Robotic Tagging Applications

Robot labelling can reduce labour costs, improve work safety and improve product appearance quality. This article provides an overview of currently available technologies such as vision systems, artificial intelligence, and robotics.

This article shows an automated solution for the automatic labelling of long profiles and steel bars in bundles. The main benefits of the system are to improve safety and productivity and reduce costs. The equipment is efficient can be used continuously, and fully automatically tracks bundles. For steel, labels are automatically printed on-site.

The topics discussed in this article are:

  • Labeling robot process layout.

  • 3D vision system.

  • Material tracking.

  • Labeling solutions for billets/bundles of bars/wire coils.


Manually labelling semi-finished or finished products often leads to errors in identifying the correct label. Material tracking, if it is not directly bound to the equipment that can automatically mark the product, is limited to the approximate identification of the object to be labelled. There is no feedback confirmation from the production line, and there is no tracking information given by the production line. The approximate identification accuracy is limited, Which requires an automatic marking system in the steel mill. The automatic marking system is directly connected to the factory’s material tracking system, which completely eliminates the previous steel mill number mixing errors. Such an automatic marking system can keep employees away from dangerous locations and work, such as marking at high temperatures or marking material safety hazards in places where frequent operations are carried out in the sky. Eliminating the tasks of manual labelling helps liberate employees to work on system maintenance, which is not just a simple operator. According to the results of the manual labelling workplace of the steel plant, an average of 5% of the steel bales were not marked or marked incorrectly, and an average of 600 bales per shift. This means that during manual marking, in the finished product warehouse in the inventory area, an average of 30 bundles of steel mill markings are missing or incorrectly marked per shift. Figure 1 shows the manual labelling work site.

Picture 1: Workplace marked by hand, all labels are printed in advance, there is a certain percentage of human error

The automatic labelling and labelling work site is mainly an anthropomorphic six-axis robot, the 3D vision system is installed on the robot’s wrist, a set of identification label printers, a machine to create or distribute labels, welding machines, and electronic panels have a complete robot work site Including related human-machine interfaces (HMIs), diagnostics and alarms. The robot is installed in a limited site space and a safety fence is designed to contain all the machines in the protected area. The drawing of the labelling robot system is shown in Figure 2.

Figure 2: Typical labelling robot working area

Simulated robots are time-tested equipment that can be used on different work sites in the steel industry. The “casting” mode of the robot is specifically designed to work in harsh environments, and applications in different fields are universal. The 3D vision system adopted by AIC is a dual-camera vision system that does not use laser beams and is designed for harsh environments. With this system, there is no need to “scan” the product to create a 3D point cloud. Using a simple picture is enough to reconstruct the 3D contour of the product. The matrix sensor is used instead of profile measurement. In this way, there is no need for the special movement of the robot to complete the scanning work. It only requires the bundled steel to be positioned during the conveying process. The shooting processing time is about 1.2 seconds. Due to the rapid processing of the program, the robot island in the steel plant can handle the steel The output is 180 tons per hour. Thanks to the advanced analysis algorithm, the 3D vision system can automatically detect the product type without any specific settings, as shown in Figure 3.

Figure 3: 3D vision system for collecting end-face information
Figure 4: Example of 3D vision system results. Keywords: white and black cross=select the item to be marked; green/yellow=alternative location; orange=appropriate but limited; red=inappropriate; blue square=obstacle area; blue circle=area of interest
Figure 5 Metal bracket, metal bracket machine, nailing distributor

Installed inside the label robot is a thermal transfer printer, designed with an external label replenishment device, which can process up to 10,000 labels, and does not need to replenish labels and ribbons during a full week of production. The label can be applied using a metal support built on the island by a specially designed machine, or it can be applied by studs. The metal support is used to keep the printed label at a distance from the end to prevent the higher steel temperature from affecting the label. When nailing and labeling labels on relatively cold materials, the distance between the label and the end does not need to be considered, and a welding machine can also be used to weld the label holder to the end of the steel.

Figure 6 The end face of the steel bundle is driven or welded with a metal bracket label
Figure 7 Application of nailing (a) and labelling of strapping steel end face (b)

in conclusion 

The material tracking system is directly connected with the automatic labelling system to ensure that the label of each product has the correct identification data and improves the traceability of the finished product. Connect to the database to achieve complete traceability in the production process of the product, and follow the production process sequence to the end user. In the case of 600 bundles of steel produced per shift, automatic marking can reduce the number of unmarked bundles to less than 0.2%, which means that on average, there is only one bundle of marking errors per shift. Corresponding measures have been taken on the rebar mill. The robot labelling application on the production line can reach the output requirement of 180 tons per hour. The entire identification and labelling time is 8 seconds. On this equipment, the strapped steel is stopped on the conveyor and 1.2 seconds is the time used to perform 3D scanning on the bundled steel end face.

With machine vision as the core, DBM can provide intelligent equipment R&D, manufacturing, and transformation services such as unmanned factories in the metallurgical industry and other industries, machine vision, and robot application design.

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