and under the direction of expert experience, this method completes the definition and classification of ontology classes and constructs the schema layer of the knowledge graph for converter steelmaking process scenarios using a top-down approach. Concurrently, it leverages a knowledge extraction tool based on LLM Graph Transformer to achieve automatic extraction of entities, relations, and attributes, thereby constructing the data layer of the knowledge graph for converter steelmaking process scenarios through a bottom-up approach. By integrating the schema layer and data layer, this method efficiently constructs the knowledge graph for converter steelmaking process scenarios.
  • ZHANG Jiaxu, ZHANG Yungui, QI Zheng
    Metallurgical Industry Automation. 2025, 49(5): 139-147. https://doi.org/10.3969/j.issn.1000-7059.2025.05.20250199
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    In steel production, full-process billet tracking is critical for ensuring production efficiency and product quality. Traditional technologies rely on multi-source sensors, which suffer from issues such as information errors and delays. Existing visual cross-domain algorithms are difficult to apply due to the limitations of steel plant scenarios. To address these issues, a cross-domain tracking algorithm based on Agent trajectory information association was proposed. Leveraging the fixed transportation paths and well-defined process nodes of billets, the algorithm integrates visual perception data with production rules through the collaboration of overhead crane and roller table Agent at network nodes, achieving precise cross-camera trajectory association without overlapping views or significant appearance features. Experimental results show that the algorithm achieves multi-object tracking accuracy (MOTA) of 95.8% and 82.1% in roller table and overhead crane cross-domain scenarios, respectively, outperforming comparative algorithms. This research breaks through the limitations of traditional tracking technologies and existing vision algorithms, providing an effective solution for intelligent tracking in metallurgical production. Future work will focus on enhancing performance through multi-modal data fusion and model optimization.

  • XIN Yan, WANG Fengqin, WANG Ce, WANG Jiaqing, LI Feng, WANG Baodong
    Metallurgical Industry Automation. 2025, 49(5): 148-154. https://doi.org/10.3969/j.issn.1000-7059.2025.05.20250213
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    Incomplete cuts during the continuous casting slab cutting process can seriously affect production stability. The phenomenon of sparks reflection was regarded as an indicator of such incomplete cuts. However, relying on manual visual monitoring leads to high labor intensity and risks of missed detections. This paper proposes and implements a real-time automatic detection method for sparks reflection during slab cutting, based on the EfficientDet object detection algorithm. Experimental results show that the trained EfficientDet-D2 model achieves a precision of 97.87%, Recall of 92.00%, and a mean average precision (mAP) of approximately 97.76%. During a two-month on-site deployment, the method successfully detected sparks reflection events, significantly enhancing the level of automation in continuous casting process monitoring.
  • ZHANG Shunhu, GE Mingkun, WAN Liangwei, CHEN Weijian, LUO Xianlong
    Metallurgical Industry Automation. 2025, 49(5): 155-163. https://doi.org/10.3969/j.issn.1000-7059.2025.05.20250193
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    To address the insufficient accuracy of traditional rolling force models, this paper proposes a method that integrates rolling mechanism analysis with industrial measurement. Based on the geometric characteristics of the rolling deformation zone, an Elliptical Velocity Field (EVF) is established, and the corresponding mechanistic model of rolling force is derived. To overcome the inherent prediction bias of the mechanistic model, the exponential smoothing method is incorporated. Furthermore, a trend adjustment factor is introduced to dynamically compensate for errors, thereby enhancing the adaptive correction capability. This process ultimately yields a high-precision Double Parameters Exponential Smoothing Prediction (DPESP) model. Verification using Q345 steel production data shows that the DPESP model achieves an average error of only 2.83%, which signifies a significant improvement in prediction accuracy. The research results can provide scientific guidance for constructing high-precision rolling force models and optimizing the rolling procedures for hot-rolled strips.
  • LI Jingdong, WANG Xiaochen, WANG Xiangchen, YANG Quan, SUN Youzhao, WU Zedong, XIE Tianyi
    Metallurgical Industry Automation. 2025, 49(5): 164-175. https://doi.org/10.3969/j.issn.1000-7059.2025.05.20250244
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    Against the backdrop of advanced and intelligent manufacturing, the product structure of China’s cold-rolled strip industry is continuously upgrading, shifting from homogeneous competition dominated by general-purpose sheets toward a high-end pattern centered on advanced high-strength steels and premium coated sheets with higher added value. However, the control systems and their associated models, as the core support of cold rolling production, have long relied on imported technologies, leading to repeated introduction and poor adaptability, which makes it difficult to meet the requirements of high precision, high stability, and fast response under complex operating conditions. To address these challenges, a cross-process collaborative digital modeling and dynamic pre-control technology framework for cold-rolling quality was established. Relying on a cross-process metallurgical industrial internet platform, the framework enables data integration between cold rolling and its upstream and downstream processes, and introduces fusion and governance methods for multi-source heterogeneous data, thereby consolidating the data foundation for quality modeling and control optimization. Building on this foundation, hot rolling information was further utilized to develop head-tail trimming optimization and side scrap blockage pre-control strategies, enhancing feed-forward identification and dynamic intervention capabilities. Meanwhile, by combining hot rolling and cold rolling process parameters, an intelligent high-precision setup model for the cold-rolling process was developed to improve the accuracy and adaptability of rolling parameter setting. Finally, by integrating data-driven and mechanism-based modeling approaches, a quality prediction and multi-objective collaborative optimization strategy was proposed, enabling dynamic process regulation and continuous quality improvement under complex conditions. The above research provides systematic technical support for building an intelligent cold rolling quality control system with cross process perception and dynamic regulation capabilities.
  • XIAO Fan, ZHANG Xinjian, LIU Fen, XUE Renjie, ZENG Guanghui, TAN Qilian
    Metallurgical Industry Automation. 2025, 49(5): 176-182. https://doi.org/10.3969/j.issn.1000-7059.2025.05.20250253
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    In the strip steel grinding operation of iron and steel enterprises, although the use of robots to replace manual labor can significantly improve grinding efficiency, stabilize grinding quality, and reduce missed grinding areas, the adaptability between robots and moving strip steel still needs to be improved, and the core issue is the path planning of robots for grinding moving strip steel. Aiming at this key technical problem in practical scenarios, this paper first analyzes the relative motion law between the robot and the strip steel through a graphical method, and accordingly determines that the robot end adopts a grinding path of reverse grinding-oblique connection. On this basis, the relationship between the robot’s motion parameters and the width, length of the ground area of the strip steel, and the strip steel speed is established, and a corresponding algorithm is proposed based on this relationship. To verify the effectiveness of the designed grinding path, a simulation experiment was carried out in Robotstudio. The results show that the path can ensure no missed grinding areas during the strip steel grinding process, verifying its feasibility and reliability.