Robot automatic material supplementing system for aluminum smelting

Abstract

The invention discloses a robot automatic material supplementing system for aluminum smelting, which comprises a detector, a robot, a material storage area and a stirring mechanism, the detector can detect the composition proportion of an aluminum-iron solution in a smelting furnace, the robot is provided with a moving mechanism and a grabbing mechanism to enable the robot to have grabbing, feeding and moving functions, the stirring mechanism is arranged in the smelting furnace, and the material storage area is arranged in the smelting furnace. The material storage area comprises an iron material area where iron blocks are stored and an aluminum material area where aluminum blocks are stored. According to the method, a detector is configured to detect and obtain the composition proportion of an aluminum-iron solution, a robot is configured to compare current composition proportion information, current aluminum block weight information and a preset aluminum-iron formula to obtain material supplementing data, and according to the material supplementing data, an iron block is grabbed from an iron material area and put into a smelting furnace, and then an aluminum block is grabbed from an aluminum material area and put into the smelting furnace; and automatic detection and feeding of the ferro-aluminum solution are realized.

Description

Technical Field

[0001] This invention relates to the field of aluminum smelting technology, and in particular to a robotic automatic feeding system for aluminum smelting. Background Technology

[0002] Aluminum smelting technology is divided into multiple stages based on purity requirements: primary aluminum is purified into refined aluminum through a three-layer electrolysis method. High-purity aluminum is produced by using refined aluminum as raw material and achieving purity through zone melting or organic solution electrolysis. Recycled aluminum is produced by remelting scrap aluminum, which requires less energy. Scrap aluminum is mainly used to produce aluminum alloys. Before remelting, the scrap aluminum must be sorted, classified, and appropriately blended and processed to achieve the desired alloy composition. Currently, scrap aluminum is collected, compacted into blocks, and then conveyed to the feeding location via a conveyor. It is then fed into the scrap aluminum smelting furnace for melting as needed using grab buckets or conveyors. The automation of aluminum molten metal inspection and replenishment in scrap aluminum smelting is not high. Summary of the Invention

[0003] The purpose of this invention is to address the above-mentioned problems by providing a robotic automatic feeding system for aluminum smelting, which enables automated detection and feeding of molten aluminum.

[0004] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0005] The robotic automatic feeding system for aluminum smelting includes a detector and robot, a storage area, and a stirring mechanism. The detector can detect the composition ratio of aluminum-iron molten metal in the smelting furnace. The robot is equipped with a moving mechanism and a gripping mechanism, enabling it to grip, feed, and move materials. The stirring mechanism is located in the smelting furnace. The storage area includes an iron material area containing iron blocks and an aluminum material area containing aluminum blocks.

[0006] The robot is equipped with a feeding model to receive the current component ratio information from the detector, obtain the weight information of all current aluminum blocks, and obtain feeding data by comparing the current component ratio information, the current aluminum block weight information and the preset aluminum-iron formula. The feeding data includes the selected aluminum blocks and iron blocks. Based on the feeding data, the robot first grabs the iron block from the iron material area and puts it into the smelting furnace, and then grabs the aluminum block from the aluminum material area and puts it into the smelting furnace. At the same time, the stirring mechanism is triggered to run the stirring operation.

[0007] The robot is equipped with an explosion-proof vision system to identify the location of each aluminum block in the storage area.

[0008] As mentioned above, the configuration of the detector is used to detect and obtain the composition ratio of the aluminum-iron solution. The configuration of the robot is used to obtain the replenishment data by comparing the current composition ratio information, the current aluminum block weight information and the preset aluminum-iron formula. Based on the replenishment data, the robot first grabs the iron block from the iron material area and puts it into the smelting furnace, and then grabs the aluminum block from the aluminum material area and puts it into the smelting furnace, thereby realizing the automated detection and replenishment of the aluminum-iron solution.

[0009] Based on the aforementioned solution, in an improved solution, to detect and identify the timing of feeding materials into the smelting furnace, the robotic automatic feeding system for aluminum smelting is equipped with an image recognition model. An explosion-proof vision mechanism acquires video data of the aluminum-iron molten metal surface. The image recognition module processes the image of the aluminum-iron molten metal surface and outputs a judgment that the slag amount meets the feeding requirements when the slag area change is less than or equal to the slag amount threshold. The feeding model obtains the judgment that the slag amount meets the feeding requirements, then monitors and controls the temperature of the aluminum-iron molten metal in the smelting furnace to stabilize within a preset temperature range (e.g., 660~700℃), and then runs the stirring mechanism to perform stirring operations for 5-10 minutes. Afterwards, the composition ratio of the aluminum-iron molten metal at different locations in the smelting furnace is obtained by a detector. When the difference in the composition ratio of the aluminum-iron molten metal at each location is less than the error value, it is determined that the aluminum-iron molten metal is uniform, triggering the robot to perform feeding operations. The image recognition model, built upon deep learning, periodically identifies slag on the surface of the molten aluminum-iron solution during slag removal intervals. It calculates the current slag area percentage and compares it to the previous percentage to obtain a numerical change in slag area. This change is then compared to a preset slag quantity threshold. If the change is less than or equal to the threshold, the slag quantity is deemed sufficient for replenishment. Thus, by recognizing slag on the molten aluminum-iron solution surface through image analysis and preliminarily determining replenishment timing based on slag area changes, and further considering the temperature control and uniformity of the molten aluminum-iron solution, automated detection and identification of replenishment timing can be achieved.

[0010] Based on the aforementioned solution, in an improved version, to promptly detect and trigger the stirring operation, the robotic automatic feeding system for aluminum smelting uses an image recognition module to process the image of the aluminum-iron molten metal surface during the process of placing iron or aluminum blocks into the smelting furnace. The module determines whether an iron or aluminum block is present in the current image; if not, it determines that an iron or aluminum block has been placed and triggers the stirring mechanism to perform the stirring operation. This automates the detection and triggering of the stirring operation.

[0011] By adopting the above technical solution, the present invention has the following beneficial effects:

[0012] 1. This invention uses a detector to detect and obtain the composition ratio of the aluminum-iron solution. A robot is configured to compare the current composition ratio information, the current aluminum block weight information, and the preset aluminum-iron formula to obtain replenishment data. Based on the replenishment data, the robot first grabs the iron block from the iron material area and puts it into the smelting furnace, and then grabs the aluminum block from the aluminum material area and puts it into the smelting furnace, thereby realizing automated detection and replenishment of the aluminum-iron solution.

[0013] 2. This invention identifies slag on the surface of an aluminum-iron solution through image recognition, and then preliminarily determines the timing of material replenishment based on changes in slag area. Furthermore, based on the temperature control and uniformity of the aluminum-iron solution, it can achieve automated detection and identification of the timing of material replenishment. Attached Figure Description

[0014] Figure 1 This is the material replenishment operation process of the material replenishment system of the present invention. Detailed Implementation

[0015] The specific implementation of the invention will be further described below with reference to the accompanying drawings.

[0016] As mentioned above, this application includes basic solutions and improved solutions. For example, an improved solution is to configure an explosion-proof vision mechanism to identify the position of each aluminum block in the storage area. Another improved solution is to automatically identify the timing of feeding materials into the smelting furnace. The feature combinations of each application example can be combined according to actual needs. The following will use a preferred example of all feature combinations as an example to illustrate.

[0017] The robotic automatic feeding system for aluminum smelting disclosed in this application includes a detector and robot, a storage area, and a stirring mechanism. The detector can detect the composition ratio of aluminum-iron molten metal in the smelting furnace. The robot is equipped with a moving mechanism and a gripping mechanism, enabling it to grip, feed, and move materials. The stirring mechanism is located in the smelting furnace. The storage area includes an iron material area containing iron blocks and an aluminum material area containing aluminum blocks. The feeding process is as follows: Figure 1 As shown, the following will be explained in detail with reference to specific components.

[0018] The robot is equipped with a feeding model to receive the current component ratio information from the detector, obtain the weight information of all current aluminum blocks, and obtain feeding data by comparing the current component ratio information, the current aluminum block weight information and the preset aluminum-iron formula. The feeding data includes the selected aluminum blocks and iron blocks. Based on the feeding data, the robot first grabs the iron block from the iron material area and puts it into the smelting furnace, and then grabs the aluminum block from the aluminum material area and puts it into the smelting furnace. At the same time, the stirring mechanism is triggered to run the stirring operation.

[0019] The robot is equipped with an autonomous driving system and a robotic arm, which, in conjunction with laser navigation and vision, navigates to the storage area and uses the robotic arm's gripper to pick up materials. All of these are existing equipment. The detector, robot, smelting furnace, and its mixing mechanism are briefly described here and will not be elaborated upon in detail.

[0020] To adapt to the working environment of the smelting furnace, the front end of the robotic arm is equipped with an explosion-proof vision mechanism to identify the position of each aluminum block in the storage area; moreover, the explosion-proof camera (explosion-proof vision mechanism) monitors the temperature of the smelting furnace or aluminum-iron molten metal in real time through a built-in thermal imaging sensor. For example, Hikvision's DS-2TD2528T-3 / Q model supports dual-spectrum temperature measurement (-20℃~150℃ or 0℃~550℃ range) with an accuracy of ±2℃ and has a temperature abnormality alarm function.

[0021] The stirring mechanism can be a stirring cart or an electromagnetic stirrer to thoroughly stir the molten aluminum and iron in the melting furnace, thereby ensuring a uniform mixing of the internal concentration gradient and promoting the diffusion of alloying elements. The stirring time needs to be long enough (usually 5-10 minutes) to ensure that forced convection covers the entire melting furnace.

[0022] The smelting process requires slag removal and stirring. During the feeding process, the timing of adding scrap aluminum blocks depends on the slag volume. A decrease in slag volume may indicate a reduction in the degree of melt oxidation or the initial removal of impurities. If there is too much slag at the beginning of smelting, slag removal or air refining (nitrogen / argon treatment for 10-15 minutes) should be used to reduce impurities before considering feeding. Then, the melt temperature needs to be stabilized within the appropriate range, such as 660-700℃ (flame furnace) or 680-750℃ (medium frequency furnace). Furthermore, the melt state needs to be assessed. After the initial charge has completely melted, stirring should confirm uniform composition before feeding in stages and stirring evenly to prevent bridging of unmelted aluminum blocks that could lead to localized overheating. When initially adding material to the furnace, iron blocks should be added first, placed low and gently to avoid excessive impact on the furnace lining and potential cracking. A separate stirring mechanism should be installed for stirring operations as needed.

[0023] The amount of slag on the surface of the molten aluminum can be determined by using an explosion-proof camera and human observation, along with slag removal and changes in slag quantity. To achieve automated identification, this application employs an AI slag quantity identification model. Based on existing artificial intelligence and image recognition technologies such as neural networks and deep learning algorithms, a slag quantity identification model is constructed for preliminary judgment, automatically calculating a value called the slag area change value. This slag area change value is used as a correlation value for the feeding operation, serving as a preliminary judgment for the timing of feeding into the smelting furnace. The temperature of the molten aluminum can be monitored using an explosion-proof camera, and a smelting furnace temperature control system is configured to maintain the molten aluminum in the furnace within a preset temperature range, achieving stable temperature control and meeting temperature requirements. The uniformity of the molten aluminum can be detected at various locations using a detector and a robotic arm. The obtained data from each location is compared with error data. If the data is within the error range, the molten aluminum is considered uniform; otherwise, it is considered non-uniform, and the process is repeated for stirring and further evaluation of uniformity.

[0024] Specifically, in order to detect and identify the timing of feeding materials into the smelting furnace, the robotic automatic feeding system for aluminum smelting is equipped with an image recognition model. The explosion-proof vision mechanism acquires video data of the aluminum-iron molten metal surface. The image recognition module processes the image of the aluminum-iron molten metal surface and outputs a judgment that the slag amount meets the feeding requirements when the slag area change is less than or equal to the slag amount threshold. The feeding model obtains the judgment that the slag amount meets the feeding requirements, and then monitors and controls the temperature of the aluminum-iron molten metal in the smelting furnace to stabilize within a preset temperature range (e.g., 660~700℃). Then, the stirring mechanism is run to stir for 5-10 minutes. After that, the composition ratio of the aluminum-iron molten metal at different locations in the smelting furnace is obtained by the detector. When the difference in the composition ratio of the aluminum-iron molten metal at each location is less than the error value, it is determined that the aluminum-iron molten metal is uniform, and the robot is triggered to perform the feeding operation. The image recognition model, built upon deep learning, periodically identifies slag on the surface of the molten aluminum-iron solution during slag removal intervals. It calculates the current slag area percentage and compares it to the previous percentage to obtain a numerical change in slag area. This change is then compared to a preset slag quantity threshold. If the change is less than or equal to the threshold, the slag quantity is deemed sufficient for replenishment. Thus, by recognizing slag on the molten aluminum-iron solution surface through image analysis and preliminarily determining replenishment timing based on slag area changes, and further considering the temperature control and uniformity of the molten aluminum-iron solution, automated detection and identification of replenishment timing can be achieved.

[0025] To promptly detect and trigger stirring operations, this robotic automatic feeding system for aluminum smelting employs an image recognition module that processes images of the molten aluminum-iron molten metal surface during the process of placing iron or aluminum blocks into the furnace. The module determines the presence of either iron or aluminum blocks in the current image; if none are found, it indicates that either has been placed, triggering the stirring mechanism to begin stirring. This automates the detection and triggering of stirring operations.

[0026] As described above, a detector is configured to obtain the aluminum-iron molten metal composition ratio. A robot then compares the current composition ratio, the current aluminum block weight, and the preset aluminum-iron formula to obtain replenishment data. Based on this data, the robot first grabs the iron block from the iron block area and places it into the smelting furnace, then grabs the aluminum block from the aluminum block area and places it into the smelting furnace, thus automating the detection and replenishment of the aluminum-iron molten metal. Since the aluminum blocks are pressed by a press, their weight varies considerably, while the weight variation of the iron blocks is controllable. Selecting the appropriate iron block based on the known aluminum block weight allows for faster replenishment.

[0027] It should be noted that the examples of the above embodiments can preferably be combined with one or more of each other according to actual needs, and the accompanying drawings of multiple examples adopt a set of combined technical features, which will not be described in detail here.

[0028] The above description is a detailed explanation and illustration of the preferred embodiments of the present invention. However, these descriptions are not intended to limit the scope of protection claimed by the present invention. All equivalent changes or modifications made under the technical teachings of the present invention should fall within the patent protection scope covered by the present invention.

Claims

1. A robotic automatic feeding system for aluminum smelting, characterized in that: The system includes a detector and robot, a storage area, and a stirring mechanism. The detector can detect the composition ratio of the aluminum-iron molten metal in the smelting furnace. The robot is equipped with a moving mechanism and a gripping mechanism, enabling it to grip, feed, and move materials. The stirring mechanism is located in the smelting furnace. The storage area includes an iron material area containing iron blocks and an aluminum material area containing aluminum blocks. The robot is equipped with a feeding model to receive the current composition ratio information from the detector, obtain the weight information of all current aluminum blocks, and compare the current composition ratio information, current aluminum block weight information, and a preset aluminum-iron formula to obtain feeding data. This feeding data includes the selected aluminum and iron blocks. Based on this feeding data, the robot first grabs the iron block from the iron material area and puts it into the smelting furnace, then grabs the aluminum block from the aluminum material area and puts it into the smelting furnace, while simultaneously triggering the stirring mechanism to perform a stirring operation.

2. The robotic automatic feeding system for aluminum smelting according to claim 1, characterized in that: The robot is equipped with an explosion-proof vision mechanism to identify the location of each aluminum block in the storage area.

3. The robotic automatic feeding system for aluminum smelting according to claim 2, characterized in that: The robot is equipped with an image recognition model and an explosion-proof vision mechanism to acquire video data of the aluminum-iron molten metal surface. The image recognition module processes the image of the aluminum-iron molten metal surface and outputs a slag quantity judgment that meets the feeding requirements when the slag area change is less than or equal to the slag quantity threshold. The feeding model obtains the slag quantity judgment that meets the feeding requirements, then monitors and controls the temperature of the aluminum-iron molten metal in the smelting furnace to stabilize within the preset temperature range, and then runs the stirring mechanism to stir for 5-10 minutes. After that, the composition ratio of the aluminum-iron molten metal at different locations in the smelting furnace is obtained by the detector. When the difference in the composition ratio of the aluminum-iron molten metal at each location is less than the error value, it is determined that the aluminum-iron molten metal is uniform, and the robot is triggered to perform the feeding operation.

4. The robotic automatic feeding system for aluminum smelting according to claim 3, characterized in that: The image recognition model is built based on deep learning. During the slag removal interval, it periodically identifies slag on the surface of the aluminum-iron solution and calculates the current slag area ratio. It compares the current slag area ratio with the previous slag area ratio to obtain the slag area change value. It then compares the slag area change value with a preset slag quantity threshold. When the slag area change value is less than or equal to the slag quantity threshold, it is determined that the slag quantity meets the feeding requirements.

5. The robotic automatic feeding system for aluminum smelting according to claim 3, characterized in that: The image recognition module processes the image of the aluminum-iron molten metal surface during the process of placing iron or aluminum blocks into the smelting furnace, and determines whether there are iron or aluminum blocks in the current image. If not, it determines that iron or aluminum blocks have been placed and triggers the stirring mechanism to perform stirring.

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