# Digital-Twin-Based Monitoring System for Slab Production Process
###### Abstract  
The growing demand for high-quality steel across various industries has led to an increasing need for superior-grade steel. The quality of slab ingots is a pivotal factor influencing the final quality of steel production. However, the current level of intelligence in the steelmaking industry's processes is relatively insufficient. Consequently, slab ingot quality inspection is characterized by high-temperature risks and imprecision. The positional accuracy of quality detection is inadequate, and the precise quantification of slab ingot production and quality remains challenging. This paper proposes a digital twin (DT)-based monitoring system for the slab ingot production process that integrates DT technology with slab ingot process detection. A neural network is introduced for defect identification to ensure precise defect localization and efficient recognition. Concurrently, environmental production factors are considered, leading to the introduction of a defect prediction module. The effectiveness of this system is validated through experimental verification. digital twin; defect recognition; process monitoring +
Footnote †: journal:  
0000-0001-659X/0000-0000-0000-0000-0000-0000-0000-0000-0000-00000-0000-00  
In addressing the quality control challenges in steel production, slab defects emerge as a significant factor influencing the ultimate product quality. Serving as the initial product in the transformation from liquid steel to solid steel, slabs bear a crucial responsibility for ensuring the quality of steel. However, existing methods for slab quality inspection present issues related to high-temperature risks and insufficient precision. Conducting quality inspections in high-temperature working environments not only poses potential hazards to operators but also constrains the accuracy of inspections. Furthermore, current quality inspection methods are unable to effectively pinpoint defects, complicating issue tracking and resolution. Most importantly, existing quality inspection methods fall short in achieving precise quantification of slab production yield and quality. This limitation hampers the transparency and controllability of the production process, impeding the maximization of production efficiency. Consequently, there is an urgent need for the adoption of innovative technologies and methods to enhance the safety, precision, and quantifiability of slab quality inspection while meeting the modern steel industry's demand for high-quality products. Digital twin (DT) technology has the capability of accurately reflecting the real-time status of physical entities [7]. By digitally modeling the slab production process, detailed information about the slab, including temperature, humidity, and quality, can be obtained. This real-time monitoring aids in the timely detection of potential issues, enhancing the overall responsiveness of the entire production system. DT technology offers a comprehensive visualization model, rendering the production process more transparent. Through digital twins, operators can actively observe the virtual model of the slab in real-time, gaining insights into its status and potential defects. This contributes to improving the controllability of the production process and reducing the occurrence of human errors. Furthermore, conducting simulated experiments on digital twin models allows for the evaluation of the impact of different parameters and strategies on the production process, thereby optimizing production efficiency and product quality. DT technology provides robust support for decision making in production. Through digital modeling of the slab production process, more in-depth analysis and prediction of production trends can be achieved, facilitating the formulation of scientifically sound decisions to enhance the overall efficiency of production.