Coil Shuttle Car Assistant

The Coil Shuttle Car Assistant is a software developed to automatically detect and track coils in coil shuttle cars. It aids operators, quality managers, logistics managers, maintenance managers, and production managers in rolling mills, processing lines, coil yards, and warehouses. The Coil Shuttle Car Assistant enhances productivity, ensures precise material tracking, and maintains quality and production standards. This is accomplished by automating coil detection, improving quality and production assurance, and monitoring coil car movements for enhanced security.

The Coil Shuttle Car Assistant streamlines tracking moving coil cars for security purposes; evaluates loading status and car ID; and ensures material transport aligns with planned positions. It utilizes automated detection of moving coil cars, assesses loading status and car ID, and employs OCR to identify text on coils.

The coil car identification feature of the Coil Shuttle Car Assistant is a sophisticated solution designed to streamline operations in steel and aluminum plants. It leverages advanced technologies to ensure precise identification and tracking of coil cars, significantly enhancing productivity, security, and quality assurance. Utilizing high-resolution cameras, the system detects coil cars in motion and captures detailed images for accurate identification. It automatically assesses whether a coil car is loaded or empty, and if the coil sits correctly in the middle of the coil car, providing real-time data to operators. The feature also reads and records car IDs when visible, ensuring accurate tracking and inventory management.

The system employs optical character recognition (OCR) technology to read written text on coils, facilitating precise identification and tracking. This reduces manual input errors by automatically capturing and logging coil IDs into the system. Detection results are visualized on existing or additional monitors, providing operators with immediate feedback. All results are saved with timestamps, enabling detailed historical analysis and reporting. Detected coil cars are highlighted with signal-colored frames, clearly indicating their status. The system displays messages with detection confidence levels, enhancing decision-making accuracy, and shows text such as "car with coil” or "car without coil” based on detection results.

The feature confirms that material transport corresponds to planned positions, preventing errors and unauthorized movements. It informs operators about material positions and discrepancies, allowing for immediate corrective actions. An intuitive dashboard provides a clear and concise interface for operators, quality managers, logistics managers, maintenance managers, and production managers. Users can set specific alerts and notifications based on operational requirements and preferences. The system can be integrated into existing systems or deployed as a standalone solution, offering versatility to various plant setups. Its scalable architecture is designed to accommodate future expansions and upgrades, ensuring long-term usability and adaptability.

The coil car identification feature of the Coil Shuttle Car Assistant transforms how steel and aluminum plants manage coil transportation. By automating detection and tracking processes, it boosts productivity and enhances security and quality assurance, making it an essential component for modern industrial operations.

Benefits of the Coil Shuttle Car Assistant

• Increased productivity: automates coil detection, reducing manual monitoring and enhancing operational efficiency
• Quality and production assurance: ensures material transport corresponds to planned positions, maintaining high standards
• Improved security: monitors coil car movements to prevent unauthorized or incorrect material handling
• Accurate material tracking: utilizes OCR for precise identification of written text on coils, aiding in logistics and inventory management
• Enhanced visualization: provides real-time visual feedback and detailed analysis through annotated video streams