Intelligent detection system and method for precision forging and stamping forming motor gear

Abstract

The application discloses a precision forging stamping forming motor gear intelligent detection system and method, relates to the technical field of stamping forming gear intelligent detection, and comprises an intelligent stamping machine, a data acquisition unit, an intelligent analysis unit, a processing execution unit and a warning unit. In the prior art, there are problems of real-time monitoring and feedback lag, unclear defect evaluation standard and insufficient defect repair and adjustment capacity, etc. The system combines various technologies such as hydraulic monitoring, temperature monitoring, image recognition and laser scanning to realize real-time monitoring of the whole process of precision forging stamping gears. Through high-precision sensors and advanced image analysis algorithms, cracks, bubbles and depressions and other defects on the gear can be accurately identified, and automatic processing decisions can be made according to the defect severity. Compared with the traditional manual detection mode, the system greatly improves the detection accuracy, reduces human error, reduces labor costs and improves production efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent detection of stamped gears, and in particular to an intelligent detection system and method for precision forging and stamping motor gears. Background Art

[0002] Precision forged and stamped motor gears are widely used in automobiles, power tools, smart homes and other fields. Since their manufacturing process involves high-temperature plastic deformation and stamping, the dimensional accuracy, tooth shape integrity and internal material defects of the gears are crucial to their service life and transmission performance. Traditional detection methods mainly rely on manual visual inspection, vernier calipers or three-coordinate measuring machines for dimensional measurement, but these methods are inefficient, easily affected by human factors, and it is difficult to fully detect microscopic defects in gears. With the development of intelligent manufacturing, intelligent detection systems based on technologies such as computer vision, laser scanning and ultrasonic detection are gradually applied to the quality inspection of motor gears to improve detection accuracy and efficiency and realize automated production quality control;

[0003] Current motor gear inspection systems still have certain limitations. On the one hand, traditional stamping equipment lacks intelligent monitoring capabilities and cannot collect and analyze the operating status of key components (such as hydraulic rods, heating systems, and stamping dies) in real time. On the other hand, traditional inspection methods are usually unable to quantitatively assess the severity of cracks, bubbles, and dents, and cannot automatically adjust stamping parameters or perform secondary processing. Summary of the Invention

[0004] The purpose of the present invention is to solve the above-mentioned problems and to provide an intelligent detection system and method for precision forging and stamping motor gears.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] In a first aspect, the present invention provides an intelligent detection system for precision forging and stamping motor gears, comprising: an intelligent stamping machine and an intelligent analysis unit;

[0007] The intelligent stamping machine includes a stamping lower body, a stamping upper body, a heating movable arm, hydraulic rods 1, 2, 3, 4, an infrared ultra-clear camera, a stamping gear die 1, 2, an electric heating copper tube, an LED fill light, and a precision forged stamping gear blank. The stamping lower body and the stamping upper body are connected by hydraulic rods 1, 2, 3, and 4. The heating movable arm and the stamping gear die 1 are installed on the stamping lower body. The infrared ultra-clear camera, the stamping gear die 2, and the LED fill light are installed on the stamping upper body. The electric heating copper tube is installed on the heating movable arm. The precision forged stamping gear blank is placed on the stamping gear die.

[0008] The intelligent analysis unit is used to detect the motor gear formed by precision forging and stamping. The process is as follows:

[0009] Intelligent detection of gear blanks after precision forging and stamping: Identification of cracks: Calculation of the gradient value of each pixel to obtain the horizontal gradient G x (x,y) and vertical gradient G y (x, y), then calculate the gradient amplitude to obtain the gradient amplitude G(x, y), and calculate the gradient direction to obtain the gradient direction θ(x, y); identify bubbles: accumulate through the parameterized circle equation to form an accumulator; for each edge point, calculate the corresponding circle center coordinates and radius; then count the number of occurrences based on the calculated circle center coordinates (a, b) and radius value r, and map them to the size and number of circles to obtain the bubble coefficient BC; identify depressions, and compare the obtained depression outline with the preset standard outline. If a depression exists, analyze the depression coefficient δZ based on the area size of the depression area, the perimeter of the depression outline, and the circularity, rectangularity, and aspect ratio of the depression outline.

[0010] As a preferred embodiment of the present invention, it also includes a data acquisition unit, a processing execution unit and an early warning unit;

[0011] The data acquisition unit is used to monitor and collect the operating data, environmental data, and status information of each component in the intelligent stamping machine in real time. It collects the operating status data of the hydraulic rod, heating movable arm, and stamping die components in real time, including displacement, pressure, and temperature data. It monitors the environmental conditions around the equipment. It captures image data of the stamping process using an infrared ultra-high-definition camera and LED fill light. It records the power consumption of the electrically heated copper tube and the overall energy efficiency data of the equipment. The collected data is transmitted to the intelligent analysis unit via the network for further processing.

[0012] The processing execution unit performs corresponding operations based on the detection results of the intelligent analysis unit. When it detects that the hydraulic pressure, temperature or gear size exceeds the preset standards, the processing execution unit adjusts the punching machine parameters. When a gear defect is detected, the processing execution unit takes different measures based on the severity of the defect: minor defects are repaired and retested; gears with more serious or severe defects are directly removed.

[0013] The early warning unit is responsible for issuing an alarm when an abnormal situation is detected and notifying management personnel to intervene; if the data acquisition unit monitors that the hydraulic pressure exceeds the safety range, the heating temperature is abnormal, or the intelligent analysis unit detects serious cracks, bubbles and dents in the gear, the early warning unit immediately triggers the alarm mechanism and provides detailed abnormal information.

[0014] As a preferred embodiment of the present invention, the specific process of testing the hydraulic system is as follows:

[0015] The real-time pressure and displacement data of the hydraulic rod are collected through the hydraulic sensor; the collected real-time data is compared with the preset interval. If the collected pressure exceeds the preset threshold range, a warning is triggered; if the displacement of the hydraulic rod exceeds the preset range, it means a mechanical failure or hydraulic system abnormality, and the early warning unit triggers a warning.

[0016] As a preferred embodiment of the present invention, the specific process of detecting the heating system is as follows:

[0017] By installing a temperature sensor on the electric heating copper tube, the temperature is monitored in real time; the real-time temperature is compared with the preset temperature range. If the temperature exceeds the preset temperature range, a corresponding control instruction is generated, and the processing execution unit stops the heating movable arm from working to avoid overheating and damage to the equipment.

[0018] As a preferred embodiment of the present invention, the specific process of testing the precision forged stamped gear blank is as follows:

[0019] The offset and size of the gear blank are monitored in real time through image recognition and laser scanning technology; the data from image recognition and laser scanning are compared with preset standard values; if the offset or size exceeds the preset standard range, a corresponding control instruction is generated, and the processing execution unit adjusts the stamping machine parameters or notifies the operator to intervene.

[0020] As a preferred embodiment of the present invention, the specific process of identifying cracks is as follows:

[0021] By calculating the gradient of the image in the horizontal and vertical directions, we can identify the intensity change of the edge in the image and calculate the gradient value of each pixel: for each pixel I (x, y), extract a 3x3 neighborhood, recorded as I (x-1, y-1) to I (x + 1, y + 1), according to the formula: Get the horizontal gradient G x (x,y) and vertical gradient G y (x,y), S x (x,y) and S y (x, y) are the horizontal and vertical Sobel operators respectively; then the horizontal gradient G x (x,y) and vertical gradient G y (x,y), calculate the magnitude and direction of the gradient: by the formula: Output gradient magnitude G(x,y); Output gradient direction θ(x,y), where atan2 is a function that calculates radians.

[0022] As a preferred embodiment of the present invention, the specific process of identifying bubbles is as follows:

[0023] The detected edge points are transformed in Hough space; the equation of the parameterized circle is accumulated to form an accumulator; for each edge point, the corresponding circle center coordinates and radius are calculated; if the circle center coordinates are (a, b) and the radius is r, then the calculation is: a=xi-rcos(θ1), b=yi-rsin(θ1), where θ1 is an angle parameter; according to the calculated circle center coordinates (a, b) and radius value r, the number of occurrences is counted; if the number of votes for some circle centers exceeds the threshold and the size and position of the circle meet the expected characteristics of the bubble, the circular area is identified as a bubble; then the number is mapped according to the size of the circle to obtain the bubble coefficient; if N bubbles are detected in the image, the center coordinates of each bubble are (a i1 ,b i1 ), with a radius of r i1 , where i1∈[1,N], the mapping formula of the bubble coefficient is: Output bubble coefficient BC, where N is the number of bubbles detected, r i1 is the radius of the i1th bubble, ω(r i1 ) is a weight function related to the bubble size.

[0024] As a preferred embodiment of the present invention, the specific process of identifying the depression is as follows:

[0025] Use the contour extraction algorithm to obtain the outline of the concave; compare the obtained concave outline with the preset standard outline to determine whether there is a concave or convex. If there is a concave, calculate the area of ​​the concave area, and calculate the perimeter, circularity, rectangularity, and aspect ratio of the concave outline for analysis:

[0026] Using the weighted average formula Output the concave coefficient δZ, A1 is the concave area, P1 is the contour perimeter, Y1 is the circularity, and Y1=4πA1 / P 2 When Y1 is close to 1, it means that the depression is close to a circle, which is caused by uniform force. When Y1 is less than 1, it means that the depression is irregular in shape, which is caused by mold problems or stamping defects. R1 is the rectangularity, and R1=A1 / (L1×W1). L1 and W1 are the length and width of the minimum circumscribed rectangle of the depression area. When the rectangularity is closer to 1, it means that the depression is close to a rectangle. AR is the aspect ratio, AR=L1 / W1; o1, o2, o3, o4 and o5 are all preset weight factors, o1+o2+o3+o4+o5=1.

[0027] In a second aspect, the present invention provides an intelligent detection method for precision forging and stamping motor gears, comprising:

[0028] Step 1: Real-time monitoring of the pressure, temperature, and displacement data of the hydraulic rod, heating movable arm, and key components of the stamping die, and the collection of stamping process images through infrared cameras and LED fill lights

[0029] Step 2: Monitor the equipment status, determine whether the pressure and temperature are out of limit, and adjust the stamping parameters; use image recognition and laser scanning to detect the offset and size of the gear blank, and analyze the cracks, bubbles, and dents in the stamped gear;

[0030] Step 3: Calculate the crack coefficient, bubble coefficient, and dent coefficient for each detected defect. Crack detection uses image gradient analysis to determine the length and depth of the crack and assess its impact level. Bubble detection uses the Hough transform method to identify circular defects and map the bubble coefficient by size and number. Depression detection uses contour analysis to calculate the depression area, perimeter, circularity, rectangularity, and aspect ratio, ultimately outputting the depression coefficient and classifying the impact level.

[0031] Step 4: If it is a minor defect, no treatment is required. If it is a moderate defect, repair it. If it is a serious defect or above, the gear will be directly eliminated and an early warning will be triggered.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] 1. Compared with existing manual detection or single-sensor detection technology, the present invention integrates ultra-high-definition infrared cameras, laser scanning technology and intelligent image recognition algorithms to achieve high-precision automated detection of gear defects. The system can capture the morphological features of gears in real time and accurately identify defects such as cracks, bubbles, and dents through algorithms such as gradient analysis, Hough transform, and contour matching. At the same time, it automatically classifies defects according to preset standards to ensure the accuracy and consistency of detection results, reduce human errors, and improve product quality.

[0034] 2. The present invention can not only detect defects in precision forged stamped gears, but also adjust stamping parameters in real time through the processing execution unit to optimize the production process; when the system detects an abnormality, it can automatically adjust the stamping pressure, temperature or mold position to avoid the occurrence of continuous defects; at the same time, for medium and above level defects, the system can automatically perform repair or removal operations, and link the early warning unit to remind maintenance personnel, greatly improving production efficiency and reducing the scrap rate and manual intervention costs caused by defects. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] To facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0036] Figure 1 It is a principle block diagram of the present invention;

[0037] Figure 2A diagram showing the steps of the method of the present invention;

[0038] Figure 3 Schematic diagram of the intelligent punching machine of the present invention Figure 1 ;

[0039] Figure 4 Schematic diagram of the intelligent punching machine of the present invention Figure 2 ;

[0040] Figure 5 Schematic diagram of the intelligent punching machine of the present invention Figure 3 .

[0041] Description of the drawings: 1. Stamping lower body; 2. Heating movable arm; 3. Hydraulic rod 1; 4. Infrared ultra-clear camera; 5. Stamping upper body; 6. Stamping gear die 1; 7. Electric heating copper tube; 8. LED fill light; 9. Precision forged stamping gear blank; 10. Stamping gear die 2; 11. Hydraulic rod 2; 12. Hydraulic rod 3; 13. Hydraulic rod 4. DETAILED DESCRIPTION

[0042] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0043] It should be understood that the terms “include” and “comprising” used in the specification and claims of the present disclosure indicate the presence of described features, integers, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0044] It should also be understood that the terminology used in this disclosure is for the purpose of describing specific embodiments only and is not intended to limit the disclosure. As used in this disclosure and the claims, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly indicates otherwise. It should be further understood that the term "and / or" as used in this disclosure and the claims refers to any and all possible combinations of one or more of the associated listed items, including and including these combinations.

[0045] Example 1:

[0046] See also Figure 1 As shown, an intelligent detection system for precision forging and stamping motor gears includes: an intelligent stamping machine, a data acquisition unit, an intelligent analysis unit, a processing execution unit and an early warning unit;

[0047] See also Figure 3 、 4 As shown in Figure 5, the intelligent stamping machine includes: a stamping lower body 1, a stamping upper body 5, a heating movable arm 2, a hydraulic rod 1 3, a hydraulic rod 2 11, a hydraulic rod 3 12, a hydraulic rod 4 13, an infrared ultra-clear camera 4, a stamping gear die 1 6, a stamping gear die 2 10, an electric heating copper tube 7, an LED fill light 8 and a precision forging stamping gear blank 9; wherein, the stamping lower body 1 and the stamping upper body 5 are connected by the hydraulic rod 1 3, the hydraulic rod 2 11, the hydraulic rod 3 12 and the hydraulic rod 4 13; the heating movable arm 2 and the stamping gear die 1 6 are installed on the stamping lower body 1; the infrared ultra-clear camera 4, the stamping gear die 2 10 and the LED fill light 8 are installed on the stamping upper body 5; the electric heating copper tube 7 is installed on the heating movable arm 2; the precision forging stamping gear blank 9 is placed on the stamping gear die 1 6;

[0048] The data acquisition unit is used to monitor and collect the operating data, environmental data, and status information of each component in the intelligent stamping machine in real time; collect the operating status data of components such as the hydraulic rod, the heating movable arm, and the stamping die in real time, including displacement, pressure, temperature, and other data; monitor the environmental conditions around the equipment, such as temperature, humidity, and light intensity, which may affect the quality and efficiency of the stamping process; capture image data during the stamping process through an infrared ultra-clear camera 4 and an LED fill light 8 to help detect the condition of the stamping gear die and the quality of the precision forged stamped gear blank; record the power consumption of the electric heating copper tube and the overall energy efficiency data of the equipment; finally, transmit the collected data to the intelligent analysis unit via the network for further processing;

[0049] The intelligent analysis unit is used to detect motor gears that have been precision forged or stamped. The specific process is as follows:

[0050] First, monitor the intelligent stamping machine itself, specifically:

[0051] Monitor the hydraulic system, collecting real-time pressure and displacement data of the hydraulic rod through hydraulic sensors. Compare the collected real-time data with the preset range. If the collected pressure exceeds the preset threshold range (such as above 1500 bar or below 1000 bar), a warning is triggered. If the displacement of the hydraulic rod exceeds the preset range (such as more than 50mm), it may indicate a mechanical failure or hydraulic system abnormality, and the early warning unit will trigger a warning.

[0052] Monitor the heating system by installing a temperature sensor on the electric heating copper tube to monitor the temperature in real time. Compare the real-time temperature with the preset temperature range. If the temperature exceeds the preset temperature range, a corresponding control instruction is generated, and the processing execution unit stops the heating arm to avoid overheating and damage to the equipment.

[0053] Monitor precision forged and stamped gear blanks, using image recognition and laser scanning technology to monitor the offset and size of the gear blank in real time. The image recognition and laser scanning data are compared with preset standard values. If the offset or size exceeds the preset standard range, a corresponding control instruction is generated, and the processing execution unit adjusts the stamping machine parameters or notifies the operator to intervene.

[0054] Secondly, the gear blank after precision forging and stamping is intelligently tested, specifically:

[0055] The infrared ultra-clear camera 4 and LED fill light 8 on the intelligent stamping machine are used to capture the image data of the stamped gear blank, and feature analysis is performed on the image data corresponding to the gear blank (features include defects such as cracks, bubbles, and dents);

[0056] Identify cracks: By calculating the horizontal and vertical gradients of the image, we can identify changes in the intensity of the edges in the image. (Cracks usually cause strong brightness changes in the image, so convolution operations are performed in the horizontal and vertical directions of the image to extract areas with significant brightness changes, that is, areas where cracks may exist.) The operator calculates the gradient value of each pixel: for each pixel I(x,y), a 3x3 neighborhood is extracted, recorded as I(x-1,y-1) to I(x+1,y+1), according to the formula: Get the horizontal gradient G x (x,y) and vertical gradient G y (x,y), where S x (x,y) and S y (x, y) are the horizontal and vertical Sobel operators respectively; then the obtained flat gradient G x (x,y) and vertical gradient G y (x,y), calculate the magnitude and direction of the gradient: by the formula: Output gradient magnitude G(x,y); Output the gradient direction θ(x, y), where atan2 is a function that calculates radians and returns the angle of the gradient direction. Compare the gradient amplitude G(x, y) with two preset high and low thresholds. If the gradient amplitude G(x, y) does not fall between the two preset high and low thresholds, determine whether the edge is significant and whether it is a crack. Then, map the corresponding crack coefficient based on the crack length: assuming a crack is 5mm long and 0.8mm deep, the system may classify it as a medium crack, and the processing execution unit will notify the operator to fill it. If the crack is 10mm long and 1.5mm deep, it is considered a severe crack, and the processing execution unit will notify the operator to handle it and replace stamping gear die 6 and stamping gear die 10.

[0057] Identify bubbles: transform the detected edge points in Hough space; accumulate them through the parameterized circle equation to form an accumulator; for each edge point, calculate the possible corresponding circle center coordinates and radius; if the circle center coordinates are (a, b) and the radius is r, then the calculation is: a=xi-rcos(θ1), b=yi-rsin(θ1), where θ1 is an angle parameter, indicating the position of each point on the circle relative to the circle center; count the number of occurrences based on the calculated circle center coordinates (a, b) and radius value r; if the number of votes for some circle centers exceeds the threshold, and the size and position of the circle meet the expected characteristics of bubbles, these circular areas will be identified as bubbles; then map the number according to the size of the circle to obtain the bubble coefficient; if N bubbles are detected in the image, the center coordinates of each bubble are (a i1 ,b i1 ), with a radius of r i1 , where i1∈[1,N], the mapping formula of the bubble coefficient is: Output bubble coefficient BC, where N is the number of bubbles detected, r i1 is the radius of the i1th bubble, ω(r i1 ) A weight function related to bubble size can generally be set based on the size of the bubble; for example, a larger bubble may have a greater weight; comparing the bubble coefficient BC with a preset instruction threshold, the preset instruction threshold including BC1 and BC2, and BC1 < BC2; if BC1 ≤ BC < BC2, the processing execution unit notifies the operator to fill the gap; if BC ≥ BC2, the processing execution unit notifies the operator to perform processing and replace the stamping gear die 6 and the stamping gear die 10;

[0058] To identify the depression, similarly, use the contour extraction algorithm to obtain the outline of the depression; compare the obtained depression outline with the preset standard outline to see if there is a depression or a bulge. If there is a depression, calculate the area of ​​the depression area, and calculate the perimeter, circularity, rectangularity, and aspect ratio of the depression outline for analysis:

[0059] By establishing the formula: Output the concave coefficient δZ, where A1 is the concave area (the area of ​​the concave region, the larger the area, the more severe the concave), P1 is the contour perimeter (the contour perimeter indicates the edge length of the concave, usually used in combination with the area to reflect the shape complexity of the concave), and Y1 is the circularity, and Y1=4πA1 / P 2When Y1 is close to 1, it means that the depression is close to a circle, which may be caused by uniform force. When Y1 is less than 1, it means that the depression is irregular in shape, which may be caused by mold problems or stamping defects. R1 is the rectangularity, and R1=A1 / (L1×W1). L1 and W1 are the length and width of the minimum circumscribed rectangle of the depression area. When the rectangularity is closer to 1, it means that the depression is close to a rectangle. AR is the aspect ratio, AR=L1 / W1. If the aspect ratio AR is greater than 1, the depression shape is relatively slender, which may be caused by uneven local pressure of the mold. o1, o2, o3, o4 and o5 are all preset weight factors. 1-Y1 means that the lower the circularity, the larger the depression. The more irregular the shape, the greater the impact on the gear. The inverse of 1-R1 indicates that the lower the rectangularity, the more complex the shape, and the greater the impact. The impact of the sag on the gear is then divided according to the size of the sag coefficient δZ. Four thresholds are set: AX1, AX2, AX3, and AX4, and AX1 < AX2 < AX3 < AX4. If δZ < AX1, the impact level is slight; if AX1 ≤ δZ < AX2, the impact level is moderate; if AX2 ≤ δZ < AX3, the impact level is severe; if δZ ≥ AX4, the impact level is severe. If there is no sag but a bulge, the processing execution unit will re-pressurize and re-identify the sag.

[0060] If the impact level received by the processing execution unit is minor, the impact is small, it is a normal process error, it does not affect the use, and no processing is performed; if the impact level received by the processing execution unit is medium, it affects the gear accuracy and needs to be monitored but does not affect short-term use, and repair is performed, and the repaired gear is subjected to dent analysis; if the impact level received by the processing execution unit is severe or serious, it is directly removed, and the early warning unit issues an alarm, notifies the operator to handle it, and replaces the stamping gear die 6 and the stamping gear die 10;

[0061] Example 2:

[0062] See also Figure 2 As shown in FIG, a method for intelligent detection of motor gears formed by precision forging and stamping is provided, and the specific steps are as follows:

[0063] Step 1: The intelligent stamping machine is equipped with a data acquisition unit to monitor the operating status of key components (hydraulic rod, heating movable arm 2, stamping die, etc.) in real time, including pressure, displacement, temperature and other data. At the same time, the infrared ultra-high-definition camera 4 and LED fill light 8 are used to capture image data of the stamping process to monitor the quality of the gear blank. The energy consumption of the electric heating copper tube 7 and the equipment energy efficiency data are also recorded, and all collected information is transmitted to the intelligent analysis unit for processing.

[0064] Step 2: Inspect the stamping machine and the stamped motor gear. First, the system monitors the hydraulic and heating systems to ensure pressure and temperature are within preset ranges. If thresholds are exceeded, an alarm is triggered or parameters are adjusted. Second, the system uses image recognition and laser scanning technology to analyze the offset and dimensions of the gear blank. If deviations exceed the standard range, adjustment instructions are generated. After stamping, the system further identifies gear surface defects (cracks, bubbles, dents, etc.) and calculates the corresponding defect coefficient to quantify the impact of defects on gear quality.

[0065] Step 3: Calculate the crack coefficient, bubble coefficient, and dent coefficient for each detected defect. Crack detection uses image gradient analysis to determine the length and depth of the crack and assess its impact level. Bubble detection uses the Hough transform method to identify circular defects and map the bubble coefficient based on size and number. Depression detection uses contour analysis to calculate the depression area, perimeter, circularity, rectangularity, and aspect ratio. Finally, the depression coefficient is output and the impact level is classified (mild, moderate, severe, severe).

[0066] Step 4: The processing execution unit performs appropriate actions based on the defect level. Minor defects do not affect use and require no action. Moderate defects affect accuracy but are repairable. Severe defects or above result in the immediate removal of unqualified products, triggering the early warning unit to notify management. If a convexity is detected during the concave detection, the system re-executes the press process to make corrections and ensure consistent gear quality. Furthermore, if severe defects occur repeatedly, the system recommends replacing the stamping die to ensure production quality.

[0067] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to specific embodiments. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. An intelligent detection system for precision forging and stamping motor gears, comprising: Intelligent punching machine and intelligent analysis unit, characterized by: The intelligent stamping machine comprises a stamping lower body (1), a stamping upper body (5), a heating movable arm (2), a hydraulic rod 1 (3), a hydraulic rod 2 (11), a hydraulic rod 3 (12), a hydraulic rod 4 (13), an infrared ultra-clear camera (4), a stamping gear die 1 (6), a stamping gear die 2 (10), an electric heating copper tube (7), an LED fill light (8) and a precision forging stamping gear blank (9); the intelligent analysis unit is used to detect the precision forging stamping motor gear in the following process: First, the intelligent stamping machine itself is monitored, including monitoring of the hydraulic system, monitoring of the heating system, monitoring of the precision forging and stamping gear blank, and intelligent detection of the gear blank after precision forging and stamping; After precision forging and stamping, the gear blank is intelligently inspected, including crack identification: the gradient value of each pixel is calculated to obtain the horizontal gradient Gx(x,y) and the vertical gradient Gy(x,y), and then the gradient amplitude is calculated to obtain the gradient amplitude G(x,y), and the gradient direction is calculated to obtain the gradient direction θ(x,y); the gradient amplitude G(x,y) is compared with the preset high and low thresholds. If the gradient amplitude G(x,y) does not fall between the preset high and low thresholds, it is judged whether the edge is significant and whether it is a crack; then the corresponding crack coefficient is mapped according to the crack length; bubble identification: the equation of the parameterized circle is accumulated to form Accumulator; for each edge point, calculate the corresponding circle center coordinates and radius; count the number of occurrences based on the calculated circle center coordinates (a, b) and radius value r; if the number of votes for the corresponding circle center exceeds the threshold, and the size and position of the circle meet the expected characteristics of a bubble, it is identified as a bubble; then map the size and number of circles to obtain the bubble coefficient BC; identify depressions, use the contour extraction algorithm to obtain the contour of the depression, and compare the obtained depression contour with the preset standard contour for overlap. If a depression exists, analyze the area size of the depression area, the perimeter of the depression contour, and the circularity, rectangularity, and aspect ratio of the depression contour to obtain the depression coefficient δZ.

2. The intelligent detection system for precision forging and stamping motor gears according to claim 1 is characterized in that: The stamping lower body (1) and the stamping upper body (5) are connected via hydraulic rod 1 (3), hydraulic rod 2 (11), hydraulic rod 3 (12) and hydraulic rod 4 (13); the heating movable arm (2) and the stamping gear die 1 (6) are mounted on the stamping lower body (1); the infrared ultra-clear camera (4), the stamping gear die 2 (10) and the LED fill light (8) are mounted on the stamping upper body (5); the electric heating copper tube (7) is mounted on the heating movable arm (2); the precision forged stamping gear blank (9) is placed on the stamping gear die 1 (6); It also includes a data acquisition unit, a processing execution unit and an early warning unit; The data acquisition unit is used to monitor and collect the operation data, environmental data and status information of each component in the intelligent stamping machine in real time; collect the operation status data of the hydraulic rod, the heating movable arm and the stamping die components in real time, including displacement, pressure and temperature data; monitor the environmental conditions around the equipment; capture image data during the stamping process through an infrared ultra-clear camera (4) and an LED fill light (8); record the power consumption of the electric heating copper tube and the overall energy efficiency data of the equipment; and transmit the collected data to the intelligent analysis unit through the network for further processing; The processing execution unit performs corresponding operations based on the detection results of the intelligent analysis unit. When it detects that the hydraulic pressure, temperature or gear size exceeds the preset standards, the processing execution unit adjusts the punching machine parameters. When a gear defect is detected, the processing execution unit takes different measures based on the severity of the defect: minor defects are repaired and retested; gears with more serious or severe defects are directly removed. The early warning unit is responsible for issuing an alarm when an abnormal situation is detected and notifying management personnel to intervene; if the data acquisition unit monitors that the hydraulic pressure exceeds the safety range, the heating temperature is abnormal, or the intelligent analysis unit detects serious cracks, bubbles and dents in the gear, the early warning unit immediately triggers the alarm mechanism and provides detailed abnormal information.

3. The intelligent detection system for precision forging and stamping motor gears according to claim 1 is characterized in that: The specific process of testing the hydraulic system is as follows: The real-time pressure and displacement data of the hydraulic rod are collected through the hydraulic sensor; the collected real-time data is compared with the preset interval. If the collected pressure exceeds the preset threshold range, a warning is triggered; if the displacement of the hydraulic rod exceeds the preset range, it means a mechanical failure or hydraulic system abnormality, and the early warning unit triggers a warning.

4. The intelligent detection system for precision forging and stamping motor gears according to claim 3 is characterized in that: The specific process of detecting the heating system is as follows: By installing a temperature sensor on the electric heating copper tube (7), the temperature is monitored in real time; the real-time temperature is compared with a preset temperature range. If the temperature exceeds the preset temperature range, a corresponding control instruction is generated, and the processing execution unit stops the heating movable arm from working, thereby avoiding overheating and damaging the equipment.

5. The intelligent detection system for precision forging and stamping motor gears according to claim 4, characterized in that: The specific process of testing the precision forged stamped gear blank is as follows: The offset and size of the gear blank are monitored in real time through image recognition and laser scanning technology; the data from image recognition and laser scanning are compared with preset standard values; if the offset or size exceeds the preset standard range, a corresponding control instruction is generated, and the processing execution unit adjusts the stamping machine parameters or notifies the operator to intervene.

6. The intelligent detection system for precision forging and stamping motor gears according to claim 1, characterized in that: The specific process of identifying cracks is as follows: By calculating the gradient of the image in the horizontal and vertical directions, we can identify the intensity change of the edge in the image and calculate the gradient value of each pixel: for each pixel I (x, y), extract a 3x3 neighborhood, recorded as I (x-1, y-1) to I (x + 1, y + 1), according to the formula: Get the horizontal gradient G x (x,y) and vertical gradient G y (x,y), S x (x,y) and S y (x, y) are the horizontal and vertical Sobel operators respectively; then the horizontal gradient G x (x,y) and vertical gradient G y (x,y), calculate the magnitude and direction of the gradient: by the formula: Output gradient magnitude G(x,y); Output gradient direction θ(x,y), where atan2 is a function that calculates radians.

7. The intelligent detection system for precision forging and stamping motor gears according to claim 6, characterized in that: The specific process of identifying bubbles is as follows: The detected edge points are transformed in Hough space; the equation of the parameterized circle is accumulated to form an accumulator; for each edge point, the corresponding circle center coordinates and radius are calculated; if the circle center coordinates are (a, b) and the radius is r, then the calculation is: a=xi-rcos(θ1), b=yi-rsin(θ1), where θ1 is an angle parameter; according to the calculated circle center coordinates (a, b) and radius value r, the number of occurrences is counted; if the number of votes for some circle centers exceeds the threshold and the size and position of the circle meet the expected characteristics of the bubble, the circular area is identified as a bubble; then the number is mapped according to the size of the circle to obtain the bubble coefficient; if N bubbles are detected in the image, the center coordinates of each bubble are (a i1 ,b i1 ), with a radius of r i1 , where i1∈[1,N], the mapping formula of the bubble coefficient is: Output bubble coefficient BC, where N is the number of bubbles detected, r i1 is the radius of the i1th bubble, ω(r i1 ) is a weight function related to the bubble size.

8. The intelligent detection system for precision forging and stamping motor gears according to claim 7, characterized in that: The specific process of identifying the depression is as follows: Use the contour extraction algorithm to obtain the outline of the concave; compare the obtained concave outline with the preset standard outline to determine whether there is a concave or convex. If there is a concave, calculate the area of ​​the concave area, and calculate the perimeter, circularity, rectangularity, and aspect ratio of the concave outline for analysis: Using the weighted average formula Output the concave coefficient δZ, A1 is the concave area, P1 is the contour perimeter, Y1 is the circularity, and Y1=4πA1 / P1 2 When Y1 is close to 1, it means that the concave shape is close to a circle, which is caused by uniform force. When Y1 is less than 1, it means that the concave shape is irregular, which is caused by mold problems or stamping defects. R1 is the rectangularity, and R1=A1 / (L1×W1). L1 and W1 are the length and width of the minimum circumscribed rectangle of the concave area. When the rectangularity is closer to 1, it means that the concave is close to a rectangle. AR is the aspect ratio, AR=L1 / W1; o1, o2, o3, o4, and o5 are all preset weight factors, o1+o2+o3+o4+o5=1.

9. An intelligent detection method for precision forging and stamping motor gears, characterized in that: An intelligent detection system for motor gears used to implement the precision forging and stamping process described in any one of claims 1 to 8; comprising: Step 1: Real-time monitoring of the pressure, temperature, and displacement data of the hydraulic rod, the heating movable arm (2), and the key components of the stamping die, and collecting images of the stamping process through an infrared ultra-clear camera (4) and an LED fill light (8); Step 2: Monitor the equipment status, determine whether the pressure and temperature are out of limit, and adjust the stamping parameters; use image recognition and laser scanning to detect the offset and size of the gear blank, and analyze the cracks, bubbles, and dents in the stamped gear; Step 3: Calculate the crack coefficient, bubble coefficient, and dent coefficient for each detected defect. Crack detection uses image gradient analysis to determine the length and depth of the crack and assess its impact level. Bubble detection uses the Hough transform method to identify circular defects and map the bubble coefficient by size and number. Depression detection uses contour analysis to calculate the depression area, perimeter, circularity, rectangularity, and aspect ratio, ultimately outputting the depression coefficient and classifying the impact level. Step 4: If it is a minor defect, no treatment is required. If it is a moderate defect, repair it. If it is a serious defect or above, the gear will be directly eliminated and an early warning will be triggered.

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