# Digital-Twin-Based Monitoring System for Slab Production Process
## 4 Experiment and Verification
### Verification Process
#### 4.2.2 Monitoring System Verification  
The smelting plant utilizing a DT-based slab production process monitoring system is a large-scale steel production facility with an annual output of 3.42 million tons of crude steel. The validation process for this monitoring system spanned nearly a year and involved comprehensive testing through actual steel production to ensure its stability and reliability under various environmental conditions. Throughout the validation period, we utilized the monitoring system to record a substantial amount of data, including critical parameters, such as temperature, humidity, and slab quality. Typical production temperatures hover around 300 \({}^{\circ}\)C, while humidity is maintained at approximately 60%. The daily volume of generated data amounts to approximately 20 MB. Through the collection and analysis of these data, the system can optimize parameters using a wealth of information, thereby ensuring the production quality of slabs. This provides the smelting plant with a more intelligent and sustainable production solution, effectively enhancing production efficiency and product quality. Monitoring the slab production process supports users in obtaining a clear understanding of production data and relevant environmental information. Detailed data not only enable a comprehensive grasp of the production status but also allow for improvements in the production environment data through the embedded system, thereby reducing the probability of defects and enhancing production efficiency. The DT-based slab production process monitoring system is divided into real-time monitoring and defect database modules, outlined as follows:  
1. Real-time monitoring module: As depicted in Figure 7, the interface displays real-time information related to slab production, including slab models, defect severity, weight, temperature, humidity, working duration, daily output, and the probability of defect occurrence along with adjustment recommendations. The severity of defects and the likelihood of their occurrence are represented by red, orange, and blue colors. Red indicates high severity, orange indicates moderate severity, and blue indicates normal conditions. 2. Defect database module: Illustrated in Figure 8, this module encompasses all historical data on slab defects. Personnel can query defect information, such as the date of occurrence, defect type, temperature, humidity, and weight, through this module. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Models** & **Time (ms)** & **Rec (\%)** & **AP** & **AP\({}_{50}\)** & **AP\({}_{75}\)** & **Accuracy** \\ \hline CBAM & 26.3 & 94.241 & 41.1 & 61.7 & 48.1 & 76.82 \\ \hline EW-YOLOv7 & 26.8 & 93.415 & 41.4 & 59.2 & 45.3 & 71.61 \\ \hline DS-Cascade RCNN & 29.1 & 97.682 & 43.2 & 64.8 & 47.1 & 84.67 \\ \hline Cascade R-CNN & 26.4 & 87.132 & 36.7 & 54.9 & 43.5 & 52.34 \\ \hline SSD & 27.4 & 88.454 & 38.9 & 57.3 & 44.1 & 70.64 \\ \hline Ours & 23.3 & 98.435 & 43.9 & 62.3 & 47.8 & 88.42 \\ \hline \hline \end{tabular} Gray represents best performance.  
\end{table} 
Table 1: Test metric scores of the different methods for each test set. Figure 6: Effective identification of slab defects. Through experiments, the DT-based slab production process monitoring system achieved real-time synchronization and simulation with the actual production process. In the production process monitoring system, users can achieve a full-angle observation of the slab by dragging and rotating, enabling a comprehensive understanding of defect information. When defects are present, the interface marks them in red or orange based on severity. Red indicates a more severe defect, while orange indicates a less severe one. Users can perform subsequent slab processing, such as repair, re-smelting, or discard operations, according to the color indications. In the upper right corner, the system provides a probability prediction of defect existence. If the probability exceeds 70%, it is marked in red; if it exceeds 50%, it is marked in orange.