Automatic photographing track method for appearance detection of aluminum alloy hub and track generation system thereof

Abstract

The invention discloses an aluminum alloy hub appearance detection automatic photographing track method and a track generation system thereof, and the method comprises the steps: based on a camera image collection sensor and a machine vision algorithm, determining and distinguishing key detection positions through the positions of a hub center hole, a hub A surface excircle and a spoke; a detection position is automatically determined and a robot motion track curve is automatically generated; the system is slightly interfered by environmental changes, the defect that different hubs need to teach a manipulator again is overcome, manual intervention is little, and the hub surface defect detection efficiency is improved. The appearance detection efficiency of the automobile hub can be improved, the detection workload can be reduced, secondary damage of the detected automobile hub can be avoided or reduced, an automatic photographing track detection mode can be formed in the appearance detection of the hub, and the accuracy and effectiveness of the appearance detection quality of the hub can be improved.

Description

Technical Field

[0001] The present invention relates to a wheel hub appearance detection, in particular to an automatic photographing trajectory method for detecting appearance defects of automobile wheel hubs and a trajectory generation system thereof. Background Art

[0002] Automobile wheel hubs are important safety components for the safe driving of automobiles. At the same time, their appearance quality not only affects the appearance quality of the wheel hubs themselves, but also affects the safety of automobile driving. During the production of automobile wheel hubs, due to the appearance quality defects such as bumps and scratches on the surface of the wheel hubs, in order to ensure the ex-factory quality of automobile wheel hubs, it is necessary to detect the appearance of automobile wheel hubs before the finished products of aluminum alloy automobile wheel hubs leave the factory. At present, the detection of wheel hub appearance is usually carried out by manual detection and identification by personnel, resulting in low efficiency of wheel hub appearance detection, large detection workload, and difficulty in effectively ensuring the stability of detection quality. Although there are also some that use wheel hub appearance detection tables or detection machines to detect the appearance of wheel hubs, the wheel hub appearance detection machines or detection tables used also only assist in the step-by-step detection of wheel hubs through the detection tables or detection machines, and there are also manual auxiliary operations such as manually loading and unloading the wheel hubs, with low detection operation efficiency, easy to cause secondary damage to the automobile wheel hubs after detection, and difficult to effectively and automatically detect all areas related to the appearance quality of automobile wheel hubs, and there is no automatic photographing trajectory detection mode formed from wheel hub appearance detection, which is not conducive to the accuracy and effectiveness of wheel hub appearance detection quality.

[0003] The authorized patent number ZL202121387835.9 with a publication date of January 25, 2022 discloses a new type of double-station aluminum alloy wheel hub appearance detection table. The main body of the new type of double-station aluminum alloy wheel hub appearance detection table is a main frame. A fixed nylon tabletop is installed on the top of the main frame. Two rotating nylon tabletops are installed on both sides of the fixed nylon tabletop. A rotating tabletop bracket is installed under the rotating nylon tabletop. Four bull's eye wheels are installed under the rotating tabletop bracket. Four limit guide wheels are installed at the bottom of the rotating tabletop bracket. One adjusting screw is installed outside each limit guide wheel. Two nylon rollers are installed in each rotating nylon tabletop. One bolt plug funnel is installed under each rotating nylon tabletop. One bolt plug collection box is installed under each bolt plug funnel. The new type of double-station aluminum alloy wheel hub appearance detection table has a simple structure and low failure rate. However, this solution also has the possible defects of low detection operation efficiency, easy to cause secondary damage to the automobile wheel hubs after detection, and difficult to effectively and automatically detect all areas related to the appearance quality of automobile wheel hubs. Summary of the Invention

[0004] The present invention provides an automatic photographing trajectory method and a trajectory generation system for the appearance inspection of aluminum alloy wheels, which can improve the efficiency of the appearance inspection of automobile wheels, reduce the inspection workload, avoid or reduce the secondary damage of the automobile wheels after inspection, and form an automatic photographing trajectory inspection mode for the appearance inspection of the wheels, which is conducive to improving the accuracy and effectiveness of the appearance inspection quality of the wheels. The present invention is provided to solve the current situation that the existing appearance inspection of automobile wheels has low inspection operation efficiency, large inspection workload, and is prone to cause secondary damage to the automobile wheels after inspection, and there is no automatic photographing trajectory inspection mode formed from the appearance inspection of the wheels, which is not conducive to the accuracy and effectiveness of the appearance inspection quality of the wheels.

[0005] The specific technical solution adopted by the present invention to solve the above technical problems is: an automatic photographing trajectory method for the appearance inspection of aluminum alloy wheels, which is characterized in that it includes the following trajectory photographing methods A1. Select an aluminum alloy automobile wheel to be inspected; A2. Place the selected automobile wheel on the automatic conveying roller path line. The wheel centering mechanism on the automatic conveying roller path line performs the centering task of the automobile wheel, and continues to be conveyed by the automatic conveying roller path line to the wheel center hole photographing trajectory station, and performs the following steps A3 to A10 tasks in the wheel center hole photographing trajectory station; A3. The wheel center hole photographing trajectory station performs the trajectory photographing of the appearance inspection of the wheel center hole to obtain the original global image of the wheel; A4. Use a Gaussian convolution kernel to perform Gaussian filtering on the original global image of the wheel to remove image noise; A5. Enhance the high-frequency region of the image processed in step A4 above to increase the image contrast; A6. Find a suitable segmentation threshold in the enhanced contrast neighborhood, select all pixels darker than their neighborhood, and perform dynamic binarization on the wheel image; A7. Identify the wheel center hole circle from the wheel image processed in step A6 above, and crop the redundant interference image; A8. According to the image cropped in step A7 above, identify the pixel coordinates of the center point position of the wheel center hole, and at the same time identify the pixel radius size of the center hole, and transmit the identified data to the image manipulator; A9. Calculate the manipulator coordinates of the center point of the wheel center hole and the manipulator coordinates of the center hole radius according to the calibrated position relationship of the image manipulator; A10. According to the manipulator points calculated in step A9 above, with the center point coordinates of the wheel center hole as the center and the center hole radius coordinates as the radius, generate the manipulator photographing motion trajectory of the center hole diameter of the wheel center hole photographing trajectory station conveyed by the roller path line; A11. After completing the above tasks at the Hub Center Hole Photo Track Station, the vehicle wheel moves from the automated conveyor roller conveyor to the Hub A-Side Photo Track Station for image capture of the spoke area on this side. Robots on either side of the automated conveyor roller conveyor are responsible for capturing images of half of the wheel hub area. Steps A12 through A16 are then executed at the Hub A-Side Photo Track Station. A12. Perform image smoothing and denoising on the image acquired in step A11 above; A13. Dynamically binarize the wheel image after processing the image in step A12 above. A14. Identify the spoke edges, spoke tops, and the intersection of the spokes and the front rim on side A of the hub. A15. Based on the differences in the identified area locations in step A14 above, pixel coordinates are generated along the spoke edges. For the spoke tops and the spoke-front rim junction, pixel coordinates are generated for several points within the identified area. A16. After generating the pixel coordinates of step A15 above, the corresponding coordinates of the robot are obtained by hand-eye calibration, and the generated points are optimized to form the robot photography motion trajectory of the wheel hub A surface photography trajectory station.

[0006] Based on camera image acquisition sensors and machine vision algorithms, the system automatically determines key inspection locations by analyzing the wheel hub center hole, the outer diameter of the hub A surface, and the spokes. This system automatically determines inspection locations and generates robot motion trajectory curves. This system is less susceptible to environmental changes, eliminates the need to retrain the robot for each wheel, reduces manual intervention, and improves the efficiency of wheel hub surface defect detection. This system improves the efficiency of wheel hub appearance inspection, reduces inspection workload, and avoids or minimizes secondary damage to the wheel hub after inspection. It also enables an automatic camera trajectory detection mode for wheel hub appearance inspection, which helps improve the accuracy and effectiveness of wheel hub appearance inspection quality.

[0007] Preferably, in the above step A4, a 5*5 Gaussian convolution kernel is used for Gaussian filtering with a standard deviation of sigma .3+0.8, remove image noise; Where sigma is the standard deviation and n is the Gaussian convolution kernel size. This improves image denoising.

[0008] Preferably, in the above step A5, the high-frequency area of the image is enhanced to increase the image contrast by using the method of Des=Round((original-mean)*Factor)+original; Wherein: original is the gray value of the pixel point on the original image; mean is the average gray value of all pixel points within the convolution kernel size range; Factor is the scale factor; Round means rounding in the mathematical formula; Des is the gray value of the new pixel point. It improves the image contrast enhancement effect.

[0009] Preferably, the configuration of the photographing trajectory station for the hub center hole includes a hoisting gantry, a 6-axis robot, a zoom intelligent camera, a light source system, and an identification system, which is used to perform automatic detection trajectory photographing and identification processing on the appearance of the position area of the hub A-side center hole.

[0010] Preferably, the configuration of the photographing trajectory station for the hub A-side includes two six-axis robots, two sets of zoom intelligent cameras, and an identification system. The two sets of zoom intelligent cameras are arranged oppositely on both sides of the automatic transfer conveyor line, and during operation, they simultaneously perform automatic detection trajectory photographing and identification processing on the appearance of the automotive hub A-side passing under the two sets of zoom intelligent cameras on the automatic transfer conveyor line.

[0011] Preferably, both the photographing trajectory station for the hub center hole and the photographing trajectory station for the hub A-side are respectively configured with a camera and lens, a manipulator body, a manipulator control system, a vision algorithm system, and a roller path control system. It improves the simplicity, convenience, reliability, and effectiveness of generating the photographing trajectory.

[0012] Another object of the present invention application is to provide an automatic photographing trajectory generation system for the appearance detection of aluminum alloy wheels, which is characterized in that: it adopts the automatic photographing trajectory method for the appearance detection of aluminum alloy wheels as described in one of the above technical solutions, and includes a wheel centering mechanism, a photographing trajectory station for the hub center hole, and a photographing trajectory station for the hub A-side that are arranged on or cooperate with the automatic transfer conveyor line and are arranged in sequence. It can not only improve the efficiency of automotive wheel appearance detection, reduce the detection workload, avoid or reduce secondary damage to the automotive wheels after detection, but also form an automatic photographing trajectory detection mode for wheel appearance detection, which is beneficial to improving the accuracy and effectiveness of wheel appearance detection quality.

[0013] The beneficial effects of the present invention are as follows: Based on the camera image acquisition sensor and machine vision algorithms, an automatic photographing camera image formed by multiple high-speed photographs of the stationary hub on the workbench taken by the camera is used to obtain the original global image of the hub. By determining and distinguishing the key detection positions based on the positions of the hub center hole, the outer circle of the hub A surface, and the spokes, the automatic determination of the detection position and the automatic generation of the robot motion trajectory curve are realized. This system is less affected by environmental changes, gets rid of the drawback of having to re-teach the manipulator for different hubs, has less manual intervention, and improves the detection efficiency of hub surface defects. It can not only improve the appearance detection efficiency of automotive hubs, reduce the detection workload, avoid or reduce secondary damage to automotive hubs after detection, but also form an automatic photographing trajectory detection mode for hub appearance detection, which is beneficial to improving the accuracy and effectiveness of hub appearance detection quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The present invention will be further described in detail below in conjunction with the drawings and specific embodiments.

[0015] Figure 1 is a schematic structural diagram of the automatic photographing trajectory method and its trajectory generation system for the appearance detection of aluminum alloy wheels of the present invention.

[0016] Figure 2 is a schematic structural diagram of the automatic photographing trajectory method and its trajectory generation system for the appearance detection of aluminum alloy wheels of the present invention observed from another direction.

[0017] Figure 3 is a front view structural schematic diagram of the automatic photographing trajectory method and its trajectory generation system for the appearance detection of aluminum alloy wheels of the present invention.

[0018] Figure 4 is a side view structural schematic diagram of the automatic photographing trajectory method and its trajectory generation system for the appearance detection of aluminum alloy wheels of the present invention.

[0019] Figure 5 is a photograph of the original image of the automotive wheel detected by the automatic photographing trajectory method and its trajectory generation system for the appearance detection of aluminum alloy wheels of the present invention.

[0020] Figure 6 is an image diagram of the filtered processed image of the automotive wheel image detected by the automatic photographing trajectory method and its trajectory generation system for the appearance detection of aluminum alloy wheels of the present invention.

[0021] Figure 7 is an image diagram of the image of the automotive wheel detected by the automatic photographing trajectory method and its trajectory generation system for the appearance detection of aluminum alloy wheels of the present invention after contrast enhancement processing.

[0022] Figure 8 is an image diagram of the image of the automotive wheel detected by the automatic photographing trajectory method and its trajectory generation system for the appearance detection of aluminum alloy wheels of the present invention after dynamic binarization processing.

[0023] Figure 9 This is the image of the automobile wheel hub after cropping and removing redundant interference from the image detected by the automatic photographing trajectory method and its trajectory generation system for the appearance detection of aluminum alloy wheel hubs of the present invention.

[0024] Figure 10 This is the image of the automobile wheel hub detected by the automatic photographing trajectory method and its trajectory generation system for the appearance detection of aluminum alloy wheel hubs of the present invention after identifying the pixel coordinates of the center point of the wheel hub center hole.

[0025] Figure 11 This is the circular coordinate data map of the center point position of the wheel hub center hole identified from the image of the automobile wheel hub detected by the automatic photographing trajectory method and its trajectory generation system for the appearance detection of aluminum alloy wheel hubs of the present invention.

[0026] Figure 12 This is the image of a half area of the spokes on the A side of the wheel hub collected at the photographing trajectory station of the A side of the wheel hub from the image of the automobile wheel hub detected by the automatic photographing trajectory method and its trajectory generation system for the appearance detection of aluminum alloy wheel hubs of the present invention.

[0027] Figure 13 This is the edge point coordinate map of the half area of the spokes on the A side of the wheel hub collected at the photographing trajectory station of the A side of the wheel hub from the image of the automobile wheel hub detected by the automatic photographing trajectory method and its trajectory generation system for the appearance detection of aluminum alloy wheel hubs of the present invention. Detailed implementation manners

[0028] Example 1: Figure 1 , Figure 2 , Figure 3 , Figure 4 , Figure 5 , Figure 6 , Figure 7 , Figure 8 , Figure 9 , Figure 10 , Figure 11 , Figure 12 , Figure 13 In the embodiment shown, an automatic photographing trajectory method for the appearance detection of aluminum alloy wheel hubs includes the following trajectory photographing methods A1. Select an aluminum alloy automobile wheel hub to be detected; A2. Place the selected automotive wheel onto the automated conveyor roller conveyor. The wheel centering mechanism on the conveyor roller conveyor performs the centering task and continues to transport the wheel to the wheel center hole tracking station, where steps A3 through A10 are performed. There is a gap between adjacent rollers, and the centering jaws are positioned within the gap. When no wheel is passing, the jaws are positioned on either side of the roller conveyor. When a wheel passes, the jaws automatically retract toward the center of the roller conveyor, clamping the wheel. A3. The hub center hole photography track station performs track photography for the hub center appearance inspection, acquiring the original global image of the hub; A4. Perform Gaussian filtering on the original global image of the wheel hub using a Gaussian convolution kernel to remove image noise. A5. Enhance the high-frequency region of the image processed in step A4 to increase image contrast. A6. By finding an appropriate segmentation threshold in a contrast-enhanced neighborhood, all pixels darker than their neighbors are selected, and the wheel image is dynamically binarized. Dynamic binarization typically involves dynamically calculating a threshold based on certain image features and using this threshold for image binarization. A7. Identify the wheel hub center hole circle from the wheel hub image processed in step A6 above and crop any unnecessary interference images. A8. After cropping the image according to step A7 above, identify the pixel coordinates of the center point of the wheel hub center hole and the pixel radius of the center hole, and transmit the identified data to the image robot; A9. Calculate the robot coordinates of the center point of the hub center hole and the center hole radius based on the image robot calibration position relationship; A10. Based on the robot position calculated in step A9 above, with the midpoint coordinates of the hub center hole as the center and the center hole radius coordinates as the radius, a roller track is generated to transport the robot to the hub center hole photography track station. A11. After completing the above tasks at the Hub Center Hole Photo Track Station, the vehicle wheel moves from the automated conveyor roller conveyor to the Hub A-Side Photo Track Station for image capture of the spoke area on this side. Robots on either side of the automated conveyor roller conveyor are responsible for capturing images of half of the wheel hub area. Steps A12 through A16 are then executed at the Hub A-Side Photo Track Station. A12. Perform image smoothing and denoising on the image acquired in step A11 above; A13. Dynamically binarize the wheel image after processing the image in step A12 above. A14. Identify the spoke edges, spoke tops, and the intersection of the spokes and the front rim on side A of the hub. A15. According to the differences in the identified regional positions in the above A14 step, generate the pixel point coordinates along the edge for the spoke edge, and generate several point pixel coordinates within the identified area for the spoke top surface and the area where the spoke intersects with the front wheel rim; A16. After generating the pixel coordinates in the above A15 step, obtain the corresponding coordinates of the robot manipulator through hand-eye calibration, optimize the generated points, and form the photographing motion trajectory of the robot manipulator at the hub A surface photographing trajectory station. Hand-eye calibration is to align the coordinate systems between the end effector of the robot (such as a robotic arm) and the sensor (such as a camera). Specifically, hand-eye calibration establishes the conversion relationship between the two by obtaining the position and pose information of the robot arm and the pose information of the camera, so as to achieve precise motion control and visual navigation.

[0029] In the above A4 step, use a 5*5 Gaussian convolution kernel for Gaussian filtering with a standard deviation of sigma .3 + 0.8 to remove image noise; where: sigma is the standard deviation; n is the size of the Gaussian convolution kernel.

[0030] In the above A5 step, enhance the high-frequency region of the image and increase the image contrast by the method of Des = Round((original - mean)*Factor)+original; where: original is the gray value of the pixel point on the original image; mean is the average gray value of all pixel points within the size range of the convolution kernel; Factor is the scaling factor; Round means rounding in the mathematical formula; Des is the gray value of the new pixel point.

[0031] The hub center hole photographing trajectory station configuration includes a hoisting gantry, a 6-axis robot, a zoom intelligent camera, a light source system, and an identification system, which are used to perform automatic detection trajectory photographing and identification processing on the appearance of the hub A surface center hole position area.

[0032] The hub A surface photographing trajectory station configuration includes two six-axis robots, two sets of zoom intelligent cameras, and an identification system. The two sets of zoom intelligent cameras are arranged oppositely on both sides of the automatic transfer conveyor line, and during operation, they simultaneously perform automatic detection trajectory photographing and identification processing on the appearance of the automotive hub A surface passing under the two sets of zoom intelligent cameras on the automatic transfer conveyor line.

[0033] Both the hub center hole photographing trajectory station and the hub A surface photographing trajectory station are respectively configured with a camera and lens, a robot manipulator body, a robot manipulator control system, a visual algorithm system, and a roller path control system.

[0034] Example 2: Figure 1 、 Figure 2 、Figure 3 , Figure 4 , Figure 5 , Figure 6 , Figure 7 , Figure 8 , Figure 9 , Figure 10 , Figure 11 , Figure 12 , Figure 13 In the embodiment shown, an automatic photographing trajectory generation system for appearance detection of aluminum alloy wheels is characterized in that: the automatic photographing trajectory method for appearance detection of aluminum alloy wheels described in the above-mentioned Embodiment 1 is adopted, including a wheel centering mechanism, a photographing trajectory station for the wheel center hole, and a photographing trajectory station for the A surface of the wheel, which are arranged on the automatic transfer conveyor line or in cooperation with the automatic transfer conveyor line and arranged in sequence. The others are the same as those in Embodiment 1.

[0035] The present invention is an industrial manipulator image acquisition trajectory generation system for automatic detection of wheel surface defects, including a camera, a light source, a 6-axis manipulator, a roller table mechanism, a positioning mechanism, a motion control module, and a vision processing module. Based on the camera image acquisition sensor and machine vision algorithm, this system determines and distinguishes key detection positions by the positions of the wheel center hole, outer circle, and spokes, so as to automatically determine the detection positions and automatically generate the robot motion trajectory curve; this system is less affected by environmental changes, gets rid of the drawback of needing to re-teach the manipulator for different wheels, has less manual intervention, and improves the detection efficiency of wheel surface defects.

[0036] In the description of the positional relationship of the present invention, terms such as "inner", "outer", "upper", "lower", "left", "right", etc. indicating the orientation or positional relationship are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the embodiments and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be construed as a limitation of the present invention.

[0037] The above content and structure describe the basic principle, main features, and advantages of the product of the present invention, which should be understood by those skilled in the art. What is described in the above examples and specifications only illustrates the principle of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. An automatic photographing trajectory method for the appearance inspection of aluminum alloy wheels, characterized in that: Including the following trajectory photography methods A1. Select an aluminum alloy automobile wheel to be tested; A2. Place the selected wheel onto the automated roller conveyor. The wheel centering mechanism on the automated roller conveyor performs the centering task. The wheel is then transported by the automated roller conveyor to the wheel center hole tracking station, where steps A3 through A10 are performed. A3. The hub center hole photography track station performs track photography for the hub center appearance inspection, acquiring the original global image of the hub; A4. Perform Gaussian filtering on the original global image of the wheel hub using a Gaussian convolution kernel to remove image noise. A5. Enhance the high-frequency region of the image processed in step A4 to increase image contrast; A6. Select all pixels darker than their neighbors and dynamically binarize the wheel image by finding a suitable segmentation threshold in the contrast-enhanced neighborhood. A7. Identify the wheel hub center hole circle from the wheel hub image processed in step A6 above and crop any unnecessary interference images. A8. After cropping the image according to step A7 above, identify the pixel coordinates of the center point of the wheel hub center hole and the pixel radius of the center hole, and transmit the identified data to the image robot. A9. Calculate the robot coordinates of the center point of the hub center hole and the center hole radius based on the image robot calibration position relationship; A10. Based on the robot position calculated in step A9 above, with the midpoint coordinates of the hub center hole as the center and the center hole radius coordinates as the radius, a roller track is generated to transport the robot to the hub center hole photography track station. A11. After completing the above tasks at the Hub Center Hole Photo Track Station, the vehicle wheel moves from the automated conveyor roller conveyor to the Hub A-Side Photo Track Station for image capture of the spoke area on this side. Robots on either side of the automated conveyor roller conveyor are responsible for capturing images of half of the wheel hub area. Steps A12 through A16 are then executed at the Hub A-Side Photo Track Station. A12. Perform image smoothing and denoising on the image acquired in step A11 above; A13. Dynamically binarize the wheel image after processing the image in step A12 above. A14. Identify the spoke edges, spoke tops, and the intersection of the spokes and the front rim on side A of the hub. A15. Based on the differences in the identified area locations in step A14 above, pixel coordinates are generated along the spoke edges. For the spoke tops and the spoke-front rim junction, pixel coordinates are generated for several points within the identified area. A16. After generating the pixel coordinates of step A15 above, the corresponding coordinates of the robot are obtained by hand-eye calibration, and the generated points are optimized to form the robot photography motion trajectory of the wheel hub A surface photography trajectory station.

2. The automatic photographing trajectory method for the appearance inspection of aluminum alloy wheels according to claim 1, wherein: In step A4 above, a 5*5 Gaussian convolution kernel is used for Gaussian filtering with a standard deviation of sigma.3+0.8 to remove image noise; Where: sigma is the standard deviation; n is the Gaussian convolution kernel size.

3. The automatic photographing trajectory method for the appearance inspection of aluminum alloy wheels according to claim 1, wherein: In the above step A5, the high-frequency region of the image is enhanced and the image contrast is increased by the method of Des = Round((original - mean)*Factor)+original; where: original is the gray value of the pixel on the original image; mean is the average gray value of all pixels within the range of the convolution kernel size; Factor is the scale factor; Round means rounding in the mathematical formula; Des is the gray value of the new pixel.

4. The automatic photographing trajectory method for the appearance inspection of aluminum alloy wheels according to claim 1, wherein: The configured working station for photographing the trajectory of the hub center hole includes a hoisting gantry, a 6-axis robot, a zoom intelligent camera, a light source system and an identification system, which are used to perform automatic detection, trajectory photographing and identification processing on the appearance of the position area of the center hole of the A surface of the hub.

5. The automatic photographing trajectory method for the appearance inspection of aluminum alloy wheels according to claim 1, characterized in that: The configured working station for photographing the trajectory of the A surface of the hub includes two six-axis robots, two sets of zoom intelligent cameras and an identification system. The two sets of zoom intelligent cameras are arranged oppositely on both sides of the automatic transfer line, and during operation, they simultaneously perform automatic detection, trajectory photographing and identification processing on the appearance of the A surface of the automotive hub passing below the two sets of zoom intelligent cameras on the automatic transfer line.

6. The automatic photographing trajectory method for the appearance inspection of aluminum alloy wheels according to claim 1, wherein: Both the configured working station for photographing the trajectory of the hub center hole and the configured working station for photographing the trajectory of the A surface of the hub are respectively equipped with a camera and lens, a manipulator body, a manipulator control system, a vision algorithm system and a roller path control system.

7. An automatic photographing trajectory generation system for appearance inspection of aluminum alloy wheels, characterized in that: The automatic photographing trajectory method for the appearance detection of the aluminum alloy hub according to any one of claims 1 to 6 above is adopted, and it includes a hub centering mechanism, a configured working station for photographing the trajectory of the hub center hole, and a configured working station for photographing the trajectory of the A surface of the hub, which are arranged on the automatic transfer line or cooperate with the automatic transfer line and are arranged in sequence.

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