Search Results (4)

A ladle tracking system in steel production plants is essential for optimizing the ladle transportation between different processing units. The currently used technologies for ladle tracking, including Radio Frequency Identification (RFID) systems, are not effective due to their high maintenance costs and poor performance in harsh conditions, leaving a significant gap in developing an automated ladle tracking system. This paper proposes two innovative solutions to address these problems: a computer-vision-based ladle tracking system and an integrated approach of preprocessing techniques with optical character recognition (OCR) algorithms. The first method utilizes a YOLOv8 framework for detecting the two classes from the input images, such as the ladles and their unique numbers. This method achieved a precision of 0.983 and a recall of 0.998 in detecting the classes. The second method involves several preprocessing steps prior to the application of OCR. This is suitable for challenging environments, where the clarity of the images may be compromised. EasyOCR with enhanced preprocessing was able to extract the ladle number with a confidence score of 0.9948. The results demonstrate that vision-based automated ladle tracking is feasible in steel plants, improving operational efficiency, ensuring safety, and minimizing human intervention. Full article

This manuscript presents a model of a system implementing individual stages of production for long steel products resulting from rolling. The system encompasses the order registration stage, followed by production planning based on information about the billet inventory status, then offers the possibility of scheduling orders for the melt shop in the form of melt sequences, manages technological knowledge regarding the principles of sequencing, and utilizes machine learning and optimization methods in melt sequencing. Subsequently, production according to the implemented plan is monitored using IoT and vision tracking systems for ladle tracking. During monitoring, predictions of energy demand and energy consumption in LMS processes are made concurrently, as well as predictions of metal overheating at the CST station. The system includes production optimization at two levels: optimization of the heat sequence and at the production level through the prediction of heating time. Optimization models and machine learning tools, including mainly neural networks, are utilized. The system described includes key components: optimization models for sequencing heats using Ant Colony Optimization (ACO) algorithms and neural network-based prediction models for power-on time. The manuscript mainly focuses on process modeling issues rather than implementation or deployment details. Machine learning models have significantly improved process efficiency and quality; the optimization of planning has reduced sequencing plan execution time; and power-on time prediction models estimate the main ladle heating time with 97% precision, enabling precise production control and reducing overheating. The system serves as an example of implementing the concept of a cyber–physical system. Full article

Ladle metallurgy serves as a crucial component of the steelmaking industry, where it plays a pivotal role in manipulating the molten steel to exercise precise control over its composition and properties. Turbulence in ladle metallurgy influences various important aspects of the steelmaking process, including mixing and distribution of additives, alongside the transport and removal of inclusions within the ladle. Consequently, gaining a clear understanding of the stirred flow field holds the potential of optimizing ladle design, improving control strategies, and enhancing the overall efficiency and steel quality. In this project, an advanced Particle-Tracking-Velocimetry system known as “Shake-the-Box” is implemented on a cylindrical water ladle model while compressed air injections through two circular plugs positioned at the bottom of the model are employed to actively stir the flow. To mitigate the particle images distortion caused by the cylindrical plexi-glass walls, the method of refractive matching is utilized with an outer polygon tank filled with a sodium iodide solution. The volumetric flow measurement is achieved on a 6 × 6 × 2 cm domain between the two plugs inside the cylindrical container while the flow rate of gas injection is set from 0.1 to 0.4 L per minute. The volumetric flow field result suggests double gas injection at low flow rate (0.1 L per minute) produce the least disturbed flow while highly disturbed and turbulent flow can be created at higher flow rate of gas injection. Full article

In this paper, we study an advanced pouring control system using a tilting-ladle-type automatic pouring machine. In such a machine, it is difficult to precisely pour the molten metal into the pouring basin of the mold, as the outflow from the ladle can be indirectly controlled by controlling its tilt. Therefore, model-based pouring control systems have been developed as a part of conventional studies to solve this problem. In the results of a recent study, the efficacy of a pouring flow rate control system based on differential flatness has been verified, by performing a simulation. In this study, we apply the flow rate control system based on differential flatness to a tilting-ladle-type automatic pouring machine, using experiments to verify the efficacy of the flow rate control system in suppressing any disturbances. In these experiments, the tracking performance using the developed flow rate control system was better than the performance obtained using a conventional feed-forward-type flow rate control system. Full article