Vacuum pump processing system and processing device based on groove opening

Abstract

The application provides a vacuum pump machining system and a machining device based on a groove, which comprises a cooling spray optimization subsystem, an online detection feedback correction subsystem and a machining execution module. The cooling spray optimization subsystem realizes cooling water spray mode optimization through water flow distribution feature extraction, spray parameter calculation and dynamic adjustment. The online detection feedback correction subsystem generates deviation information and calibrates machining and detection parameters by using multispectral imaging and geometric feature extraction technology. Through the synergistic effect of the two systems, the application overcomes the interference of the traditional cooling method on the detection accuracy and improves the monitoring accuracy and efficiency of the vacuum pump groove machining.

Description

Technical Field

[0001] This invention belongs to the field of machining and vacuum equipment manufacturing technology, specifically a vacuum pump machining system and machining device based on a tank. Background Technology

[0002] In modern machining, high-precision and high-efficiency machining systems are the core technological support for manufacturing complex parts. Especially in the machining of vacuum pump tanks, traditional machining methods are insufficient to meet the ever-increasing industrial demands due to the stringent requirements for dimensional accuracy, shape accuracy, and surface quality. As a core component, the machining quality of the vacuum pump tank directly affects the performance and lifespan of the entire vacuum pump. However, in actual machining processes, especially during the grooving stage, friction between the tool and the workpiece, along with material removal, leads to a rapid increase in localized temperature. Excessive temperature causes oxidation and accelerated wear on the tool surface, even resulting in chipping or deformation, thus reducing tool life and further affecting machining accuracy. Simultaneously, high temperatures can alter the physical and chemical properties of the workpiece material, such as dimensional deviations caused by thermal expansion, internal cracks induced by thermal stress, and changes in the material's microstructure. These factors combined may cause the final dimensional accuracy and surface quality of the vacuum pump tank to fail to meet design requirements, thereby affecting the overall performance of the equipment.

[0003] To address the aforementioned issues, water spray cooling is commonly used to lower the temperature of the machining area. By spraying cooling water onto the contact point between the tool and the workpiece, heat generated during cutting can be effectively absorbed and carried away, thereby reducing the temperature of both the tool and the workpiece, and minimizing thermal deformation and stress. This method improves machining conditions to some extent, extends tool life, and enhances machining stability. However, the application of water spray cooling also brings new technical challenges, particularly in the implementation of online inspection and feedback correction systems. Online inspection and feedback correction systems are a crucial component of modern precision machining. Their main function is to monitor key parameters (such as dimensional accuracy and shape error) in real time during machining and adjust machining parameters promptly through feedback control mechanisms to ensure that the final product meets design requirements. However, when water spray cooling is performed simultaneously with cutting operations, the presence of cooling water significantly interferes with the imaging effect of the inspection system. The dynamic changes in water flow can create obstructions or reflections in the camera's captured image, making it difficult to accurately capture the actual state of the target area. Furthermore, water droplets adhering to the workpiece surface can also cause image blurring or distortion, further affecting the reliability of the inspection results. These problems directly restrict the performance of the online detection and feedback correction system, preventing it from fully utilizing its real-time monitoring and precise control capabilities, thereby reducing the overall efficiency and accuracy of the processing system. Summary of the Invention

[0004] In view of this, the problem to be solved by the present invention is to provide a vacuum pump processing system and processing device based on a tank.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: The vacuum pump machining system based on the tank includes a cooling jet optimization subsystem, an online detection feedback correction subsystem, and a machining execution module. The cooling jet optimization subsystem includes a water flow distribution feature extraction module, a jet parameter calculation module, a dynamic adjustment module, and a water flow influence model construction module. The water flow distribution feature extraction module is configured with a preset water flow state recognition strategy to obtain the water flow distribution characteristics of the machining area in real time. The jet parameter calculation module is configured with optimal jet angle and shape parameter calculation algorithms to calculate the optimal cooling water jet pattern based on the water flow distribution characteristics. The dynamic adjustment module generates dynamic adjustment commands to change the cooling water jet angle and shape parameters in real time. The water flow influence model construction module analyzes the impact of cooling water on the imaging effect of the target area, pre-constructs a water flow influence model, and generates corresponding theoretical detection features. The online detection feedback correction subsystem includes an image acquisition module, a geometric feature extraction module, a deviation information generation module, and a parameter calibration module. The image acquisition module acquires image information of the target area in the optimal spray mode. The geometric feature extraction module extracts actual detection features from the acquired images. The deviation information generation module compares the actual detection features with the theoretical detection features using a preset feature comparison algorithm to generate deviation information. The parameter calibration module generates calibration instructions based on the deviation information and adjusts the processing parameters and detection parameters through the calibration instructions.

[0006] The water flow state identification strategy includes collecting water flow distribution data within the processing area at preset time intervals, calculating the water flow coverage value at each time point using a preset water flow coverage calculation algorithm, and selecting time points with water flow coverage values ​​greater than a preset benchmark value as key sampling points; calculating the water flow uniformity value of the key sampling points using a preset water flow uniformity calculation algorithm, and selecting the key sampling point with the largest water flow uniformity value as the key water flow state to be analyzed.

[0007] The expression for the water flow coverage calculation algorithm is as follows:

[0008] C represents water flow coverage. Indicates the first The area covered by water flow This represents the total area of ​​the processing area. Indicates the number of covered areas; The expression for the algorithm for calculating the uniformity of water flow is:

[0009] Indicates the uniformity of water flow. Indicates the first The area covered by water flow This represents the average area of ​​all covered regions. Indicates the number of areas covered.

[0010] The expression for the algorithm for calculating the optimal injection angle and shape parameters is as follows:

[0011] Indicates the optimal injection angle. Indicates the optimal jet shape parameters. This indicates the temperature change at the point of contact between the tool and the workpiece. This indicates the change in displacement in the direction of the cooling water jet. and These are the weighting coefficients.

[0012] The logic for generating dynamic adjustment commands by the dynamic adjustment module includes: generating a continuous sequence of injection adjustment commands based on the changing trends of the optimal injection angle and shape parameters, combined with the real-time thermal load status of the processing area; the injection adjustment command sequence is optimized by a preset smooth transition algorithm to avoid interference from sudden changes in the injection mode to the processing process.

[0013] The expression for the smooth transition algorithm is:

[0014] This indicates the injection adjustment command from the previous moment. Indicates a target injection adjustment command. This represents the smoothing factor, with a value range of 0 < ≤1.

[0015] The water flow influence model construction module analyzes the imaging effect of cooling water under different spray modes to construct an initial water flow influence model, and uses AI to complete the boundary part of the initial model. The completed water flow influence model generates an index image, and after estimating the theoretical detection features of the index image, the images are stitched together to form the final water flow influence model.

[0016] When the image acquisition module acquires image information of the target area in the optimal spray mode, it adopts a preset multispectral imaging technology to eliminate the interference of cooling water reflection and refraction on the imaging effect. The multispectral imaging technology selects a light source of a specific wavelength to illuminate the target area and extracts the characteristic spectral information of the target area by combining a filter.

[0017] When the geometric feature extraction module extracts actual detection features from the acquired image, it preprocesses the image using a preset edge enhancement algorithm to improve the clarity of the target region boundary; the expression of the edge enhancement algorithm is:

[0018] This represents the pixel values ​​of the enhanced image. This represents the Gaussian filter function. Indicates the image in The gradient magnitude at that point.

[0019] The deviation information generation module compares the actual detection features with the theoretical detection features using a preset feature comparison algorithm to generate deviation information. The feature comparison algorithm includes calculating the size deviation, shape deviation, and position deviation of the geometric features, and generating a deviation feature vector based on the deviation values. The deviation information also includes the ambient illuminance value, the spray parameter value, and the acquisition time value.

[0020] The expression for the feature comparison algorithm is:

[0021] This represents the total deviation value. Indicates dimensional deviation. Indicates shape deviation, This indicates positional deviation.

[0022] A machining apparatus employing a vacuum pump machining system based on a grooved design is disclosed. The frame serves as the fundamental support component of the entire apparatus, bearing all other components and ensuring their stable operation. A cooling water circulation tank is located at the top of the frame, containing cooling water nozzles. These nozzles are connected to an external water source via pipes, and a built-in water pump circulates the cooling water. The cooling water nozzles correspond to the position of the cutting blade. When the cutting blade performs a grooving operation on the rotor, the cooling water nozzles continuously spray cooling water onto the contact area between the cutting blade and the rotor, effectively reducing the high temperatures generated by friction. The cooling water circulation tank not only provides cooling but also collects debris generated during the cutting process and transports it to a filtration device for treatment, thus maintaining a clean machining environment. Furthermore, the cooling water circulation tank is designed with a sloping bottom and guide channels to facilitate smooth water discharge and reduce water accumulation, further optimizing cooling efficiency and resource utilization.

[0023] The cutter is connected to one side of the frame via a sliding mechanism, which includes a linear guide rail and a drive motor. The drive motor, through a lead screw, moves the cutter along the linear guide rail to achieve precise cutting of the rotor. The cutter's trajectory is controlled by the control system according to preset parameters, including cutting depth, speed, and angle. A monitoring camera is mounted on the top of the frame to acquire image information in real time during the cutting process and transmits the acquired data to the central processing unit for analysis. The central processing unit adjusts the cutter's cutting parameters based on the vacuum pump processing system; this real-time feedback mechanism significantly improves processing accuracy and efficiency.

[0024] The automatic clamping mechanism, located in the middle of the frame, is responsible for securing the rotor to ensure its stability during machining. The mechanism mainly consists of grippers, a cylinder, and a pressure sensor. The cylinder drives the grippers to clamp or release the rotor via a piston rod, while the pressure sensor monitors the clamping force to prevent deformation due to excessive force or loosening due to insufficient force. In practical applications, once the rotor is placed on the automatic clamping mechanism, the cylinder activates and the grippers quickly clamp the rotor. Simultaneously, the pressure sensor provides real-time feedback of the clamping force data to the control system, ensuring the clamping force remains within a reasonable range. The automatic clamping mechanism is simple and efficient to operate, enabling rapid rotor fixation and providing reliable assurance for subsequent machining.

[0025] The transverse rotation mechanism, positioned above the automatic clamping mechanism, drives the rotor to rotate axially. This mechanism includes a servo motor, a coupling, and a rotary table. The servo motor is connected to the rotary table via the coupling, and the rotary table directly contacts the rotor. The servo motor features high precision and high response speed, enabling precise control of the rotary table's rotation angle and speed according to control system commands. During processing, when grooving is required on different parts of the rotor, the transverse rotation mechanism rotates the rotor to a specified angle according to a preset program and maintains its position through a locking device, ensuring accurate grooving at different angles. Furthermore, the transverse rotation mechanism has a self-checking function, using an encoder to monitor and compensate for rotation angle errors in real time, further improving processing accuracy.

[0026] An automatic loading and unloading structure is located on one side of the frame and is used to replace rotors that have been processed and those awaiting processing. The automatic loading and unloading structure includes a robotic arm, a gripping device, and a conveyor belt. The robotic arm is driven by a joint motor and can move flexibly in multiple directions. The gripping device is installed at the end of the robotic arm and is used to grip and place the rotors. The conveyor belt is responsible for transporting rotors awaiting processing to designated positions or removing processed rotors from the processing area.

[0027] The advantages and positive effects of this invention are: The cooling jet optimization subsystem determines the water flow distribution characteristics from the real-time state of the processing area and pre-configures the water flow influence model. When the tool-workpiece contact point is under high heat load, the cooling optimization subsystem calculates the optimal jet angle and shape parameters based on the water flow distribution characteristics and generates dynamic adjustment commands to change the cooling water jet mode in real time. This ensures the cooling effect while minimizing interference with the online detection system. Image information of the target area is acquired under the optimal jet mode, and the corresponding geometric features are extracted as actual detection features. The theoretical detection features under the corresponding jet parameters are extracted from the water flow influence model as benchmark detection features. The benchmark detection features and actual detection features are compared using a preset feature comparison algorithm to generate deviation information. This allows for the calibration of detection and processing parameters at each moment, configuring the online detection unit in the optimal working state. This improves the monitoring accuracy and feedback correction capability during the vacuum pump tank processing, providing support for the manufacturing of complex parts with high precision and high efficiency. Attached Figure Description

[0028] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0029] In the attached diagram: Figure 1 This is a block diagram of the overall structure of the vacuum pump processing system based on the tank of the present invention, showing the connection relationship between the cooling jet optimization subsystem, the online detection feedback correction subsystem, and the processing execution module.

[0030] Figure 2 The diagram illustrates the functional modules of the cooling jet optimization subsystem, detailing the composition and interaction flow of the water flow distribution feature extraction module, jet parameter calculation module, dynamic adjustment module, and water flow influence model construction module.

[0031] Figure 3 This diagram illustrates the functional modules of the online detection feedback correction subsystem, focusing on the structure and working logic of the image acquisition module, geometric feature extraction module, deviation information generation module, and parameter calibration module.

[0032] Figure 4 A flowchart illustrating the optimization process for cooling water jet patterns, including water flow state identification strategies, algorithms for calculating optimal jet angle and shape parameters, and steps for generating dynamic adjustment commands.

[0033] Figure 5 This is a schematic diagram illustrating the application of multispectral imaging technology in an image acquisition module, demonstrating the process of extracting characteristic spectral information of a target region by combining a specific wavelength light source with a filter.

[0034] Figure 6This diagram illustrates the working principle of the deviation information generation module, detailing the specific implementation methods for geometric feature extraction, feature comparison algorithms, and deviation feature vector generation.

[0035] Figure 7 This is a three-dimensional structural diagram of the processing device.

[0036] Figure 8 This is a three-dimensional structural diagram of the sliding mechanism.

[0037] Figure 9 This is a three-dimensional structural diagram of an automatic loading and unloading system.

[0038] Figure 10 This is a three-dimensional structural diagram of a cooling water storage tank.

[0039] Figure 11 This is a three-dimensional structural diagram of the automatic clamping mechanism and the transverse rotation mechanism.

[0040] In the attached diagram: 1. Frame; 2. Cooling water tank; 3. Cutter; 4. Automatic clamping mechanism; 5. Cooling water nozzle; 6. Automatic loading and unloading structure; 7. Lateral rotation mechanism; 8. Monitoring camera; 9. Rotor; 10. Sliding mechanism; 11. Linear guide rail; 12. Drive motor; 13. Gripper; 14. Cylinder; 15. Pressure sensor; 16. Servo motor; 17. Coupling; 18. Rotary table; 19. Robotic arm; 20. Gripping device; 21. Conveyor belt; 22. Joint motor. Detailed Implementation

[0041] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0042] It should be noted that when a component is described as "fixed to" another component, it can be directly on the other component or may have a component in between. When a component is considered "connected to" another component, it can be directly connected to the other component or may have a component in between. When a component is considered "set on" another component, it can be directly set on the other component or may have a component in between. The terms "vertical," "horizontal," "left," "right," and similar expressions used in this document are for illustrative purposes only.

[0043] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0044] like Figure 1 As shown in the diagram, the overall structural block diagram of this invention illustrates the connection relationship between the cooling jet optimization subsystem, the online detection feedback correction subsystem, and the machining execution module. The cooling jet optimization subsystem includes a water flow distribution feature extraction module, a jet parameter calculation module, a dynamic adjustment module, and a water flow influence model construction module. The online detection feedback correction subsystem consists of an image acquisition module, a geometric feature extraction module, a deviation information generation module, and a parameter calibration module. These modules cooperate with each other in actual operation to complete the entire process from cooling water jet mode optimization to machining parameter calibration.

[0045] Specifically, the vacuum pump machining system includes a cooling jet optimization subsystem, an online detection feedback correction subsystem, and a machining execution module; The cooling jet optimization subsystem includes a water flow distribution feature extraction module, a jet parameter calculation module, a dynamic adjustment module, and a water flow influence model construction module. The water flow distribution feature extraction module is configured with a preset water flow state recognition strategy, which acquires the water flow distribution features of the processing area in real time. The jet parameter calculation module is configured with optimal jet angle and shape parameter calculation algorithms to calculate the optimal jet pattern of the cooling water based on the water flow distribution features. The dynamic adjustment module generates dynamic adjustment commands to change the jet angle and shape parameters of the cooling water in real time. The water flow influence model construction module analyzes the impact of cooling water on the imaging effect of the target area, pre-constructs a water flow influence model, and generates corresponding theoretical detection features. The online detection feedback correction subsystem includes an image acquisition module, a geometric feature extraction module, a deviation information generation module, and a parameter calibration module. The image acquisition module acquires image information of the target area in the optimal spraying mode. The geometric feature extraction module extracts actual detection features from the acquired images. The deviation information generation module compares the actual detection features with the theoretical detection features using a preset feature comparison algorithm to generate deviation information. The parameter calibration module generates calibration instructions based on the deviation information and adjusts the processing parameters and detection parameters using the calibration instructions.

[0046] The specific operation process of the cooling spray optimization subsystem is as follows: Figure 2 As shown, firstly, the water flow distribution feature extraction module acquires the water flow distribution features of the processing area in real time through a preset water flow state recognition strategy. The water flow state identification strategy includes collecting water flow distribution data within the processing area at preset time intervals, calculating the water flow coverage value at each time point using a preset water flow coverage calculation algorithm, and selecting time points with water flow coverage values ​​greater than a preset benchmark value as key sampling points; calculating the water flow uniformity value of the key sampling points using a preset water flow uniformity calculation algorithm, and selecting the key sampling point with the largest water flow uniformity value as the key water flow state to be analyzed. Specifically, this strategy collects water flow distribution data within the processing area at preset time intervals, and calculates the water flow coverage value and water flow uniformity value at each time point using a water flow coverage calculation algorithm and a water flow uniformity calculation algorithm, respectively. The expression for the water flow coverage calculation algorithm is as follows:

[0047] C represents water flow coverage. Indicates the first The area covered by water flow This represents the total area of ​​the processing area. Indicates the number of covered areas; This algorithm is used to evaluate the coverage of cooling water in the processing area, selecting time points where the water flow coverage value is greater than a preset benchmark value as key sampling points. Subsequently, the water flow uniformity value of the key sampling points is further analyzed using a water flow uniformity calculation algorithm, the expression of which is:

[0048] Indicates the uniformity of water flow. Indicates the first The area covered by water flow This represents the average area of ​​all covered regions. Indicates the number of covered areas; Finally, the key sampling point with the largest water flow uniformity value was determined as the key water flow state to be analyzed. After determining the key water flow state, the injection parameter calculation module calculated the optimal injection angle and shape parameters of the cooling water based on the water flow distribution characteristics.

[0049] The calculation process used an algorithm to calculate the optimal injection angle and shape parameters, the expression of which is:

[0050] Indicates the optimal injection angle. Indicates the optimal jet shape parameters. This indicates the temperature change at the point of contact between the tool and the workpiece. This indicates the change in displacement in the direction of the cooling water jet. and The weighting coefficients are used. This algorithm comprehensively considers the changing trends of heat load and injection direction, thereby ensuring that the cooling water can act on the target area in the optimal mode.

[0051] By adjusting the angle and fixed flow rate of the spray pattern, and dynamically adjusting according to the real-time status of the processing area, the cooling effect is ideal and it is not easy to interfere with the detection system.

[0052] The dynamic adjustment module generates dynamic adjustment commands based on the results from the injection parameter calculation module to change the injection angle and shape parameters of the cooling water in real time. The generation logic of the dynamic adjustment commands includes generating a continuous sequence of injection adjustment commands by combining the real-time thermal load status of the processing area, and optimizing the command sequence through a smooth transition algorithm. The expression of the smooth transition algorithm is as follows:

[0053] This indicates the injection adjustment command from the previous moment. Indicates a target injection adjustment command. This represents the smoothing factor, with a value range of 0 < ≤1.

[0054] This algorithm avoids interference from sudden changes in the injection mode during the processing by gradually adjusting the injection parameters, thereby ensuring the stability of the cooling effect and the consistency of the processing quality.

[0055] One embodiment of the operation of the cooling jet optimization subsystem is as follows: Assuming that during the vacuum pump tank machining process, water flow distribution data within the machining area is collected at preset time intervals of 0.5 seconds, and the total area of ​​the machining area is... At a certain moment, five areas covered by water flow were detected, with areas of... According to the water flow coverage calculation algorithm, The water flow coverage at that moment can be obtained as follows: If the preset baseline value is 50%, then this moment is the critical sampling point; Calculate the water flow uniformity at this key sampling point, and the average area of ​​all covered regions. Based on the algorithm for calculating water flow uniformity achievable ; Among multiple key sampling points, the one with the highest water flow uniformity value is selected as the key water flow state to be analyzed.

[0056] The temperature change at the point of contact between the tool and the workpiece is known. The change in displacement in the direction of cooling water jet Weighting coefficient The optimal injection angle is calculated using an algorithm based on the optimal injection angle and shape parameters. Optimal jet shape parameters The dynamic adjustment module generates a sequence of injection adjustment commands based on the above calculation results and the real-time heat load status of the processing area. Assuming the injection adjustment command from the previous moment... Target injection adjustment command Smoothing factor According to the smooth transition algorithm This gives the injection adjustment command for the current moment. This allows for smooth adjustment of the cooling water spray angle and shape parameters.

[0057] The water flow influence model construction module analyzes the imaging effects of cooling water under different jet modes to construct an initial water flow influence model. Then, using A1 (artificial intelligence technology), the boundary regions of the initial model are completed. The completed water flow influence model generates indexed images, and the theoretical detection features of these indexed images are estimated before being stitched together to form the final water flow influence model. This model provides benchmark detection features for the subsequent online detection feedback correction subsystem, effectively reducing the interference of cooling water reflection and refraction on the imaging effect.

[0058] The specific operation process of the online detection feedback correction subsystem is as follows: Figure 3 As shown, when the image acquisition module acquires image information of the target area in the optimal spray mode, it uses multispectral imaging technology, such as... Figure 5 As shown, this technology eliminates the interference of cooling water reflection and refraction on the imaging effect by illuminating the target area with a light source of a specific wavelength and extracting the characteristic spectral information of the target area using a filter. The acquired image information is then transmitted to the geometric feature extraction module, which uses an edge enhancement algorithm to preprocess the image to improve the clarity of the target area boundary. The expression of the edge enhancement algorithm is as follows:

[0059] This represents the pixel values ​​of the enhanced image. This represents the Gaussian filter function. Indicates the image in The gradient magnitude at the location is used to further extract the actual detection features from the preprocessed image information.

[0060] The deviation information generation module compares actual detection features with theoretical detection features using a preset feature comparison algorithm to generate deviation information. The expression for the feature comparison algorithm is:

[0061] This represents the total deviation value. Indicates dimensional deviation. Indicates shape deviation, This indicates positional deviation.

[0062] The algorithm comprehensively calculates the size deviation, shape deviation, and position deviation of the geometric features and generates a deviation feature vector. In addition, the deviation information also includes the ambient illumination value, the spray parameter value, and the acquisition time value, so as to fully reflect the state changes of the target area.

[0063] The parameter calibration module generates calibration instructions based on deviation information and adjusts machining and detection parameters accordingly. The generation logic of the calibration instructions includes adjusting parameters such as cutting speed and feed rate of the machining execution module based on the magnitude and direction of the deviation feature vector, while simultaneously adjusting parameters such as light source intensity and filter settings of the online detection unit to ensure the optimal operating state of the detection system. In this way, the present invention achieves real-time monitoring and feedback correction of the machining process, significantly improving the monitoring accuracy and feedback correction capability during the machining of the vacuum pump tank. One embodiment of the operation of the online detection feedback correction subsystem is as follows: After the cooling jet optimization subsystem is adjusted to the optimal jet mode, the image acquisition module begins to operate. For example, it selects light sources with wavelengths of 500nm and 700nm to illuminate the target area, extracts the characteristic spectral information of the target area through filters, and acquires a clear image.

[0064] The geometric feature extraction module uses an edge enhancement algorithm to preprocess the acquired image. Assuming a pixel (x, y) in a target region of the image has a Gaussian filter function G(x, y) = 0.8, the gradient magnitude of the image at that point... According to edge enhancement algorithm The enhanced image pixel values ​​can be obtained. This allows for a clearer extraction of the actual detection features.

[0065] The deviation information generation module compares the actual detected features with the theoretical detected features obtained from the flow influence model. It assumes that the actual detected features have a size deviation compared to the theoretical detected features. , shape deviation Positional deviation According to feature comparison algorithm The total deviation value can be obtained as follows: ; Simultaneously, the ambient illuminance value was recorded as 500 lux, the spray parameter values ​​(such as spray angle, spray shape parameters, etc.), and the acquisition time value.

[0066] The parameter calibration module generates calibration instructions based on the deviation information. If the deviation feature vector shows a large dimensional deviation, the cutting speed of the machining execution module can be appropriately reduced, such as from 500 mm / min to 450 mm / min; the feed rate can be adjusted, such as from 0.2 mm / r to 0.18 mm / r. For the online detection unit, if the image clarity is found to be affected, the light source intensity can be appropriately increased, such as from 300 cd to 350 cd, and the filter setting parameters can be adjusted to optimize the detection effect and ensure machining accuracy.

[0067] Furthermore, this invention also proposes a processing device employing a vacuum pump processing system based on a slotted surface. The frame 1 serves as the fundamental support component of the entire device, bearing all other components and ensuring their stable operation. A cooling water storage tank 2 is located inside the frame 1, and a cooling water nozzle 5 is mounted on the top of the frame 1. The cooling water nozzle 5 is connected to a water pump via pipes, enabling the circulation of cooling water. The cooling water nozzle 5 corresponds to the position of the cutter 3. When the cutter 3 performs slotting operations on the rotor 9, the cooling water nozzle 5 continuously sprays cooling water onto the contact area between the cutter 3 and the rotor 9, effectively reducing the high temperature generated by friction. The chiller on the cooling water storage tank 2 cools the cooling water and also collects debris generated during the cutting process, which is then transported to a filtration device for treatment via water flow, thus maintaining a clean processing environment. In addition, the cooling water storage tank 2 is designed with an inclined bottom and a guide channel, facilitating smooth water drainage and reducing water accumulation, further optimizing cooling efficiency and resource utilization.

[0068] The cutter 3 is connected to one side of the frame 1 via a sliding mechanism 10. The sliding mechanism 10 includes a first moving component, a second moving component, and a third moving component, enabling the cutter 3 to move along the X, Y, and Z axes. The moving components include a linear guide rail 11 and a drive motor 12. The drive motor 12 drives the cutter 3 to move along the linear guide rail 11 via a lead screw drive, thereby achieving precise cutting of the rotor 9. The motion trajectory of the cutter 3 is controlled by the control system according to preset parameters, including cutting depth, speed, and angle. A monitoring camera 8 is mounted on the top of the frame 1 to collect image information during the cutting process in real time and transmit the collected data to the central processing unit for analysis. The central processing unit adjusts the cutting parameters of the cutter 3 according to the vacuum pump processing system. This real-time feedback mechanism significantly improves processing accuracy and efficiency.

[0069] The automatic clamping mechanism 4, located in the middle of the frame 1, is responsible for fixing the rotor 9 to ensure its stability during processing. The automatic clamping mechanism 4 mainly includes grippers 13, a cylinder 14, and a pressure sensor 15. The cylinder 14 drives the grippers 13 to clamp or release the rotor 9 via a piston rod, while the pressure sensor 15 monitors the clamping force to prevent deformation of the rotor 9 due to excessive clamping force or loosening of the rotor 9 due to insufficient clamping force. In practical applications, after the rotor 9 is placed on the automatic clamping mechanism 4, the cylinder 14 activates and quickly clamps the rotor 9 through the grippers 13. Simultaneously, the pressure sensor 15 feeds back the clamping force data to the control system in real time, ensuring that the clamping force is within a reasonable range. The automatic clamping mechanism 4 is simple and efficient to operate, capable of fixing the rotor 9 in a short time, providing a reliable guarantee for subsequent processing.

[0070] The transverse rotation mechanism 7 is positioned to the side of the automatic clamping mechanism 4 and is used to drive the rotor 9 to rotate axially. The transverse rotation mechanism 7 includes a servo motor 16, a coupling 17, and a rotary table 18. The servo motor 16 is connected to the rotary table 18 via the coupling 17. The servo motor 16 features high precision and high response speed, enabling it to precisely control the rotation angle and speed of the rotary table 18 according to control system commands. During processing, when grooving is required on different parts of the rotor 9, the transverse rotation mechanism 7 rotates the rotor 9 to a specified angle according to a preset program and maintains its stable position through a locking device, thereby ensuring that the cutter 3 can accurately groove at different angles. Furthermore, the transverse rotation mechanism 7 also has a self-checking function, which can monitor and compensate for rotation angle errors in real time via an encoder, further improving processing accuracy.

[0071] An automatic loading and unloading structure 6 is located on one side of the frame 1 and is used to replace completed and unprocessed rotors 9. The automatic loading and unloading structure 6 includes a robotic arm 19, a gripping device 20, and a conveyor belt 21. The robotic arm 19 is driven by a joint motor 22 and can move flexibly in multiple directions. The gripping device 20 is installed at the end of the robotic arm 19 and is used to grip and place the rotors 9. The conveyor belt 21 is responsible for transporting the rotors 9 to be processed to a designated position or removing the processed rotors 9 from the processing area. In actual operation, after a rotor 9 is processed, the robotic arm 19 uses the gripping device 20 to remove it from the automatic clamping mechanism 4 and place it on the conveyor belt 21. Then, it grips the next rotor 9 to be processed and installs it onto the automatic clamping mechanism 4. This process is fully automated, requiring no manual intervention, greatly improving production efficiency and reducing the possibility of human error.

[0072] When a batch of rotors 9 needs to be slotted, the rotors 9 to be processed are first placed on the conveyor belt 21. After the conveyor belt 21 transports them to the designated position, the robotic arm 19 uses the gripping device 20 to grip the rotors 9 and install them onto the automatic clamping mechanism 4. At this time, the automatic clamping mechanism 4 is activated, clamping the rotors 9 with the grippers 13 and the pressure sensor 15 confirms whether the clamping force is appropriate. Then, the transverse rotation mechanism 7 drives the rotors 9 to rotate to the initial processing position, while the sliding mechanism 10 drives the cutter 3 to move to the starting point. Before processing begins, the monitoring camera 8 scans the cutting area and transmits the image information to the central processing unit (CPU). The CPU calculates the optimal cutting parameters based on the vacuum pump processing system and sends them to the sliding mechanism 10 and the transverse rotation mechanism 7. Subsequently, the cutter 3 begins grooving the rotor 9. At the same time, the cooling water nozzle 5 continuously sprays cooling water to reduce the cutting temperature. The monitoring camera 8 captures the cutting status in real time and dynamically adjusts the cutting depth and speed of the cutter 3. After the first groove is processed, the transverse rotation mechanism 7 rotates the rotor 9 to the next processing position. The above steps are repeated until all grooves are processed. Finally, the robotic arm 19 uses the gripping device 20 to remove the processed rotor 9 and place it on the conveyor belt 21. Simultaneously, a new rotor 9 to be processed is installed on the automatic clamping mechanism 4, entering the next processing cycle.

[0073] The embodiments of the present invention have been described in detail above, but the content described is only a preferred embodiment of the present invention and should not be considered as limiting the scope of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of this patent.

Claims

1. A vacuum pump processing system based on a tank, characterized in that: This includes a cooling spray optimization subsystem, an online detection feedback correction subsystem, and a machining execution module; The cooling jet optimization subsystem includes a water flow distribution feature extraction module, a jet parameter calculation module, a dynamic adjustment module, and a water flow influence model construction module. The water flow distribution feature extraction module is configured with a preset water flow state recognition strategy for real-time acquisition of water flow distribution features in the processing area. The injection parameter calculation module is equipped with algorithms for calculating the optimal injection angle and shape parameters, which are used to calculate the optimal injection mode of cooling water based on the water flow distribution characteristics. The dynamic adjustment module generates dynamic adjustment commands to change the spray angle and shape parameters of the cooling water in real time. The water flow influence model construction module analyzes the impact of cooling water on the imaging effect of the target area, pre-constructs the water flow influence model, and generates corresponding theoretical detection features. The online detection feedback correction subsystem includes an image acquisition module, a geometric feature extraction module, a deviation information generation module, and a parameter calibration module; The image acquisition module acquires image information of the target area in the optimal spray mode; The geometric feature extraction module extracts actual detection features from the acquired images; The deviation information generation module generates deviation information by comparing actual detection features with theoretical detection features using a preset feature comparison algorithm. The parameter calibration module generates calibration instructions based on the deviation information, and adjusts the processing parameters and detection parameters through the calibration instructions. The water flow state identification strategy includes collecting water flow distribution data within the processing area at preset time intervals, calculating the water flow coverage value at each time point using a preset water flow coverage calculation algorithm, and selecting time points with water flow coverage values ​​greater than a preset benchmark value as key sampling points. The water flow uniformity value of key sampling points is calculated by a preset water flow uniformity calculation algorithm, and the key sampling point with the largest water flow uniformity value is taken as the key water flow state to be analyzed. The logic for generating dynamic adjustment commands by the dynamic adjustment module includes generating a continuous sequence of injection adjustment commands based on the changing trends of the optimal injection angle and shape parameters combined with the real-time heat load status of the processing area; the injection adjustment command sequence is optimized through a preset smooth transition algorithm. The expression for the water flow coverage calculation algorithm is as follows: C represents water flow coverage. Indicates the first The area covered by water flow This represents the total area of ​​the processing area. Indicates the number of covered areas; The expression for the algorithm for calculating the uniformity of water flow is: Indicates the uniformity of water flow. Indicates the first The area covered by water flow This represents the average area of ​​all covered regions. Indicates the number of covered areas; The expression for the algorithm for calculating the optimal injection angle and shape parameters is as follows: Indicates the optimal injection angle. Indicates the optimal jet shape parameters. This indicates the temperature change at the point of contact between the tool and the workpiece. This indicates the change in displacement in the direction of the cooling water jet. and These are the weighting coefficients; The logic for generating dynamic adjustment commands by the dynamic adjustment module includes: generating a continuous sequence of injection adjustment commands based on the changing trends of the optimal injection angle and shape parameters, combined with the real-time thermal load status of the processing area. The injection adjustment command sequence is optimized using a preset smooth transition algorithm to avoid interference from sudden changes in the injection mode to the processing. The expression for the smooth transition algorithm is: This indicates the injection adjustment command from the previous moment. Indicates a target injection adjustment command. This represents the smoothing factor, with a value range of 0 < ≤1.

2. The vacuum pump processing system based on a groove as described in claim 1, characterized in that, The water flow influence model construction module analyzes the imaging effect of cooling water under different spray modes to construct an initial water flow influence model, and uses AI to complete the boundary part of the initial model. After generating an index image from the completed water flow influence model, the theoretical detection features of the index image are estimated, and then stitched together to form the final water flow influence model.

3. The vacuum pump processing system based on a groove as described in claim 1, characterized in that, When the image acquisition module acquires image information of the target area in the optimal spray mode, it adopts a preset multispectral imaging technology. The multispectral imaging technology selects light sources with wavelengths of 500nm and 700nm to illuminate the target area, and combines filters to extract the characteristic spectral information of the target area.

4. The vacuum pump processing system based on a groove as described in claim 1, characterized in that, When the geometric feature extraction module extracts actual detection features from the acquired image, it uses a preset edge enhancement algorithm to preprocess the image.

5. The vacuum pump processing system based on a groove as described in claim 1, characterized in that, The deviation information generation module compares the actual detection features with the theoretical detection features using a preset feature comparison algorithm to generate deviation information. The feature comparison algorithm includes calculating the size deviation, shape deviation, and position deviation of the geometric features, and generating a deviation feature vector based on the deviation values. The deviation information also includes the ambient illuminance value, the spray parameter value, and the acquisition time value.

6. The vacuum pump processing system based on a groove as described in claim 1, characterized in that, The parameter calibration module generates calibration instructions based on the deviation information, and adjusts the cutting speed and feed parameters of the machining execution module, as well as the light source intensity and filter setting parameters of the online detection unit through the calibration instructions.

7. The vacuum pump processing system based on a groove as described in claim 1, characterized in that, The water flow distribution feature extraction module obtains the water flow distribution features of the processing area in real time through a water flow state recognition strategy, and transmits the water flow distribution features to the jet parameter calculation module.

8. The vacuum pump processing system based on a groove as described in claim 1, characterized in that, The theoretical detection features generated by the water flow influence model construction module are stored in the database, and the theoretical detection features under the corresponding jet parameters are mapped by index.

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