Process detection method and system for electronic connector pin

Abstract

The invention provides a process detection method and system for an electronic connector pin, and the method comprises the steps: obtaining the inclined plane image data of the pin, carrying out the preprocessing according to the surface reflection characteristics of the pin and environment illumination interference, and obtaining a clear inclined plane image; applying an edge detection algorithm to the clear inclined plane image, and extracting inclined plane contour data including contact pin inclined plane features; according to the inclined plane contour data, inclined plane angle deviation is calculated, and whether the inclined plane angle deviation exceeds a preset angle deviation threshold value or not is judged; when the inclined plane angle deviation exceeds a preset angle deviation threshold value, obtaining the insertion depth and contact position data of the pin; performing correlation analysis on the slope angle deviation, the insertion depth and the contact position data, judging the influence of the slope angle deviation on the insertion depth and the contact position, and obtaining a correlation analysis result; and according to a correlation analysis result, adjusting pin processing technological parameters and re-processing the pin. Precise control of the inclined plane angle of the contact pin is realized, and the insertion performance and the contact reliability of the contact pin are improved.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and in particular, to a process detection method and system for pins of an electronic connector. Background Art

[0002] The bevel design of the pins of an electronic connector is a key factor affecting the matching degree between the pins and the sockets of the connector. During the actual production process, the angle and shape of the bevel of the pins are easily affected by factors such as processing technology and mold wear, resulting in deviations or inconsistencies. The accumulation of these small deviations will cause uneven insertion depths of the pins and offset contact positions, thereby leading to problems such as increased contact resistance and unstable signal transmission, seriously affecting the reliability and service life of the connector.

[0003] In order to monitor the changes in the angle and shape of the bevel of the pins in real time, it is necessary to introduce high-precision optical non-contact measurement equipment to perform on-line detection of the pins. The measurement equipment needs to have a sufficiently high resolution and sampling frequency to accurately capture the subtle changes in the bevel of the pins. At the same time, factors such as the reflection characteristics of the pin surface and environmental light interference need to be considered during the measurement process to ensure the accuracy and reliability of the acquired image data. After obtaining the image data of the bevel of the pins, it is necessary to further analyze and process it in combination with intelligent algorithms.

[0004] Traditional image processing algorithms are easily interfered by defects such as contamination and scratches on the surface of the pins when identifying the bevel features of the pins, resulting in deviations in the detection results. Summary of the Invention

[0005] In a first aspect, the present invention provides a process detection method for pins of an electronic connector, mainly including: Using an optical non-contact measurement device to obtain image data of the bevel of the pins; for the image data of the bevel of the pins, performing preprocessing according to the reflection characteristics of the pin surface and environmental light interference to remove noise and interference and obtain a clear bevel image; applying an edge detection algorithm to the clear bevel image to extract bevel contour data including the bevel features of the pins; calculating the bevel angle deviation according to the bevel contour data, and determining whether the bevel angle deviation exceeds a preset angle deviation threshold; when the bevel angle deviation exceeds the preset angle deviation threshold, obtaining the insertion depth and contact position data of the pins; Performing correlation analysis on the bevel angle deviation, the insertion depth, and the contact position data to determine the influence of the bevel angle deviation on the insertion depth and the contact position, and obtaining a correlation analysis result; Adjust the processing parameters of the pin according to the association analysis results and reprocess the pin. The processing parameters of the pin include cutting speed, feed rate, tool angle, inclination of the pin bevel or pin length. Preferably, for the image data of the pin bevel, preprocessing is performed according to the surface reflection characteristics of the pin and environmental light interference to remove noise and interference and obtain a clear bevel image, including: Detect the image data of the pin bevel. If a defect or abnormality is detected, determine the position of the defect or abnormality and classify it according to the morphological characteristics of the defect or abnormality; For different types of the defects or abnormalities, use image enhancement algorithms and image segmentation algorithms to process the image data of the pin bevel to obtain a clear bevel image.

[0006] Preferably, apply an edge detection algorithm to the clear bevel image to extract bevel contour data including the characteristics of the pin bevel, including: Preprocess the clear bevel image, including denoising and contrast enhancement operations; Use an edge detection algorithm to perform edge detection on the preprocessed clear bevel image to extract the initial bevel contour; According to a preset pin bevel feature template, perform feature matching on the initial bevel contour to identify the feature area of the pin bevel; By analyzing the continuity and smoothness of the feature area, determine whether there are surface contaminants and scratches on the feature area; If there are surface contaminants and scratches, use a contour repair algorithm to repair the disturbed feature area; Perform refinement processing on the repaired bevel contour, including contour smoothing and contour simplification, to obtain a fine bevel contour; Compare the fine bevel contour with a preset standard bevel contour, calculate the deviation degree of the contour, and determine whether the bevel contour meets the requirements; If the deviation degree exceeds the preset contour threshold, it does not meet the requirements and is marked as a defective bevel; If the deviation degree does not exceed the preset contour threshold, it meets the requirements and is marked as a qualified bevel; Output the fine bevel contour corresponding to the qualified bevel as bevel contour data.

[0007] Preferably, according to the bevel contour data, calculate the bevel angle deviation and determine whether the bevel angle deviation exceeds a preset angle deviation threshold, including: According to the bevel contour data, use the least squares method to fit the bevel contour curve equation; Based on the bevel contour curve equation, calculate the angle values at each position of the bevel and the bevel angle deviation; Obtain a preset angle deviation threshold, compare the bevel angle deviation at each position of the bevel with the angle deviation threshold, and if the bevel angle deviation exceeds the angle deviation threshold, it is determined that there is an angle deviation at this position.

[0008] Preferably, the correlation analysis is performed on the bevel angle deviation, the insertion depth, and the contact position data to determine the influence of the bevel angle deviation on the insertion depth and the contact position, and the correlation analysis result is obtained, including: Adopt a correlation analysis algorithm to calculate the correlation coefficients between the bevel angle deviation, the insertion depth, and the contact position data, and determine whether there is a correlation; If there is a correlation, then adopt a regression analysis method to establish a regression model between the bevel angle deviation, the insertion depth, and the contact position data; According to the results of the regression model, judge the change range of the insertion depth and the contact position when the angle deviation changes by one unit, and obtain a quantitative evaluation result of the influence of the angle deviation on the insertion depth and the contact position; Take the quantitative evaluation result as the correlation analysis result.

[0009] Preferably, taking the quantitative evaluation result as the correlation analysis result further includes: Compare the quantitative evaluation result with a preset evaluation threshold to determine whether the influence of the bevel angle deviation on the insertion depth and the contact position exceeds the allowable range; If it exceeds, adjust the pin processing process parameters according to the bevel angle deviation.

[0010] Preferably, adjusting the pin processing process parameters according to the bevel angle deviation includes: Obtain the actual angle of the pin bevel, compare the actual angle with a preset target angle, and calculate the actual angle deviation value; If the actual angle deviation value exceeds the preset threshold, trigger the adjustment process of the pin processing process parameters; According to the magnitude and direction of the actual angle deviation value, use a decision tree algorithm to determine the processing process parameters that need to be adjusted. The processing process parameters include cutting speed, feed rate, tool angle, bevel inclination of the pin, or pin length; Transmit the adjustment value output by the decision tree algorithm to the numerical control system and modify the corresponding parameters in the pin processing program; Re - execute the pin processing program to perform secondary processing on the pin until the actual bevel angle deviation value meets the requirements.

[0011] Second aspect, the present invention provides a process detection system for pins of an electronic connector, mainly including: an image acquisition module, configured to acquire image data of the pin bevel surface by using an optical non-contact measurement device; an image preprocessing module, configured to preprocess the image data of the pin bevel surface according to the reflection characteristics of the pin surface and environmental light interference, remove noise and interference, and obtain a clear bevel surface image; an edge detection module, configured to apply an edge detection algorithm to the clear bevel surface image to extract bevel profile data including the characteristics of the pin bevel surface; an angle calculation module, configured to calculate the bevel angle deviation according to the bevel profile data, and determine whether the bevel angle deviation exceeds a preset angle deviation threshold; a data acquisition module, configured to acquire the insertion depth and contact position data of the pin when the bevel angle deviation exceeds the preset angle deviation threshold; a correlation analysis module, configured to perform a correlation analysis on the bevel angle deviation, the insertion depth, and the contact position data, determine the influence of the bevel angle deviation on the insertion depth and the contact position, and obtain a correlation analysis result; a process adjustment module, configured to adjust the pin processing process parameters and reprocess the pin according to the correlation analysis result, where the pin processing process parameters include cutting speed, feed rate, tool angle, pin bevel inclination, or pin length. Third aspect, the present invention further provides an electronic device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, where when the processor executes the computer program, the process detection method for pins of an electronic connector described in any one of the above is implemented.

[0012] Fourth aspect, the present invention further provides a computer-readable storage medium, where the computer-readable storage medium includes a stored computer program, and when the computer program runs, it controls the device where the computer-readable storage medium is located to execute the process detection method for pins of an electronic connector described in any one of the above.

[0013] The technical solution provided by the embodiments of the present invention may include the following beneficial effects: The present invention discloses a process detection method for pins of an electronic connector. An image of the pin bevel surface is obtained by a high-precision optical non-contact measurement device, and an accurate bevel profile is extracted through preprocessing and edge detection, and the angle deviation is calculated. If the deviation exceeds the threshold, the processing process parameters are adjusted and reprocessed. At the same time, the insertion depth and contact position data are acquired, and a correlation analysis is performed with the angle deviation to evaluate the influence on the insertion performance. Based on the analysis result, the bevel angle and shape are adjusted, including cutting speed, feed rate, tool angle, pin bevel inclination, or pin length. The present invention realizes the precise control of the pin bevel angle through precise measurement, data analysis, and process optimization, improves the insertion performance and contact reliability of the pin, and provides an effective solution for the manufacture of precision electronic connectors. Description of the Drawings

[0014] Figure 1 It is a flowchart of a process detection method for the pins of an electronic connector according to the present invention.

[0015] Figure 2 It is a schematic structural diagram of a process detection system for the pins of an electronic connector according to the present invention. Specific embodiments

[0016] Next, the technical solutions of the present invention will be described clearly and completely in conjunction with the embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0017] The inclined surface design of the pins of an electronic connector is a key factor affecting the matching degree between the pins and the connector sockets. During the actual production process, the angle and shape of the inclined surface of the pins are easily affected by factors such as processing technology and mold wear, resulting in deviations or inconsistencies. The accumulation of these tiny deviations will cause uneven insertion depths of the pins and offset contact positions, thereby leading to problems such as increased contact resistance and unstable signal transmission, seriously affecting the reliability and service life of the connector.

[0018] In order to monitor the changes in the angle and shape of the inclined surface of the pins in real time, it is necessary to introduce high-precision optical non-contact measurement equipment to perform on-line detection of the pins. The measurement equipment needs to have a sufficiently high resolution and sampling frequency to accurately capture the subtle changes in the inclined surface of the pins. At the same time, factors such as the reflection characteristics of the pin surface and environmental light interference also need to be considered during the measurement process to ensure the accuracy and reliability of the acquired image data. After obtaining the image data of the inclined surface of the pins, it is necessary to further analyze and process it in combination with intelligent algorithms.

[0019] When traditional image processing algorithms identify the features of the inclined surface of the pins, they are easily interfered by defects such as surface contamination and scratches of the pins, resulting in deviations in the detection results.

[0020] To solve the above problems, as Figure 1 shown, a process detection method for the pins of an electronic connector in this embodiment may specifically include: S11. Use an optical non-contact measurement device to obtain the image data of the pin bevel surface; S12. For the image data of the pin bevel surface, perform preprocessing according to the reflection characteristics of the pin surface and environmental light interference to remove noise and interference, and obtain a clear bevel image; S13. Apply an edge detection algorithm to the clear bevel image to extract the bevel contour data including the characteristics of the pin bevel surface; S14. According to the bevel contour data, calculate the bevel angle deviation, and determine whether the bevel angle deviation exceeds the preset angle deviation threshold; S15. When the bevel angle deviation exceeds the preset angle deviation threshold, obtain the insertion depth and contact position data of the pin; S16. Perform correlation analysis on the bevel angle deviation, insertion depth, and contact position data to judge the influence of the bevel angle deviation on the insertion depth and contact position, and obtain the correlation analysis result; S17. Adjust the pin processing process parameters and reprocess the pin according to the correlation analysis result. The pin processing process parameters include cutting speed, feed rate, tool angle, pin bevel inclination, or pin length.

[0021] The present invention discloses a process detection method for pins of an electronic connector. An image of the pin bevel surface is obtained through a high-precision optical non-contact measurement device. After preprocessing and edge detection, an accurate bevel contour is extracted, and the angle deviation is calculated. If the deviation exceeds the threshold, adjust the processing process parameters and reprocess. At the same time, obtain the insertion depth and contact position data, and perform correlation analysis with the angle deviation to evaluate the influence on the insertion performance. Based on the analysis result, adjust the bevel angle and shape, including cutting speed, feed rate, tool angle, pin bevel inclination, or pin length. The present invention realizes precise control of the pin bevel angle through precise measurement, data analysis, and process optimization, improves the insertion performance and contact reliability of the pin, and provides an effective solution for the manufacture of precision electronic connectors.

[0022] In step S11, a high-precision optical non-contact measurement device is used to obtain the image data of the pin bevel surface.

[0023] Exemplarily, in order to obtain high-resolution image data of the pin bevel surface, an industrial camera with a pixel resolution of 4096×3072 can be used, combined with a high-magnification macro lens, to image the pin bevel surface under appropriate light source conditions. The image data is transmitted to a computer through an image acquisition card for processing.

[0024] In step S12, for the image data of the pin bevel surface, perform preprocessing according to the reflection characteristics of the pin surface and environmental light interference to remove noise and interference, and obtain a clear bevel image.

[0025] Detect the image data of the pin bevel surface. If a defect or abnormality is detected, determine the position of the defect or abnormality, and classify it according to the morphological characteristics of the defect or abnormality; For different types of the defects or abnormalities, an image enhancement algorithm and an image segmentation algorithm are used to process the image data of the pin bevel surface, and a clear bevel surface image is obtained.

[0026] It should be noted that after obtaining the image data of the pin bevel surface, first, a Gaussian filtering algorithm is used to smooth the image. Gaussian filtering is a commonly used image smoothing method. By performing a convolution operation on the image and using a Gaussian kernel function to perform weighted averaging on each pixel point of the image, high-frequency noise in the image is removed. For example, a 5×5 Gaussian kernel can be selected, with σ taken as 5, to filter the image and obtain a smooth bevel surface image. Regarding the reflection characteristics of the pin surface, by establishing a Phong reflection illumination model, the reflection situation of the pin surface under different illumination conditions is simulated. By adjusting the coefficients of ambient light, diffuse reflection, and specular reflection, and considering the position and direction of the light source, the optimal illumination compensation parameters are calculated. For example, the ambient light coefficient is taken as 2, the diffuse reflection coefficient is taken as 6, the specular reflection coefficient is taken as 2, and the light source is located at a 45° direction above the pin bevel surface to eliminate the influence of ambient light interference. When performing edge detection on the smoothed bevel surface image, the Canny algorithm is used to extract the edge contour of the pin bevel surface. The Canny algorithm obtains a clear bevel surface image by calculating the gradient magnitude and direction of the image, performing non-maximum suppression, and double-threshold detection.

[0027] Step S13, applying an edge detection algorithm to the clear bevel surface image, and extracting bevel surface contour data including the features of the pin bevel surface, including: Preprocessing the clear bevel surface image, including denoising and contrast enhancement operations; Using an edge detection algorithm to perform edge detection on the preprocessed clear bevel surface image, and extracting the initial bevel surface contour; According to a preset pin bevel surface feature template, performing feature matching on the initial bevel surface contour to identify the feature area of the pin bevel surface; By analyzing the continuity and smoothness of the feature area, determining whether there are surface contaminants and scratches on the feature area; If there are surface contaminants and scratches, then a contour repair algorithm is used to repair the disturbed feature area; Performing refinement processing on the repaired bevel surface contour, including contour smoothing and contour simplification, to obtain a fine bevel surface contour; Comparing the fine bevel surface contour with a preset standard bevel surface contour, calculating the deviation degree of the contour, and determining whether the bevel surface contour meets the requirements; If the deviation degree exceeds the preset contour threshold, it does not meet the requirements and is marked as a defective bevel surface; If the deviation degree does not exceed the preset contour threshold, it meets the requirements and is marked as a qualified bevel surface; Output the fine bevel profile corresponding to the qualified bevel as bevel profile data.

[0028] Preprocess the clear bevel image, including operations such as denoising and enhancing contrast, to improve the image quality and lay a foundation for subsequent processing. Use an edge detection algorithm, such as the Canny algorithm, to perform edge detection on the preprocessed bevel image and extract the bevel profile information. According to the preset pin bevel feature template, perform feature matching on the extracted bevel profile to identify the feature area of the pin bevel. By analyzing the continuity and smoothness of the bevel profile, judge whether there are interference factors such as surface contamination and scratches on the profile. If so, use a contour repair algorithm, such as spline interpolation, to repair the interfered contour and eliminate the influence of interference. Perform refined processing on the repaired bevel profile, including operations such as contour smoothing and contour simplification, to obtain accurate bevel profile data. Compare the accurate bevel profile data with the preset standard bevel profile, calculate the deviation degree of the profile, and judge whether the bevel profile meets the requirements. If the deviation degree exceeds the preset profile threshold, it does not meet the requirements and is marked as a defective bevel; if the deviation degree does not exceed the preset profile threshold, it meets the requirements and is marked as a qualified bevel.

[0029] Exemplarily, in order to obtain a clear image of the inclined plane, a high-resolution industrial camera can be used, such as a CCD camera with a resolution of 20 million pixels, in combination with an appropriate light source and lens, to image the inclined plane. After obtaining the image, the median filtering algorithm is used to denoise the image, removing the high-frequency noise in the inclined plane image and improving the signal-to-noise ratio. Then, the histogram equalization algorithm is used to enhance the image, stretching the gray dynamic range of the image to make the inclined plane contour clearer. The Canny edge detection algorithm is used for the preprocessed image. By setting appropriate high and low thresholds, such as a high threshold of 200 and a low threshold of 50, the precise contour of the inclined plane is extracted. According to the characteristics of the pin inclined plane, such as the inclined plane angle is usually between 30° and 45°, and the inclined plane width is between 2 mm and 5 mm, a feature template of the pin inclined plane is established. Algorithms such as the Hough transform are used to perform feature matching on the inclined plane contour to identify the feature area of the inclined plane. By analyzing the first derivative and the second derivative of the inclined plane contour, the continuity and smoothness of the contour are judged. When there is a large mutation in the first derivative or the value of the second derivative exceeds the preset threshold, it is considered that there is contamination or a scratch on the contour. For the disturbed contour area, the cubic spline interpolation algorithm is used for contour repair to generate a smooth and continuous contour curve, excluding the influence of interference factors. The repaired inclined plane contour is smoothed using the Gaussian smoothing algorithm to remove the high-frequency noise and burrs on the contour. Then, the Douglas-Peucker algorithm is used to simplify the contour, reducing the data points of the contour and improving the efficiency of subsequent processing. The extracted inclined plane contour is compared with the standard inclined plane template, and the iterative closest point algorithm is used to calculate the average distance between the two contours. When the average distance exceeds 1 mm, it is determined as a defective inclined plane.

[0030] Step S14, according to the inclined plane contour data, calculate the inclined plane angle deviation, and judge whether the inclined plane angle deviation exceeds the preset angle deviation threshold, including: According to the inclined plane contour data, use the least squares method to fit the inclined plane contour curve equation; Based on the inclined plane contour curve equation, calculate the angle value and the inclined plane angle deviation at each position of the inclined plane; Obtain the preset angle deviation threshold, compare the inclined plane angle deviation at each position of the inclined plane with the angle deviation threshold. If the inclined plane angle deviation exceeds the angle deviation threshold, it is judged that there is an angle deviation at this position.

[0031] Exemplarily, obtaining the bevel profile data is a key step in accurately evaluating the quality of the bevel. Usually, a high-precision laser scanner or an optical measurement system is used to collect the three-dimensional coordinate point cloud data of the bevel surface. These raw data often contain noise and outliers and need to be preprocessed to improve the accuracy of subsequent analysis. During the preprocessing, the statistical outlier filtering method can be used to remove the significantly deviated points, and then smoothing algorithms such as Gaussian filtering are applied to reduce the influence of random noise. The preprocessed profile data is smoother and more continuous, laying the foundation for fitting the bevel profile curve. In this embodiment, the curve fitting method of the least squares method is adopted, which determines the optimal fitting parameters by minimizing the sum of the squares of the errors between the measured points and the fitting curve. For a linear bevel, a first-order polynomial fitting can be used; for a curved bevel, a second-order or higher-order polynomial can be selected. The obtained equation can not only describe the overall shape of the bevel but also provide a mathematical basis for subsequent angle calculation. Based on the fitted bevel profile curve equation, the tangent slope of the bevel can be calculated at any position, and then the angle value at that point can be obtained. Specifically, for a linear bevel, the angle value remains constant throughout the bevel; while for a curved bevel, the angle value changes with the position. By uniformly sampling on the bevel, a series of representative angle values can be obtained to comprehensively reflect the angle distribution of the bevel. The preset angle deviation threshold is an important basis for judging the quality of the bevel. This threshold is usually determined according to the product design requirements and processing accuracy. For example, it is specified that the allowable deviation range of the bevel angle is ±0.5°. The calculated actual angle value is compared with the standard angle value. If the difference exceeds the threshold range, it is determined that there is an angle deviation at that position.

[0032] Furthermore, by statistically analyzing the angle deviation results at each position, the overall angle deviation distribution can be obtained. This distribution can be presented in a visual way such as a histogram or a heat map, intuitively reflecting the change trend and abnormal areas of the bevel angle.

[0033] Step S15, when the bevel angle deviation exceeds the preset angle deviation threshold, obtain the insertion depth and contact position data of the pin.

[0034] Obtain the real-time depth data of the pin during the insertion process through a dedicated depth sensor. Obtain the real-time contact position data of the pin during the insertion process through a dedicated position sensor. The obtained insertion depth data and contact position data of the pin are fused and processed to obtain the comprehensive data of the pin insertion state. Feature extraction is performed on the comprehensive data of the pin insertion state to obtain the key feature parameters reflecting the pin insertion state.

[0035] Step S16, perform correlation analysis on the bevel angle deviation, insertion depth, and contact position data to judge the influence of the bevel angle deviation on the insertion depth and contact position, and obtain the correlation analysis results, including: Using the correlation analysis algorithm, calculate the correlation coefficients between the bevel angle deviation and the insertion depth and contact position data, and determine whether there is a correlation; If there is a correlation, use the regression analysis method to establish a regression model between the bevel angle deviation and the insertion depth and contact position data; According to the results of the regression model, judge the change range of the insertion depth and contact position when the angle deviation changes by one unit, and obtain the quantitative evaluation result of the influence of the angle deviation on the insertion depth and contact position; Take the said quantitative evaluation result as the association analysis result.

[0036] Exemplarily, during the pin processing, the bevel angle deviation, insertion depth, and contact position data are key parameters, and their relationship has an important impact on the processing quality. First, the original data of these parameters need to be obtained and preprocessed. For example, for the bevel angle deviation, a high-precision angle sensor can be used to measure the angle between the pin and the target plane; the insertion depth can be recorded by a displacement sensor; and the contact position can be obtained using a pressure sensor array. Data preprocessing includes removing outliers, filling in missing values, etc., to ensure data quality. Next, analyze the correlation between these parameters. Suppose in a certain batch of production, it is found that the Pearson correlation coefficient between the angle deviation and the insertion depth is 0.85, indicating a strong positive correlation between the two. This means that the larger the angle deviation, the deeper the insertion depth. And the Spearman rank correlation coefficient between the angle deviation and the contact position is -0.72, indicating a strong negative correlation, that is, when the angle deviation increases, the contact position deviates from the expected value.

[0037] Based on the correlation analysis results, establish a regression model. Taking the angle deviation as the independent variable and the insertion depth as the dependent variable, the linear regression equation is obtained: insertion depth = 2.5 + 0.3 × angle deviation. This shows that for every 1-degree increase in the angle deviation, the insertion depth increases by an average of 0.3 millimeters. Similarly, establish a model for the contact position: contact position offset = -0.2 × angle deviation + 0.5, indicating that for every 1-degree increase in the angle deviation, the contact position deviates by an average of 0.2 millimeters. Finally, take the above quantitative evaluation result as the association analysis result.

[0038] Furthermore, if it is found that the influence of the angle deviation on the insertion depth is more significant, while the influence on the contact position is relatively small. This suggests that during process optimization, the angle deviation should be controlled preferentially to ensure the accuracy of the insertion depth. At the same time, the pin design can be considered to be adjusted to increase its adaptive ability within a certain angle range, thereby reducing the influence of the angle deviation on the contact position. Through this series of analyses, not only the influence of the angle deviation on the insertion depth and contact position is quantified, but also specific guidance for process parameter adjustment is provided. This helps to improve the processing accuracy, reduce the defective rate, and ultimately achieve a double improvement in production efficiency and product quality.

[0039] Step S17: Adjust the processing parameters of the pin according to the correlation analysis results and reprocess the pin. The processing parameters of the pin include cutting speed, feed rate, tool angle, inclination of the pin bevel, or pin length.

[0040] In this embodiment, taking the quantitative evaluation result as the correlation analysis result further includes: Compare the quantitative evaluation result with a preset evaluation threshold to determine whether the influence of the bevel angle deviation on the insertion depth and contact position exceeds the allowable range; If it exceeds, adjust the processing parameters of the pin according to the bevel angle deviation.

[0041] Specifically, adjusting the processing parameters of the pin according to the bevel angle deviation includes: Obtain the actual angle of the pin bevel, compare the actual angle with a preset target angle, and calculate the actual angle deviation value; If the actual angle deviation value exceeds the preset threshold, trigger the adjustment process of the pin processing parameters; According to the magnitude and direction of the actual angle deviation value, use a decision tree algorithm to determine the processing parameters to be adjusted. The processing parameters include cutting speed, feed rate, tool angle, inclination of the pin bevel, or pin length; Transfer the adjustment value output by the decision tree algorithm to the numerical control system to modify the corresponding parameters in the pin processing program; Re - execute the pin processing program to perform secondary processing on the pin until the actual bevel angle deviation value meets the requirements.

[0042] In one implementation, obtain the actual angle of the pin bevel, compare the actual angle with a preset target angle, and calculate the angle deviation value; if the angle deviation value exceeds the preset threshold, trigger the adjustment process of the pin processing parameters; according to the magnitude and direction of the actual angle deviation value, use a decision tree algorithm to determine the processing parameters to be adjusted. The processing parameters include cutting speed, feed rate, and tool angle; transfer the adjustment value output by the decision tree algorithm to the numerical control system to modify the corresponding parameters in the pin processing program; re - execute the pin processing program to perform secondary processing on the pin until the actual bevel angle deviation value meets the requirements; during the secondary processing, continuously monitor the bevel angle of the pin and feedback the real - time data to the decision tree algorithm to dynamically optimize the processing parameters; when the bevel angles of multiple consecutive pins all meet the preset threshold, determine that the pin processing parameters have reached the optimal state, and save the optimized parameter settings for subsequent mass production.

[0043] Exemplarily, the machine vision system obtains the actual angle of the pin bevel through a high-precision camera and image processing algorithms. For example, edge detection and Hough transform are used to extract the bevel contour, and then a straight line is fitted by the least squares method to calculate the angle between it and the horizontal plane. Assuming the target angle is 45°, and the actual measured value is 46.2°, then the angle deviation is 1.2°. The preset threshold is the allowable error range determined according to product quality requirements, such as ±0.5°. When the deviation exceeds the threshold, the system will trigger the process parameter adjustment process. The pin processing parameters include cutting speed, feed rate, tool angle, pin bevel inclination or pin length. This automatic adjustment mechanism can correct machining errors in a timely manner and improve the product qualification rate. The decision tree algorithm determines the machining parameters that need to be adjusted according to the magnitude and direction of the angle deviation. For example, when the angle is too large, the cutting speed needs to be reduced or the tool inclination angle needs to be increased; when the angle is too small, the feed rate needs to be increased or the tool inclination angle needs to be reduced. This intelligent decision-making method can quickly find the optimal parameter combination and reduce the manual trial-and-error time. The numerical control system receives the adjustment values output by the decision tree and modifies the corresponding parameters in the machining program. For example, the cutting speed is reduced from the original 100 m / min to 90 m / min, or the tool angle is adjusted from 44.5° to 45.2°. This precise parameter adjustment can directly affect the machining accuracy and effectively improve the bevel angle. During the secondary machining process, the system continuously monitors the bevel angle and feeds it back to the decision tree algorithm in real time. This closed-loop control method can dynamically optimize the machining parameters and quickly converge to the best process state. For example, if there is still a deviation of 0.3° after the first adjustment, the system will further fine-tune the parameters until the requirements are met. When the bevel angles of multiple consecutive pins (such as 10) all meet the preset threshold, the system determines that the machining process parameters have reached the optimal state. At this time, the optimized parameter settings will be saved for subsequent batch production. This method not only ensures the quality of single-piece products but also improves the overall production efficiency. Through this intelligent machining parameter adjustment system, the accuracy and stability of pin bevel machining can be greatly improved. It can not only cope with machining fluctuations caused by factors such as materials and tools but also adapt to the characteristic changes of different batches of products, realizing high-quality and high-efficiency flexible production.

[0044] The present invention provides a process detection system for an electronic connector pin, mainly including: an image acquisition module, which is used to obtain the image data of the pin bevel surface by using an optical non-contact measurement device; an image preprocessing module, which is used to preprocess the image data of the pin bevel surface according to the reflection characteristics of the pin surface and the environmental light interference, remove noise and interference, and obtain a clear bevel surface image; an edge detection module, which is used to apply an edge detection algorithm to the clear bevel surface image and extract the bevel contour data including the characteristics of the pin bevel surface; an angle calculation module, which is used to calculate the bevel angle deviation according to the bevel contour data and judge whether the bevel angle deviation exceeds a preset angle deviation threshold; a data acquisition module, which is used to obtain the insertion depth and contact position data of the pin when the bevel angle deviation exceeds the preset angle deviation threshold; a correlation analysis module, which is used to perform a correlation analysis on the bevel angle deviation, the insertion depth, and the contact position data, judge the influence of the bevel angle deviation on the insertion depth and the contact position, and obtain a correlation analysis result; a process adjustment module, which is used to adjust the pin processing process parameters according to the correlation analysis result and reprocess the pin. The pin processing process parameters include cutting speed, feed rate, tool angle, pin bevel inclination, or pin length.

[0045] Preferably, the image preprocessing module is used for: detect the image data of the pin bevel surface. If a defect or an abnormality is detected, determine the position of the defect or the abnormality, and classify it according to the morphological characteristics of the defect or the abnormality; For different types of the defects or abnormalities, use an image enhancement algorithm and an image segmentation algorithm to process the image data of the pin bevel surface, and obtain a clear bevel surface image.

[0046] Preferably, the edge detection module is used for: Preprocess the clear bevel surface image, including denoising and contrast enhancement operations; Use an edge detection algorithm to perform edge detection on the preprocessed clear bevel surface image and extract an initial bevel contour; According to a preset pin bevel surface feature template, perform feature matching on the initial bevel contour to identify the feature area of the pin bevel surface; By analyzing the continuity and smoothness of the feature area, judge whether there is surface contamination and scratches on the feature area; If there is surface contamination and scratches, use a contour repair algorithm to repair the disturbed feature area; Perform refinement processing on the repaired bevel contour, including contour smoothing and contour simplification, to obtain a fine bevel contour; Compare the fine bevel contour with a preset standard bevel contour, calculate the deviation degree of the contour, and judge whether the bevel contour meets the requirements; If the deviation degree exceeds the preset profile threshold, it does not meet the requirements and is marked as a defective bevel; If the deviation degree does not exceed the preset profile threshold, it meets the requirements and is marked as a qualified bevel; Output the fine bevel profile corresponding to the qualified bevel as bevel profile data.

[0047] Preferably, the angle calculation module is used for: According to the bevel profile data, use the least squares method to fit the bevel profile curve equation; Based on the bevel profile curve equation, calculate the angle values and bevel angle deviations at various positions of the bevel; Obtain the preset angle deviation threshold, compare the bevel angle deviations at various positions of the bevel with the angle deviation threshold. If the bevel angle deviation exceeds the angle deviation threshold, it is determined that there is an angle deviation at this position.

[0048] Preferably, the correlation analysis module is used for: Adopt the correlation analysis algorithm to calculate the correlation coefficients between the bevel angle deviation and the insertion depth and contact position data, and judge whether there is a correlation; If there is a correlation, use the regression analysis method to establish a regression model between the bevel angle deviation and the insertion depth and contact position data; According to the results of the regression model, judge the change amplitude of the insertion depth and contact position when the angle deviation changes by one unit, and obtain the quantitative evaluation result of the influence of the angle deviation on the insertion depth and contact position; Take the quantitative evaluation result as the correlation analysis result.

[0049] Preferably, taking the quantitative evaluation result as the correlation analysis result further includes: Compare the quantitative evaluation result with the preset evaluation threshold to judge whether the influence of the bevel angle deviation on the insertion depth and contact position exceeds the allowable range; If it exceeds, adjust the processing process parameters of the pin according to the bevel angle deviation.

[0050] Preferably, the process adjustment module is used for: Obtain the actual angle of the pin bevel, compare the actual angle with the preset target angle, and calculate the actual angle deviation value; If the actual angle deviation value exceeds the preset threshold, trigger the adjustment process of the pin processing process parameters; According to the magnitude and direction of the actual angle deviation value, use the decision tree algorithm to determine the processing process parameters that need to be adjusted. The processing process parameters include cutting speed, feed rate, tool angle, pin bevel inclination or pin length; Transfer the adjustment value output by the decision tree algorithm to the numerical control system to modify the corresponding parameters in the pin processing program; Re - execute the pin processing program to perform secondary processing on the pin until the actual bevel angle deviation value meets the requirements.

[0051] The present invention discloses a process detection system for pins of an electronic connector. By using a high - precision optical non - contact measurement device to obtain the bevel image of the pin, accurately extract the bevel contour through pre - processing and edge detection, and calculate the angle deviation. If the deviation exceeds the threshold, adjust the processing process parameters and re - process. At the same time, obtain the insertion depth and contact position data, and conduct correlation analysis with the angle deviation to evaluate the impact on the insertion performance. Based on the analysis results, adjust the bevel angle and shape, including cutting speed, feed rate, tool angle, bevel inclination of the pin, or pin length. The present invention realizes the precise control of the bevel angle of the pin through precise measurement, data analysis, and process optimization, improves the insertion performance and contact reliability of the pin, and provides an effective solution for the manufacture of precision electronic connectors. It should be noted that the process detection system for pins of an electronic connector provided in the embodiments of the present invention is used to execute all the process steps of the process detection method for pins of an electronic connector in the above - mentioned embodiments, and the working principles and beneficial effects of the two correspond one by one, so they will not be elaborated here.

[0052] The embodiments of the present invention also provide an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a process detection program for pins of an electronic connector. When the processor executes the computer program, it implements the steps in the embodiments of the above - mentioned process detection methods for pins of an electronic connector, such as Figure 1 the step S11 shown. Or, when the processor executes the computer program, it implements the functions of each module / unit in the above - mentioned system embodiments, such as the image acquisition module.

[0053] Exemplarily, the computer program can be divided into one or more modules / units. The one or more modules / units are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of performing specific functions, and these instruction segments are used to describe the execution process of the computer program in the electronic device.

[0054] The electronic device may be a computing device such as a desktop computer, notebook, handheld computer, and smart tablet, etc. The electronic device may include, but is not limited to, a processor and a memory. Those skilled in the art can understand that the above components are only examples of the electronic device and do not constitute a limitation on the electronic device. It may include more or fewer components than the above, or combine certain components, or different components. For example, the electronic device may also include input / output devices, network access devices, a bus, etc.

[0055] The so-called processor may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The processor is the control center of the electronic device and connects various parts of the entire electronic device through various interfaces and lines.

[0056] The memory may be used to store the computer programs and / or modules. The processor realizes various functions of the electronic device by running or executing the computer programs and / or modules stored in the memory and by calling the data stored in the memory. The memory may mainly include a program storage area and a data storage area. Among them, the program storage area may store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area may store data created according to the use of the mobile phone (such as audio data, phone book, etc.). In addition, the memory may include high-speed random access memory and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one magnetic disk storage device, flash device, or other volatile solid-state storage devices.

[0057] Among them, if the modules / units integrated in the electronic device are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above-described embodiment methods of the present invention, it can also be completed by a computer program instructing relevant hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-described various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or system, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0058] It should be noted that the system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, in the attached drawings of the system embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which can be specifically implemented as one or more communication buses or signal lines. Those of ordinary skill in the art can understand and implement it without creative effort.

[0059] The above-described specific embodiments have further elaborated on the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above is only the specific embodiments of the present invention and is not used to limit the protection scope of the present invention. It is particularly pointed out that for those skilled in the art, any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A process detection method for pins of an electronic connector, characterized in that, The method includes: Using an optical non-contact measurement device to obtain the image data of the pin bevel; for the image data of the pin bevel, preprocessing is performed according to the reflection characteristics of the pin surface and environmental light interference to remove noise and interference, and a clear bevel image is obtained; applying an edge detection algorithm to the clear bevel image to extract the bevel contour data including the pin bevel features; according to the bevel contour data, calculating the bevel angle deviation and determining whether the bevel angle deviation exceeds a preset angle deviation threshold; when the bevel angle deviation exceeds the preset angle deviation threshold, obtaining the insertion depth and contact position data of the pin; Performing correlation analysis on the bevel angle deviation, insertion depth, and contact position data to judge the influence of the bevel angle deviation on the insertion depth and contact position, and obtaining a correlation analysis result; Adjusting the pin processing process parameters and reprocessing the pin according to the correlation analysis result. The pin processing process parameters include cutting speed, feed rate, tool angle, pin bevel inclination, or pin length.

2. The process detection method of the pin of the electronic connector according to claim 1, wherein, The preprocessing of the pin bevel image data according to the reflection characteristics of the pin surface and environmental light interference to remove noise and interference and obtain a clear bevel image includes: Detecting the pin bevel image data. If a defect or abnormality is detected, determining the position of the defect or abnormality and classifying it according to the morphological characteristics of the defect or abnormality; For different types of the defects or abnormalities, using an image enhancement algorithm and an image segmentation algorithm to process the pin bevel image data to obtain a clear bevel image.

3. The process detection method of the pin of the electronic connector according to claim 1, characterized in that, The applying an edge detection algorithm to the clear bevel image to extract the bevel contour data including the pin bevel features includes: Preprocessing the clear bevel image, including denoising and enhancing the contrast operation; Using an edge detection algorithm to perform edge detection on the preprocessed clear bevel image to extract the initial bevel contour; According to a preset pin bevel feature template, performing feature matching on the initial bevel contour to identify the feature area of the pin bevel; By analyzing the continuity and smoothness of the feature area, judging whether there are surface contaminations and scratches on the feature area; If there are surface contaminations and scratches, using a contour repair algorithm to repair the disturbed feature area; Performing refinement processing on the repaired bevel contour, including contour smoothing and contour simplification, to obtain a fine bevel contour; Comparing the fine bevel contour with a preset standard bevel contour, calculating the deviation degree of the contour, and judging whether the bevel contour meets the requirements; If the deviation degree exceeds the preset contour threshold, it does not meet the requirements and is marked as a defective bevel; If the deviation degree does not exceed the preset contour threshold, it meets the requirements and is marked as a qualified bevel; Outputting the fine bevel contour corresponding to the qualified bevel as the bevel contour data.

4. The process detection method of the pin of the electronic connector according to claim 1, characterized in that The calculating the bevel angle deviation according to the bevel contour data and determining whether the bevel angle deviation exceeds a preset angle deviation threshold includes: According to the bevel contour data, using the least squares method to fit the bevel contour curve equation; Based on the bevel contour curve equation, calculating the angle values at various positions of the bevel and the bevel angle deviation; Obtain a preset angle deviation threshold, compare the bevel angle deviation at each position of the bevel with the angle deviation threshold. If the bevel angle deviation exceeds the angle deviation threshold, it is determined that there is an angle deviation at this position.

5. The process inspection method of the pin of the electronic connector according to claim 1, characterized in that, The correlation analysis of the bevel angle deviation with the insertion depth and contact position data, and the judgment of the influence of the bevel angle deviation on the insertion depth and contact position to obtain the correlation analysis result, includes: Adopt a correlation analysis algorithm to calculate the correlation coefficients between the bevel angle deviation and the insertion depth and contact position data, and judge whether there is a correlation; If there is a correlation, adopt a regression analysis method to establish a regression model between the bevel angle deviation and the insertion depth and contact position data; According to the results of the regression model, judge the change range of the insertion depth and contact position when the angle deviation changes by one unit, and obtain a quantitative evaluation result of the influence of the angle deviation on the insertion depth and contact position; Take the quantitative evaluation result as the correlation analysis result.

6. The process detection method of the pin of the electronic connector according to claim 5, wherein, After taking the quantitative evaluation result as the correlation analysis result, it further includes: Compare the quantitative evaluation result with a preset evaluation threshold to judge whether the influence of the bevel angle deviation on the insertion depth and contact position exceeds the allowable range; If it exceeds, adjust the processing parameters of the pin according to the bevel angle deviation.

7. The process inspection method of the pin of the electronic connector according to claim 6, characterized in that, The adjustment of the pin processing parameters according to the bevel angle deviation includes: Obtain the actual angle of the pin bevel, compare the actual angle with a preset target angle, and calculate the actual angle deviation value; If the actual angle deviation value exceeds the preset threshold, trigger the adjustment process of the pin processing parameters; According to the magnitude and direction of the actual angle deviation value, use a decision tree algorithm to determine the processing parameters to be adjusted. The processing parameters include cutting speed, feed rate, tool angle, bevel inclination of the pin or pin length; Transmit the adjustment value output by the decision tree algorithm to the numerical control system and modify the corresponding parameters in the pin processing program; Re - execute the pin processing program to perform secondary processing on the pin until the actual bevel angle deviation value meets the requirements.

8. A process detection system for an electronic connector pin, characterized in that, The system includes: An image acquisition module for obtaining pin bevel image data using an optical non - contact measurement device; an image pre - processing module for pre - processing the pin bevel image data according to the surface reflection characteristics of the pin and environmental light interference to remove noise and interference and obtain a clear bevel image; an edge detection module for applying an edge detection algorithm to the clear bevel image to extract bevel contour data including pin bevel features; an angle calculation module for calculating the bevel angle deviation according to the bevel contour data and judging whether the bevel angle deviation exceeds a preset angle deviation threshold; a data acquisition module for obtaining the insertion depth and contact position data of the pin when the bevel angle deviation exceeds the preset angle deviation threshold; A correlation analysis module for performing a correlation analysis on the bevel angle deviation with the insertion depth and contact position data, judging the influence of the bevel angle deviation on the insertion depth and contact position, and obtaining a correlation analysis result; A process adjustment module is used to adjust the processing parameters of the pin according to the correlation analysis result and reprocess the pin. The processing parameters of the pin include cutting speed, feed rate, tool angle, inclination of the pin bevel or pin length.

9. An electronic device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the process detection method for the pin of the electronic connector according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program. When the computer program runs, it controls the device where the computer-readable storage medium is located to execute the process detection method for the pin of the electronic connector according to any one of claims 1 to 7.

Images