1 Introduction
Intelligent warehousing is a rapidly developing and important component of modern logistics systems. Two-dimensional warehouses integrate mechanical, electronic, control, and computer technologies, offering advantages such as low labor intensity, high technological content, high space utilization, high cargo storage and retrieval efficiency, and a high degree of automation. PLCs, on the other hand, are powerful, highly reliable, have strong anti-interference capabilities, are easy to maintain, and readily integrate with electromechanical systems. They fully meet the requirements of the working environment and control system of automated warehouses. The intelligent loading of two-dimensional silos by a single mobile loading point has numerous applications in daily production and life, such as automated parking systems, blast furnace ore bin loading, and processed product warehousing.
2 System Analysis
2.1 Silo Group Issues
The loading structure of the two-dimensional silo assembly is shown in Figure 1. The two-dimensional silo is equipped with material level detection, and both the lateral and longitudinal moving loading points have position detection. The loading point can be determined based on the material level in the silo (or the presence or absence of workpieces), or it can be classified for loading. Currently, the easiest control method to implement is to manually move the loading point to the selected loading position based on the silo status or classification information reflected by the detection elements (remote or local operation). However, this loading method requires heavy manual labor, has a high degree of arbitrariness in silo selection, and the rational utilization of each silo is directly related to the operator's skill level, and is also prone to errors.
Figure 1. Structure of the silo assembly
2.2 Intelligent Loading and Silo Selection Principles
How can we achieve intelligent silo loading to reduce manual labor intensity, improve work efficiency and accuracy, and enable scientific silo selection? Intelligent silo selection is based on the following principles:
(1) Do not load materials when the hopper is full (or has been loaded with components). If the hopper is full during the loading process, move the loading point to automatically find an unfulfilled (or unloaded) hopper to load materials.
(2) Prioritize loading materials into silos that are not filled (or have not been loaded with components). If other silos in the two-dimensional silo group become empty during the loading process, the loading point can be set to immediately move to the empty silo to load materials (suitable for situations where empty silos do not occur frequently in the three-dimensional silo group, which can ensure that each silo is not empty and improve the silo utilization rate), or continue to complete the loading of this silo (suitable for situations where empty silos occur frequently, which can avoid frequent movement of the loading point and reduce equipment wear).
(3) If there are no full or empty bins in the two-dimensional bin group, the loading order can be based on the set priority level, and the bins in the three-dimensional bin group can be loaded in order.
(4) If multiple empty bins appear in the two-dimensional bin group at the same time, the loading order can be based on the set priority level, and the empty bins in the three-dimensional bin group can be loaded in turn.
(5) If all bins are full, stop the assembly of the two-dimensional bins.
2.3 Design Principles
By controlling the process according to the above principles, the silo loading process is made intelligent. To achieve these principles, the mathematical model needs to solve the following problems:
(1) Automatic selection of loading points. When loading points are manually selected, there is no problem with automatic selection. However, in the case of automatic loading, the loading points need to be determined according to the selection principles and priority order. This process is dynamic, and the state of the two-dimensional silo group is different at different times. The system must make the correct decision.
(2) Control of the moving loading point. After determining the loading point, make a judgment based on the existing position of the moving loading point to determine the moving direction and stopping position of the moving loading point.
To address the above issues, I developed an intelligent loading control method for two-dimensional silo groups, which solved the problems.
3 System Implementation
The intelligent three-dimensional silo is equipped with a full material position h and an empty material position l; the moving loading point is equipped with a position switch w11～wnn at the loading position.
(1) Selection of three-dimensional silos: Boolean variables s11 to snn are set to correspond one-to-one with the silos. When sxy=1, the loading point will move to the silo in row x and column y to load materials. When sxy=0, the loading point will not move to the silo to load materials. Only one silo in the three-dimensional silo group will have sxy=1 at any given time. Determining which silo has sxy=1 is the process of intelligent silo selection. For a single silo, the flowchart of the sxy value setting process is shown in Figure 2 [1].
Figure 2. Flowchart for setting sxy values
In the flowchart shown in Figure 2, "end" indicates the end of the current detection within one scan cycle. Using ladder diagrams to program the control system in a PLC is relatively simple. Within one scan cycle of the PLC, all silos in the automated silo group can be detected once, and the silo scanned first is the one with the highest priority in the automated silo group. When selecting, the order can be determined based on the position in the x and y directions. For example, the silo with the smaller sum of x and y values has higher priority. When the sum of x and y values is equal, the silo with the smaller x (or y) value has higher priority. Of course, this control method can also be used with other controllers to achieve intelligent material selection for two-dimensional automated silos.
(2) After determining the hopper for loading, the next step is to move the loading point to the selected hopper location.
Set up a number of data registers equal to the number of hoppers. Number each hopper according to the priority level mentioned above, from 1, 2, 3… in ascending order of priority. Pass the number to the corresponding data register for each hopper. For example, if the highest priority hopper's data register is d0, then the number 1 is passed to that register. Add two more data registers: one, d100 (d100 is an unused data register from the previous hopper), to store the number of the hopper currently being filled; and another, d101 (d101 is an unused data register from the previous hopper), to store the hopper number where sxy=1. Compare the numbers stored in these two data registers. If the number in d100 is greater than the number in d101, it means there is a hopper with a higher priority than the hopper currently being fed. Move the loading point to the hopper corresponding to the number in d101. If the number in d100 is less than the number in d101, it means the hopper currently being fed has a higher priority than the other hopper. Feed the hopper currently being fed first, and then move the loading point to feed the other hopper after the hopper is full. If the number in d100 is equal to the number in d101, it means the loading point has already moved to the hopper and will not move further.
The movement control of the moving loading point involves comparing the current position of the moving loading point with the expected position as two values within each scanning cycle to determine the movement path of the moving loading point.
4. Conclusion
Two-dimensional intelligent warehouse control can reduce labor intensity, increase silo utilization, and improve loading accuracy, thereby enhancing work efficiency. This method can prioritize tasks and implement different loading methods based on the actual needs of different production processes. This is significant for reducing production costs and increasing automation.
