A control system and operating process for precise and intelligent cutting of sandpaper

Abstract

This invention discloses a control system and operating process for precise intelligent cutting of sandpaper, relating to the field of cutting control technology. It involves vacuum adsorption to fix the sandpaper while simultaneously acquiring and preprocessing images and multi-source pore blockage data; positioning the sandpaper through image recognition; and analyzing blockage trends using cosine similarity. If there is no risk, a path is planned and cutting is executed; otherwise, an early warning is issued. This invention simultaneously acquires pressure, flow rate, and current to construct a multi-dimensional fusion system. Through vectorization and cosine similarity calculation, it accurately distinguishes between blockage and operating condition fluctuations, solving the problem of misjudgment based solely on pressure. Furthermore, by using successive differential and positive acceleration proportional statistics to quantify the acceleration trend of the difference value, it captures early signs of blockage, providing an early warning before adsorption failure leads to displacement, significantly improving the timeliness of intervention.

Description

Technical Field

[0001] This invention relates to the field of cutting control technology, specifically to a control system and operating process for precise and intelligent cutting of sandpaper. Background Technology

[0002] In fields with extremely high requirements for surface treatment quality, such as precision manufacturing, 3C electronics, and automotive painting, sandpaper is a key consumable. Its cutting accuracy directly affects the sanding effect and product yield. The existing precision intelligent cutting control system consists of four modules working together: a vision recognition module uses a high-precision industrial camera and image algorithms to acquire the sandpaper outline, texture, and positioning points to achieve precise sandpaper alignment; a path planning and control module generates the optimal cutting trajectory based on visual data and uses servo drives and high-response motion control cards to ensure that the cutter head maintains trajectory accuracy during high-speed movement; a process adaptive module has a built-in sandpaper material database and adjusts the vacuum adsorption pressure in real time; and a status monitoring module collects the vacuum adsorption pressure in real time through a high-precision pressure sensor and combines it with static abnormal alarm thresholds to ensure stable system operation.

[0003] During the cutting process, the micropores and air channels of the vacuum adsorption platform maintain adsorption force to fix the sandpaper. However, when the adhesive layer in the sandpaper's own structure is squeezed out, it mixes with the abrasive dust generated during cutting and the oil mist emitted during equipment operation, gradually clogging the micropores and air channels of the vacuum adsorption platform. In the existing technology, the status monitoring module only monitors a single vacuum pressure value, which cannot accurately distinguish between the clogging state and normal operating condition fluctuations, and is prone to false alarms. Moreover, the static abnormal alarm threshold lacks dynamic perception of the attenuation trend of airflow in the micropores and air channels after clogging, and cannot intervene in adsorption failure in time, which in turn causes slight displacement of the sandpaper, ultimately causing a mismatch between the positioning coordinate system of the visual recognition module and the position of the sandpaper. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a control system and operating process for precise and intelligent cutting of sandpaper. This solves the problem in existing technologies where the status monitoring module relies solely on a single vacuum pressure value and static threshold for judgment, failing to distinguish between blockages and operating condition fluctuations, and lacking dynamic perception of airflow attenuation trends, making it difficult to intervene in adsorption failure in a timely manner, ultimately leading to a mismatch between sandpaper displacement and visual positioning.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a precise and intelligent cutting process for sandpaper, comprising the following specific steps: Step 1: Vacuum adsorption is used to fix the sandpaper, and image data and multi-source pore blockage data are acquired in real time and preprocessed; Step 2: The sandpaper is identified and located based on the image data; Step 3: The multi-source pore blockage data is calculated using the cosine similarity method to obtain the deviation value, and the trend of the deviation value is analyzed to predict pore blockage. If no blockage is predicted, Step 4 is executed; otherwise, Step 5 is executed; Step 4: The cutting path is planned, and a cutting command is sent to the cutting blade, and the cutting blade is controlled according to the cutting path; Step 5: An early warning is issued.

[0006] Furthermore, the deviation value is obtained as follows: the multi-source pore blockage data includes vacuum adsorption pressure value, airway flow rate value and vacuum pump motor current value. The adsorption pressure value, airway flow rate value and vacuum pump motor current value are standardized, and then the adsorption pressure value, airway flow rate value and vacuum pump motor current value are vectorized to obtain the actual adsorption vector. A normal adsorption vector is preset, and the cosine similarity between the actual adsorption vector and the normal adsorption vector is calculated to obtain the deviation value.

[0007] Furthermore, the actual adsorption vector is obtained by arranging the adsorption pressure value, the gas flow rate value, and the vacuum pump motor current value in a fixed order to form a multi-dimensional array, which is the actual adsorption vector, wherein the adsorption pressure value is in the first dimension, the gas flow rate value is in the second dimension, and the vacuum pump motor current value is in the third dimension.

[0008] Furthermore, the preset method for the normal adsorption vector is as follows: First, the adsorption pressure value, gas flow rate value and vacuum pump motor current value are averaged in a single normal operating period to obtain a feature vector representing this operating period. Then, the feature vectors in multiple normal operating periods are averaged twice to obtain the normal adsorption vector.

[0009] Furthermore, the specific method for determining the deviation value is as follows: ;in, Indicates the deviation value. This represents the actual adsorption vector. This represents the normal adsorption vector. This represents the magnitude of the actual adsorption vector. This represents the modulus of the normal adsorption vector.

[0010] Furthermore, the method for predicting pore blockage by analyzing the trend of the deviation value is as follows: calculate the difference between the deviation value and 1 to obtain the gap value, analyze the acceleration of the change of the gap value, if acceleration is found, it indicates that blockage is predicted, and if no acceleration is found, it indicates that no blockage is predicted.

[0011] Furthermore, the method for analyzing the accelerated change of the difference value is as follows: a preset detection time period is set. During the detection time period, the difference value at the next moment is calculated with the difference value at the previous moment to obtain the acceleration value. The positive or negative value of the acceleration value is analyzed and the proportion of positive acceleration values ​​is calculated to determine whether acceleration has occurred. If the positive acceleration value is greater than one-half, it indicates acceleration; otherwise, it indicates no acceleration.

[0012] Furthermore, the positive or negative analysis method for the acceleration value is as follows: compare the acceleration value with zero. If the acceleration value is greater than zero, it represents a positive acceleration value; if the acceleration value is less than zero, it represents a negative acceleration value; and if the acceleration value is equal to zero, it represents a zero acceleration value.

[0013] Furthermore, the calculation method for the proportion of positive acceleration values ​​is as follows: count the number of positive acceleration values, negative acceleration values, and zero acceleration values ​​respectively to obtain the number of positive acceleration values, the number of negative acceleration values, and the number of zero acceleration values. Count the number of positive acceleration values, the number of negative acceleration values, and the number of zero acceleration values ​​to obtain the total number. Calculate the ratio of the number of positive acceleration values ​​to the total number to obtain the proportion of positive acceleration values.

[0014] A control system for precise and intelligent cutting of sandpaper includes the following modules: a data acquisition module: acquiring image data and multi-source pore blockage data in real time and preprocessing them; a positioning module: identifying and locating the sandpaper based on the image data; a blockage trend prediction module: calculating the deviation value of the multi-source pore blockage data using the cosine similarity method, analyzing the trend of the deviation value to predict pore blockage, and if the prediction indicates no blockage, executing the path planning and control module; otherwise, executing the early warning module; a path planning and control module: planning the cutting path, sending cutting commands to the cutting blade, and controlling the cutting blade according to the cutting path; and an early warning module: issuing an early warning.

[0015] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects: 1. By simultaneously collecting vacuum adsorption pressure, airway flow rate, and vacuum pump motor current, a multi-dimensional data fusion system is constructed. The three physical quantities are vectorized in a fixed order and then the cosine similarity is calculated. This allows the characteristic pattern of pressure increase leading to flow decrease and then current increase caused by blockage to be mathematically represented as a clear deviation in vector direction. This accurately distinguishes between blockage and normal operating condition fluctuations, solving the technical problem that existing technologies can only monitor a single pressure value and cannot identify false vacuum conditions and are prone to false alarms.

[0016] 2. By calculating the acceleration value through successive differentials and statistically analyzing the proportion of positive acceleration values, the acceleration trend of the difference value is quantitatively analyzed, thereby capturing early signs of blockage deterioration. Compared with the traditional static threshold alarm method, it can issue an early warning before adsorption failure causes sandpaper displacement and visual mismatch, significantly improving the timeliness of intervention.

[0017] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0018] Figure 1 This is a flowchart of the dynamic posture analysis method of the present invention. Detailed Implementation

[0019] 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.

[0020] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus.

[0021] Example 1: like Figure 1 As shown, this embodiment of the invention provides a process for precise and intelligent cutting of sandpaper, including the following specific steps: Step 1: The system continuously outputs PWM signals to control the speed of the vacuum pump motor and dynamically adjusts the duty cycle to maintain the set negative pressure, ensuring that the sandpaper does not shift during the cutting process. It also acquires image data in real time at a fixed frame rate using a high-resolution industrial camera, performs Gaussian filtering for noise reduction and grayscale processing on the image data, and simultaneously samples through pressure sensors, flow sensors, and current sensors to acquire multi-source pore blockage data. The multi-source pore blockage data is then cleaned to remove redundant values. Step 2: Input the preprocessed image data into a deep learning-based semantic segmentation network to extract the sandpaper outline and feature points. Map the pixel coordinates to the world coordinate system through coordinate transformation and match them with a preset positioning point template to output the precise pose of the sandpaper. This achieves spatial alignment between the sandpaper and the cutting head, providing a benchmark for high-precision trajectory planning and avoiding cutting deviations caused by positioning errors. Step 3: Calculate the deviation value of the multi-source pore blockage data using the cosine similarity method, analyze the trend of the deviation value to predict pore blockage, and if the prediction indicates no blockage, proceed to Step 4; otherwise, proceed to Step 5. Step 4: Generate a trajectory including start and end points, transition arcs, and cutting speed based on the precise pose of the sandpaper. Send the trajectory interpolation points to the servo driver that drives the cutting blade in real time via the EtherCAT bus. The servo driver uses a position-speed-current three-closed-loop PID control algorithm to drive the cutting blade to move along the predetermined trajectory. Step 5: Send alarm codes and fault types to the HMI via the MODBUS TCP protocol, trigger the audible and visual alarms, and record the current sensor data and deviation values ​​to the log database. This will promptly notify operators to intervene and clean up or inspect the blockage, preventing further deterioration of the blockage that could lead to production interruptions or batch scrapping of products.

[0022] Example 2 differs from Example 1 in that: The deviation value is obtained as follows: Multi-source pore blockage data includes vacuum adsorption pressure, airway flow rate, and vacuum pump motor current. It comprehensively characterizes the blockage state from three dimensions: airway resistance, gas flow, and energy consumption, avoiding the limitations of single-parameter judgment. The adsorption pressure, airway flow rate, and vacuum pump motor current values ​​are standardized to eliminate dimensional differences and transform values ​​of different orders of magnitude into a unified numerical range. Furthermore, these values ​​are vectorized, fusing multi-dimensional physical signals into a single spatial point. This provides a unified mathematical carrier for subsequent similarity calculations. Vectorization unifies multi-source data into a single mathematical object, simplifying the complexity of subsequent calculations and logical judgments, significantly improving the system's processing efficiency and real-time performance. The actual adsorption vector is obtained, and a pre-defined normal adsorption vector is used. Cosine similarity calculation is performed between the actual and normal adsorption vectors to obtain the deviation value.

[0023] The actual adsorption vector is obtained as follows: The adsorption pressure value, airway flow rate value, and vacuum pump motor current value are arranged in a fixed order to form a multidimensional array. According to the fixed dimension order of the physical causal chain, the change in the direction of the vector directly corresponds to the blockage characteristics of pressure increase leading to flow decrease and then current increase, which is convenient for subsequent trend analysis. This array is the actual adsorption vector, in which the adsorption pressure value is in the first dimension, the airway flow rate value is in the second dimension, and the vacuum pump motor current value is in the third dimension.

[0024] The default setting for the normal adsorption vector is as follows: First, the adsorption pressure, gas flow rate, and vacuum pump motor current are averaged within a single normal operating period to obtain a feature vector representing this operating period. Sensor noise and short-term random fluctuations are eliminated by averaging within the operating period to obtain the stable state under this period. Then, the feature vectors from multiple normal operating periods are averaged twice. By combining the stable states of multiple different periods, accidental process disturbances are further eliminated to obtain the normal adsorption vector.

[0025] The specific method for determining the deviation value is as follows: ; in, The deviation value is represented by cosine similarity, which measures the directional consistency between two vectors. When congestion causes changes in the proportional relationship of multidimensional parameters, the deviation value decreases from 1, providing a sensitive and stable quantitative basis for trend judgment. This represents the actual adsorption vector. This represents the normal adsorption vector. This represents the magnitude of the actual adsorption vector. This represents the modulus of the normal adsorption vector.

[0026] The analytical method for predicting pore blockage based on the trend of deviation values ​​is as follows: The difference between the deviation value and 1 is calculated to obtain the gap value. The accelerated change of the gap value is analyzed, and the cosine similarity is converted into a gap index. The larger the difference value, the further it deviates from the normal pattern, which is convenient for subsequent analysis of the change trend. That is, when the mixture of adhesive layer, dust and oil mist adheres to the inner wall of the micropore, the effective flow cross-sectional area decreases and the air resistance increases accordingly. The increase in resistance leads to a decrease in airflow velocity and a decrease in the ability to carry particulate matter, making it easier for blockages to accumulate more rapidly at the existing blockage. At the same time, the increase in resistance forces the vacuum pump to increase the load, and the temperature rise caused by the increase in motor current may further soften the adhesive layer, making it easier to flow and adhere. This creates a vicious cycle, causing the blockage to deteriorate nonlinearly over time rather than at a uniform rate. Based on this characteristic, by monitoring whether the deviation of the actual state from the normal pattern shows an accelerating trend, signs of deterioration can be captured in the early stage of blockage, achieving an earlier warning than the fixed threshold method. If acceleration is analyzed, it indicates that blockage is predicted; if no acceleration is analyzed, it indicates that no blockage is predicted.

[0027] The method for analyzing the accelerated change in the gap value is as follows: A preset detection time period is used. Within this period, the difference between the next time interval and the previous time interval is calculated to obtain the acceleration value. The time series of the difference value is converted into a rate of change series through successive differencing to quantify the rate of change in each sampling interval. The positive or negative value of the acceleration value is analyzed and the proportion of positive acceleration values ​​is calculated to determine whether acceleration has occurred. The overall direction of change of the difference value is judged from a probabilistic perspective to avoid misjudgment due to single fluctuations. If the positive acceleration value is greater than half, it indicates acceleration; otherwise, it indicates no acceleration.

[0028] The method for analyzing the positive and negative values ​​of acceleration values ​​is as follows: The acceleration value is compared with zero. If the acceleration value is greater than zero, it indicates a positive acceleration value, marking the period when the difference value is increasing, reflecting that the congestion is worsening. If the acceleration value is less than zero, it indicates a negative acceleration value, marking the period when the difference value is decreasing, reflecting that the congestion may be temporarily relieved or the system has recovered. If the acceleration value is equal to zero, it indicates a zero acceleration value, marking the period when the difference value remains unchanged, reflecting that the state is stable.

[0029] The proportion of positive acceleration values ​​is calculated as follows: The number of positive, negative, and zero acceleration values ​​is counted separately to obtain the total number of positive, negative, and zero acceleration values. The ratio of the positive acceleration values ​​to the total number is calculated to obtain the proportion of positive acceleration values. The ratio is used to quantify the strength of the acceleration trend. The higher the ratio, the longer the time that the gap value continues to increase, and the more significant the congestion acceleration.

[0030] Example 3: A control system for precise and intelligent cutting of sandpaper includes the following specific modules: Data acquisition module: acquires image data and multi-source pore blockage data in real time, and performs preprocessing; Positioning module: Identifies and locates the sandpaper based on image data; Blockage Trend Prediction Module: Calculates the deviation value from the multi-source pore blockage data using the cosine similarity method, analyzes the changing trend of the deviation value to predict pore blockage, and executes the path planning and control module if no blockage is predicted; otherwise, it executes the early warning module. Path planning and control module: plans the cutting path, sends cutting commands to the cutting blade, and controls the cutting blade according to the cutting path; Early warning module: Issues an early warning.

[0031] The preferred embodiments disclosed in this invention are merely illustrative examples of feasible implementation methods and are not intended to exhaustively cover all technical details of the invention, nor do they constitute a limitation on the scope of protection of this invention. In practical applications, those skilled in the art can make appropriate adjustments, combinations, or substitutions to the methods or systems described in these embodiments based on specific production conditions, equipment configurations, and process requirements, without departing from the core concept of this invention. For example, the acquisition method, data processing algorithm, control threshold, or specific implementation form of the execution unit can all be reasonably modified according to the actual situation.

[0032] Furthermore, the technical concepts disclosed in this invention have universal extensibility and adaptability. They are not only applicable to the specific scenarios described in the embodiments, but can also be applied in similar technical fields or related industrial processes through analogy, transplantation, or improvement. Any technical solution formed by making logically equivalent substitutions, reasonable adjustments to the order of steps, or recombination of module functions based on the principles, ideas, or framework disclosed in this specification should be considered to fall within the spirit and scope of this invention.

[0033] It should be further clarified that the specific descriptions and drawings in the patent documents are for the purpose of assisting in understanding the present invention only, and their details should not be interpreted as limitations on the claims. The true scope of protection of the present invention should be determined by the content of the claims recorded in the authorized text, and should cover all equivalent technical solutions that comply with the provisions of patent law under these claims. Any implementation method that has the same or similar function and achieves similar effects through reasonable changes in technical means under the guidance of the concept of the present invention falls within the scope of protection sought by the present invention.

[0034] Therefore, the descriptions in this specification are merely illustrative. Any adjustments to implementation methods, equivalent substitutions of technical features, or further applications based on the concept of this invention, as long as they do not depart from the overall technical approach described in this invention, should be included within the scope of protection of this invention. We encourage those skilled in the art to innovate and optimize based on their understanding of the core of this invention and in conjunction with specific practices, so as to jointly promote the progress and development of related technologies.

Claims

1. A process for precise and intelligent cutting of sandpaper, characterized in that: The specific steps include the following: Step 1: Vacuum adsorption keeps the sandpaper fixed, and real-time image data and multi-source pore blockage data are acquired and pre-processed; Step 2: Identify and locate the sandpaper based on the image data; Step 3: Calculate the deviation value of the multi-source pore blockage data using the cosine similarity method, analyze the trend of the deviation value to predict pore blockage, and if the prediction indicates no blockage, proceed to Step 4; otherwise, proceed to Step 5. Step 4: Plan the cutting path, send cutting instructions to the cutting blade, and control the cutting blade according to the cutting path; Step 5: Issue an early warning.

2. The process for precise and intelligent cutting of sandpaper according to claim 1, characterized in that: The deviation value is obtained in the following way: The multi-source pore blockage data includes vacuum adsorption pressure value, airway flow rate value, and vacuum pump motor current value. The adsorption pressure value, airway flow rate value, and vacuum pump motor current value are standardized and then vectorized to obtain the actual adsorption vector. A normal adsorption vector is preset, and the cosine similarity between the actual adsorption vector and the normal adsorption vector is calculated to obtain the deviation value.

3. The operation process for precise and intelligent cutting of sandpaper according to claim 2, characterized in that: The actual adsorption vector is obtained as follows: The adsorption pressure value, gas flow rate value, and vacuum pump motor current value are arranged in a fixed order to form a multidimensional array. This array is the actual adsorption vector, where the adsorption pressure value is in the first dimension, the gas flow rate value is in the second dimension, and the vacuum pump motor current value is in the third dimension.

4. The operation process for precise and intelligent cutting of sandpaper according to claim 3, characterized in that: The preset method for the normal adsorption vector is as follows: First, the average values ​​of adsorption pressure, gas flow rate, and vacuum pump motor current are calculated for each single normal operating period to obtain a feature vector representing this operating period. Then, the feature vectors from multiple normal operating periods are averaged twice to obtain the normal adsorption vector.

5. The operation process for precise and intelligent cutting of sandpaper according to claim 4, characterized in that: The specific method for determining the deviation value is as follows: ； in, Indicates the deviation value. This represents the actual adsorption vector. This represents the normal adsorption vector. This represents the magnitude of the actual adsorption vector. This represents the modulus of the normal adsorption vector.

6. The operation process for precise and intelligent cutting of sandpaper according to claim 5, characterized in that: The method for analyzing the trend of the deviation value to predict pore blockage is as follows: The difference between the deviation value and 1 is calculated to obtain the gap value. The acceleration change of the gap value is analyzed. If acceleration is found, it indicates that a blockage is predicted. If no acceleration is found, it indicates that no blockage is predicted.

7. The operation process for precise and intelligent cutting of sandpaper according to claim 6, characterized in that: The method for analyzing the accelerated change in the gap value is as follows: A preset detection time period is set. Within the detection time period, the difference between the difference value at the next moment and the difference value at the previous moment are calculated to obtain the acceleration value. The positive and negative values ​​of the acceleration value are analyzed and the proportion of positive acceleration values ​​is calculated to determine whether acceleration has occurred. If the positive acceleration value is greater than half, it indicates acceleration; otherwise, it indicates no acceleration.

8. The operation process for precise and intelligent cutting of sandpaper according to claim 7, characterized in that: The method for analyzing the positive or negative value of the acceleration is as follows: The acceleration value is compared with zero. If the acceleration value is greater than zero, it represents a positive acceleration value. If the acceleration value is less than zero, it represents a negative acceleration value. If the acceleration value is equal to zero, it represents a zero acceleration value.

9. The operation process for precise and intelligent cutting of sandpaper according to claim 8, characterized in that: The proportion of positive acceleration values ​​is calculated as follows: Count the number of positive acceleration values, negative acceleration values, and zero acceleration values ​​respectively to obtain the number of positive acceleration values, negative acceleration values, and zero acceleration values. Count the number of positive acceleration values, negative acceleration values, and zero acceleration values ​​to obtain the total number. Calculate the ratio of the number of positive acceleration values ​​to the total number to obtain the proportion of positive acceleration values.

10. A control system for precise intelligent cutting of sandpaper, used to implement the operation process for precise intelligent cutting of sandpaper as described in any one of claims 1-9, characterized in that, The control system for precise and intelligent cutting of sandpaper includes: Data acquisition module: acquires image data and multi-source pore blockage data in real time, and performs preprocessing; Positioning module: Identifies and locates the sandpaper based on image data; Blockage Trend Prediction Module: Calculates the deviation value from the multi-source pore blockage data using the cosine similarity method, analyzes the changing trend of the deviation value to predict pore blockage, and executes the path planning and control module if no blockage is predicted; otherwise, it executes the early warning module. Path planning and control module: plans the cutting path, sends cutting commands to the cutting blade, and controls the cutting blade according to the cutting path; Early warning module: Issues an early warning.

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