Intelligent adaptive polishing system and control method thereof

Abstract

The application discloses an intelligent adaptive polishing system and a control method thereof. The system comprises a polishing execution unit, a sensing unit, an intelligent control unit and a storage and optimization unit. The polishing execution unit is used for adjusting the polishing speed and pressure; the sensing unit is used for monitoring the pressure, temperature and vibration parameters in the polishing process in real time; the intelligent control unit is used for automatically adjusting the polishing parameters through data analysis and feedback control algorithm to realize the adaptive control of the polishing process; and the storage and optimization unit is used for storing and analyzing the polishing data and gradually optimizing the polishing strategy through self-learning. The control method comprises the steps of initialization setting, real-time monitoring and data acquisition, data analysis and feedback adjustment, self-learning and data optimization, and realizes the high-precision polishing effect of different workpieces. Through the application, the polishing process is more intelligent and efficient, is suitable for various materials and workpiece shapes, and meets the precision and consistency requirements of industrial surface processing.

Description

Technical Field

[0001] This invention relates to the field of industrial surface treatment, and in particular to an intelligent adaptive polishing system and its control method, which achieves high-precision machining of workpiece surfaces by automatically adjusting polishing parameters. Background Technology

[0002] Polishing is a widely used process in manufacturing. Traditional polishing methods rely on manual operation, lacking precise and consistent control, making it difficult to adapt to the surface shapes of complex workpieces and the properties of various materials, easily leading to unstable processing quality. Some existing automated polishing systems have basic parameter setting functions, but they cannot dynamically respond to changes in the polishing process, resulting in inconsistent quality. To solve these problems, there is an urgent need for an intelligent adaptive polishing system that can monitor and adjust polishing parameters in real time to ensure stable and consistent surface treatment results for different workpieces. Summary of the Invention

[0003] The purpose of this invention is to provide an intelligent adaptive polishing system and its control method. Through real-time data monitoring, intelligent algorithm regulation and self-learning mechanism, it can adapt to workpieces with different materials, shapes and surface treatment requirements, optimize the polishing process, and achieve high-precision and high-consistency surface processing effects.

[0004] The intelligent adaptive polishing system of the present invention includes:

[0005] 1. Polishing execution unit:

[0006] It includes an adjustable speed motor, polishing head, pressure control device, and positioning table. The polishing execution unit performs polishing operations under intelligent control and can dynamically adjust the polishing speed and pressure to adapt to the needs of different workpieces.

[0007] 2. Sensing Unit:

[0008] It includes pressure sensors, temperature sensors, and vibration sensors, which are used to monitor the pressure, temperature, and equipment vibration during the polishing process in real time, ensuring that the polishing parameters are within a stable range.

[0009] 3. Intelligent control unit:

[0010] It includes a data processing module and an adaptive control module, integrating various control algorithms (such as PID control, fuzzy control, etc.) and machine learning models. It can analyze sensor data and make real-time adjustments to achieve adaptive control.

[0011] 4. Storage and Optimization Unit:

[0012] This data is used to store the data collected during the polishing process, forming a historical dataset. The polishing strategy is then optimized through machine learning to achieve intelligent self-learning and adaptive capabilities.

[0013] The control method of the present invention includes the following steps:

[0014] 1. Initialization settings:

[0015] Based on the workpiece's material, surface roughness requirements, and machining accuracy, set the initial polishing parameters, including polishing speed, pressure range, and temperature range.

[0016] 2. Real-time monitoring and data acquisition:

[0017] During the polishing process, the sensing unit monitors pressure, temperature, and vibration in real time, with a data acquisition frequency of 100 times per second to ensure timely response to subtle changes. For example, in experiments on metal workpieces, the real-time pressure is maintained between 19N and 21N, and the temperature between 25°C and 30°C.

[0018] 3. Data analysis and feedback adjustments:

[0019] After receiving sensor data, the intelligent control unit analyzes the data changes through a feedback control algorithm. Taking pressure control as an example, if the pressure exceeds 25N, the system will reduce the pressure of the polishing head; when the temperature exceeds the set 40°C, the system will reduce the polishing speed to cool down. For example, when the temperature exceeds 40°C, the polishing speed will decrease from 1500RPM to 1000RPM until the temperature recovers.

[0020] 4. Self-learning and data optimization:

[0021] After polishing, the system stores the operational data in the storage unit. Based on historical data analysis using machine learning algorithms, the system gradually optimizes the polishing strategy. Through analysis of multiple processing runs of metal workpieces, the system automatically adjusts the initial metal polishing speed to the range of 1300 RPM to 1400 RPM to improve processing results.

[0022] The beneficial effects of this invention are as follows:

[0023] The shaft cover grinding fixture of the present invention has significant advantages. First, the fixture has a simple structure, concise design, low manufacturing cost, and is easy to maintain and operate, making it suitable for large-scale production and use. The adjustable support structure allows for precise adjustment according to the size and shape of the workpiece, ensuring accurate positioning during processing and thus improving machining accuracy. The clamping mechanism securely fixes the workpiece with multiple locking screws, preventing loosening or displacement during processing and ensuring workpiece stability and machining quality. The adjustable design of the support structure allows it to adapt to workpieces of different sizes and shapes, increasing the fixture's applicability and meeting diverse processing needs. Both the base and support structure are made of high-strength aluminum alloy, which not only reduces the fixture's weight but also improves its corrosion resistance and durability, extending its service life. Furthermore, the fixture design of the present invention considers rapid workpiece loading and unloading, simplifying the operation process, improving processing efficiency, and making it suitable for high-efficiency production environments. Through the above effects, the shaft cover grinding fixture of the present invention has significant advantages in improving machining accuracy, stability, and efficiency, and is suitable for various machining scenarios. Attached Figure Description

[0024] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0025] Figure 1 This is a flowchart of the intelligent adaptive polishing system of the present invention; Detailed Implementation

[0026] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. 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.

[0027] like Figure 1 As shown, the specific implementation of the intelligent adaptive polishing system and its control method of the present invention is as follows:

[0028] System initial settings

[0029] Before the polishing operation begins, initial polishing parameters are set based on the workpiece's material, surface roughness requirements, and target machining accuracy. The system references historical data stored in the unit to more accurately set the initial polishing speed, pressure, and temperature range. For example:

[0030] 1) For metal workpieces, the initial polishing speed is set to 1500 RPM, the initial pressure is 20 N, and the temperature control range is 20℃ to 40℃.

[0031] 2) For plastic workpieces, the initial polishing speed is set to 1000 RPM, the initial pressure is 10 N, and the temperature is controlled between 15℃ and 30℃.

[0032] By setting these initial parameters, the system has better adaptability in the processing of workpieces made of different materials.

[0033] Real-time monitoring and data acquisition

[0034] During the polishing process, the pressure sensor, temperature sensor, and vibration sensor of the sensing unit monitor various parameters in real time. For example:

[0035] 1) The pressure sensor monitors the pressure applied to the workpiece surface to ensure that it remains within the initially set range of ±5N.

[0036] 2) The temperature sensor detects the temperature change on the workpiece surface. If the temperature rises too quickly, the system will adjust it through the intelligent control unit to prevent the temperature from exceeding the set maximum value.

[0037] 3) Vibration sensors monitor the stability of the polishing equipment to ensure smooth system operation and avoid affecting polishing quality due to vibration.

[0038] Data analysis and feedback adjustment

[0039] After receiving the sensor data, the intelligent control unit analyzes the data fluctuations in real time through a feedback control algorithm and makes dynamic adjustments based on the set tolerance range.

[0040] 1) Pressure feedback control: When the pressure detection value exceeds the set range (e.g., exceeding 25N), the control unit will reduce the contact pressure of the polishing head to avoid excessive wear on the workpiece surface. When the detected pressure is lower than the set range (e.g., below 15N), the system will increase the polishing head pressure to ensure a uniform surface polishing effect.

[0041] 2) Temperature Feedback Control: If the temperature sensor detects that the temperature exceeds the set value (e.g., above 40°C), the control unit will cool the area by reducing the polishing speed or decreasing the contact pressure. For example, if the temperature exceeds 40°C...

[0042] C. The polishing speed will automatically decrease from 1500 RPM to 1000 RPM until the temperature returns to a safe range. 3) Vibration monitoring and adjustment: If the vibration sensor detects that the vibration frequency exceeds the normal range, the system will temporarily stop or fine-tune the operation. For example, if the vibration frequency exceeds the set 3Hz, the system will reduce the polishing speed to 800 RPM and gradually adjust it to maintain stability.

[0043] The data acquisition frequency was set to 100 times per second to ensure timely response to subtle changes during the processing. For example, in one experiment, the pressure range was maintained between 19N and 21N, and the temperature was stabilized between 25℃ and 30℃ during real-time data acquisition of the metal workpiece.

[0044] Example 1: Polishing of metal workpieces

[0045] In a polishing test of a batch of metal workpieces, the system was set with an initial speed of 1500 RPM, an initial pressure of 20 N, and a temperature control range of 20℃ to 40℃. The real-time data collected by the sensing unit during the polishing process is analyzed as follows:

[0046] 1) Pressure: Real-time data is maintained between 19N and 21N. After detecting brief pressure fluctuations, the system immediately maintains stability through feedback control.

[0047] 2) Temperature: The temperature fluctuation range is 26℃ to 35℃. When the temperature reaches 35℃, the system automatically reduces the polishing speed to 1200RPM and resumes the original speed after the temperature returns to 30℃.

[0048] 3) Vibration: The vibration frequency is kept below 2.5Hz during the polishing process, and the system operates smoothly.

[0049] After 20 minutes of polishing, the surface roughness of the metal workpiece reached the preset standard of 0.8μm, and the polished surface was smooth and free of scratches.

[0050] Example 2: Polishing of plastic workpieces

[0051] For a batch of plastic workpieces, the system was set with an initial speed of 1000 RPM, an initial pressure of 10 N, and a temperature controlled between 15℃ and 30℃. Data acquisition and analysis during the polishing process are as follows:

[0052] 1) Pressure: During polishing, the pressure sensor data showed 9N to 11N. The system detected several fluctuations, but rapid adjustment was achieved through feedback control.

[0053] 2) Temperature: During the polishing process, the temperature gradually increases from 20℃ to 30℃ without exceeding the set range. The system adjusts the speed to 800RPM as needed based on temperature changes to ensure temperature stability.

[0054] 3) Vibration: The vibration sensor detected a frequency between 1.8Hz and 2.2Hz, indicating stable equipment operation.

[0055] After polishing, the surface roughness of the plastic workpiece reached the standard of 1.2μm, with no obvious surface damage, meeting the requirements for the smoothness of the plastic surface.

[0056] Self-learning and data optimization

[0057] After each polishing cycle, the system stores all data from that operation, including initial settings, adjustments made during the process, and the final result, in a storage unit. Through machine learning algorithms, the system comprehensively analyzes all historical data to continuously optimize the polishing strategy. For example:

[0058] 1) Parameter optimization: After polishing metal workpieces multiple times, the system found that when the polishing speed varies between 1300 RPM and 1400 RPM, it can reduce surface temperature fluctuations without sacrificing efficiency, thus setting the initial speed for future metal workpiece polishing within this range.

[0059] 2) Adaptability Enhancement: For different types of materials, the system analyzes historical data on temperature, pressure and vibration to gradually form polishing schemes that are more adaptable to the characteristics of various materials. For example, it automatically distinguishes the polishing temperature control range of plastic and metal materials and stores the adjustment strategy in the data model.

[0060] Through a self-learning mechanism, the system adjusts parameters according to the different characteristics of the workpiece after each polishing operation, ensuring that it can handle different types of workpieces more efficiently in future operations and meet the requirements of different surface finishes.

[0061] The embodiments described herein are preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Therefore, all equivalent changes made in accordance with the structure, shape, and principle of the present invention should be covered within the scope of protection of the present invention.

Claims

1. An intelligent adaptive polishing system, comprising a polishing execution unit, a sensing unit, an intelligent control unit, and a storage and optimization unit, characterized in that: The polishing execution unit includes an adjustable speed motor, a polishing head, a pressure control device, and a positioning stage, used to adjust the polishing speed and pressure during the polishing process to adapt to the polishing requirements of different workpieces. The sensing unit includes a pressure sensor, a temperature sensor, and a vibration sensor, used to monitor the pressure, temperature, and vibration parameters during the polishing process in real time. The intelligent control unit includes a data processing module and an adaptive control module. The data processing module receives monitoring data from the sensing unit, and the adaptive control module automatically adjusts the operating parameters of the polishing execution unit based on the monitoring data. The storage and optimization unit stores the operating data during the polishing process and analyzes and optimizes historical data based on machine learning to form the best polishing scheme adapted to different workpieces.

2. The intelligent adaptive polishing system according to claim 1, characterized in that, The adjustable speed motor of the polishing execution unit is used to control the rotation speed of the polishing head. The polishing head polishes the workpiece by being driven by the motor. The pressure control device is used to adjust the pressure applied by the polishing head to the surface of the workpiece. The positioning table is used to stabilize the position of the workpiece.

3. The intelligent adaptive polishing system according to claim 1 or 2, characterized in that, The intelligent control unit includes a feedback control module, which analyzes the data from the sensing unit in real time, detects fluctuations in various parameters during the polishing process, and dynamically adjusts the speed or pressure of the polishing execution unit based on a set tolerance range.

4. The intelligent adaptive polishing system according to claim 3, characterized in that, The feedback control module, based on real-time data from the pressure sensor, reduces the pressure of the polishing head to avoid excessive wear on the workpiece surface when the pressure exceeds the set range; and increases the pressure of the polishing head to ensure a uniform surface polishing effect when the pressure is below the set range.

5. The intelligent adaptive polishing system according to claim 3, characterized in that, The feedback control module adjusts the temperature by reducing the polishing head speed or reducing the pressure when the temperature exceeds the set safety range, based on the real-time data from the temperature sensor, in order to prevent the workpiece surface from being damaged by overheating.

6. The intelligent adaptive polishing system according to claim 3, characterized in that, The feedback control module, based on real-time data from the vibration sensor, temporarily stops the polishing operation or adjusts the polishing speed to maintain equipment stability when it detects that the vibration frequency exceeds the set normal range.

7. An intelligent adaptive polishing control method, applied to the intelligent adaptive polishing system according to any one of claims 1 to 6, characterized in that, The process includes the following steps: initial setup, setting the initial polishing speed, pressure, and temperature range according to the workpiece's material, surface roughness requirements, and processing accuracy; real-time monitoring and data acquisition, collecting pressure, temperature, and vibration data in real time through the sensing unit during the polishing process; data analysis and feedback adjustment, where the intelligent control unit receives the sensing data and analyzes fluctuations through the feedback control module, dynamically adjusting the speed or pressure of the polishing execution unit based on a preset tolerance range; and self-learning and data optimization, where after polishing, the data from this operation is stored in the storage and optimization unit, and machine learning is used to analyze historical data to form optimal polishing parameters suitable for different workpieces, thereby improving subsequent polishing effects.

8. The intelligent adaptive polishing control method according to claim 7, characterized in that, The initial setting parameters in the initialization setting step can be optimized based on historical data in the storage unit to improve the system's adaptability to different workpieces.

9. The intelligent adaptive polishing control method according to claim 7 or 8, characterized in that, In the data analysis and feedback adjustment step, when the pressure or temperature exceeds the preset range, the intelligent control unit adjusts the polishing speed or pressure based on the feedback control algorithm to maintain the consistency of the polishing effect.

10. The intelligent adaptive polishing control method according to any one of claims 7 to 9, characterized in that, In the self-learning and data optimization step, the parameter settings are optimized by analyzing historical data from multiple polishing operations, so that the system can form the best polishing scheme applicable to various materials and workpiece shapes.

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