Background
One of Taiwan’s largest steel corporation. The main products include plates, bars, electrical steel coils, and hot-dip galvanized coils. The company ships out steel coils that weigh tens of tons each. These 'heavy' loads are transported, processed, and shipped by the heavy-duty lifting device called the 'EOT Crane.' By moving horizontally and vertically in the factory area, it can lift and transport steel coils to designated positions, managing the schedule and transporting thousands of tons of steel coils in and out of the factory and warehouse.
Challenge
Operating the EOT crane used to require manual labor. Operators would enter the driver's seat, hanging from the ceiling, to control the lifting and transport of steel coils. The environment was high-temperature and risky. Additionally, human workers had limitations in terms of working hours, needing at least four hours of rest.
Once the EOT crane malfunctions, it can lead to a complete halt in the entire production line, resulting in losses of up to millions of dollars. Manual inspections required regular maintenance. The onsite environment was extremely harsh. Frequent on-site visits for measurements posed safety risks.
Solution
Machine Vision Analyzer: NISE 3910E
To overcome these challenges, the steel company developed an automated EOT crane system using unmanned operation. The system consists of PoE cameras, a 3D scanner, an industrial computer for machine vision (AI/ML algorithms) and communication, a PLC, and an HMI.
The NISE 3910E, installed in EOT cranes, offers robust protection against a range of environmental challenges. Notably, it is equipped to handle wide temperature variations, shocks, and vibrations, making it particularly suitable for the demanding and critical operating environment of a steel manufacturer's warehouse. With a COM port isolation design, NISE 3910E enhances system reliability by preventing surges in harsh environments.
To create an unmanned EOT crane, the cranes needed machine vision. The steel company aimed to convert the visual information operators see into logical control commands through an IPC, which would then be interpreted and executed by the crane itself. NISE 3910E, powered by 12th/13th Gen Intel® Core™ i processors, integrates high-resolution cameras and 3D scanners via PoE LAN ports as image inputs for machine vision. It maximizes graphic processing performance through GPU cards on PCIe slots to effectively meet industrial AI requirements, from image processing/optimization to machine/deep learning and machine vision/object recognition.
Additionally, NEXCOM offers a comprehensive software service called OT-X, a powerful new embedded IoT OS that can run on x86 platforms as an OT and IoT integrated gateway, supporting OPC UA. Industrial computers can be effortlessly transformed into software components. It enables intuitive Docker image deployment from cloud to edge and effortlessly extends microservices for OT and AI applications.
The high-resolution cameras were installed on the unmanned EOT crane to capture two-dimensional images of the trailer platform, while the 3D scanner reconstructed vertical coordinates on these images. This development significantly improved computer vision technology. These images were then transmitted to an HMI used by the crane operator. The operator would indicate the storage position for the steel coils on the screen, and the control computer would automatically convert this storage position into coordinates and send them to the EOT crane control system for lifting.
In addition to using a machine vision system to accurately position steel coil storage slots, the company also developed various intelligent logistics-related technologies. These included optimizing the lifting schedule, calculating the best lifting path using AI, and establishing a queue optimization system for unmanned warehouses. This system could accurately predict operation times, allowing drivers to arrive at the warehouse at the appropriate times for loading and unloading tasks.
Furthermore, by analyzing the processes of hundreds of thousands of steel coils entering and leaving the warehouse, the steel company created an optimized storage slot prediction feature in their warehouse management system. This ensured that each steel coil could be delivered to the customer using the fewest lifting operations and the shortest distance.
During lifting operations, the steel company designed a versatile smart lifting clamp that could identify the coil's identity, detect the coil's center, and accurately lift and transport the steel coil. Additionally, the clamp featured an active safety protection mechanism. Utilizing deep learning technology, it could detect personnel movement within the EOT crane's operating range, automatically identify and avoid obstacles.
Benefit and Result
1. Minimizing Labor Operations and Management:Unmanned EOT cranes can operate continuously for 24 hours without interruption. This automation even functions overnight when all employees have left. With backend management systems, the system can perform automatic stacking and inventory management the night before.
2. Optimizing Operation:After achieving full automation in lifting operations, the steel company integrated their unmanned EOT crane system with logistics and information flows, reducing the need for additional manual operations and management. For example, by connecting the EOT crane to logistics information, they could determine the coils required for the following day's shipment. The system would automatically rearrange the coils the previous night, positioning them near the loading bay. This shortened preparation time for the next day's shipment.
Alternatively, when a delivery driver checked in outside the warehouse, they could swipe an ID card. Once the driver's identity and vehicle number were confirmed, the warehouse management system received the mission details and automatically initiated the lifting task for the designated steel coils.
3. Reducing Downtime and Maintenance Costs: The unmanned EOT crane system continuously monitors the condition of critical crane components such as motors, gears, and brakes. By analyzing data from these components, AI can assess their health and performance, enabling maintenance teams to intervene at the right time and prevent catastrophic failures. AI can also identify abnormal crane behavior by establishing baseline patterns and flagging deviations. If the crane's performance deviates from the norm, AI can issue alerts, allowing maintenance teams to investigate and address potential issues before they escalate into more significant problems.
Since its deployment in 2018, the system has completed over 60,000 trips and lifted more than 300,000 steel coils. Following the success of the first system, the steel company introduced a second system in the same warehouse in November of last year. This warehouse can accommodate around 20,000 metric tons of steel coils, and the two unmanned EOT cranes achieve full warehouse automation for steel coil lifting.
The steel company not only utilized this system internally but also exported it to steel plants in China. In 2019, they sold 12 systems, and during the COVID-19 pandemic last year, they provided remote assistance to customers for system calibration and implementation.
