Intelligent flange processing device

Abstract

The application relates to the technical field of flange end face machining, and discloses an intelligent flange machining device, which comprises a flange clamping mechanism and a flange machining mechanism fixed on a workbench. The flange clamping mechanism comprises a clamping base, a placing assembly and two groups of clamping assemblies. Each clamping assembly is provided with a clamping jaw and a force sensor, and the clamping is realized through a clamping jaw driving cylinder. The flange machining mechanism is connected with the clamping mechanism through first, second and third sliding assemblies, and respectively comprises a driving motor, a transmission screw and a sliding rod, so that the X, Y and Z direction movements of the machining mechanism are realized. A cutter driving unit comprises a machining head assembly, is provided with a cutter box and a milling cutter, and is provided with image acquisition devices on both sides of the machining head. A control module is arranged at the bottom of the workbench and is electrically connected with the image acquisition devices, the force sensors, the clamping jaw driving cylinders and the driving motors. According to the application, the machining scheme can be dynamically adjusted according to the flatness deviation of different regions, and the automatic compensation and optimization of the uneven errors of the end face are realized.

Description

A smart flange processing device Technical Field

[0001] This invention relates to the field of flange end face processing technology, and more specifically, to an intelligent flange processing device. Background Technology

[0002] As an important connecting component in pipelines, pressure vessels, and mechanical equipment, the flatness, perpendicularity, and surface roughness of the flange end face directly affect the sealing performance and assembly accuracy. Especially in large equipment or pipeline systems, flanges have large diameters and heavy weights, and the processing environment is complex, which places higher demands on the accuracy, efficiency, and equipment stability of end face processing.

[0003] In a large flange end face on-site machining intelligent machine tool with existing patent number CN113369549B, a base, rotating arm, drive device, milling mechanism, laser collimation system, hydraulic leveling structure, and machine vision measurement module are included, which can improve machining accuracy and monitoring capabilities to a certain extent. However, the coordinated control between the various systems is not yet perfect, there is a lack of real-time closed-loop feedback between the laser collimation signal and the support leveling action, and the measurement range and resolution of the machine vision system are also limited, making it impossible to achieve three-dimensional dynamic measurement and adaptive adjustment of machining parameters for the entire flange end face.

[0004] Therefore, it is necessary to design an intelligent flange processing device to solve the problems existing in the current technology. Summary of the Invention

[0005] In view of this, the present invention proposes an intelligent flange processing device, which aims to solve the problem of being unable to achieve three-dimensional dynamic measurement and adaptive adjustment of processing parameters across the entire flange end face.

[0006] This invention proposes an intelligent flange processing device, including a worktable. A flange clamping mechanism and a flange processing mechanism are fixed on the upper surface of the worktable. The flange clamping mechanism includes a clamping base, a placement component and two sets of clamping components are arranged above the clamping base. The placement component includes a disc base, and a placement column is arranged in the middle of the disc base. The two sets of clamping components are respectively arranged on both sides of the placement component. Each set of clamping components includes a jaw and a jaw driving cylinder. A force sensor is also arranged on the inner surface of the jaw.

[0007] The flange processing mechanism is located above the flange clamping mechanism. The flange processing mechanism is connected to the flange clamping mechanism via a first sliding assembly. The first sliding assembly includes a first drive motor, a first transmission screw, and two first sliding rods. The bottom of the flange processing mechanism is connected to the first transmission screw through a first transmission screw mating hole, and to the corresponding first sliding rods through first sliding rod mating guide holes. A second sliding assembly is located at the top of the flange processing mechanism, and a flange processing drive unit is connected to the second sliding assembly. The second sliding assembly includes a second drive motor, a second transmission screw, and two second sliding rods. The flange processing drive unit... The second transmission screw mating hole is connected to the second transmission screw, and the second sliding rod mating guide hole is connected to the corresponding second sliding rod respectively; the flange processing drive unit is provided with a third sliding assembly, and the tool drive unit is connected based on the third sliding assembly. The third sliding assembly includes a third drive motor, a third transmission screw, and two third sliding rods. The tool drive unit is connected to the third transmission screw through the third transmission screw mating hole, and is connected to the corresponding third sliding rod respectively through the third sliding rod mating guide hole; the tool drive unit includes a processing head assembly, which includes a tool box and a milling cutter; image acquisition devices are provided on both sides of the processing head assembly;

[0008] The bottom of the workbench is also equipped with a control module, which is electrically connected to the image acquisition device, force sensor, gripper drive cylinder, first drive motor, second drive motor and third drive motor.

[0009] Furthermore, the control module includes a first position confirmation unit, a clamping control unit, a second position confirmation unit, a processing scheme generation unit, a processing control unit, and a quality detection unit;

[0010] The first position confirmation unit is used to determine whether the flange is accurately placed at the specified position on the workbench based on the first image data acquired by the image acquisition device and by using the Canny edge detection algorithm and template matching operation.

[0011] The clamping control unit is used to control the clamping jaw drive cylinder to drive the clamping jaw to clamp the flange after determining that the flange is accurately placed, and to monitor the clamping pressure in real time through the force sensor to ensure that the clamping force is within the safe working range.

[0012] The second position confirmation unit is used to accurately obtain the actual position parameters after the flange is clamped by using the Hough transform parameter space accumulation method based on the second image data acquired by the image acquisition device. The actual position parameters include the actual center coordinates and radius parameters of the outer circle of the flange.

[0013] The processing scheme generation unit is used to identify the high point region and low point region of the flange end face according to the actual position parameters through a sub-pixel level edge extraction algorithm, and generate a flange end face processing scheme.

[0014] The machining control unit is used to generate a three-dimensional cutting path according to the flange end face machining scheme, convert the three-dimensional cutting path into control commands for each drive motor, coordinate the movement of the first sliding component, the second sliding component and the third sliding component, so that the milling cutter continuously cuts the flange end face along the spiral cutting path.

[0015] The quality inspection unit is used to analyze the quality of the flange end face based on the third image data acquired by the image acquisition device after processing, and to analyze the end face surface and extract geometric feature parameters, and compare them with the preset quality standard library to determine whether the flange end face meets the quality requirements.

[0016] Furthermore, when the first position confirmation unit determines whether the flange is accurately placed at the designated position on the workbench, it includes:

[0017] The first image data is processed by grayscale conversion and Gaussian filtering for noise reduction; the image gradient magnitude and direction are calculated to construct a gradient intensity map; non-maximum suppression is performed on the gradient intensity map to retain the maximum pixel in the gradient direction; a dual threshold detection method is used to classify the maximum pixel after non-maximum suppression into edges, marking the maximum pixel with a gradient magnitude greater than the high magnitude threshold as a strong edge, and marking the maximum pixel with a gradient magnitude greater than the low magnitude threshold and less than or equal to the high magnitude threshold as a weak edge; through edge connection operation, strong edges and weak edges connected to strong edges are retained to generate effective edges; continuous closed effective edges are extracted, and the main edge chain matching the flange outer contour is selected through contour topology analysis; the main edge chain is matched with the preset workbench positioning area template for feature points, and the feature point matching degree value is calculated;

[0018] When the feature point matching degree value is greater than the preset matching degree threshold, it is determined that the flange is accurately placed in the designated position on the workbench; when the feature point matching degree value is less than or equal to the preset matching degree threshold, it is determined that the flange is not accurately placed in the designated position on the workbench.

[0019] Furthermore, when the second position confirmation unit confirms the actual position parameters after the flange is clamped, it includes:

[0020] The second image data is subjected to edge sharpening processing. By calculating the image gray-level gradient direction histogram, the geometric feature direction of the flange edge is determined. The edge point set after edge sharpening processing is mapped to the parameter space, an accumulator array is constructed, and voting statistics are performed on the parameter space to identify the peak region in the parameter space. The actual center coordinates and radius parameters of the outer circle of the flange are extracted based on the coordinates of the peak region.

[0021] Furthermore, when the processing scheme generation unit generates the flange end face processing scheme, it includes:

[0022] The end face quality analysis is performed on the second image data, a polynomial fitting model of the edge point neighborhood is constructed, and the sub-pixel level coordinates of the end face edge are calculated; the flatness deviation value of each region of the end face is calculated based on the sub-pixel level coordinate sequence, and the region with the flatness deviation value greater than the first deviation threshold is marked as the high point region, and the region with the flatness deviation value less than the second deviation threshold is marked as the low point region.

[0023] Based on the spatial distribution characteristics of the high and low point regions, the cutting parameters of each segment in the helical cutting path are adjusted to generate machining trajectory data, which includes region identification, cutting depth, feed rate and compensation coordinate offset.

[0024] Furthermore, when the processing scheme generation unit constructs a polynomial fitting model of the edge point neighborhood, it includes:

[0025] The second image data is preprocessed with Gaussian filtering to eliminate image noise while preserving the clear features of the end face edge; the gradient operator is used to calculate the gradient change of each pixel in the image to preliminarily determine the approximate pixel-level position of the end face edge;

[0026] A square neighborhood window of fixed size is set around each initially determined edge point, and the gray value distribution information of all pixels within the square neighborhood window is collected; a coordinate system is established along the normal direction of the edge point, a quadratic function relationship describing the gray value change law is constructed, and the quadratic function relationship is used as a polynomial fitting model;

[0027] The coefficients of the quadratic function relationship are optimized using the least squares method to minimize the deviation between the function curve and the actual gray value, thus ensuring the accuracy of the fitting results.

[0028] Furthermore, when the machining scheme generation unit adjusts the cutting parameters of each segment in the helical cutting path to generate machining trajectory data, it includes:

[0029] The flatness deviation value of the high point region is proportionally calculated with a preset cutting depth reference value to obtain a cutting depth increase coefficient. The cutting depth increase coefficient is equal to the ratio of the flatness deviation value to the cutting depth reference value. The cutting depth of the corresponding high point region in the helical cutting path is set to the sum of the standard cutting depth multiplied by the cutting depth increase coefficient plus one. The flatness deviation value of the high point region is inversely proportionally calculated with a preset feed rate reference value to obtain a feed rate decrease coefficient. The feed rate decrease coefficient is equal to the feed rate reference value divided by the sum of the flatness deviation value and the feed rate reference value. The feed rate of the corresponding high point region in the helical cutting path is set to the standard feed rate multiplied by the feed rate decrease coefficient.

[0030] The absolute value of the flatness deviation in the low-point region is proportionally calculated to a preset cutting depth reference value to obtain a cutting depth reduction coefficient. The cutting depth reduction coefficient is equal to the ratio of the absolute value of the flatness deviation to the cutting depth reference value. The cutting depth of the corresponding low-point region in the helical cutting path is set as the difference between the standard cutting depth multiplied by one and the cutting depth reduction coefficient. The absolute value of the flatness deviation in the low-point region is inversely proportionally calculated to a preset feed rate reference value to obtain a feed rate improvement coefficient. The feed rate improvement coefficient is equal to the sum of the absolute value of the flatness deviation and the feed rate reference value divided by the feed rate reference value. The feed rate of the corresponding low-point region in the helical cutting path is set as the standard feed rate multiplied by the feed rate improvement coefficient.

[0031] For the spiral cutting path segment at the junction of the high point region and the low point region, the length of the parameter transition section is calculated based on the magnitude of the flatness deviation value on both sides of the junction. The length of the parameter transition section is proportional to the magnitude of the flatness deviation value. Within the parameter transition section, the cutting depth and feed rate change linearly from the parameter value of one side region to the parameter value of the other side region.

[0032] The adjusted depth of cut, feed rate, and coordinate position information of the helical cutting path are precisely mapped, and corresponding cutting parameter values ​​are assigned to each coordinate point on the helical cutting path to generate the machining trajectory data.

[0033] Furthermore, when the machining control unit converts the three-dimensional cutting path into control commands for each drive motor, it includes:

[0034] Based on the geometric features of the flange end face, a helical cutting path is generated, which is determined by the starting point, rotation angle, and radial feed. The motion parameters of each sliding component are calculated by the inverse kinematics algorithm, and the helical cutting path is converted into motion commands for the X-axis, Y-axis, and Z-axis.

[0035] The first drive motor is controlled to drive the first sliding component to move along the X-axis, and the second drive motor is controlled to drive the second sliding component to move along the Y-axis, so as to realize the radial movement of the milling cutter on the flange end face; the third drive motor is controlled to drive the third sliding component to move along the Z-axis, so as to realize the axial feed of the milling cutter; the movement speed of each drive motor is coordinated so that the milling cutter continuously cuts the flange end face along a spiral cutting path.

[0036] Furthermore, when the quality inspection unit analyzes the end face surface and extracts geometric feature parameters, it includes:

[0037] Based on the edge coordinate sequence of sub-pixel level coordinate positioning, the flatness error of the flange end face is calculated. The ideal plane is fitted by the least squares method and compared with the actual measurement points. The micro-texture features of the end face surface are analyzed to evaluate the surface roughness level.

[0038] Calculate the perpendicularity error between the flange end face and the flange axis by measuring the radial deviation at different positions on the end face edge and performing statistical analysis; identify possible surface defects on the end face, including scratches, pits, and irregular protrusions.

[0039] Furthermore, when the quality inspection unit determines whether the flange end face meets the quality requirements, it includes:

[0040] The calculated flatness error, surface roughness, perpendicularity error, and surface defect characteristics are compared with the threshold ranges in the preset quality standard library in multiple dimensions. When all quality indicators fall within the qualified range, the flange end face is determined to meet the quality requirements. When any quality indicator exceeds the qualified range, the flange end face is determined to not meet the quality requirements.

[0041] Compared with existing technologies, the advantages of this invention are as follows: By combining an image acquisition device with the Canny edge detection algorithm and template matching technology, the placement position of the flange on the worktable is automatically identified, realizing intelligent positioning and attitude confirmation of the flange. This avoids manual alignment errors and repeated adjustments, improving clamping accuracy and loading efficiency. A clamping force sensor embedded in the gripper monitors the clamping pressure in real time. The control module adjusts the air pressure of the gripper drive cylinder through feedback to achieve adaptive control of the clamping force, ensuring clamping stability and avoiding flange deformation or surface damage caused by over-clamping. The flange processing mechanism uses three sets of independent sliding components (first, second, and third sliding components), respectively responsible for displacement in the X, Y, and Z axes. The control module achieves three-axis coordination through inverse kinematics algorithm, enabling the milling cutter to continuously process along a helical cutting path. The cutting trajectory is smooth and the positioning is accurate, improving the flatness and surface quality of the flange end face. The second position confirmation unit collects the precise position parameters of the flange after clamping, and combined with a sub-pixel-level edge extraction algorithm to identify the distribution of high and low points on the end face, it can automatically generate a personalized processing scheme including cutting depth, feed rate, and offset compensation. The machining path can be dynamically adjusted according to the flatness deviation of different areas, realizing automatic compensation and optimization of unevenness error on the end face. In the machining scheme generation unit, the cutting depth and feed rate of each segment of the helical cutting path are adjusted in real time according to the deviation value of the high and low point areas. The cutting depth is automatically deepened and the feed rate is reduced in the high point area, and the cutting depth is automatically reduced and the feed rate is increased in the low point area. The transition section parameters are smoothly connected, thus balancing machining efficiency and surface consistency. After machining, a third image data is acquired through an image acquisition device. A sub-pixel level edge positioning algorithm is used to perform multi-dimensional feature analysis of end face flatness, perpendicularity, and surface roughness, and the data is compared with a preset quality standard library. This realizes automatic detection and judgment of machining quality, reduces manual inspection, and improves detection accuracy and reliability. Attached Figure Description

[0042] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0043] Figure 1 is a schematic diagram of the intelligent flange processing device provided in an embodiment of the present invention;

[0044] Figure 2 is a front view of the intelligent flange processing device provided in an embodiment of the present invention;

[0045] Figure 3 is a top view of the intelligent flange processing device provided in an embodiment of the present invention;

[0046] Figure 4 is a block diagram of the control module structure of the intelligent flange processing device provided in an embodiment of the present invention.

[0047] The components include: 1. Workbench; 2. Flange clamping mechanism; 21. Clamping base; 22. Disc base; 23. Placement column; 24. Gripper; 25. Gripper drive cylinder; 3. Flange processing mechanism; 311. First drive motor; 312. First transmission screw; 313. First sliding rod; 321. Second drive motor; 322. Second transmission screw; 323. Second sliding rod; 331. Third drive motor; 332. Third transmission screw; 333. Third sliding rod; 34. Tool box; 35. Milling cutter; 36. Image acquisition device; 4. Control module. Detailed Implementation

[0048] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the disclosure to those skilled in the art. It should be noted that, unless otherwise specified, embodiments and features in the embodiments of the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0049] Existing large flange end-face machining equipment mostly adopts manual or semi-automatic structures, relying heavily on operator experience for positioning, clamping, and cutting control. Such equipment generally suffers from low machining accuracy, insufficient automation, and weak real-time monitoring capabilities. Traditional machine tools lack high-precision flange placement identification and position correction methods, often requiring multiple manual adjustments to flange center positioning errors; clamping force cannot be detected and fed back in real time, easily leading to insufficient or excessive clamping, resulting in flange displacement or deformation; machining paths are usually fixed trajectories, unable to dynamically optimize cutting depth and feed rate based on end-face flatness deviations, resulting in uneven machining allowances and high surface roughness; machining quality inspection is mostly done manually after each process, lacking online inspection and self-feedback quality control, severely restricting the accuracy and efficiency of large flange on-site machining.

[0050] For example, in the on-site processing of wind turbine tower flanges, because the flange diameter is typically over 2 meters, and the installation location is high with a complex environment, traditional rotary end-face processing machines rely on manual positioning and leveling. Operators must use tools such as levels and feeler gauges to measure and adjust multiple times to barely ensure the flatness of the flange end face. During processing, due to slight deformation of the equipment support surface or uneven cutting forces, the flange end face often exhibits a flatness error of more than 0.3 mm. If the processing accuracy is not up to standard, it will lead to uneven connection gaps between tower sections and uneven stress on flange bolts, thus affecting the overall assembly quality and long-term operational safety. This example reflects the significant shortcomings of existing technologies in automated leveling, intelligent path planning, and online quality inspection.

[0051] Referring to Figures 1-4, an intelligent flange processing device includes a worktable 1. A flange clamping mechanism 2 and a flange processing mechanism 3 are fixed on the upper surface of the worktable 1. The flange clamping mechanism 2 includes a clamping base 21. A placement component and two sets of clamping components are arranged above the clamping base 21. The placement component includes a disc base 22, and a placement column 23 is arranged in the middle of the disc base 22. The two sets of clamping components are respectively arranged on both sides of the placement component. Each set of clamping components includes a gripper 24 and a gripper drive cylinder 25. A force sensor is also arranged on the inner surface of the gripper 24.

[0052] A flange processing mechanism 3 is provided above the flange clamping mechanism 2. The flange processing mechanism 3 is connected to the flange clamping mechanism 2 based on a first sliding assembly. The first sliding assembly includes a first drive motor 311, a first transmission screw 312, and two first sliding rods 313. The bottom of the flange processing mechanism 3 is connected to the first transmission screw 312 through a mating hole, and is connected to the corresponding first sliding rod 313 through a mating guide hole. A second sliding assembly is provided on the upper part of the flange processing mechanism 3, and a flange processing drive unit is connected based on the second sliding assembly. The second sliding assembly includes a second drive motor 321, a second transmission screw 322, and two second sliding rods 323. The flange processing drive unit is connected to the flange clamping mechanism 2 through the second transmission screw 322. The rod 322 is connected to the second transmission screw 322 through the mating hole, and is connected to the corresponding second sliding rod 323 through the mating guide hole. The flange processing drive unit is provided with a third sliding assembly, and the tool drive unit is connected based on the third sliding assembly. The third sliding assembly includes a third drive motor 331, a third transmission screw 332, and two third sliding rods 333. The tool drive unit is connected to the third transmission screw 332 through the mating hole, and is connected to the corresponding third sliding rod 333 through the mating guide hole. The tool drive unit includes a processing head assembly, which includes a tool box 34 and a milling cutter 35. Image acquisition devices 36 are provided on both sides of the processing head assembly.

[0053] The bottom of the workbench 1 is also equipped with a control module 4, which is electrically connected to the image acquisition device 36, the force sensor, the gripper drive cylinder 25, the first drive motor 311, the second drive motor 321 and the third drive motor 331.

[0054] Specifically, an intelligent flange processing device is disclosed, mainly used to achieve automated positioning, clamping, processing, and quality inspection of flanges. The overall structure of the device includes a worktable 1, a flange clamping mechanism 2, a flange processing mechanism 3, and a control module 4. The worktable 1 serves as the basic support platform for the device, on which the flange clamping mechanism 2 and the processing mechanism are fixed. The clamping mechanism consists of a clamping base 21, a placement component, and two sets of symmetrical clamping components. The placement component supports the flange and achieves initial concentric positioning through positioning pins; each set of clamping components is equipped with a jaw 24, a jaw drive cylinder 25, and a force sensor. The jaw 24 achieves clamping action through the cylinder, and the force sensor monitors the clamping force in real time to ensure stable clamping without damaging the workpiece. The flange processing mechanism 3 is connected to the clamping mechanism through three sets of sliding components. The first sliding component achieves forward and backward translation, the second sliding component achieves left and right translation, and the third sliding component achieves up and down feeding. Each set of sliding components includes a drive motor, a transmission screw, and a guide sliding rod. High-precision position adjustment is achieved by driving the screw rotation through the motor. The machining mechanism's end is the machining head assembly, which houses the toolbox 34 and milling cutter 35, supporting automatic tool changing. Image acquisition devices 36 are installed on both sides of the machining head to acquire images of the flange surface during the loading, machining, and inspection stages. These images, combined with the vision algorithm built into the control module 4, enable workpiece positioning and machining quality assessment. The control module 4 is located below the worktable 1 and includes a main control chip, signal acquisition unit, drive control unit, and human-machine interface. The control module 4 is connected to the image acquisition device 36, force sensor, drive motor, and cylinder, receiving sensor data in real time and generating control commands. Before machining, the image acquisition device 36 identifies and positions the workpiece. The control module 4 adjusts the position of the sliding component based on the identification results to automatically align the flange center with the machining coordinate system. During the clamping stage, the control module 4 uses a closed-loop adjustment strategy to control the clamping force of the gripper 24 based on real-time data from the force sensor, ensuring the flange is securely fixed.

[0055] The working principle and process are as follows: When the device is working, the flange is placed on the placement assembly of the workbench 1 by manual or automatic feeding mechanism, so that the inner hole of the flange fits into the placement column 23 to achieve coarse positioning; the image acquisition devices 36 on both sides capture images of the placement area, and the control module 4 performs grayscale conversion and Gaussian filtering to reduce noise, and then uses Canny edge detection to extract the outer contour, and judges whether the placement position is within the allowable range by template matching or feature point matching; after the positioning is qualified, the control module 4 issues a command to drive the gripper drive cylinder 25 to clamp the gripper 24. The gripper 24 has a built-in force sensor to sample the clamping force in real time, and feeds it back in the closed-loop control (such as PID) to control the air supply pressure of the cylinder, and adopts "rapid approach - touch" The segmented strategy of "collision detection - fine clamping" ensures both clamping speed and prevents over-clamping deformation. After clamping, a high-resolution image is acquired again. Image sharpening and Hough transform are used to identify the outer circle of the flange using the parameter space accumulation method to extract the actual center coordinates and radius. Sub-pixel-level edge coordinates are obtained by quadratic polynomial least squares fitting along the edge normal direction in the pixel neighborhood, thereby constructing the end face high and low point distribution (flatness deviation field) and edge micro-geometric features. Based on the extracted center, radius, and deviation field, the path generation module constructs a reference plane in the workpiece coordinate system and divides it into high point area, low point area, and neutral area. A helical cutting strategy is used to generate a three-dimensional cutting path (composed of the starting radius, radial step size per revolution, and angle). The depth of cut (D&D increment) is determined, and the cutting depth, feed rate, and tool compensation are allocated to each segment of the path based on the regional characteristics: the cutting depth is deepened and the feed rate is reduced in the high-point area to remove the protrusion, and the cutting depth is reduced or skipped in the low-point area to improve efficiency. A linear transition segment is inserted at the boundary between the high and low areas to avoid vibration caused by abrupt parameter changes. The three-dimensional trajectory is converted into three-axis drive commands through inverse kinematics: the first sliding component performs forward and backward translation, the second sliding component performs left and right translation, and the third sliding component performs up and down feed. Each axis is driven by a drive motor that drives a transmission screw and is guided by double guide slides to ensure straightness. The servo / stepper driver receives the interpolated motion trajectory and executes the velocity profile (S-curve or fifth-order polynomial) and forward... Look-ahead processing enables smooth acceleration / deceleration and high-precision interpolation. During cutting, real-time fusion of spindle current / torque, vibration spectrum, force sensor, and visual feedback is used: spindle current and vibration are used to monitor cutting load and tool wear, force sensors can be used for collision detection or fixture abnormality alarms, and the image acquisition unit performs online scanning of the end face at set intervals or feature positions and compares the current contour with the planned allowance through sub-pixel edge comparison. If an excessive deviation is detected, local compensation (adjusting the cutting depth / feed of subsequent segments) or tool change is paused; the toolbox 34 supports a quick change mechanism, and the control module 4 automatically initiates tool change and updates the tool radius and offset compensation based on tool life estimation and online wear signals.After processing, a comprehensive scan is performed. The quality inspection unit calculates flatness error using least-squares fitting, assesses perpendicularity by statistically analyzing edge radial deviation, and estimates surface roughness using texture / contour analysis. Simultaneously, it detects defects such as scratches and dents and compares them item by item with a preset quality standard library. If the defects are acceptable, processing parameters and a report are recorded; otherwise, an anomaly log is recorded, and rework or manual processing is determined.

[0056] As a preferred embodiment, the solution of this application is implemented as follows: For example, drilling, milling, or chamfering operations are performed on flanges of different specifications. Upon startup, the control module 4 receives flange processing task parameters, including the flange model, diameter, thickness, and required processing technology information. The flange clamping mechanism 2 is activated, and the gripper drive cylinder 25 controls the two sets of grippers 24 to open. The operator or robotic arm places the flange to be processed on the placement column 23 of the disc base 22. The force sensor detects the force distribution during the clamping process in real time, ensuring that the flange is centered and firmly clamped. After clamping, the feedback signal from the force sensor is transmitted to the control module 4 to determine whether the clamping is stable and safe. The flange processing mechanism 3 is activated. The first drive motor 311 drives the first transmission screw 312 to rotate, causing the flange processing mechanism 3 to precisely descend vertically to a preset height, achieving alignment between the processing head and the flange surface. The second drive motor 321 drives the second sliding component to move, thereby controlling the processing drive device to perform coarse positioning in the horizontal direction. The third drive motor 331 drives the third sliding component, enabling precise displacement adjustment of the machining head assembly within a small range. After the machining head assembly starts, the milling cutter 35 in the tool box 34 performs drilling, milling, or chamfering operations according to process requirements. During machining, the image acquisition devices 36 on both sides monitor the flange surface and machining area in real time, using image recognition algorithms to determine tool position, machining accuracy, and surface finish. When machining deviations or tool wear are detected, the control module 4 immediately issues adjustment commands, automatically correcting the feed rate or adjusting the machining path to ensure the stability and consistency of machining quality. After machining is completed, the gripper 24 automatically releases, and the robotic arm removes the machined flange from the worktable 1.

[0057] Through the above technical solution, this application achieves rapid, precise positioning and stable clamping of the flange by the synergistic action of two sets of clamping and placement components. Force sensors monitor the clamping force in real time, ensuring the safety and stability of the flange during processing and avoiding processing errors or equipment damage caused by improper clamping. The multi-axis linkage design of the multi-sliding components allows the processing head to move precisely and finely in the front-back, left-right, and up-down directions, achieving high-precision control of complex processing paths and improving processing accuracy and consistency. The image acquisition devices 36 on both sides of the processing head can monitor the flange processing status in real time. Combined with the intelligent feedback and automatic adjustment functions of the control module 4, it can dynamically correct processing deviations, optimize tool paths and feed rates, thereby improving processing efficiency and extending tool life.

[0058] This application further proposes that the control module 4 includes a first position confirmation unit, a clamping control unit, a second position confirmation unit, a machining scheme generation unit, a machining control unit, and a quality detection unit;

[0059] The first position confirmation unit is used to determine whether the flange is accurately placed at the specified position on the workbench 1 based on the first image data acquired by the image acquisition device 36 and by using the Canny edge detection algorithm and template matching operation.

[0060] The clamping control unit is used to control the clamping jaw drive cylinder 25 to drive the clamping jaw 24 to clamp the flange after determining that the flange is accurately placed, and to monitor the clamping pressure in real time through the force sensor to ensure that the clamping force is within the safe working range.

[0061] The second position confirmation unit is used to accurately obtain the actual position parameters of the flange after clamping by using the Hough transform parameter space accumulation method based on the second image data acquired by the image acquisition device 36. The actual position parameters include the actual center coordinates and radius parameters of the outer circle of the flange.

[0062] The processing scheme generation unit is used to identify the high and low points of the flange end face based on the actual position parameters using a sub-pixel level edge extraction algorithm, and generate a flange end face processing scheme.

[0063] The machining control unit is used to generate a three-dimensional cutting path according to the flange end face machining scheme, convert the three-dimensional cutting path into control commands for each drive motor, coordinate the movement of the first sliding component, the second sliding component and the third sliding component, so that the milling cutter 35 continuously cuts the flange end face along the helical cutting path.

[0064] The quality inspection unit is used to analyze the quality of the flange end face based on the third image data acquired by the image acquisition device 36 after processing, and to analyze the end face surface and extract geometric feature parameters. The results are compared with the preset quality standard library to determine whether the flange end face meets the quality requirements.

[0065] Specifically, the control module 4 of the intelligent flange processing device is the core of the entire system for achieving high-precision automated processing. It includes multiple functional units working together to ensure the accuracy, stability, and efficiency of the flange processing process. The first position confirmation unit acquires the initial image of the flange placement through the image acquisition device 36, extracts the flange edge contour using the Canny edge detection algorithm, and then combines it with template matching calculation to determine whether the flange is correctly placed in the designated position on the worktable 1. This step ensures the accuracy of the initial positioning of the flange before processing, providing a reliable foundation for subsequent clamping and processing. After the first position confirmation unit confirms that the flange position is accurate, the clamping control unit activates the gripper drive cylinder 25 to firmly clamp the flange on the worktable 1. The force sensor monitors the clamping pressure in real time and automatically adjusts the clamping force according to the set safe working range, ensuring firm clamping while avoiding flange deformation or fixture damage due to excessive tightness or looseness. The second position confirmation unit acquires the second image data and uses the Hough transform parameter space accumulation method to accurately calculate the actual position parameters of the flange after clamping, including the outer circle center coordinates and radius parameters, to achieve accurate positioning of the flange in the clamped state and provide accurate geometric information for processing path planning. During the machining scheme generation stage, the machining scheme generation unit uses a sub-pixel-level edge extraction algorithm to identify high and low points on the flange end face based on actual position parameters, generating a fine machining scheme for the flange end face. This scheme not only considers the overall contour of the flange but also allows for targeted cutting of minute uneven areas to ensure end face flatness and machining accuracy. The machining control unit then generates a three-dimensional cutting path based on the generated machining scheme and converts it into control commands for each drive motor, coordinating the synchronous movement of the three sliding components (front / back, left / right, and up / down) to allow the milling cutter 35 to continuously cut the flange end face along a spiral or other complex path, achieving high-precision machining. The cutting speed and feed rate can be dynamically adjusted to handle different geometric characteristics of the flange surface, ensuring a smooth and efficient machining process. After machining, the quality inspection unit uses a third image acquisition and a sub-pixel-level edge extraction algorithm to analyze the surface quality of the flange end face, extract geometric feature parameters, and compare them with a preset quality standard library to determine whether the machined flange meets quality requirements. If deviations exist, the data can be recorded to guide corrections or secondary machining, thereby achieving closed-loop control and quality traceability in machining.

[0066] As a preferred embodiment, the solution of this application is implemented as follows: For example, on a flange processing line, when a batch of standard flanges requires end face finishing, the operator places the flanges on the workbench 1. The first position confirmation unit immediately acquires an initial image through the image acquisition device 36, extracts the outer edge of the flange using the Canny edge detection algorithm, and matches it with a preset template to ensure that the flange is accurately placed in the designated position, avoiding processing errors caused by offset or tilt. The clamping control unit activates the gripper drive cylinder 25 to firmly clamp the flange onto the disc base 22 of the placement component. At the same time, the force sensor monitors the clamping force in real time and automatically adjusts the clamping pressure to maintain it within a safe range. Even if the flange has slight deformation or weight difference, it can ensure that the clamping is stable and does not damage the workpiece. After clamping is completed, the second position confirmation unit acquires the image after clamping and accurately calculates the center coordinates and radius parameters of the flange outer circle using the Hough transform parameter space accumulation method, thereby obtaining the actual position data and providing an accurate basis for generating the processing plan. The machining scheme generation unit further utilizes a sub-pixel-level edge extraction algorithm to identify high and low points on the flange end face. Based on this data, a detailed machining scheme is generated to ensure the end face is flat and meets machining accuracy requirements. During the machining stage, the machining control unit converts the generated machining scheme into a three-dimensional cutting path and generates control commands for each drive motor, coordinating the sliding components in the front-back, left-right, and up-down directions to allow the milling cutter 35 to continuously cut the flange end face along a helical path. In actual operation, this device can machine flanges with a diameter of 500 mm and a thickness of 50 mm. The milling cutter 35 adjusts the feed rate in real time during the cutting process to ensure smooth end face cutting without vibration marks. After machining, the quality inspection unit performs a third image acquisition to perform a detailed analysis of the flange end face, extracting geometric feature parameters and comparing them with the enterprise's quality standard library to determine whether the end face flatness, roundness, and surface defects meet the requirements. If slight deviations are found, they can be automatically marked and correction data can be generated to guide secondary machining or trimming, achieving closed-loop control of the machining process.

[0067] Through the above technical solution, the combination of the first position confirmation unit and the second position confirmation unit in this application can achieve precise positioning of the flange after placement and clamping, ensuring the absolute accuracy of the starting position and the workpiece position during processing, avoiding processing errors caused by offset or tilt, thereby improving the consistency and reliability of processing. The clamping control unit monitors the clamping pressure in real time through a force sensor, which can prevent excessive clamping and damage to the flange while ensuring the stability of the workpiece, thus improving processing safety. The processing scheme generation unit and the processing control unit work together to generate personalized processing schemes based on the actual geometric features of the flange end face, and convert them into three-dimensional cutting paths to drive each sliding component and the milling cutter 35 to complete helical cutting, achieving a high-precision, continuous and smooth processing effect on the end face, which is especially suitable for complex or batch processing needs. The quality inspection unit can analyze the geometric features of the end face and compare them with the standard library immediately after processing, realizing closed-loop control of processing. It can not only detect and correct processing deviations in a timely manner, but also ensure that the end face quality of each flange meets the preset standard, improving product consistency and pass rate.

[0068] This application further proposes that when the first position confirmation unit determines whether the flange is accurately placed in the designated position on the workbench 1, it includes:

[0069] The first image data is processed by grayscale conversion and Gaussian filtering for noise reduction; the gradient magnitude and direction of the image are calculated to construct a gradient intensity map; non-maximum suppression is performed on the gradient intensity map to retain the maximum pixel in the gradient direction; a double threshold detection method is used to classify the maximum pixel after non-maximum suppression into edges, marking the maximum pixel with a gradient magnitude greater than the high magnitude threshold as a strong edge, and marking the maximum pixel with a gradient magnitude greater than the low magnitude threshold and less than or equal to the high magnitude threshold as a weak edge; through edge connection operation, strong edges and weak edges connected to strong edges are retained to generate effective edges; continuous closed effective edges are extracted, and the main edge chain matching the outer contour of the flange is selected through contour topology analysis; the main edge chain is matched with the preset workbench 1 positioning area template for feature points, and the feature point matching degree value is calculated;

[0070] When the feature point matching degree value is greater than the preset matching degree threshold, it is determined that the flange is accurately placed at the designated position on the workbench 1; when the feature point matching degree value is less than or equal to the preset matching degree threshold, it is determined that the flange is not accurately placed at the designated position on the workbench 1.

[0071] Specifically, the first position confirmation unit is used to accurately determine whether the flange has been accurately placed in the designated position on the workbench 1. The first image data acquired by the image acquisition device 36 is converted into a grayscale image to simplify subsequent calculations. Simultaneously, Gaussian filtering is used to denoise the grayscale image, eliminating interference caused by uneven lighting or stray noise in the shooting environment. The gradient magnitude and gradient direction of each pixel are calculated to generate a gradient intensity map, which represents the edge information with the most significant brightness changes in the image. When performing non-maximum suppression on the gradient intensity map, local maxima pixels are retained along the gradient direction, and non-edge points are removed, further enhancing edge clarity. After obtaining the image after non-maximum suppression, a dual-threshold detection method is used to classify pixels according to their gradient magnitude: pixels above the high threshold are marked as strong edges, and pixels between the high and low thresholds are marked as weak edges. Through edge connection operations, all strong edges and their connected weak edges are retained to form continuous effective edges, thereby eliminating isolated noise points. Continuous closed effective edges are extracted, and through contour topology analysis, the main edge chain that best matches the flange's outer contour is selected. This main edge chain represents the flange's actual outer contour position on workbench 1. The main edge chain is matched with the pre-set positioning area template of workbench 1 using feature points, and the feature point matching degree value is calculated to measure the consistency between the flange's actual position and its ideal position. When the matching degree value is higher than a preset threshold, it is determined that the flange has been accurately placed in the designated position on workbench 1, and the next clamping operation can proceed; conversely, when the matching degree value is lower than or equal to the threshold, it is determined that the flange is not accurately placed, prompting the operator to reposition it or triggering an automatic adjustment process.

[0072] The high-amplitude threshold is generally set based on the statistical distribution of the gradient amplitude of the flange image under standard lighting conditions, ensuring that obvious edges can be reliably identified as strong edges. It is typically chosen to be a certain multiple greater than the average gradient amplitude of the image to filter out weak noise interference. The low-amplitude threshold is set to a gradient amplitude between the background noise level and the high-amplitude threshold. It is used to mark weak edges that may belong to the flange edge, and their validity is judged through subsequent edge connection operations, balancing completeness and accuracy. The matching degree threshold is determined based on statistical experience regarding the matching of the flange's outer contour feature points with the feature points of the workbench 1 positioning template. It is generally taken as the high percentile value of the feature point matching ratio, such as above 95%, ensuring that accurate placement is only determined when the flange position matches the template height, thus avoiding misjudgment due to minor deviations.

[0073] Through the above technical solutions, this application utilizes grayscale conversion and Gaussian filtering to reduce the interference of image noise on edge recognition, ensuring the accuracy of edge detection. By calculating gradient magnitude and direction and suppressing non-maximum values, key contour information of the flange can be extracted, eliminating irrelevant pixels. Dual threshold detection and edge connection operations further enhance edge continuity, enabling the main edge chain to completely and reliably represent the flange's outer contour. Matching the main edge chain with a preset positioning template for feature points not only achieves automatic flange position determination but also avoids processing deviation risks caused by human error or placement deviation through matching degree threshold control.

[0074] This application further proposes that when the second position confirmation unit confirms the actual position parameters after the flange is clamped, it includes:

[0075] The second image data is subjected to edge sharpening processing. By calculating the image gray-level gradient direction histogram, the geometric feature direction of the flange edge is determined. The edge point set after edge sharpening is mapped to the parameter space, an accumulator array is constructed, and voting statistics are performed on the parameter space to identify the peak region in the parameter space. The actual center coordinates and radius parameters of the outer circle of the flange are extracted based on the coordinates of the peak region.

[0076] Specifically, when confirming the actual position parameters after flange clamping, the second position confirmation unit performs edge sharpening processing on the second image data to enhance the contrast and detail of the flange edges, making the edge points clearer and more continuous, thus facilitating subsequent geometric feature extraction. By calculating the image grayscale gradient direction histogram, the gradient direction distribution of the flange edges is analyzed to determine the main geometric feature directions of the flange edges, including the tangent direction of the circular edge and the local normal direction of the flange outer circle. The edge point set after edge sharpening is mapped to the parameter space, generally using polar coordinates or a parameterized circular representation method. The possible center position and radius value corresponding to each edge point are mapped to the accumulator array to form a voting matrix. The accumulator array performs statistical voting on the parameter space. By analyzing the intensity and distribution of the voting set, peak regions in the parameter space are identified. These peak regions correspond to the most likely circular edge positions in the image. Based on the coordinates of the peak regions, the actual center coordinates and radius parameters of the flange outer circle are accurately extracted and output as the actual position parameters after flange clamping.

[0077] Through the above technical solution, the edge sharpening processing of this application can enhance the edge features of the flange, eliminate the interference of image noise and uneven illumination on edge recognition, and make the edge points clearer and more continuous. By analyzing the geometric feature direction of the flange edge through gray-level gradient direction histogram analysis, the main direction and contour features of the flange outer circle can be determined, providing a reliable basis for parameter space mapping. Mapping the edge points to the parameter space and performing accumulator voting can identify the center coordinates and radius of the flange outer circle, ensuring high accuracy of center position and radius measurement even in the presence of small clamping offsets or mechanical errors.

[0078] This application further proposes that when the processing scheme generation unit generates a flange end face processing scheme, it includes:

[0079] The end-face quality is analyzed on the second image data, a polynomial fitting model of the edge point neighborhood is constructed, and the sub-pixel level coordinates of the end-face edge are calculated. Based on the sub-pixel level coordinate sequence, the flatness deviation value of each region of the end face is calculated. Regions with flatness deviation values ​​greater than the first deviation threshold are marked as high-point regions, and regions with flatness deviation values ​​less than the second deviation threshold are marked as low-point regions.

[0080] Based on the spatial distribution characteristics of high and low point regions, the cutting parameters of each segment in the helical cutting path are adjusted to generate machining trajectory data, which includes region identification, cutting depth, feed rate and compensation coordinate offset.

[0081] Specifically, end-face quality analysis is performed on the second image data. First, camera distortion correction and coordinate system transformation (pixel to machine tool coordinate system) are performed on the input image. Then, Gaussian denoising, contrast enhancement, and edge sharpening are applied to the region of interest (flange end face), extracting the edge point set. To reduce noise impact, morphological opening and closing operations and connected component filtering are used to remove isolated small fragments. A polynomial fitting model is constructed in the neighborhood of each edge point to obtain sub-pixel level coordinates. A fixed-size square neighborhood window (e.g., 11×11 or 15×15 pixels, adjusted according to resolution) is taken at each edge point. A local coordinate system is established along the edge normal direction. The grayscale values ​​of the pixels in the neighborhood are fitted with a quadratic polynomial (or a cubic polynomial if necessary) to the grayscale change curve. The weighted least squares method is used to solve for the fitting coefficients (weights can be allocated according to distance or gradient magnitude). The fitting peak / zero intersection position gives the sub-pixel level edge position. The fitting residuals are statistically analyzed, outliers are removed, and an iterative fitting can be performed to improve robustness. The complete sub-pixel edge coordinate sequence is converted into a 3D surface point set (combining camera calibration and depth or structured light measurement results). Plane fitting is performed on the end face in the workpiece coordinate system (using the least squares method to fit an ideal plane). The local vertical deviation of each edge point from this ideal plane is calculated, and then a flatness deviation field of the entire end face is generated through spatial interpolation (e.g., based on triangular meshes or radial basis functions). To suppress high-frequency noise, low-pass filtering or Gaussian smoothing is applied to the deviation field. Regions are divided based on the deviation field: a first deviation threshold and a second deviation threshold are set (positive and negative directions can be set separately). Connected pixel blocks with deviations greater than the first threshold are marked as "high-point regions," connected blocks with deviations less than the second threshold are marked as "low-point regions," and the rest are neutral or acceptable regions. Minimum area / minimum width filtering is applied to the regions, adjacent small regions are merged to avoid over-segmentation, and attributes such as geometric center, area, maximum / average deviation value, and normal gradient are calculated for each marked region. In the helical cutting path generation stage, a basic helical trajectory is constructed (determined by the starting radius, radial step size, angular increment, and tool diameter), and then the trajectory is mapped into several segments according to the regions it passes through. For each trajectory segment, cutting parameters are assigned based on the region's attributes: cutting depth, which is linearly or proportionally amplified from the baseline cutting depth for high-point regions, and appropriately reduced or skipped for low-point regions; feed rate, which is decelerated for high-point regions and appropriately increased for low-point regions; tool compensation and coordinate offset, which calculate radial / axial compensation based on the deviation vector at the region center, update tool radius compensation and tool tip offset, and record them as compensation fields for trajectory points. To avoid vibration or surface steps caused by abrupt parameter changes, transition segments are inserted between adjacent parameter intervals. The length of the transition segment is calculated proportionally to the difference in deviation between the two sides, and parameter changes are smoothed using linear or cubic spline methods. The trajectory segment also includes safety constraints (maximum cutting depth, maximum cutting force estimation threshold, minimum pass time, etc.). If the calculated parameters exceed the limits, the trajectory is split or the single-circle cutting amount is reduced to ensure safety.The final machining trajectory data table is output in point-by-point or segmented format. Each record includes: trajectory coordinates (X, Y, Z), area identifier, depth of cut, feed rate, tool compensation amount, maximum allowable load, and detection point marker.

[0082] The setting of the first and second deviation thresholds is crucial for ensuring the machining accuracy and surface quality of the flange end face. The setting rules are primarily based on flange design requirements, machining tolerance standards, and tool cutting capabilities. The first deviation threshold is used to identify high-point areas of local protrusions on the end face. Its setting is typically slightly lower than the maximum allowable flatness deviation of the end face to ensure effective trimming of these protrusions during machining, avoiding tool overload or insufficient machining depth. During machining, the cutting depth in high-point areas is appropriately increased based on the threshold information, and the feed rate is adjusted so that the milling cutter 35 can fully trim the end face protrusions along the helical cutting path, thereby ensuring the overall flatness of the end face. The second deviation threshold is used to identify low-point areas of local depressions on the end face. Its setting is typically slightly higher than the minimum allowable flatness deviation to prevent overcutting of depressed areas or damage to the workpiece during machining. Low-point areas can be corrected in the machining path by reducing the cutting amount or performing tool compensation to ensure end face flatness and improve machining safety. The threshold setting also needs to be comprehensively considered in conjunction with machining parameters such as the 35mm diameter of the milling cutter, the spindle speed, and the feed rate. At the same time, it can be appropriately adjusted based on historical machining data or machine tool measurement feedback to make the threshold closer to the actual state of the workpiece.

[0083] Through the above technical solution, this application can obtain sub-pixel-level coordinates of the end face by constructing a fitting model of the edge point neighborhood, enabling accurate identification of high and low point regions and targeted adjustments in the helical cutting path; it corrects local protrusions and depressions on the end face, ensuring overall flatness, and can also optimize cutting parameters, including depth of cut, feed rate, and coordinate compensation, improving machining efficiency and tool life. The generated machining trajectory data enables controllable and intelligent machining, ensuring that each end face region is cut according to predetermined standards, improving the surface quality and geometric accuracy of the flange end face, and reducing rework rate.

[0084] This application further proposes that when the processing scheme generation unit constructs a multinomial fitting model for the neighborhood of edge points, it includes:

[0085] The second image data is preprocessed with Gaussian filtering to eliminate image noise while preserving the clear features of the end face edge; the gradient operator is used to calculate the gradient change of each pixel in the image to preliminarily determine the approximate pixel-level position of the end face edge;

[0086] A square neighborhood window of fixed size is set around each initially determined edge point, and the gray value distribution information of all pixels within the square neighborhood window is collected; a coordinate system is established along the normal direction of the edge point, a quadratic function relationship describing the gray value change law is constructed, and the quadratic function relationship is used as a polynomial fitting model;

[0087] The coefficients of the quadratic function relationship are optimized by using the least squares method to minimize the deviation between the function curve and the actual gray value, thus ensuring the accuracy of the fitting results.

[0088] Specifically, in the process of constructing the polynomial fitting model for the edge point neighborhood, the processing scheme generation unit performs Gaussian filtering preprocessing on the second image data. This step aims to eliminate random noise in the image while preserving the clear contour features of the end face edges, ensuring the accuracy of subsequent processing. Gradient operators are used to calculate the grayscale changes of each pixel in the image, determining the approximate location of the end face edges and providing a preliminary reference for polynomial fitting. Based on this, for each preliminarily determined edge point, a square neighborhood window of fixed size is set around it, collecting the grayscale values ​​of all pixels within that neighborhood. This grayscale information reflects the grayscale variation patterns in the local area of ​​the end face and is an important basis for constructing the fitting model. A local coordinate system is established along the normal direction of the edge points, allowing grayscale changes to be accurately described within this coordinate system. Based on the grayscale distribution characteristics within the neighborhood, a quadratic function relationship is used to model the grayscale value variation patterns, serving as the basis for the polynomial fitting model. To ensure fitting accuracy, the least squares method is used to optimize the coefficients of the quadratic function, minimizing the deviation between the fitted curve and the actual grayscale values.

[0089] Through the above technical solution, this application achieves sub-pixel-level accurate identification of flange end face edges by constructing a polynomial fitting model of the edge point neighborhood, thereby improving the accuracy and reliability of end face topography measurement.

[0090] This application further proposes that when the machining scheme generation unit adjusts the cutting parameters of each segment in the helical cutting path to generate machining trajectory data, it includes:

[0091] The flatness deviation value of the high point area is proportionally calculated with the preset cutting depth reference value to obtain the cutting depth increase coefficient. The cutting depth increase coefficient is equal to the ratio of the flatness deviation value to the cutting depth reference value. The cutting depth of the corresponding high point area in the helical cutting path is set to the standard cutting depth multiplied by the cutting depth increase coefficient plus one. The flatness deviation value of the high point area is inversely proportionally calculated with the preset feed rate reference value to obtain the feed rate decrease coefficient. The feed rate decrease coefficient is equal to the feed rate reference value divided by the sum of the flatness deviation value and the feed rate reference value. The feed rate of the corresponding high point area in the helical cutting path is set to the standard feed rate multiplied by the feed rate decrease coefficient.

[0092] The absolute value of the flatness deviation in the low-point region is proportionally calculated to obtain the cutting depth reduction coefficient. The cutting depth reduction coefficient is equal to the ratio of the absolute value of the flatness deviation to the cutting depth reference value. The cutting depth of the corresponding low-point region in the helical cutting path is set to the difference between the standard cutting depth multiplied by one and the cutting depth reduction coefficient. The absolute value of the flatness deviation in the low-point region is inversely proportionally calculated to obtain the feed rate improvement coefficient. The feed rate improvement coefficient is equal to the sum of the absolute value of the flatness deviation and the feed rate reference value divided by the feed rate reference value. The feed rate of the corresponding low-point region in the helical cutting path is set to the standard feed rate multiplied by the feed rate improvement coefficient.

[0093] For the spiral cutting path segment at the junction of the high point region and the low point region, the length of the parameter transition section is calculated based on the magnitude of the flatness deviation value on both sides of the junction. The length of the parameter transition section is proportional to the magnitude of the flatness deviation value. Within the parameter transition section, the cutting depth and feed rate change linearly from the parameter values ​​of one side region to the parameter values ​​of the other side region.

[0094] The adjusted depth of cut, feed rate, and coordinate position information of the helical cutting path are precisely mapped, and corresponding cutting parameter values ​​are assigned to each coordinate point on the helical cutting path to generate machining trajectory data.

[0095] Specifically, during flange end-face machining, the machining scheme generation unit analyzes the flatness deviations of the high and low points of the end face and dynamically adjusts the cutting parameters of each segment of the helical cutting path to generate accurate machining trajectory data. For high-point areas, the machining system calculates the ratio of the flatness deviation value to a preset cutting depth reference value to obtain a cutting depth increase coefficient. Then, the cutting depth of the corresponding high-point area in the helical cutting path is set as a combination of the standard cutting depth and the cutting depth increase coefficient to ensure that the protruding part can be fully flattened. At the same time, the feed rate of the high-point area is calculated inversely to obtain a reduction coefficient, and the feed rate in the helical path is adjusted according to this coefficient to reduce the cutting load of the milling cutter 35 in the high-point area, avoiding machining vibration and increased surface roughness. For low-point areas, the machining unit first calculates the ratio of the absolute value of the flatness deviation to the cutting depth reference value to obtain a cutting depth reduction coefficient, and reduces the cutting depth of the corresponding area accordingly to avoid over-cutting that leads to material loss or surface depressions. The feed rate in the low-point region is increased by an inverse proportional calculation, adjusting the feed rate of the helical path to ensure the tool can pass quickly through the low-point region, guaranteeing machining efficiency and overall flatness uniformity. At the boundary between the high-point and low-point regions, the machining unit calculates the length of the parameter transition section based on the difference in flatness deviation values ​​on both sides, and achieves a linear transition of cutting depth and feed rate within this section to avoid tool impact or uneven surface texture caused by abrupt changes in cutting parameters. Finally, the machining scheme generation unit accurately maps the adjusted cutting depth, feed rate, and three-dimensional coordinate information of the helical cutting path, assigning corresponding cutting parameter values ​​to each coordinate point, thereby generating complete machining trajectory data. This trajectory data not only accurately reflects the differences in end face morphology but also enables continuous, stable, and intelligent machining control during the cutting process, ensuring the flatness, dimensional accuracy, and surface quality of the flange end face, while improving machining efficiency and reducing rework rate.

[0096] As a preferred embodiment, the solution of this application is implemented as follows: For example, in a flange end face machining process, high and low point areas of the end face are identified through second image data. The flatness deviation of the high point area is 0.15 mm, while that of the low point area is 0.08 mm. For the high point area, the machining scheme generation unit calculates the ratio of the deviation value to the preset standard cutting depth to obtain a cutting depth increase coefficient, and sets the cutting depth of this area to be increased by a certain amount based on the standard cutting depth to ensure that the tool can effectively flatten the protruding part. At the same time, considering the increased load on the tool in the high point area, the feed rate is appropriately reduced to make the tool cutting smooth and avoid vibration and increased surface roughness. In the above example, the standard cutting depth of the high point area is 0.2 mm, the cutting depth is adjusted to 0.23 mm according to the deviation, and the feed rate is reduced from the original setting of 100 mm / min to 85 mm / min. For the low point area, the cutting depth is reduced according to the absolute value of the flatness deviation, and the feed rate is appropriately increased to avoid over-cutting or wasting time in the recessed area. In this example, the standard depth of cut in the low-point area is 0.2 mm, while the actual depth of cut is adjusted to 0.184 mm. The feed rate is increased from 100 mm / min to 110 mm / min to ensure machining efficiency and maintain the overall flatness of the end face. At the boundary between the high-point and low-point areas, the machining scheme generation unit calculates the difference in flatness deviation between the two sides and sets the length of the parameter transition section. This allows the depth of cut and feed rate to change linearly within the transition section, thus smoothly connecting different areas and avoiding tool impact or surface ripples caused by abrupt changes in cutting parameters. The machining scheme generation unit accurately maps the adjusted depth of cut, feed rate, and three-dimensional coordinate information of the helical cutting path, assigning corresponding cutting parameter values ​​to each coordinate point and generating complete machining trajectory data. In this way, the tool can continuously cut the end face along the helical path, achieving a machining effect of flattening the high points, lightly cutting the low points, and smoothly transitioning the boundary. This ensures that the flatness and machining accuracy of the flange end face meet the preset standards, while improving machining efficiency and reducing rework rate.

[0097] Through the above technical solution, the cutting depth in the high-point region of this application increases accordingly due to the larger flatness deviation, while the feed rate decreases to ensure that the tool does not vibrate or overload when cutting protruding parts, thereby flattening the high points and improving the end face flatness. The cutting depth in the low-point region is moderately reduced, while the feed rate is appropriately increased to avoid over-cutting and end face concavity, while also improving machining efficiency. The parameter transition section at the junction of high and low points achieves a smooth transition between different regions by linearly adjusting the cutting depth and feed rate, avoiding cutting impact and end face ripples.

[0098] This application further proposes that when the machining control unit converts the three-dimensional cutting path into control commands for each drive motor, it includes:

[0099] Based on the geometric features of the flange end face, a helical cutting path is generated, which is determined by the starting point, rotation angle, and radial feed. The motion parameters of each sliding component are calculated using an inverse kinematics algorithm, and the helical cutting path is converted into motion commands for the X, Y, and Z axes.

[0100] The first drive motor 311 drives the first sliding component to move along the X-axis, and the second drive motor 321 drives the second sliding component to move along the Y-axis, thereby realizing the radial movement of the milling cutter 35 on the flange end face; the third drive motor 331 drives the third sliding component to move along the Z-axis, thereby realizing the axial feed of the milling cutter 35; the movement speed of each drive motor is coordinated so that the milling cutter 35 continuously cuts the flange end face along a spiral cutting path.

[0101] Specifically, the machining control unit converts the generated three-dimensional helical cutting path into control commands for each drive motor, achieving high-precision machining of the flange end face. Based on the actual geometric features of the flange end face and the machining plan, a complete helical cutting path is generated. This path is determined by information such as the starting point position, the rotation angle of each revolution, and the radial feed, ensuring that the milling cutter 35 can cover the entire end face. The machining control unit uses an inverse kinematics algorithm to perform analytical calculations on each trajectory point in the helical cutting path, obtaining the corresponding X, Y, and Z-axis coordinate motion commands, and calculating the required velocity and acceleration of each sliding component at each trajectory point. During execution, the first drive motor 311 controls the first sliding component to move along the X-axis, and the second drive motor 321 controls the second sliding component to move along the Y-axis, achieving precise radial movement of the milling cutter 35 on the flange end face, thus following the helical path to complete in-plane cutting. The third drive motor 331 drives the third sliding component to move along the Z-axis, achieving axial feed of the milling cutter 35, performing layer-by-layer cutting on the end face, ensuring that the cutting depth is consistent with the machining trajectory. By coordinating the movement speed and start-up sequence of the three-axis drive motors, the milling cutter 35 moves smoothly along a continuous helical path, avoiding cutting vibration and tool skipping, and ensuring the flatness and smoothness of the end face. The machining control unit adjusts the speed of each motor based on real-time feedback signals to achieve precise correction of high and low points on the end face, ensuring that the machining results meet the design accuracy and quality requirements.

[0102] Through the above technical solution, this application generates a helical cutting path based on the actual geometric features of the flange end face, and calculates the motion parameters of each sliding component using an inverse kinematics algorithm, accurately decomposing the path into synchronous motion commands for the X, Y, and Z axes. The first drive motor 311 and the second drive motor 321 control the X-axis and Y-axis sliding components respectively, enabling the milling cutter 35 to achieve precise radial movement within the plane along the flange end face; the third drive motor 331 controls the Z-axis sliding component, realizing the layer-by-layer axial feed of the milling cutter 35. The coordinated movement speeds of each drive motor ensure continuous and smooth cutting along the helical path, avoiding tool skipping or vibration, and improving the flatness and finish of the end face machining.

[0103] This application further proposes that when the quality inspection unit analyzes the end face surface and extracts geometric feature parameters, it includes:

[0104] Based on the edge coordinate sequence of sub-pixel level coordinate positioning, the flatness error of the flange end face is calculated. The ideal plane is fitted by the least squares method and compared with the actual measurement points. The micro-texture features of the end face surface are analyzed to evaluate the surface roughness level.

[0105] Calculate the perpendicularity error between the flange end face and the flange axis by measuring the radial deviation at different positions on the end face edge and performing statistical analysis; identify possible surface defects on the end face, including scratches, pits, and irregular protrusions.

[0106] Specifically, the quality inspection unit performs a comprehensive and quantitative geometric and surface quality analysis of the flange end face based on the sub-pixel level edge coordinate sequence. The sub-pixel level edge coordinates are transformed from the pixel system to the machine tool / workpiece coordinate system, and obvious outliers are removed (using residual-based outlier removal or RANSAC iterative fitting). Then, the ideal plane is fitted using the least squares method or weighted least squares method. The vertical residual from each sampling point to the fitted plane is calculated, and the flatness index (including maximum deviation, peak-to-valley difference, root mean square error, etc.) is statistically obtained. The maximum residual or root mean square value is compared with the preset flatness tolerance to determine whether it exceeds the tolerance. During the fitting process, the spatial autocorrelation of the residual distribution is calculated or interpolated to generate a deviation field heat map, so as to locate concentrated convex or concave areas and provide reference coordinates for rework. Regarding the evaluation of surface roughness and microtexture, local profiles or small surfaces are extracted based on high-resolution two-dimensional contours or three-dimensional reconstructed point clouds. First, the large-scale matrix morphology is removed from the measurement signal (using polynomial detrending or low-pass filtering). Then, commonly used roughness parameters (such as Ra, Rq, Rz or 3D indices such as Sa, Sq) are calculated. Frequency domain analysis (Fourier transform or wavelet decomposition) can be used to determine the surface texture characteristic frequency band and tool mark characteristics. Combined with texture directionality analysis, it is determined whether there are periodic cutting marks or abnormal vibration marks. The roughness results are compared with the set threshold according to the statistical interval (mean ± confidence interval). Areas exceeding the limit are marked and their specific locations and amplitudes are recorded. Regarding the perpendicularity of the end face (the angle between the end face and the flange axis and the radial deviation), the workpiece axis direction is determined based on the flange center coordinates obtained from the second position and the fixture reference axis. By measuring the radial coordinate differences at different angular positions of the end face and calculating the corresponding normal offset, the perpendicularity error index (angular deviation and maximum radial deviation) is statistically obtained, and this is used to determine whether the end face is tilted or eccentric relative to the flange axis. By using zonal statistics (sampling every several degrees), it can be revealed whether it is a unilateral tilt, a center of gravity shift, or a periodic deviation. Surface defect identification employs a multi-scale, multi-method fusion strategy. First, local contrast enhancement and adaptive threshold segmentation are performed using image / depth data. Morphological processing is then combined to extract suspicious patches, and their shape features (area, perimeter, aspect ratio, convexity), grayscale / depth contrast, and texture features (Local Binary Pattern (LBP) or Gabor filter response) are calculated. Simple defects (scratches, dents, debris marks) can be classified based on geometric / grayscale threshold rules. For complex or ambiguous defects, a pre-trained classifier (lightweight CNN or SVM) can be used to perform secondary judgment on candidate regions and provide defect type and confidence level. All defects are recorded with their polar coordinates, size, severity score, and visual annotation. The quality inspection unit summarizes the above indicators (flatness statistics, roughness parameters, perpendicularity error, defect list and its location) to generate a structured inspection report and performs multi-dimensional comparison with a preset quality standard library, outputting a pass / fail judgment and grading suggestion (e.g., acceptable, requires partial rework, requires full rework).

[0107] Through the above technical solution, this application can accurately fit the ideal plane based on the edge sequence of sub-pixel level coordinate positioning, and calculate the flatness error by comparing it with the actual measurement points, thereby determining whether the end face meets the design flatness requirements; in micro-texture analysis, the quality detection unit can evaluate the roughness level of the end face surface, quantify the impact of the cutting process on the surface quality, and conduct a detailed analysis of the surface texture features, providing data support for surface quality control.

[0108] This application further proposes that when a quality inspection unit determines whether a flange end face meets quality requirements, it includes:

[0109] The calculated flatness error, surface roughness, perpendicularity error, and surface defect characteristics are compared with the threshold ranges in the preset quality standard library in multiple dimensions. When all quality indicators fall within the qualified range, the flange end face is determined to meet the quality requirements. When any quality indicator exceeds the qualified range, the flange end face is determined to not meet the quality requirements.

[0110] Specifically, when determining whether the flange end face meets quality requirements, the various geometric and surface quality indicators obtained through the aforementioned inspection steps are summarized, including the flatness error of the end face, surface roughness value, perpendicularity error between the end face and the flange axis, and the detected surface defect characteristics (such as the number, size, and distribution location of scratches, pits, and irregular protrusions). These indicators are compared with threshold ranges in a preset quality standard library in multiple dimensions: the flatness error must be within the maximum allowable deviation range of the standard, the roughness value must be lower than the upper limit of the specified surface finish grade, the perpendicularity error must meet the perpendicularity accuracy required by the design, and the number and size of surface defects must be controlled within acceptable limits. The quality inspection unit independently judges each indicator and uses logical synthesis to determine the compliance of all indicators. When all indicators fall within their respective acceptable ranges, the flange end face is automatically determined to meet the quality requirements, and a qualified report is generated to record the processing batch, test data, and test time. When any indicator exceeds the standard threshold range, the flange end face is determined to not meet the quality requirements, and the non-conforming item is marked. At the same time, a detailed non-conforming report is generated, indicating the specific non-conforming indicator and its corresponding location, providing a basis for subsequent rework, processing parameter adjustment, or production process optimization.

[0111] The threshold range setting rules in the preset quality standard library are comprehensively formulated based on flange processing requirements, design drawing tolerances, and actual usage environment conditions. The maximum permissible errors for end face flatness and perpendicularity are determined according to the dimensions and geometric tolerances in the flange design drawings. The flatness threshold considers the flange fit accuracy requirements and sealing performance, while the perpendicularity threshold takes into account the smoothness of axial assembly. The surface roughness threshold is determined based on the flange material, processing method, and usage environment to ensure that the surface finish of the flange end face meets sealing and wear resistance requirements. The surface defect characteristic threshold is set based on the size, number, and distribution of scratches, pits, and protrusions that may affect the flange's service life and sealing performance; defects exceeding this threshold are considered unqualified. By comprehensively considering the above indicators and fine-tuning them based on historical processing data and empirical parameters, a multi-dimensional, quantifiable, and operable quality standard library is formed, enabling the quality inspection unit to accurately determine the conformity of the flange end face.

[0112] Through the above technical solution, this application considers key geometric and surface quality indicators such as flatness, surface roughness, perpendicularity and surface defects, avoiding the risk of missed detection caused by judging by a single indicator; based on the threshold range in the preset quality standard library, intelligent comparison can be performed to quickly determine whether the end face is qualified or unqualified, thus improving detection efficiency and reliability.

[0113] In summary, by combining an image acquisition device with the Canny edge detection algorithm and template matching technology, the flange's placement position on the worktable is automatically identified, achieving intelligent flange positioning and attitude confirmation. This avoids manual alignment errors and repetitive adjustments, improving clamping accuracy and loading efficiency. A clamping force sensor within the grippers monitors the clamping pressure in real time. The control module adjusts the air pressure of the gripper drive cylinder through feedback, achieving adaptive control of the clamping force. This ensures clamping stability while preventing flange deformation or surface damage caused by over-clamping. The flange machining mechanism employs three independent sliding components (first, second, and third sliding components), responsible for displacement in the X, Y, and Z axes respectively. The control module uses an inverse kinematics algorithm to achieve three-axis coordination, enabling the milling cutter to continuously process along a helical cutting path. This results in a smooth cutting trajectory and precise positioning, improving the flatness and surface quality of the flange end face. The second position confirmation unit acquires precise position parameters after flange clamping. Combined with a sub-pixel-level edge extraction algorithm to identify the distribution of high and low points on the end face, a personalized machining plan including cutting depth, feed rate, and offset compensation can be automatically generated. The machining path can be dynamically adjusted according to the flatness deviation of different areas, realizing automatic compensation and optimization of unevenness error on the end face. In the machining scheme generation unit, the cutting depth and feed rate of each segment of the helical cutting path are adjusted in real time according to the deviation value of the high and low point areas. The cutting depth is automatically deepened and the feed rate is reduced in the high point area, and the cutting depth is automatically reduced and the feed rate is increased in the low point area. The transition section parameters are smoothly connected, thus balancing machining efficiency and surface consistency. After machining, a third image data is acquired through an image acquisition device. A sub-pixel level edge positioning algorithm is used to perform multi-dimensional feature analysis of end face flatness, perpendicularity, and surface roughness, and the data is compared with a preset quality standard library. This realizes automatic detection and judgment of machining quality, reduces manual inspection, and improves detection accuracy and reliability.

[0114] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. An intelligent flange processing device, characterized in that, The system includes a worktable, on the upper surface of which a flange clamping mechanism and a flange processing mechanism are fixed. The flange clamping mechanism includes a clamping base, above which a placement assembly and two sets of clamping assemblies are arranged. The placement assembly includes a disc base with a placement column in the center. The two sets of clamping assemblies are respectively arranged on both sides of the placement assembly. Each set of clamping assemblies includes a jaw and a jaw drive cylinder, and a force sensor is also provided on the inner surface of the jaw. The flange processing mechanism is arranged above the flange clamping mechanism and is connected to the flange clamping mechanism based on a first sliding assembly. The first sliding assembly includes a first drive motor, a first transmission screw, and two first sliding rods. The bottom of the machining mechanism is connected to the first transmission screw through a first transmission screw mating hole, and is connected to the corresponding first sliding rod through a first sliding rod mating guide hole. The upper part of the flange machining mechanism is provided with a second sliding assembly, and a flange machining drive unit is connected based on the second sliding assembly. The second sliding assembly includes a second drive motor, a second transmission screw, and two second sliding rods. The flange machining drive unit is connected to the second transmission screw through a second transmission screw mating hole, and is connected to the corresponding second sliding rod through a second sliding rod mating guide hole. The flange machining drive unit is provided with a third sliding assembly, and a tool drive unit is connected based on the third sliding assembly. The third sliding assembly includes a third drive motor, a second transmission screw, and two second sliding rods. The tool drive unit is connected to the third drive screw via a mating hole and to the corresponding third sliding rod via mating guide holes. The tool drive unit includes a machining head assembly comprising a tool box and a milling cutter. Image acquisition devices are located on both sides of the machining head assembly. A control module is also located at the bottom of the worktable, electrically connected to the image acquisition devices, force sensor, gripper drive cylinder, first drive motor, second drive motor, and third drive motor. The control module includes a first position confirmation unit, a clamping control unit, a second position confirmation unit, a machining scheme generation unit, a machining control unit, and a quality inspection sheet. The system comprises the following components: a first position confirmation unit, configured to determine whether the flange is accurately placed at a designated position on the worktable based on the first image data acquired by the image acquisition device and through Canny edge detection algorithm and template matching calculation; a clamping control unit, configured to control the gripper drive cylinder to drive the gripper to clamp the flange after determining that the flange is accurately placed, and to monitor the clamping pressure in real time through the force sensor to ensure that the clamping force is within a safe working range; and a second position confirmation unit, configured to accurately obtain the actual position parameters of the flange after clamping based on the second image data acquired by the image acquisition device using the Hough transform parameter space accumulation method, wherein the actual position parameters include the actual center coordinates and radius parameters of the flange outer circle.The machining scheme generation unit is used to identify the high and low points of the flange end face based on the actual position parameters using a sub-pixel level edge extraction algorithm, and generate a flange end face machining scheme. The machining control unit is used to generate a three-dimensional cutting path based on the flange end face machining scheme, convert the three-dimensional cutting path into control commands for each drive motor, coordinate the movement of the first sliding component, the second sliding component, and the third sliding component, so that the milling cutter continuously cuts the flange end face along the helical cutting path. The quality inspection unit is used, after machining is completed, to analyze the flange end face quality based on the third image data acquired by the image acquisition device, using a sub-pixel level edge extraction algorithm, analyze the end face surface and extract geometric feature parameters, compare them with a preset quality standard library, and determine whether the flange end face meets the quality requirements.

2. The intelligent flange processing device according to claim 1, characterized in that, When the first position confirmation unit determines whether the flange is accurately placed at the designated position on the workbench, it includes: performing grayscale processing and Gaussian filtering noise reduction on the first image data; calculating the image gradient magnitude and direction to construct a gradient intensity map; performing non-maximum suppression on the gradient intensity map to retain the maximum value pixel in the gradient direction; and using a dual threshold detection method to perform edge classification on the maximum value pixel after non-maximum suppression, marking the maximum value pixel with a gradient magnitude greater than the high magnitude threshold as a strong edge, and marking the maximum value pixel with a gradient magnitude greater than the low magnitude threshold and less than or equal to the high magnitude threshold as a weak edge. Edges; through edge connection operations, strong edges and weak edges connected to strong edges are retained to generate effective edges; continuous closed effective edges are extracted, and the main edge chain matching the outer contour of the flange is selected through contour topology analysis; the main edge chain is matched with the preset workbench positioning area template by feature point matching, and the feature point matching degree value is calculated; when the feature point matching degree value is greater than the preset matching degree threshold, it is determined that the flange is accurately placed in the specified position of the workbench; when the feature point matching degree value is less than or equal to the preset matching degree threshold, it is determined that the flange is not accurately placed in the specified position of the workbench.

3. The intelligent flange processing device according to claim 2, characterized in that, When the second position confirmation unit confirms the actual position parameters after the flange is clamped, it includes: performing edge sharpening processing on the second image data, determining the geometric feature direction of the flange edge by calculating the image gray-level gradient direction histogram; mapping the edge point set after edge sharpening processing to the parameter space, constructing an accumulator array and performing voting statistics on the parameter space to identify the peak region in the parameter space; and extracting the actual center coordinates and radius parameters of the outer circle of the flange based on the coordinates of the peak region.

4. The intelligent flange processing device according to claim 3, characterized in that, When the processing scheme generation unit generates a flange end face processing scheme, it includes: performing end face quality analysis on the second image data, constructing a polynomial fitting model of the edge point neighborhood, and calculating the sub-pixel level coordinates of the end face edge; calculating the flatness deviation value of each region of the end face based on the sub-pixel level coordinate sequence, marking regions with flatness deviation values ​​greater than a first deviation threshold as high-point regions, and marking regions with flatness deviation values ​​less than a second deviation threshold as low-point regions; adjusting the cutting parameters of each segment in the helical cutting path based on the spatial distribution characteristics of the high-point regions and low-point regions, and generating processing trajectory data, wherein the processing trajectory data includes region identification, cutting depth, feed rate, and compensation coordinate offset.

5. The intelligent flange processing device according to claim 4, characterized in that, When the processing scheme generation unit constructs a polynomial fitting model for the neighborhood of edge points, it includes: performing Gaussian filtering preprocessing on the second image data to eliminate image noise while retaining the clear features of the end face edge; using a gradient operator to calculate the gradient change of each pixel in the image to preliminarily determine the approximate pixel-level position of the end face edge; setting a square neighborhood window of a fixed size around each preliminarily determined edge point and collecting the gray value distribution information of all pixels within the square neighborhood window; establishing a coordinate system along the normal direction of the edge point, constructing a quadratic function relationship describing the gray value change law, and using the quadratic function relationship as a polynomial fitting model; using the least squares method to optimize and solve the coefficients of the quadratic function relationship to minimize the deviation between the function curve and the actual gray value, ensuring the accuracy of the fitting result.

6. The intelligent flange processing device according to claim 5, characterized in that, When the machining scheme generation unit adjusts the cutting parameters of each segment in the helical cutting path to generate machining trajectory data, it includes: calculating the flatness deviation value of the high point region proportionally to a preset cutting depth reference value to obtain a cutting depth increase coefficient, wherein the cutting depth increase coefficient is equal to the ratio of the flatness deviation value to the cutting depth reference value; setting the cutting depth of the corresponding high point region in the helical cutting path to the standard cutting depth multiplied by the cutting depth increase coefficient plus one; calculating the flatness deviation value of the high point region inversely proportionally to a preset feed rate reference value to obtain a feed rate decrease coefficient, wherein the feed rate decrease coefficient is equal to the feed rate reference value divided by the sum of the flatness deviation value and the feed rate reference value; setting the feed rate of the corresponding high point region in the helical cutting path to the standard feed rate multiplied by the feed rate decrease coefficient; calculating the absolute value of the flatness deviation of the low point region proportionally to a preset cutting depth reference value to obtain a cutting depth decrease coefficient, wherein the cutting depth decrease coefficient is equal to the ratio of the absolute value of the flatness deviation to the cutting depth reference value; and setting the cutting depth of the corresponding high point region in the helical cutting path to the standard feed rate multiplied by the feed rate decrease coefficient; and calculating the absolute value of the flatness deviation of the low point region proportionally to a preset cutting depth reference value to obtain a cutting depth decrease coefficient, wherein the cutting depth decrease coefficient is equal to the ratio of the absolute value of the flatness deviation to the cutting depth reference value. In the spiral cutting path, the cutting depth corresponding to the low point region is set as the difference between the standard cutting depth and the cutting depth reduction coefficient. The absolute value of the flatness deviation of the low point region is inversely proportional to the preset feed rate reference value to obtain the feed rate improvement coefficient, which is equal to the sum of the absolute value of the flatness deviation and the feed rate reference value divided by the feed rate reference value. The feed rate of the corresponding low point region in the spiral cutting path is set as the standard feed rate multiplied by the feed rate improvement coefficient. For the spiral cutting path segment at the junction of the high point region and the low point region, the length of the parameter transition section is calculated according to the magnitude of the flatness deviation values ​​on both sides of the junction. The length of the parameter transition section is proportional to the magnitude of the flatness deviation value. Within the parameter transition section, the cutting depth and feed rate change linearly from the parameter values ​​of one side region to the parameter values ​​of the other side region. The adjusted cutting depth and feed rate are precisely mapped to the coordinate position information of the spiral cutting path, and a corresponding cutting parameter value is assigned to each coordinate point on the spiral cutting path to generate the machining trajectory data.

7. The intelligent flange processing device according to claim 6, characterized in that, When the machining control unit converts the three-dimensional cutting path into control commands for each drive motor, it includes: generating a helical cutting path based on the geometric features of the flange end face, wherein the helical cutting path is determined by the starting point, rotation angle, and radial feed; calculating the motion parameters of each sliding component using an inverse kinematics algorithm, and converting the helical cutting path into motion commands for the X-axis, Y-axis, and Z-axis; controlling the first drive motor to drive the first sliding component to move along the X-axis and the second drive motor to drive the second sliding component to move along the Y-axis, thereby realizing the radial movement of the milling cutter on the flange end face; controlling the third drive motor to drive the third sliding component to move along the Z-axis, thereby realizing the axial feed of the milling cutter; and coordinating the motion speeds of each drive motor to enable the milling cutter to continuously cut the flange end face along the helical cutting path.

8. The intelligent flange processing device according to claim 7, characterized in that, When the quality inspection unit analyzes the end face surface and extracts geometric feature parameters, it includes: calculating the flatness error of the flange end face based on the edge coordinate sequence of sub-pixel level coordinate positioning, fitting an ideal plane using the least squares method and comparing it with the actual measurement points; analyzing the micro-texture features of the end face surface and evaluating the surface roughness level; calculating the perpendicularity error between the flange end face and the flange axis, and performing statistical analysis by measuring the radial deviation at different positions of the end face edge; and identifying possible surface defects on the end face, including scratches, pits, and irregular protrusions.

9. The intelligent flange processing device according to claim 8, characterized in that, When the quality inspection unit determines whether the flange end face meets the quality requirements, it includes: comparing the calculated flatness error, surface roughness, perpendicularity error and surface defect characteristics with the threshold range in the preset quality standard library in multiple dimensions; when all quality indicators fall within the qualified range, the flange end face is determined to meet the quality requirements; when any quality indicator exceeds the qualified range, the flange end face is determined to not meet the quality requirements.

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