Numerical control machining center for fasteners and machining method

Abstract

The invention discloses a numerical control machining center and machining method for fasteners, and the numerical control machining center comprises a clamping module used for clamping the fasteners on a process plate based on machining specification requirements; the visual module is used for carrying out workpiece calibration treatment on the fasteners on the process plate through visual photographing; the numerical control module is used for generating a machining control strategy for the fastener subjected to the workpiece calibration processing based on the machining parameters; and the multi-axis linkage module is electrically connected with the numerical control module and comprises at least three motion axes, and each motion axis drives a cutter to move according to the machining control strategy. The problems that in traditional fastener numerical control machining, the clamping error is large, workpiece calibration is difficult, the machining control strategy generation is not accurate, and multi-axis linkage machining coordination and accuracy are insufficient are solved, so that the machining precision and production efficiency of fasteners are improved, and the rejection rate is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of parts processing, and in particular to a numerical control processing center for fasteners and a processing method thereof. Background Art

[0002] Fasteners are important components for connecting and fixing parts, and their processing accuracy and quality directly affect the overall performance and reliability of mechanical equipment. There are many technical difficulties in the traditional CNC processing of fasteners. On the one hand, the clamping method of fasteners is not accurate and efficient enough. Relying on manual or simple tooling for clamping, it is difficult to ensure that the position of the fasteners on the process board meets the processing specifications. The clamping error is large, resulting in the dimensional accuracy of the processed fasteners being difficult to meet the design standards. On the other hand, the workpiece calibration process often lacks effective technical means, and it is impossible to quickly and accurately calibrate the fasteners on the process board, affecting the subsequent processing accuracy and efficiency. In addition, in the processing control strategy generation and multi-axis linkage processing links, the existing technology also has certain limitations. It is impossible to flexibly generate accurate processing control strategies based on the specific processing parameters of the fasteners. The coordination and accuracy of multi-axis linkage motion are insufficient, making it difficult for the tool motion trajectory to accurately match the processing requirements, resulting in low processing efficiency and a high product scrap rate. Summary of the Invention

[0003] The present invention aims to at least partially address one of the technical problems encountered in the aforementioned technologies. To this end, the present invention provides a CNC machining center and machining method for fasteners that address the problems of large clamping errors, difficult workpiece calibration, inaccurate machining control strategy generation, and insufficient coordination and accuracy in multi-axis machining, as encountered in conventional CNC machining of fasteners. This approach improves fastener machining accuracy, production efficiency, and reduces scrap rates.

[0004] To achieve the above objectives, an embodiment of the present invention provides a CNC machining center for fasteners, comprising:

[0005] Clamping module, used to clamp fasteners to the process board based on processing specification requirements;

[0006] Vision module, used to calibrate the fasteners on the process board through visual photography;

[0007] A numerical control module is used to generate a machining control strategy based on machining parameters for the fastener after workpiece calibration;

[0008] The multi-axis linkage module is electrically connected to the CNC module and includes at least three motion axes. Each motion axis drives the tool to move according to the machining control strategy.

[0009] According to some embodiments of the present invention, the vision module includes:

[0010] An acquisition module, used for acquiring images of the fasteners on the process board by visual photography;

[0011] The comparison module is used to compare the captured image with the preset database to obtain a matching image, and perform workpiece calibration based on the matching image.

[0012] According to some embodiments of the present invention, the numerical control module includes:

[0013] A cutting parameter acquisition module is used to determine the material properties and tool type of the fastener after the workpiece calibration process, input the material properties and tool type into a pre-built fuzzy rule library, and output cutting parameters; the cutting parameters include cutting speed, feed rate and cutting depth;

[0014] Path planning module, used to plan tool paths;

[0015] The first generation module is used to determine the processing parameters according to the cutting parameters and the tool path, and generate the processing control strategy based on the processing parameters.

[0016] According to some embodiments of the present invention, the path planning module includes:

[0017] An extraction module is used to generate a three-dimensional model of the fastener after the workpiece calibration process, pre-process the three-dimensional model, and extract processing features;

[0018] The second generation module is used to plan and generate the initial tool path based on the processing characteristics and processing technology requirements;

[0019] The optimization module is used to optimize the initial tool path using genetic algorithm to obtain the final tool path.

[0020] According to some embodiments of the present invention, the present invention further includes: a tool monitoring module for monitoring the tool state and issuing an alarm when determining that the tool state is abnormal;

[0021] The tool monitoring module includes:

[0022] A first recognition module is used to obtain a monitoring image of the tool, recognize the monitoring image, and obtain first monitoring information;

[0023] The second recognition module is used to collect vibration signals and sound signals of the tool during the cutting process, recognize the vibration signals and sound signals, and obtain second monitoring information;

[0024] The first determining module is used to determine the state of the tool according to the first monitoring information and the second monitoring information, and to issue an alarm when it is determined that the state of the tool is abnormal.

[0025] According to some embodiments of the present invention, the first identification module includes:

[0026] A filtering module is used to filter the monitoring image to obtain a filtered image;

[0027] The second determining module is configured to:

[0028] Using a preset contour detection algorithm to extract the contour of the filtered image to obtain a contour image;

[0029] Extract key points from the contour image to obtain target key points;

[0030] Extract key points from the preset contour images in the preset contour image library to obtain preset key points corresponding to each preset contour image;

[0031] The lower left corner of the contour image is used as the coordinate origin to obtain the target coordinates of the target key point, and the target feature vector of the contour image is determined based on the target coordinates;

[0032] Taking the lower left corner of each preset contour image as the coordinate origin, obtaining the coordinates of the preset key points corresponding to the preset contour image, and determining the preset feature vector corresponding to the preset contour image based on the coordinates of the preset key points;

[0033] Calculate the cosine distance between the target feature vector and each preset feature vector;

[0034] The cosine distance is compared with a preset distance threshold to obtain comparison information, and the first monitoring information is obtained according to the comparison information.

[0035] According to some embodiments of the present invention, the filtering module includes:

[0036] The third determining module is configured to:

[0037] Determine any pixel point in the monitoring image as a target pixel point, and determine a target area centered on the target pixel point; the target area is an N×N pixel matrix, where N is an even number; divide the target area into four square areas with a side length of N / 2, and select one of them as the square area to be processed; obtain the pixel mean, pixel median, and pixel mode value of all pixels in the square area to be processed; obtain the sum of the pixel mean, pixel median, and pixel mode values, determine a first mean value corresponding to the pixel mean, pixel median, and pixel mode value based on the sum value, and use the first mean value as a first estimated value of the target pixel point;

[0038] Repeat the above operation for all other square areas to obtain the second estimated value, the third estimated value, and the fourth estimated value of the target pixel;

[0039] Replacement modules for:

[0040] Obtaining a target pixel value of a target pixel point, calculating the absolute values ​​of the differences between the target pixel value and the first estimated value, the second estimated value, the third estimated value, and the fourth estimated value, respectively, and taking the estimated value corresponding to the minimum absolute value as the first filtered pixel value of the target pixel point;

[0041] Determining a first weight corresponding to the first filtered pixel value according to a first preset weight function;

[0042] Obtaining a target gradient value of a target pixel point, and determining a first coefficient according to the target gradient value;

[0043] Get the target pixel mean of all pixels in the target area;

[0044] Convert the image into a grayscale image, determine the target grayscale area corresponding to the target area in the grayscale image, and obtain the grayscale pixel mean of all pixels in the target grayscale area;

[0045] Determine a second coefficient according to the first coefficient, the target pixel mean and the grayscale pixel mean;

[0046] Obtaining a target pixel value of a target pixel point, and determining a second filtered pixel value corresponding to the target pixel point based on the target pixel value, the first coefficient, the second coefficient, and a preset filtering formula;

[0047] Determining a second weight corresponding to the second filtered pixel value according to a second preset weight function;

[0048] Calculating a first product of the first filtered pixel value and the first weight and a second product of the second filtered pixel value and the second weight, and taking the sum of the first product and the second product as the filtered pixel value of the target pixel; replacing the target pixel value of the target pixel with the filtered pixel value to obtain a filtered pixel corresponding to the target pixel;

[0049] The above operation is performed on all pixels in the monitoring image to obtain the filtered pixel corresponding to each pixel, and all the filtered pixels constitute a filtered image.

[0050] According to some embodiments of the present invention, the multi-axis linkage module includes X-axis linear motion, Y-axis linear motion and Z-axis linear motion axes, and at least one rotation axis; the X-axis linear motion, Y-axis linear motion and Z-axis linear motion axes are used to realize the linear motion of the tool in three mutually perpendicular directions; the rotation axis is used to realize the rotational motion of the tool around a specific axis.

[0051] According to some embodiments of the present invention, the further device further comprises: a processing quality detection module for:

[0052] Conduct quality inspection on processed fasteners and obtain inspection results;

[0053] Determine whether the processing quality of the fasteners meets the requirements based on the test results; if not, optimize and adjust the processing control strategy and re-process; if it meets the requirements, complete the processing task.

[0054] According to some embodiments of the present invention, the above-mentioned method for machining a fastener using a CNC machining center includes:

[0055] Clamp the fasteners onto the process board based on processing specifications;

[0056] The fasteners on the process board are calibrated by visual photography;

[0057] Generate a machining control strategy based on machining parameters for the fastener after workpiece calibration;

[0058] The motion axes included in the multi-axis linkage module drive the tool to move according to the processing control strategy.

[0059] The present invention proposes a CNC machining center and machining method for fasteners. The clamping module can accurately clamp the fasteners to the process board based on the machining specifications, effectively avoiding machining errors caused by improper clamping, ensuring the machining accuracy of the fasteners from the source, reducing scrap caused by clamping problems, and improving the product qualification rate. The vision module performs workpiece calibration on the fasteners on the process board through visual photography, and can promptly detect and correct the position deviation of the fasteners. The CNC module generates a machining control strategy based on the machining parameters, realizing the intelligence and precision of the machining process. The machining control strategy can be flexibly adjusted for fasteners of different specifications and requirements to ensure that the tool is processed according to the optimal path, thereby improving machining accuracy and production efficiency and reducing dependence on operator experience. The multi-axis linkage module includes at least three motion axes, each of which drives the tool to move according to the machining control strategy, realizing a complex machining trajectory, and can meet the machining needs of various types of fasteners.

[0060] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0061] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0063] Figure 1 is a block diagram of a CNC machining center for fasteners according to one embodiment of the present invention;

[0064] Figure 2 is a block diagram of a vision module according to one embodiment of the present invention;

[0065] Figure 3 is a block diagram of a numerical control module according to one embodiment of the present invention;

[0066] Figure 4 is a block diagram of a path planning module according to one embodiment of the present invention;

[0067] Figure 5 is a block diagram of a tool monitoring module according to one embodiment of the present invention;

[0068] Figure 6 is a block diagram of a first identification module according to one embodiment of the present invention;

[0069] Figure 7 is a block diagram of a filtering module according to one embodiment of the present invention;

[0070] Figure 8 The figure is a flow chart of a machining method for a fastener using a numerical control machining center according to one embodiment of the present invention. DETAILED DESCRIPTION

[0071] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0072] like Figure 1 As shown, an embodiment of the present invention provides a CNC machining center for fasteners, comprising:

[0073] Clamping module, used to clamp fasteners to the process board based on processing specification requirements;

[0074] Vision module, used to calibrate the fasteners on the process board through visual photography;

[0075] A numerical control module is used to generate a machining control strategy based on machining parameters for the fastener after workpiece calibration;

[0076] The multi-axis linkage module is electrically connected to the CNC module and includes at least three motion axes. Each motion axis drives the tool to move according to the machining control strategy.

[0077] The working principle and beneficial effects of the above technical solution are as follows: The clamping module accurately clamps fasteners onto the process plate according to machining specifications, effectively avoiding machining errors caused by improper clamping, ensuring fastener machining accuracy from the source, reducing scrap caused by clamping issues, and improving product qualification rates. The vision module uses visual photography to align the fasteners on the process plate, enabling timely detection and correction of fastener position deviations. The CNC module generates machining control strategies based on machining parameters, enabling intelligent and precise machining. The machining control strategies can be flexibly adjusted for fasteners of varying specifications and requirements, ensuring that the tool follows the optimal machining path, improving machining accuracy and production efficiency while reducing reliance on operator experience. The multi-axis linkage module comprises at least three motion axes, each of which drives the tool according to the machining control strategy, enabling complex machining trajectories and meeting the machining requirements of various fastener types. Through the coordinated operation of the clamping module, vision module, CNC module, and multi-axis linkage module, the CNC machining center achieves high-precision and efficient machining of fasteners.

[0078] like Figure 2 As shown, according to some embodiments of the present invention, the visual module includes:

[0079] An acquisition module, used for acquiring images of the fasteners on the process board by visual photography;

[0080] The comparison module is used to compare the captured image with the preset database to obtain a matching image, and perform workpiece calibration based on the matching image.

[0081] The working principle of this technical solution is as follows: The acquisition module is a high-resolution industrial camera with an integrated autofocus function, which automatically adjusts the focus based on the fastener position. The comparison module compares the captured image with a preset database to obtain a matching image, which is then used for workpiece calibration. The preset database contains standard images and parameters of various fasteners for comparison and calibration. This matching is performed using methods such as template matching to obtain a matching image, which corresponds to the captured image. The matching result is determined based on the matching image, and the deviation between the actual and theoretical positions of the fasteners is calculated to generate calibration parameters.

[0082] The beneficial effects of this technical solution are as follows: Through the collaborative work of the acquisition and comparison modules, the vision module achieves high-precision image acquisition and calibration of fasteners on the process board. Advanced image recognition algorithms and database comparison enable high-precision workpiece calibration, reducing manual intervention and improving production efficiency and consistency.

[0083] like Figure 3 As shown, according to some embodiments of the present invention, the numerical control module includes:

[0084] A cutting parameter acquisition module is used to determine the material properties and tool type of the fastener after the workpiece calibration process, input the material properties and tool type into a pre-built fuzzy rule library, and output cutting parameters; the cutting parameters include cutting speed, feed rate and cutting depth;

[0085] Path planning module, used to plan tool paths;

[0086] The first generation module is used to determine the processing parameters according to the cutting parameters and the tool path, and generate the processing control strategy based on the processing parameters.

[0087] The working principle of the above technical solution: The cutting parameter acquisition module includes a detection sensor, which obtains the material properties of the fastener, such as hardness, toughness, thermal conductivity, etc. based on the detection sensor. The cutting parameter acquisition module includes an identification module, which is used to determine the type of tool, such as milling cutter, drill, tap, etc. Construct a fuzzy rule base containing the relationship between material properties, tool type and cutting parameters, and output the cutting parameters through fuzzy reasoning. The path planning module is used to plan the tool path. The first generation module combines the cutting parameters (cutting speed, feed rate, cutting depth) and the tool path to determine the specific processing parameters. Based on the processing parameters, the motion control strategy of the tool is generated, including motion speed, acceleration, direction, etc.

[0088] The above technical solution has the following beneficial effects: The CNC module, through the collaborative work of the cutting parameter acquisition module, the path planning module, and the first generation module, facilitates accurate generation of machining control strategies. High-precision machining is achieved through precise cutting parameters and path planning. Fuzzy logic enables intelligent control and reduces manual intervention.

[0089] like Figure 4 As shown, according to some embodiments of the present invention, the path planning module includes:

[0090] An extraction module is used to generate a three-dimensional model of the fastener after the workpiece calibration process, pre-process the three-dimensional model, and extract processing features;

[0091] The second generation module is used to plan and generate the initial tool path based on the processing characteristics and processing technology requirements;

[0092] The optimization module is used to optimize the initial tool path using genetic algorithm to obtain the final tool path.

[0093] The working principle of the above technical solution: Based on 3D scanning technology, a 3D model of the fastener is generated, and the 3D model is pre-processed such as denoising, smoothing, and simplification to improve the accuracy of subsequent processing feature extraction. Features that need to be processed, such as holes, slots, threads, etc., are extracted from the 3D model, and their geometric parameters (such as size, position, direction, etc.) are determined. Processing process requirements include milling, drilling, tapping, etc. Combining the extracted processing features and processing process requirements, the initial motion path of the tool is planned, including the starting point, end point, motion trajectory, etc. of the tool. The initial tool path is used as the initial population of the genetic algorithm, and the tool path is optimized through operations such as selection, crossover, and mutation.

[0094] The beneficial effects of the above technical solution are as follows: The path planning module achieves high-precision and efficient tool path planning for fasteners through the collaborative work of the extraction module, the second generation module, and the optimization module. High-precision tool path planning is achieved through 3D model generation and machining feature extraction. Tool path optimization using a genetic algorithm improves machining efficiency and tool life.

[0095] like Figure 5 As shown, according to some embodiments of the present invention, it further includes: a tool monitoring module, which is used to monitor the tool state and issue an alarm when it is determined that the tool state is abnormal;

[0096] The tool monitoring module includes:

[0097] A first recognition module is used to obtain a monitoring image of the tool, recognize the monitoring image, and obtain first monitoring information;

[0098] The second recognition module is used to collect vibration signals and sound signals of the tool during the cutting process, recognize the vibration signals and sound signals, and obtain second monitoring information;

[0099] The first determining module is used to determine the state of the tool according to the first monitoring information and the second monitoring information, and to issue an alarm when it is determined that the state of the tool is abnormal.

[0100] The working principle of the above technical solution is: the first recognition module obtains the monitoring image of the tool, recognizes the monitoring image, and obtains the first monitoring information. The second recognition module uses a vibration sensor and a sound sensor to collect the vibration signal and sound signal of the tool during the cutting process in real time. The collected signal is pre-processed by filtering, amplifying, etc. to improve the signal quality. Through the signal processing algorithm, the characteristic parameters in the signal (such as vibration frequency, amplitude, sound intensity, etc.) are extracted. According to the extracted characteristic parameters, the abnormal state of the tool (such as excessive vibration, abnormal sound, etc.) is identified. The recognition result is output as the second monitoring information, including the vibration state and sound state of the tool. The first determination module fuses the first monitoring information and the second monitoring information, comprehensively judges the state of the tool, and identifies the tool state through multiple dimensions such as the tool's own contour, vibration during operation, and sound, so as to improve the accuracy of determining the tool state.

[0101] The beneficial effects of the above technical solution are as follows: The tool monitoring module, through the coordinated operation of the first recognition module, the second recognition module, and the first determination module, monitors the tool status in real time, promptly detecting anomalies and avoiding machining accidents. Combining image recognition and signal recognition improves the accuracy of tool status judgment. Automatically issuing alarms reduces manual intervention and improves production efficiency.

[0102] like Figure 6 As shown, according to some embodiments of the present invention, the first identification module includes:

[0103] A filtering module is used to filter the monitoring image to obtain a filtered image;

[0104] The second determining module is configured to:

[0105] Using a preset contour detection algorithm to extract the contour of the filtered image to obtain a contour image;

[0106] Extract key points from the contour image to obtain target key points;

[0107] Extract key points from the preset contour images in the preset contour image library to obtain preset key points corresponding to each preset contour image;

[0108] The lower left corner of the contour image is used as the coordinate origin to obtain the target coordinates of the target key point, and the target feature vector of the contour image is determined based on the target coordinates;

[0109] Taking the lower left corner of each preset contour image as the coordinate origin, obtaining the coordinates of the preset key points corresponding to the preset contour image, and determining the preset feature vector corresponding to the preset contour image based on the coordinates of the preset key points;

[0110] Calculate the cosine distance between the target feature vector and each preset feature vector;

[0111] The cosine distance is compared with a preset distance threshold to obtain comparison information, and the first monitoring information is obtained according to the comparison information.

[0112] The working principle of the above technical solution is as follows: A monitoring image is filtered based on a filtering module to obtain a filtered image; contours are extracted from the filtered image using a preset contour detection algorithm, which includes Canny edge detection and the Sobel operator, to obtain a contour image. Key points are extracted from the contour image to obtain target key points; key points are extracted from preset contour images in a preset contour image library to obtain preset key points corresponding to each preset contour image; the preset contour image is a complete contour image of the tool. The lower left corner of the contour image is used as the coordinate origin to obtain target coordinates of the target key points, and a target feature vector of the contour image is determined based on the target coordinates; the lower left corner of each preset contour image is used as the coordinate origin to obtain the coordinates of the preset key points corresponding to the preset contour image, and a preset feature vector corresponding to the preset contour image is determined based on the coordinates of the preset key points; the cosine distance between the target feature vector and each preset feature vector is calculated; the cosine distance is compared with a preset distance threshold to obtain comparison information, and key points whose cosine distance is less than the preset distance threshold are identified as wear points. The degree of wear is determined based on the difference between the two, and first monitoring information is obtained based on the comparison information. The preset distance threshold is the wear threshold.

[0113] The beneficial effects of the above technical solution are as follows: the filtering module performs filtering processing on the monitoring image to obtain a filtered image, the second determination module performs contour extraction on the filtered image to obtain a contour image, based on the cosine distance of the corresponding key points between the calculated contour image and the preset contour image, the cosine distance is compared with the preset distance threshold to obtain comparison information, and the first monitoring information is obtained based on the comparison information, which facilitates the accurate determination of the wear information of the tool, and further facilitates the determination of the status information of the tool.

[0114] like Figure 7 As shown, according to some embodiments of the present invention, the filtering module includes:

[0115] The third determining module is configured to:

[0116] Determine any pixel point in the monitoring image as a target pixel point, and determine a target area centered on the target pixel point; the target area is an N×N pixel matrix, where N is an even number; divide the target area into four square areas with a side length of N / 2, and select one of them as the square area to be processed; obtain the pixel mean, pixel median, and pixel mode value of all pixels in the square area to be processed; obtain the sum of the pixel mean, pixel median, and pixel mode values, determine a first mean value corresponding to the pixel mean, pixel median, and pixel mode value based on the sum value, and use the first mean value as a first estimated value of the target pixel point;

[0117] Repeat the above operation for all other square areas to obtain the second estimated value, the third estimated value, and the fourth estimated value of the target pixel;

[0118] Replacement modules for:

[0119] Obtaining a target pixel value of a target pixel point, calculating the absolute values ​​of the differences between the target pixel value and the first estimated value, the second estimated value, the third estimated value, and the fourth estimated value, respectively, and taking the estimated value corresponding to the minimum absolute value as the first filtered pixel value of the target pixel point;

[0120] Determining a first weight corresponding to the first filtered pixel value according to a first preset weight function;

[0121] Obtaining a target gradient value of a target pixel point, and determining a first coefficient according to the target gradient value;

[0122] Get the target pixel mean of all pixels in the target area;

[0123] Convert the image into a grayscale image, determine the target grayscale area corresponding to the target area in the grayscale image, and obtain the grayscale pixel mean of all pixels in the target grayscale area;

[0124] Determine a second coefficient according to the first coefficient, the target pixel mean and the grayscale pixel mean;

[0125] Obtaining a target pixel value of a target pixel point, and determining a second filtered pixel value corresponding to the target pixel point based on the target pixel value, the first coefficient, the second coefficient, and a preset filtering formula;

[0126] Determining a second weight corresponding to the second filtered pixel value according to a second preset weight function;

[0127] Calculating a first product of the first filtered pixel value and the first weight and a second product of the second filtered pixel value and the second weight, and taking the sum of the first product and the second product as the filtered pixel value of the target pixel; replacing the target pixel value of the target pixel with the filtered pixel value to obtain a filtered pixel corresponding to the target pixel;

[0128] The above operation is performed on all pixels in the monitoring image to obtain the filtered pixel corresponding to each pixel, and all the filtered pixels constitute a filtered image.

[0129] Working principle of the above technical solution: In this embodiment, the specific implementation method of determining the first weight corresponding to the first filtered pixel value according to the first preset weight function is: determining the first preset weight function to be Wherein, W1 is the first weight, p1 is the target pixel mean of all pixels in the target area, and p2 is the pixel mean of all pixels in the square area corresponding to the first filtered pixel value.

[0130] In this embodiment, the target gradient value of the target pixel point is obtained, and the specific implementation method of determining the first coefficient according to the target gradient value is: the target pixel point is determined to be pixel point j, then the target gradient value is Among them, G(j) is the target gradient value of pixel j, is the square value of the pixel difference between pixel j and adjacent pixels in the horizontal direction, is the square value of the pixel difference between pixel j and the adjacent pixel in the vertical direction, then the first coefficient expression is Wherein, a1 is the first coefficient.

[0131] In this embodiment, the specific implementation method for determining the second coefficient based on the first coefficient, the target pixel mean and the grayscale pixel mean is: a2=p1-a1×p3, where a2 is the second coefficient, p1 is the target pixel mean, and p3 is the grayscale pixel mean.

[0132] In this embodiment, the target pixel value of the target pixel point is obtained, and the specific implementation method of determining the second filtered pixel value corresponding to the target pixel point based on the target pixel value, the first coefficient, the second coefficient and the preset filtering formula is: the preset filtering formula is Where E is the second filtered pixel value, is the target pixel value.

[0133] In this embodiment, a specific implementation method of determining the second weight corresponding to the second filtered pixel value according to the second preset weight function is: the second preset weight function is W2=1-W1, where W2 is the second weight.

[0134] An N×N neighborhood matrix is ​​constructed with the target pixel as the center, dividing the neighborhood into four equal-sized square subregions. For each subregion, the following values ​​are calculated: the pixel mean (reflecting overall brightness), the pixel median (resistance to impulse noise), and the pixel mode (reflecting the main distribution). The estimated value for each subregion is obtained by taking the arithmetic average of these three values. The absolute difference between the target pixel value and the estimated values ​​of each subregion is calculated, and the estimated value corresponding to the minimum difference is selected as the initial filtering result. Gradient information is introduced to calculate the gradient value of the target pixel. The first coefficient is determined based on the gradient value. The second filtered pixel value corresponding to the target pixel is determined based on the target pixel value, the first coefficient, the second coefficient, and a preset filtering formula. Enhancement filtering is then performed. The first product of the first filtered pixel value and the first weight, and the second product of the second filtered pixel value and the second weight, are calculated. The sum of the first and second products is used as the filtered pixel value of the target pixel. The target pixel value of the target pixel is replaced with the filtered pixel value to obtain the filtered pixel corresponding to the target pixel, thus completing the pixel filtering process.

[0135] The beneficial effects of the above technical solution are as follows: the filtering module achieves a balance between noise suppression and detail preservation through multi-level feature fusion, making it easier to obtain an accurate filtered image.

[0136] According to some embodiments of the present invention, the multi-axis linkage module includes X-axis linear motion, Y-axis linear motion and Z-axis linear motion axes, and at least one rotation axis; the X-axis linear motion, Y-axis linear motion and Z-axis linear motion axes are used to realize the linear motion of the tool in three mutually perpendicular directions; the rotation axis is used to realize the rotational motion of the tool around a specific axis.

[0137] The working principle and beneficial effects of the above technical solution: X-axis linear motion: controls the movement of the tool in the horizontal direction (the front and back direction of the machine tool). Y-axis linear motion: controls the movement of the tool in another horizontal direction (the left and right direction of the machine tool). Z-axis linear motion: controls the movement of the tool in the vertical direction (the up and down direction of the machine tool). High-precision linear guides and ball screws are used to achieve smooth and precise linear motion. Integrated servo motors and encoders are used to achieve closed-loop control of each axis and improve motion accuracy. The rotary axis realizes the rotational motion of the tool around a specific axis, increasing the flexibility and complexity of processing. The rotary axis can be at least one of rotation around the X-axis, rotation around the Y-axis, and rotation around the Z-axis. By coordinating and controlling the motion of the X-axis, Y-axis, Z-axis linear motion axes and rotary axes, complex trajectory motion of the tool in three-dimensional space is achieved.

[0138] According to some embodiments of the present invention, the further device further comprises: a processing quality detection module for:

[0139] Conduct quality inspection on processed fasteners and obtain inspection results;

[0140] Determine whether the processing quality of the fasteners meets the requirements based on the test results; if not, optimize and adjust the processing control strategy and re-process; if it meets the requirements, complete the processing task.

[0141] The working principle of the above technical solution is: perform quality inspection on the processed fasteners to obtain the inspection results. The inspection items include but are not limited to key quality indicators such as dimensional accuracy, shape accuracy, surface roughness, hardness, and material composition. According to the design requirements and industry standards of the fasteners, set the qualified quality standard range. Compare the inspection results with the set quality standards to determine whether the processing quality meets the requirements. Output the quality judgment results, including qualified, unqualified and specific unqualified items. Analyze the reasons for unqualified processing quality, such as tool wear, improper cutting parameters, insufficient machine tool accuracy, etc. According to the cause analysis results, adjust the processing control strategy, such as replacing tools, optimizing cutting parameters, calibrating machine tools, etc. Re-process according to the adjusted processing control strategy, and perform quality inspection again.

[0142] Beneficial effects of the above technical solution: The processing quality inspection module realizes comprehensive monitoring and management of the quality of processed fasteners by integrating functions such as quality inspection, quality judgment, processing control strategy optimization and adjustment, and processing task completion.

[0143] like Figure 8 As shown, according to some embodiments of the present invention, the above-mentioned method for machining a fastener using a CNC machining center includes steps S1-S4:

[0144] S1. Clamp the fasteners onto the process board based on the processing specifications;

[0145] S2. Perform workpiece calibration on the fasteners on the process board by visual photography;

[0146] S3, generating a processing control strategy based on processing parameters for the fastener after workpiece calibration;

[0147] S4. The motion axes included in the multi-axis linkage module drive the tool to move according to the processing control strategy.

[0148] The working principle and beneficial effects of the above technical solution: Based on the requirements of the processing specifications, the fasteners are accurately clamped to the process board, which effectively avoids the processing errors caused by improper clamping, ensures the processing accuracy of the fasteners from the source, reduces the waste caused by clamping problems, and improves the product qualification rate. By visually calibrating the workpiece with the fasteners on the process board, the position deviation of the fasteners can be discovered and corrected in time. The processing control strategy is generated according to the processing parameters, and the intelligent and precise processing process is realized. The processing control strategy can be flexibly adjusted for fasteners of different specifications and requirements to ensure that the tool is processed according to the optimal path, improves the processing accuracy and production efficiency, and reduces the dependence on the operator's experience. The multi-axis linkage module includes at least three motion axes, each of which drives the tool to move according to the processing control strategy, realizing a complex processing trajectory, which can meet the processing needs of various types of fasteners.

[0149] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A CNC machining center for fasteners, characterized in that: include: Clamping module, used to clamp fasteners to the process board based on processing specification requirements; Vision module, used to calibrate the fasteners on the process board through visual photography; A numerical control module is used to generate a machining control strategy based on machining parameters for the fastener after workpiece calibration; The multi-axis linkage module is electrically connected to the CNC module and includes at least three motion axes. Each motion axis drives the tool to move according to the machining control strategy.

2. The CNC machining center for fasteners according to claim 1, wherein: The visual module includes: An acquisition module, used for acquiring images of the fasteners on the process board by visual photography; The comparison module is used to compare the captured image with the preset database to obtain a matching image, and perform workpiece calibration based on the matching image.

3. The CNC machining center for fasteners according to claim 1, wherein: The numerical control module comprises: A cutting parameter acquisition module is used to determine the material properties and tool type of the fastener after the workpiece calibration process, input the material properties and tool type into a pre-built fuzzy rule library, and output cutting parameters; the cutting parameters include cutting speed, feed rate and cutting depth; Path planning module, used to plan tool paths; The first generation module is used to determine the processing parameters according to the cutting parameters and the tool path, and generate the processing control strategy based on the processing parameters.

4. The CNC machining center for fasteners according to claim 3, wherein: Path planning module, including: An extraction module is used to generate a three-dimensional model of the fastener after the workpiece calibration process, pre-process the three-dimensional model, and extract processing features; The second generation module is used to plan and generate the initial tool path based on the processing characteristics and processing technology requirements; The optimization module is used to optimize the initial tool path using genetic algorithm to obtain the final tool path.

5. The CNC machining center for fasteners according to claim 1, wherein: Also includes: Tool monitoring module, used to monitor the tool status and issue an alarm when it is determined that the tool status is abnormal; The tool monitoring module includes: A first recognition module is used to obtain a monitoring image of the tool, recognize the monitoring image, and obtain first monitoring information; The second recognition module is used to collect vibration signals and sound signals of the tool during the cutting process, recognize the vibration signals and sound signals, and obtain second monitoring information; The first determining module is used to determine the state of the tool according to the first monitoring information and the second monitoring information, and to issue an alarm when it is determined that the state of the tool is abnormal.

6. The CNC machining center for fasteners according to claim 5, wherein: The first identification module includes: A filtering module is used to filter the monitoring image to obtain a filtered image; The second determining module is configured to: Using a preset contour detection algorithm to extract the contour of the filtered image to obtain a contour image; Extract key points from the contour image to obtain target key points; Extract key points from the preset contour images in the preset contour image library to obtain preset key points corresponding to each preset contour image; The lower left corner of the contour image is used as the coordinate origin to obtain the target coordinates of the target key point, and the target feature vector of the contour image is determined based on the target coordinates; Taking the lower left corner of each preset contour image as the coordinate origin, obtaining the coordinates of the preset key points corresponding to the preset contour image, and determining the preset feature vector corresponding to the preset contour image based on the coordinates of the preset key points; Calculate the cosine distance between the target feature vector and each preset feature vector; The cosine distance is compared with a preset distance threshold to obtain comparison information, and the first monitoring information is obtained according to the comparison information.

7. The CNC machining center for fasteners according to claim 6, wherein: The filtering module includes: The third determining module is configured to: Determine any pixel point in the monitoring image as a target pixel point, and determine a target area centered on the target pixel point; the target area is an N×N pixel matrix, where N is an even number; divide the target area into four square areas with a side length of N / 2, and select one of them as the square area to be processed; obtain the pixel mean, pixel median, and pixel mode value of all pixels in the square area to be processed; obtain the sum of the pixel mean, pixel median, and pixel mode values, determine a first mean value corresponding to the pixel mean, pixel median, and pixel mode value based on the sum value, and use the first mean value as a first estimated value of the target pixel point; Repeat the above operation for all other square areas to obtain the second estimated value, the third estimated value, and the fourth estimated value of the target pixel; Replacement module for: Obtaining a target pixel value of a target pixel point, calculating the absolute values ​​of the differences between the target pixel value and the first estimated value, the second estimated value, the third estimated value, and the fourth estimated value, respectively, and taking the estimated value corresponding to the minimum absolute value as the first filtered pixel value of the target pixel point; Determining a first weight corresponding to the first filtered pixel value according to a first preset weight function; Obtaining a target gradient value of a target pixel point, and determining a first coefficient according to the target gradient value; Get the target pixel mean of all pixels in the target area; Convert the image into a grayscale image, determine the target grayscale area corresponding to the target area in the grayscale image, and obtain the grayscale pixel mean of all pixels in the target grayscale area; Determine a second coefficient according to the first coefficient, the target pixel mean and the grayscale pixel mean; Obtaining a target pixel value of a target pixel point, and determining a second filtered pixel value corresponding to the target pixel point based on the target pixel value, the first coefficient, the second coefficient, and a preset filtering formula; Determining a second weight corresponding to the second filtered pixel value according to a second preset weight function; Calculating a first product of the first filtered pixel value and the first weight and a second product of the second filtered pixel value and the second weight, and taking the sum of the first product and the second product as the filtered pixel value of the target pixel; replacing the target pixel value of the target pixel with the filtered pixel value to obtain a filtered pixel corresponding to the target pixel; The above operation is performed on all pixels in the monitoring image to obtain the filtered pixel corresponding to each pixel, and all the filtered pixels constitute a filtered image.

8. The CNC machining center for fasteners according to claim 1, wherein: The multi-axis linkage module includes X-axis linear motion, Y-axis linear motion and Z-axis linear motion axes, and at least one rotation axis; the X-axis linear motion, Y-axis linear motion and Z-axis linear motion axes are used to realize the linear motion of the tool in three mutually perpendicular directions; the rotation axis is used to realize the rotational motion of the tool around a specific axis.

9. The CNC machining center for fasteners according to claim 1, wherein: Also includes: Processing quality inspection module, used for: Conduct quality inspection on processed fasteners and obtain inspection results; Determine whether the processing quality of the fasteners meets the requirements based on the test results; if not, optimize and adjust the processing control strategy and re-process; if it meets the requirements, complete the processing task.

10. The method for machining fasteners using a CNC machining center according to any one of claims 1 to 9, wherein: include: Clamp the fasteners onto the process board based on processing specifications; The fasteners on the process board are calibrated by visual photography; Generate a machining control strategy based on machining parameters for the fastener after workpiece calibration; The motion axes included in the multi-axis linkage module drive the tool to move according to the processing control strategy.

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