Correction method and device for helical line deviation of small-modulus plastic cylindrical gear

Abstract

The invention relates to the technical field of helical line deviation correction, in particular to a correction method and device for helical line deviation of a small-modulus plastic cylindrical gear. The method comprises the following steps: acquiring a multi-azimuth image of a small-modulus gear to be detected; global histogram equalization is carried out on the to-be-detected small-modulus gear multi-azimuth image, and a brightness optimization gear image is constructed; carrying out three-dimensional point cloud reconstruction on the brightness optimized gear image, and carrying out gear total deviation calculation to generate a spiral line total deviation curve; carrying out deviation change situation analysis on the total deviation curve of the spiral line, and carrying out nonlinear shrinkage characteristic mining to obtain a nonlinear shrinkage rule; and performing error compensation calculation on the gear three-dimensional result model according to a nonlinear contraction rule to generate a spiral line contraction error compensation value, and performing error parameter fine adjustment correction on the to-be-detected small-modulus gear so as to obtain corrected gear spiral line parameters. The high-precision and high-accuracy gear helix deviation correction method is realized.

Description

Technical Field

[0001] The invention relates to the technical field of helix deviation correction, and in particular to a method and a device for correcting a helix deviation of a small-modulus plastic cylindrical gear. Background Art

[0002] As a key component of precision mechanical transmission, small modulus plastic gears are widely used in aerospace, high-end equipment and other fields. Their processing accuracy plays a vital role in the performance of the whole machine. However, due to the small size and complex structure of small modulus gears, the traditional contact measurement method has problems such as difficulty, low measurement accuracy and low efficiency, and it is difficult to meet the current high-precision and high-efficiency processing needs. Small modulus plastic gears have nonlinear shrinkage when they are manufactured by injection molding process, and the manufactured plastic gear products are also affected by many factors such as the structure, parameters, mold design scheme, mold processing accuracy, mold assembly accuracy, injection molding process parameters, and injection molding machine models of plastic gears. Therefore, the current manufacturing accuracy of plastic gears at home and abroad is distributed in 8-11 levels according to GB / T 38192-2019, and most of them are in 9~11 levels, which makes it impossible to make the overall noise of plastic gearboxes smaller, which seriously restricts the development of plastic gears. In summary, it is precisely because there are too many factors affecting the precision deviation of the helix of plastic gears that a unified shrinkage law and correction method have not yet been formed, resulting in large helix deviation of plastic gears and difficulty in improving the accuracy of plastic gears. Therefore, an intelligent gear helix deviation correction method is needed. Summary of the invention

[0003] In order to solve the above technical problems, the present invention proposes a method and device for correcting the helix deviation of a small-module plastic cylindrical gear, so as to solve at least one of the above technical problems.

[0004] To achieve the above object, the present invention provides a method for correcting the helix deviation of a small-modulus plastic cylindrical gear, comprising the following steps: Step S1: Acquire a multi-directional image of a small modulus gear to be detected; perform global histogram equalization on the multi-directional image of the small modulus gear to be detected to construct a brightness optimized gear image; Step S2: reconstructing the three-dimensional point cloud of the brightness optimized gear image, calculating the overall gear deviation, and generating a helix total deviation curve; Step S3: Analyze the deviation change trend of the total deviation curve of the spiral line, and mine the nonlinear shrinkage characteristics to obtain the nonlinear shrinkage law; Step S4: Calculate the error compensation of the gear three-dimensional result model according to the nonlinear shrinkage law to generate a helical shrinkage error compensation value; Step S5: fine-tuning and correcting the error parameters of the small-module gear to be inspected based on the helix contraction error compensation value, thereby obtaining the corrected gear helix parameters; Step S6: Perform helix accuracy correction evaluation on the corrected gear helix parameters to obtain helix deviation correction results, thereby completing the small-module gear helix deviation correction operation.

[0005] The present invention improves the image contrast and brightness uniformity through global histogram equalization, making the gear surface details clearer, constructing a brightness-optimized gear image to provide a clearer image basis for subsequent processing, which is conducive to accurate gear parameter extraction, and more accurately restores the three-dimensional shape of the gear through three-dimensional point cloud reconstruction, providing accurate geometric information for gear parameter calculation, and generating a spiral total deviation curve to understand the deviation of the entire gear, providing a basis for subsequent error correction, and discovering the variation law of gear deviation through deviation variation trend analysis, providing guidance for subsequent correction strategies, and nonlinear shrinkage feature mining to deeply understand the nonlinear characteristics of gear deviation, providing more accurate for subsequent error compensation. The method generates a helix shrinkage error compensation value, compensates the error of the gear model according to the nonlinear shrinkage law, improves the accuracy of the gear parameters, and more accurately corrects the gear deviation through error compensation calculation, making the measurement result more reliable. The gear error parameters are fine-tuned based on the helix shrinkage error compensation value to more accurately correct the gear parameters. The helix accuracy correction evaluation evaluates the accuracy of the corrected helix to ensure the effectiveness and reliability of the gear parameter correction, completes the small modulus gear helix deviation correction operation, improves the accuracy of the gear helix parameters, ensures that the gear quality meets the requirements, realizes the effective correction of the gear helix deviation, and greatly improves the accuracy of the plastic gear tooth profile.

[0006] Preferably, step S1 comprises the following steps: Step S11: Acquire multi-directional images of the small-module gear to be inspected; Step S12: performing multi-scale Gaussian blur processing on the multi-directional images of the small modulus gear to be inspected to generate multiple multi-scale blurred images; Step S13: performing original image difference calculation on multiple multi-scale fuzzy image sets to obtain multiple scale sharpened sub-images; Step S14: performing nonlinear detail enhancement on the multiple scale sharpened sub-images to obtain multiple sharpened detail enhanced sub-images; Step S15: weighting multiple sharpening detail enhancement sub-images to generate a multi-scale sharpening enhancement image; Step S16: Perform global histogram equalization on the multi-scale sharpening enhancement image to construct a brightness optimized gear image.

[0007] The present invention smoothes the image through Gaussian blur to reduce noise, providing a clearer image basis for subsequent steps, and highlights the changes in image details at different scales by calculating the difference of the image. In the subsequent detail enhancement processing, nonlinear detail enhancement highlights the detail information in the image, enhances the clarity and contrast of the image, and more accurately extracts the gear parameters. By weighted superposition of detail enhancement sub-graphs at different scales, richer and more accurate image information is obtained. Global histogram equalization improves the contrast and brightness uniformity of the image, making the gear surface details clearer. Constructing a brightness-optimized gear image provides a clearer image basis for subsequent processing, which is conducive to accurate gear parameter extraction.

[0008] Preferably, step S2 comprises the following steps: Step S21: performing gear tooth profile visual recognition on the brightness optimized gear image to extract the gear tooth profile line; Step S22: Analyze the characteristic points of the gear tooth profile and mark the key characteristic points of the tooth profile; Step S23: performing gear helix fitting calculation on key characteristic points of the tooth profile to obtain characteristic parameters of the tooth profile helix; Step S24: performing gear three-dimensional structure layout analysis on the brightness optimized gear image, thereby obtaining gear three-dimensional structure data; Step S25: reconstructing the three-dimensional point cloud of the gear three-dimensional structure data according to the characteristic parameters of the tooth profile helix line, and constructing a three-dimensional result model of the gear; Step S26: Calculate the gear overall deviation of the tooth profile helix characteristic parameters based on the preset theoretical design gear parameters to generate a helix total deviation curve.

[0009] The present invention accurately locates the key parts of the gear by marking and analyzing the key feature points, thereby providing a reliable data basis for the subsequent analysis; accurately extracts the characteristic parameters of the gear tooth profile by the helix fitting calculation, thereby providing important data support for the subsequent three-dimensional structure analysis; obtains more accurate three-dimensional structure data of the gear by performing three-dimensional structure layout analysis on the brightness-optimized gear image; reconstructs the three-dimensional point cloud of the gear three-dimensional structure data according to the characteristic parameters of the tooth profile helix, constructs a three-dimensional result model of the gear, and realizes accurate reconstruction of the gear shape; designs the gear parameters based on the preset theoretical design, calculates the overall gear deviation for the characteristic parameters of the tooth profile helix, generates a total helix deviation curve, evaluates the deviation of the gear helix, and provides a basis for correction.

[0010] Preferably, the specific steps of step S26 are: Based on the preset theoretical design gear parameters, the directional inclination angle deviation of the tooth profile helix characteristic parameters is calculated to obtain the helix inclination deviation; Perform quadrant plane projection on the preset theoretical design gear parameters to obtain theoretical helix projection data; The tooth surface crest is calculated on the theoretical helix projection data to obtain the theoretical tooth surface crest parameters; The morphological difference of the characteristic parameters of the tooth profile helix is ​​calculated based on the theoretical tooth surface crest parameters to obtain the helix shape deviation. Repeat the above operation to calculate the helical inclination deviation and helical shape deviation of all tooth profiles; Performing curve fitting on the helical inclination deviation and helical shape deviation of all tooth profiles, thereby obtaining a helical inclination deviation curve and a helical shape deviation curve; The helix tilt deviation curve and the helix shape deviation curve are fused by comprehensive deviation characterization to generate the helix total deviation curve.

[0011] The present invention evaluates the deviation between preset gear design parameters and actual helix characteristic parameters by calculating the directional inclination angle deviation, provides guidance for subsequent design optimization, obtains theoretical helix projection data by quadrant plane projection, understands the geometric characteristics of the gear, and provides a basis for subsequent morphological difference calculations, quantifies the surface morphology of the theoretical helix by calculating the tooth surface peak parameters, and provides data support for the evaluation of the helix shape deviation, calculates the helix shape deviation based on the theoretical tooth surface peak parameters, further evaluates the difference between the preset design and the actual morphology, and provides a basis for design adjustment, comprehensively calculates the helix inclination deviation and shape deviation of all tooth profiles, comprehensively evaluates the manufacturing quality and design accuracy of the gear, and provides a reference for production process improvement, obtains the helix inclination deviation and shape deviation of all tooth profiles by curve fitting, and forms a helix inclination deviation curve and a helix shape deviation curve, thereby forming a comprehensive deviation curve model for further analysis and comparison, and integrates the helix inclination deviation and shape deviation through comprehensive deviation characterization to generate a helix total deviation curve, thereby comprehensively understanding the helix deviation of the gear.

[0012] Preferably, step S3 specifically comprises the following steps: Step S31: performing gear center shrinkage difference analysis based on the helix tilt deviation curve to obtain gear shrinkage difference data; Step S32: performing an asymmetry trend analysis on the helical shape deviation curve to obtain asymmetry trend data; Step S33: performing tooth surface comprehensive shrinkage effect identification on the helix total deviation curve to obtain tooth surface comprehensive shrinkage effect data; Step S34: performing deviation change trend analysis on the gear shrinkage difference data, asymmetry trend data and tooth surface comprehensive shrinkage effect data, thereby generating helix deviation trend data; Step S35: mining the nonlinear contraction characteristics of the spiral deviation situation data to obtain the nonlinear contraction law. The present invention analyzes the helix inclination deviation curve to evaluate the shrinkage difference of the gear center, thereby providing data support for understanding the changes in the gear manufacturing process, analyzes the helix shape deviation curve to identify the asymmetry trend of the gear, and provides information for understanding the irregularity of the gear shape, analyzes the total helix deviation curve to identify the comprehensive shrinkage effect of the tooth surface, and understands the overall changes in the gear surface, comprehensively analyzes the gear shrinkage difference data, asymmetry trend data and tooth surface comprehensive shrinkage effect data, reveals the changing trend of the gear deviation, and provides a basis for adjusting the manufacturing process, and analyzes the helix deviation situation data to explore the nonlinear shrinkage characteristics in the gear deviation and understand the complex changing laws in the gear manufacturing process.

[0013] Preferably, the specific steps of step S4 are: Step S41: Calculate the gear profile parameters one by one on the gear three-dimensional result model to generate multiple gear profile parameters; Step S42: calculating the environmental shrinkage amount according to the nonlinear shrinkage law to generate the current environmental shrinkage amount; Step S43: performing error compensation calculation on multiple gear tooth profile parameters based on the current environmental shrinkage amount to generate a helical shrinkage error compensation value.

[0014] The present invention calculates the tooth profile parameters of the gear three-dimensional result model one by one to generate multiple gear tooth profile parameters, comprehensively understands the geometric characteristics of the gear, provides basic data for subsequent analysis and optimization, calculates the environmental shrinkage amount of the nonlinear shrinkage law, generates shrinkage amount data under the current environment, considers the influence of external environmental factors on gear manufacturing and performance, provides a basis for precise manufacturing, and performs error compensation calculations on multiple gear tooth profile parameters based on the current environmental shrinkage amount, generates a helical shrinkage error compensation value, corrects errors existing in the manufacturing process, and improves the geometric accuracy and performance stability of the gear.

[0015] Preferably, the helical line shrinkage error compensation value includes the upper and lower reducer compensation value of the tooth width, the waist shrinkage compensation value in the middle of the tooth width and the helical line inclination compensation value; the specific steps of step S5 are: Based on the upper and lower tooth width compensation values ​​and the middle tooth width waist compensation value, reverse compensation and fine-tuning processing is performed on the small modulus gear to be tested; Perform helix angle inclination correction on the small modulus gear to be inspected according to the helix line inclination compensation value; Based on the above operation, a corrected small modulus gear is obtained; The helix characteristic parameters of the corrected small-module gear are identified to obtain the corrected gear helix parameters.

[0016] The present invention achieves fine-tuning of the gear tooth width by correcting the compensation value of the tooth width and the compensation value of the middle waist shrinkage, thereby ensuring the dimensional accuracy and geometric accuracy of the gear during the processing. By correcting the helix line inclination compensation value, the helix angle inclination of the gear is adjusted to ensure that the helix line of the gear is at the correct angle, thereby improving the transmission efficiency and performance stability of the gear. By identifying the characteristic parameters of the helix line of the gear after correction, the accuracy and uniformity of the gear helix line are evaluated to ensure the transmission efficiency and operation stability of the gear.

[0017] Preferably, the specific steps of step S6 are: According to the preset theoretical design gear parameters, the helix accuracy correction calculation is performed on the corrected gear helix parameters to generate the helix accuracy correction deviation value; Based on the preset gear deviation accuracy range, the helix accuracy correction deviation value is corrected and evaluated to obtain a helix deviation correction result; the helix deviation correction result includes a deviation correction pass and a deviation correction failure; When the helical line deviation correction result is a deviation correction failure, repeat the error parameter fine-tuning correction, and perform the correction evaluation again until the helical line deviation correction result is a deviation correction pass; When the helix deviation correction result is qualified, the helix deviation correction operation of the small modulus gear is completed.

[0018] The present invention ensures that the gear helix meets the preset theoretical design parameters by performing precision correction calculation on the corrected gear helix parameters, generates a helix precision correction deviation value to quantify the difference between the actual gear helix and the theoretical design value, and provides data support for subsequent evaluation. Based on the preset gear deviation precision range, the helix precision correction deviation value is corrected and evaluated to determine the helix deviation correction result. The helix precision correction deviation value is compared and evaluated with the preset range to promptly find out whether the gear helix meets the requirements, and provide guidance for subsequent processing. The helix deviation correction result includes qualified deviation correction and failed deviation correction, and provides a clear judgment result. When the helix deviation correction fails, the accuracy of the gear helix is ​​gradually optimized by repeating the error parameter fine-tuning correction and re-evaluation to ensure that the final result meets the quality requirements. When the helix deviation correction result is qualified deviation correction, it indicates that the small modulus gear helix deviation correction operation is completed. Through this process, it is ensured that the accuracy of the gear helix meets the design standards and the performance and reliability of the gear are improved.

[0019] In this specification, a small modulus gear helix deviation assessment and correction system based on visual measurement is provided, which is used to perform the correction method of the helix deviation of the small modulus plastic cylindrical gear as described above, including: The helix deviation module is used to reconstruct the 3D point cloud of the brightness optimized gear image, calculate the overall gear deviation, and generate the helix total deviation curve; The nonlinear shrinkage module is used to analyze the deviation change trend of the total deviation curve of the spiral line and to mine the nonlinear shrinkage characteristics to obtain the nonlinear shrinkage law; The error compensation module is used to calculate the error compensation of the gear three-dimensional result model according to the nonlinear shrinkage law and generate the helical shrinkage error compensation value; An error correction module is used to fine-tune the error parameters of the small-module gear to be inspected based on the helix contraction error compensation value, so as to obtain the corrected gear helix parameters; The accuracy correction evaluation module is used to perform helix accuracy correction evaluation on the corrected gear helix parameters, obtain the helix deviation correction result, and complete the small modulus gear helix deviation correction operation.

[0020] The invention obtains multi-directional images of small-module gears to be detected, provides required input data for subsequent analysis, performs global histogram equalization on the images to improve the contrast and brightness of the images, highlights the detailed features of the gears, and provides better input for subsequent analysis and processing. The invention performs three-dimensional point cloud reconstruction and deviation calculation on the brightness-optimized gear images, obtains the overall deviation of the gears, generates a total helix deviation curve, quantifies the deviation of the gears, and provides a basis for subsequent analysis and correction. The invention mines the nonlinear shrinkage feature of the total helix deviation curve to reveal the nonlinear variation law of the gear deviation, understands the gear deviation more comprehensively, and provides guidance for subsequent error compensation. The invention performs error compensation calculation according to the nonlinear shrinkage law to generate a helix shrinkage error compensation value for correcting the error of the gear model. The error compensation improves the accuracy and precision of gear detection. The gear parameters are fine-tuned and corrected based on the error compensation value to obtain the corrected helix parameters, improve the accuracy of the gear parameters, ensure the accuracy and stability of the gears during use, perform precision correction evaluation on the corrected helix parameters to obtain the helix deviation correction result, ensure the quality and accuracy of the gears, verify whether the corrected gear parameters meet the requirements, and improve the tooth profile accuracy of the gears. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A schematic flow chart of the steps of a method for correcting a helical deviation of a small-modulus plastic cylindrical gear according to the present invention; Figure 2 Detailed implementation flow chart of step S1; Figure 3 Detailed implementation flow chart of step S2; Figure 4 Detailed implementation flow chart of step S3. DETAILED DESCRIPTION

[0022] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0023] This application example provides a method and device for correcting the helical deviation of a small-modulus plastic cylindrical gear. The execution subjects of the method and device include but are not limited to: mechanical equipment, data processing platform, cloud server node, network upload device, etc. equipped with the system, which can be regarded as the general computing node of this application, and the data processing platform includes but is not limited to: at least one of an audio and image management system, an information management system, and a cloud data management system.

[0024] See also Figures 1 to 4 The present invention provides a method for correcting the helical line deviation of a small-module plastic cylindrical gear, and the method for correcting the helical line deviation of a small-module plastic cylindrical gear comprises the following steps: Step S1: Acquire a multi-directional image of a small modulus gear to be detected; perform global histogram equalization on the multi-directional image of the small modulus gear to be detected to construct a brightness optimized gear image; Step S2: reconstructing the three-dimensional point cloud of the brightness optimized gear image, calculating the overall gear deviation, and generating a helix total deviation curve; Step S3: Analyze the deviation change trend of the total deviation curve of the spiral line, and mine the nonlinear shrinkage characteristics to obtain the nonlinear shrinkage law; Step S4: Calculate the error compensation of the gear three-dimensional result model according to the nonlinear shrinkage law to generate a helical shrinkage error compensation value; Step S5: fine-tuning and correcting the error parameters of the small-module gear to be inspected based on the helix contraction error compensation value, thereby obtaining the corrected gear helix parameters; Step S6: Perform helix accuracy correction evaluation on the corrected gear helix parameters to obtain helix deviation correction results, thereby completing the small-module gear helix deviation correction operation.

[0025] The present invention improves the image contrast and brightness uniformity through global histogram equalization, making the gear surface details clearer, constructing a brightness-optimized gear image to provide a clearer image basis for subsequent processing, which is conducive to accurate gear parameter extraction, and more accurately restores the three-dimensional shape of the gear through three-dimensional point cloud reconstruction, providing accurate geometric information for gear parameter calculation, and generating a spiral total deviation curve to understand the deviation of the entire gear, providing a basis for subsequent error correction, and discovering the variation law of gear deviation through deviation variation trend analysis, providing guidance for subsequent correction strategies, and nonlinear shrinkage feature mining to deeply understand the nonlinear characteristics of gear deviation, providing more accurate for subsequent error compensation. The method generates a helix shrinkage error compensation value, compensates the error of the gear model according to the nonlinear shrinkage law, improves the accuracy of the gear parameters, and more accurately corrects the gear deviation through error compensation calculation, making the measurement result more reliable. The gear error parameters are fine-tuned based on the helix shrinkage error compensation value to more accurately correct the gear parameters. The helix accuracy correction evaluation evaluates the accuracy of the corrected helix to ensure the effectiveness and reliability of the gear parameter correction, completes the small modulus gear helix deviation correction operation, improves the accuracy of the gear helix parameters, ensures that the gear quality meets the requirements, realizes the effective correction of the gear helix deviation, and greatly improves the accuracy of the plastic gear tooth profile.

[0026] In the embodiment of the present invention, refer to Figure 1 , is a schematic flow chart of the steps of a method for correcting a helical line deviation of a small-module plastic cylindrical gear according to the present invention. In this example, the steps of the method for correcting a helical line deviation of a small-module plastic cylindrical gear include: Step S1: Acquire a multi-directional image of a small modulus gear to be detected; perform global histogram equalization on the multi-directional image of the small modulus gear to be detected to construct a brightness optimized gear image; In this embodiment, a high-resolution camera or other imaging device is used to shoot the small modulus gear to be inspected at multiple angles to obtain multi-view image data of the gear surface, including images at different angles such as the front view and the oblique view. A global histogram equalization image processing algorithm is used to adjust the brightness of the multiple gear images obtained. Histogram equalization can stretch the grayscale distribution of the image, enhance the contrast, highlight the image detail information, optimize the brightness and contrast of the gear image, and provide better input for subsequent image analysis.

[0027] Step S2: reconstructing the three-dimensional point cloud of the brightness optimized gear image, calculating the overall gear deviation, and generating a helix total deviation curve; In this embodiment, a two-dimensional image is converted into a three-dimensional point cloud through machine vision algorithms, such as depth estimation and feature matching, to describe the geometric shape of the gear surface. The three-dimensional spatial information of the gear surface is obtained to provide a data basis for subsequent deviation analysis and detection. Based on the three-dimensional point cloud data, the ideal gear helix is ​​fitted. The distance deviation between the actual gear surface and the ideal helix is ​​calculated for each measuring point. The deviations of all measuring points are counted to calculate the overall geometric deviation indicators of the gear, such as the total deviation and variance. The degree of deviation between the gear geometry and the ideal model is quantitatively described to provide a basis for subsequent defect judgment. The calculated deviations of each measuring point are plotted into a curve according to the circumferential position of the gear. This spiral total deviation curve can intuitively reflect the circumferential deviation distribution characteristics of the gear geometry.

[0028] Step S3: Analyze the deviation change trend of the total deviation curve of the spiral line, and mine the nonlinear shrinkage characteristics to obtain the nonlinear shrinkage law; In this embodiment, the total deviation curve of the helix is ​​analyzed to identify abnormal fluctuations, local extreme points and other features on the curve. The overall change trend of the curve, such as fluctuation amplitude, periodicity and other features, is analyzed to determine the overall deviation of the gear surface geometry. Nonlinear analysis methods, such as neural networks and fuzzy logic, are applied to deeply mine the obtained deviation situation data, find out various nonlinear factors that affect the helix deviation, and extract the corresponding nonlinear contraction law.

[0029] Step S4: Calculate the error compensation of the gear three-dimensional result model according to the nonlinear shrinkage law to generate a helical shrinkage error compensation value; In this embodiment, a mathematical model is established to describe the nonlinear characteristics of the extracted gear geometric deviation by using the nonlinear contraction law. The nonlinear model is applied to the obtained three-dimensional point cloud data to calculate the geometric deviation compensation value of each measuring point. The compensation calculation uses surface fitting, neural network and other methods to accurately map the nonlinear deviation characteristics to the three-dimensional geometric model. Through the compensation calculation, the nonlinear geometric deviation existing in the three-dimensional point cloud data is corrected to improve the accuracy of the gear. Error compensation is performed on each point cloud data point, and its wheelbase parameter is corrected to obtain the error compensation value.

[0030] Step S5: fine-tuning and correcting the error parameters of the small-module gear to be inspected based on the helix contraction error compensation value, thereby obtaining the corrected gear helix parameters; In this embodiment, the initial geometric parameters and error compensation curve are imported into the gear design model, and numerical optimization algorithms, such as the least squares method, are used to make slight adjustments and iterative optimization to these gear parameters, so that the geometric deviation of the optimized three-dimensional gear model at each tooth position is minimized, that is, it is most consistent with the error compensation curve. After parameter fine-tuning and optimization, the corrected gear geometric dimension parameters, such as module, number of teeth, helix, etc., are finally obtained. These parameters are directly applied to the subsequent gear manufacturing process to ensure that the produced gears can meet the design requirements.

[0031] Step S6: Perform helix accuracy correction evaluation on the corrected gear helix parameters to obtain helix deviation correction results, thereby completing the small-module gear helix deviation correction operation.

[0032] In this embodiment, based on the theoretical design parameters of the gear (such as module, number of teeth, helix angle, etc.), the theoretical helix accuracy index is calculated, and the theoretical helix parameters are compared to calculate the deviation of each tooth position, and the distribution pattern of these deviation values ​​is analyzed to understand the systematic error characteristics existing in the gear manufacturing process. Through statistical analysis, key indicators such as the mean value and standard deviation of the helix deviation are determined, and the corrected gear is measured again to confirm that the helix deviation of each tooth position has been effectively controlled, and the key indicators of the correction effect, such as the average deviation and maximum deviation, are evaluated to ensure that the design requirements are met.

[0033] In this embodiment, refer to Figure 2 , is a flowchart of detailed implementation steps of step S1. In this embodiment, the detailed implementation steps of step S1 include: Step S11: Acquire multi-directional images of the small-module gear to be inspected; Step S12: performing multi-scale Gaussian blur processing on the multi-directional images of the small modulus gear to be inspected to generate multiple multi-scale blurred images; Step S13: performing original image difference calculation on multiple multi-scale fuzzy image sets to obtain multiple scale sharpened sub-images; Step S14: performing nonlinear detail enhancement on the multiple scale sharpened sub-images to obtain multiple sharpened detail enhanced sub-images; Step S15: weighting multiple sharpening detail enhancement sub-images to generate a multi-scale sharpening enhancement image; Step S16: Perform global histogram equalization on the multi-scale sharpening enhancement image to construct a brightness optimized gear image.

[0034] In this embodiment, multi-view image data (such as top view, front view, side view, etc.) of the small modulus gear to be tested are collected. These multi-view images reflect the 3D structural characteristics of the gear, which is conducive to subsequent detection and analysis. These image data are acquired using a high-definition camera or scanning equipment, and processed using a Gaussian blur filter. Different Gaussian kernel sizes (sigma Value), generate fuzzy images of different scales, multi-scale fuzzy images can better reflect the detailed features of the gear surface, perform pixel-by-pixel difference calculation between the multi-scale fuzzy image and the original image, the difference operation highlights the detailed information in the image, and obtain sharpened sub-images of multiple scales, which can highlight the texture, defects and other features of the gear surface. Apply nonlinear enhancement algorithm (such as Sigmoid function) to the generated sharpened sub-images of multiple scales, the nonlinear enhancement further amplifies the detailed information in the image, highlights the features of the gear surface, and obtains multiple sub-images after detail enhancement, which provides a basis for subsequent brightness optimization. Perform weighted superposition of multiple detail enhancement sub-images, and adjust the weights according to the importance of different scales to highlight the key detail information, and obtain a sharpened enhanced image that integrates multi-scale detail information. Apply global histogram equalization to the multi-scale sharpened enhanced image, the histogram equalization adjusts the brightness distribution of the image to make it more uniform and clear, and obtains a brightness-optimized gear image, which provides more suitable image data for subsequent defect detection and analysis.

[0035] In this embodiment, refer to Figure 3 , is a flowchart of detailed implementation steps of step S2. In this embodiment, the detailed implementation steps of step S2 include: Step S21: performing gear tooth profile visual recognition on the brightness optimized gear image to extract the gear tooth profile line; Step S22: Analyze the characteristic points of the gear tooth profile and mark the key characteristic points of the tooth profile; Step S23: performing gear helix fitting calculation on key characteristic points of the tooth profile to obtain characteristic parameters of the tooth profile helix; Step S24: performing gear three-dimensional structure layout analysis on the brightness optimized gear image, thereby obtaining gear three-dimensional structure data; Step S25: reconstructing the three-dimensional point cloud of the gear three-dimensional structure data according to the characteristic parameters of the tooth profile helix line, and constructing a three-dimensional result model of the gear; Step S26: Calculate the gear overall deviation of the tooth profile helix characteristic parameters based on the preset theoretical design gear parameters to generate a helix total deviation curve.

[0036] In this embodiment, an image edge detection algorithm (such as the Canny operator) is used to process the generated brightness optimized gear image. The gear tooth profile contour is extracted through edge detection. These tooth profiles provide basic data for subsequent feature analysis and three-dimensional reconstruction. The extracted tooth profiles are analyzed and marked with key feature points, and key feature points such as the tooth top and tooth bottom are identified to provide a reference for subsequent spiral line fitting. These key feature points carry important information about the geometric size and shape of the gear. Using the marked key feature points of the tooth profile, a curve fitting algorithm (such as the least squares method) is used to calculate the parameters of the spiral line equation for these feature points to obtain the characteristic parameters of the tooth profile spiral line. These spiral line characteristic parameters include the spiral angle, pitch, etc., which reflect the geometry of the gear. What characteristics are obtained, the gear image is optimized by brightness, and combined with the known basic parameters of the gear (module, number of teeth, etc.), the three-dimensional structural data of the gear (such as point cloud model) is constructed through the three-dimensional visual reconstruction algorithm. These three-dimensional structural data provide the basis for subsequent three-dimensional reconstruction. The three-dimensional structural data is reconstructed based on the characteristic parameters of the tooth profile helix line. By applying the helix line characteristic parameters to the three-dimensional model, a more accurate three-dimensional result model of the gear is constructed. This three-dimensional model better reflects the actual geometric shape of the gear. The actual tooth profile helix line characteristic parameters are compared with the theoretical design parameters, and the deviation between the two is calculated and drawn into a total deviation curve. This deviation curve reflects the difference between the actual gear and the theoretical design, providing a basis for quality analysis.

[0037] In this embodiment, the specific steps of step S26 are: Based on the preset theoretical design gear parameters, the directional inclination angle deviation of the tooth profile helix characteristic parameters is calculated to obtain the helix inclination deviation; Perform quadrant plane projection on the preset theoretical design gear parameters to obtain theoretical helix projection data; The tooth surface crest is calculated on the theoretical helix projection data to obtain the theoretical tooth surface crest parameters; The morphological difference of the characteristic parameters of the tooth profile helix is ​​calculated based on the theoretical tooth surface crest parameters to obtain the helix shape deviation. Repeat the above operation to calculate the helical inclination deviation and helical shape deviation of all tooth profiles; Performing curve fitting on the helical inclination deviation and helical shape deviation of all tooth profiles, thereby obtaining a helical inclination deviation curve and a helical shape deviation curve; The helix tilt deviation curve and the helix shape deviation curve are fused by comprehensive deviation characterization to generate the helix total deviation curve.

[0038] In this embodiment, the actual tooth profile helix characteristic parameters are compared with the theoretical design parameters, and the directional inclination angle difference between the actual helix and the theoretical helix is ​​calculated as the helix inclination deviation. This inclination deviation reflects the geometric deviation in the actual gear manufacturing process. According to the theoretical design parameters, the gear three-dimensional model is projected onto different quadrant planes to obtain theoretical spiral trajectory data on these projection planes. These projection data provide a benchmark for subsequent tooth surface peak calculations. The obtained theoretical spiral projection data is analyzed to calculate the tooth surface peaks on different projection planes, and the geometric parameters of these theoretical tooth surface peaks, such as peak height, peak spacing, etc., are obtained. These theoretical tooth surface peak parameters will be used as a benchmark for comparison with the actual deviation, and the obtained theoretical tooth surface peak parameters will be compared with the actual tooth profile helix characteristic parameters. , calculate the difference between the actual helix shape and the theoretical model, and get the helix shape deviation. This shape deviation reflects the degree of deviation between the actual tooth profile and the theoretical design. Repeat the above calculation process for all tooth profiles on the gear, and get the helix inclination deviation and shape deviation data of each tooth profile. These data provide the basis for subsequent curve fitting. Use curve fitting algorithm (such as polynomial fitting) to analyze all the deviation data obtained, and fit the change curve of the helix inclination deviation and the change curve of the helix shape deviation. These curves provide a basis for the final total deviation evaluation. Combine the two deviation curves and fuse them by weighted average and other methods. After fusion, get a helix total deviation curve reflecting the overall deviation change. This total deviation curve provides overall data support for the quality analysis of small modulus gears.

[0039] In this embodiment, refer to Figure 4 , is a flowchart of detailed implementation steps of step S3. In this embodiment, the detailed implementation steps of step S3 include: Step S31: performing gear center shrinkage difference analysis based on the helix tilt deviation curve to obtain gear shrinkage difference data; Step S32: performing an asymmetry trend analysis on the helical shape deviation curve to obtain asymmetry trend data; Step S33: performing tooth surface comprehensive shrinkage effect identification on the helix total deviation curve to obtain tooth surface comprehensive shrinkage effect data; Step S34: performing deviation change trend analysis on the gear shrinkage difference data, asymmetry trend data and tooth surface comprehensive shrinkage effect data, thereby generating helix deviation trend data; Step S35: mining the nonlinear contraction characteristics of the spiral deviation situation data to obtain the nonlinear contraction law. In this embodiment, the helix inclination deviation curve is analyzed to identify the deviation change trend of different positions (such as tooth top, tooth root, etc.), and the shrinkage difference of different positions, that is, the shrinkage difference between the center and the edge of the gear, is calculated according to the deviation change trend, and data characterizing the gear shrinkage difference is obtained to provide a basis for subsequent comprehensive analysis. The obtained helix shape deviation curve is analyzed to identify the asymmetric characteristics of the curve, and the asymmetric trend of the shape deviation is calculated according to the asymmetry degree of the curve, and data characterizing the asymmetry of the shape deviation is obtained to provide a basis for subsequent comprehensive analysis. The total deviation curve of the helix is ​​analyzed to identify the overall change trend of the curve, and the total deviation curve is calculated according to the change trend of the total deviation curve. Potential, calculate the comprehensive shrinkage effect of the tooth surface, and obtain data that characterizes the comprehensive shrinkage effect of the tooth surface, providing a basis for subsequent comprehensive analysis. The three types of data obtained are comprehensively analyzed to identify the relationship and overall change trend between various deviation data, and obtain a set of data reflecting the overall situation of the helix deviation, which provides a basis for subsequent nonlinear feature mining. Nonlinear analysis methods, such as neural networks and fuzzy logic, are used to deeply mine the obtained deviation situation data, find out various nonlinear factors that affect the helix deviation, and extract the corresponding nonlinear shrinkage law, and obtain a set of nonlinear models or empirical formulas that can be used to predict and control the manufacturing deviation of small modulus gears.

[0040] In this embodiment, step S4 includes the following steps: Step S41: Calculate the gear profile parameters one by one on the gear three-dimensional result model to generate multiple gear profile parameters; Step S42: calculating the environmental shrinkage amount according to the nonlinear shrinkage law to generate the current environmental shrinkage amount; Step S43: performing error compensation calculation on multiple gear tooth profile parameters based on the current environmental shrinkage amount to generate a helical shrinkage error compensation value.

[0041] In this embodiment, each tooth profile in the three-dimensional model is parametrically calculated to obtain multiple tooth profile geometric parameters including tooth profile curve, tooth height, tooth top circle diameter, tooth root circle diameter, etc. These tooth profile geometric parameters are organized into a complete data set as the basis for subsequent error compensation. The overall shrinkage in the current environment is predicted according to the nonlinear shrinkage law, that is, the current environmental shrinkage. This shrinkage reflects the overall impact of the current manufacturing environment on the gear size and is the key basis for error compensation. Error compensation is performed using each tooth profile geometric parameter in combination with the calculated current environmental shrinkage. For each tooth profile parameter, the value that should be compensated is calculated to offset the error caused by environmental shrinkage, and a complete set of spiral shrinkage error compensation values ​​is organized to provide a basis for subsequent process optimization.

[0042] In this embodiment, the helical shrinkage error compensation value includes the upper and lower reducer compensation value of the tooth width, the waist compensation value of the middle of the tooth width and the helical inclination compensation value; the specific steps of step S5 are: Based on the upper and lower tooth width compensation values ​​and the middle tooth width waist compensation value, reverse compensation and fine-tuning processing is performed on the small modulus gear to be tested; Perform helix angle inclination correction on the small modulus gear to be inspected according to the helix line inclination compensation value; Based on the above operation, a corrected small modulus gear is obtained; The helix characteristic parameters of the corrected small-module gear are identified to obtain the corrected gear helix parameters.

[0043] In this embodiment, the upper and lower tooth width compensation values ​​obtained are used to reversely correct the upper and lower parts of the tooth width of the small modulus gear to be tested to offset the tooth width deviation caused by manufacturing errors. At the same time, the middle waist shrinkage compensation value of the tooth width is used to reversely correct the middle part of the tooth width of the small modulus gear to be tested to correct the middle waist shrinkage problem caused by manufacturing errors. Through these two steps of reverse correction and fine-tuning processing, the tooth width size of the small modulus gear is corrected. The helix shrinkage error compensation value obtained is used to correct the helix angle of the small modulus gear to be tested. The electrode is processed according to the corrected helix, and the cavity is processed by electric spark discharge to obtain the corrected small modulus gear sample. The corrected small modulus gear is comprehensively measured using a three-dimensional measuring machine or other precision measuring equipment, and the measurement data is extracted and analyzed to obtain key parameters such as the helix and tooth position of the corrected gear. These parameters reflect the final state of the gear manufacturing quality and provide a basis for subsequent performance evaluation and application.

[0044] In this embodiment, the specific steps of step S6 are: According to the preset theoretical design gear parameters, the helix accuracy correction calculation is performed on the corrected gear helix parameters to generate the helix accuracy correction deviation value; Based on the preset gear deviation accuracy range, the helix accuracy correction deviation value is corrected and evaluated to obtain a helix deviation correction result; the helix deviation correction result includes a deviation correction pass and a deviation correction failure; When the helical line deviation correction result is a deviation correction failure, repeat the error parameter fine-tuning correction, and perform the correction evaluation again until the helical line deviation correction result is a deviation correction pass; When the helix deviation correction result is qualified, the helix deviation correction operation of the small modulus gear is completed.

[0045] In this embodiment, according to the theoretical design parameters of the gear (such as module, number of teeth, helix angle, etc.), the theoretical helix accuracy index is calculated, and then the actual helix parameters of the corrected gear are compared and analyzed with the theoretical design parameters, and the deviation between the actual helix and the theoretical value, that is, the helix accuracy correction deviation value, is calculated to quantify the difference between the actual manufacturing accuracy of the gear and the design requirements, and provide a basis for subsequent deviation correction. According to the predetermined allowable range of gear deviation (usually given by standards or customer requirements), the calculated helix accuracy correction deviation value is evaluated. If the deviation value is within the allowable range, it is judged as "deviation correction qualified"; if the deviation value exceeds the allowable range, it is judged as " Deviation correction failed", judge whether the current manufacturing status meets the requirements, and provide a basis for subsequent correction operations. If the evaluation result is "deviation correction failed", it is necessary to repeat the difference parameter fine-tuning correction and correction evaluation operations. The specific method is to readjust the parameter compensation value used, and calculate the helix accuracy correction deviation again, and then evaluate. Repeat this cycle until the helix deviation correction result is "deviation correction qualified". By continuously optimizing the compensation parameters, the deviation accuracy of the gear helix can finally meet the requirements. When the evaluation result is "deviation correction qualified", it means that the current gear helix accuracy has met the design requirements. At this time, the entire small modulus gear helix deviation correction operation is completed, and the final corrected gear is obtained.

[0046] In this embodiment, a small modulus gear helix deviation assessment and correction system based on visual measurement is provided, which is used to perform the correction method of the helix deviation of the small modulus plastic cylindrical gear as described above, including: An image enhancement module is used to obtain multi-directional images of small-modulus gears to be detected; perform global histogram equalization on the multi-directional images of small-modulus gears to be detected to construct brightness-optimized gear images; The helix deviation module is used to reconstruct the 3D point cloud of the brightness optimized gear image, calculate the overall gear deviation, and generate the helix total deviation curve; The nonlinear shrinkage module is used to analyze the deviation change trend of the total deviation curve of the spiral line and to mine the nonlinear shrinkage characteristics to obtain the nonlinear shrinkage law; The error compensation module is used to calculate the error compensation of the gear three-dimensional result model according to the nonlinear shrinkage law and generate the helical shrinkage error compensation value; An error correction module is used to fine-tune the error parameters of the small-module gear to be inspected based on the helix contraction error compensation value, so as to obtain the corrected gear helix parameters; The accuracy correction evaluation module is used to perform helix accuracy correction evaluation on the corrected gear helix parameters, obtain the helix deviation correction result, and complete the small modulus gear helix deviation correction operation.

[0047] The invention obtains multi-directional images of small-module gears to be detected, provides required input data for subsequent analysis, performs global histogram equalization on the images to improve the contrast and brightness of the images, highlights the detailed features of the gears, and provides better input for subsequent analysis and processing. The invention performs three-dimensional point cloud reconstruction and deviation calculation on the brightness-optimized gear images, obtains the overall deviation of the gears, generates a total helix deviation curve, quantifies the deviation of the gears, and provides a basis for subsequent analysis and correction. The invention mines the nonlinear shrinkage feature of the total helix deviation curve to reveal the nonlinear variation law of the gear deviation, understands the gear deviation more comprehensively, and provides guidance for subsequent error compensation. The invention performs error compensation calculation according to the nonlinear shrinkage law to generate a helix shrinkage error compensation value for correcting the error of the gear model. The error compensation improves the accuracy and precision of gear detection. The gear parameters are fine-tuned and corrected based on the error compensation value to obtain the corrected helix parameters, improve the accuracy of the gear parameters, ensure the accuracy and stability of the gears during use, perform precision correction evaluation on the corrected helix parameters to obtain the helix deviation correction result, ensure the quality and accuracy of the gears, verify whether the corrected gear parameters meet the requirements, and improve the tooth profile accuracy of the gears.

[0048] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0049] The above is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for correcting the helix deviation of a small-modulus plastic cylindrical gear, characterized in that: The following steps are involved: Step S1: Acquire a multi-directional image of a small modulus gear to be detected; perform global histogram equalization on the multi-directional image of the small modulus gear to be detected to construct a brightness optimized gear image; Step S2: reconstructing the three-dimensional point cloud of the brightness optimized gear image, calculating the overall gear deviation, and generating a helix total deviation curve; Step S3: Analyze the deviation change trend of the total deviation curve of the spiral line, and mine the nonlinear shrinkage characteristics to obtain the nonlinear shrinkage law; Step S4: Calculate the error compensation of the gear three-dimensional result model according to the nonlinear shrinkage law to generate a helical shrinkage error compensation value; Step S5: fine-tuning and correcting the error parameters of the small-module gear to be inspected based on the helix contraction error compensation value, thereby obtaining the corrected gear helix parameters; Step S6: Perform helix accuracy correction evaluation on the corrected gear helix parameters to obtain helix deviation correction results, thereby completing the small-module gear helix deviation correction operation.

2. The method for correcting the helix deviation of a small-modulus plastic cylindrical gear according to claim 1, characterized in that: The specific steps of step S1 are: Step S11: Acquire multi-directional images of the small-module gear to be inspected; Step S12: performing multi-scale Gaussian blur processing on the multi-directional images of the small modulus gear to be inspected to generate multiple multi-scale blurred images; Step S13: performing original image difference calculation on multiple multi-scale fuzzy image sets to obtain multiple scale sharpened sub-images; Step S14: performing nonlinear detail enhancement on the multiple scale sharpened sub-images to obtain multiple sharpened detail enhanced sub-images; Step S15: weighting multiple sharpening detail enhancement sub-images to generate a multi-scale sharpening enhancement image; Step S16: Perform global histogram equalization on the multi-scale sharpening enhancement image to construct a brightness optimized gear image.

3. The method for correcting the helix deviation of a small-modulus plastic cylindrical gear according to claim 1, characterized in that: The specific steps of step S2 are: Step S21: performing gear tooth profile visual recognition on the brightness optimized gear image to extract the gear tooth profile line; Step S22: Analyze the characteristic points of the gear tooth profile and mark the key characteristic points of the tooth profile; Step S23: performing gear helix fitting calculation on key characteristic points of the tooth profile to obtain characteristic parameters of the tooth profile helix; Step S24: performing gear three-dimensional structure layout analysis on the brightness optimized gear image, thereby obtaining gear three-dimensional structure data; Step S25: reconstructing the three-dimensional point cloud of the gear three-dimensional structure data according to the characteristic parameters of the tooth profile helix line, and constructing a three-dimensional result model of the gear; Step S26: Calculate the gear overall deviation of the tooth profile helix characteristic parameters based on the preset theoretical design gear parameters to generate a helix total deviation curve.

4. The method for correcting the helix deviation of a small-modulus plastic cylindrical gear according to claim 3, characterized in that: The specific steps of step S26 are: Based on the preset theoretical design gear parameters, the directional inclination angle deviation of the tooth profile helix characteristic parameters is calculated to obtain the helix inclination deviation; Perform quadrant plane projection on the preset theoretical design gear parameters to obtain theoretical helix projection data; The tooth surface crest is calculated on the theoretical helix projection data to obtain the theoretical tooth surface crest parameters; The morphological difference of the characteristic parameters of the tooth profile helix is ​​calculated based on the theoretical tooth surface crest parameters to obtain the helix shape deviation. Repeat the above operation to calculate the helical inclination deviation and helical shape deviation of all tooth profiles; Performing curve fitting on the helical inclination deviation and helical shape deviation of all tooth profiles, thereby obtaining a helical inclination deviation curve and a helical shape deviation curve; The helix tilt deviation curve and the helix shape deviation curve are fused by comprehensive deviation characterization to generate the helix total deviation curve.

5. The method for correcting the helix deviation of a small-modulus plastic cylindrical gear according to claim 1, characterized in that: The specific steps of step S3 are: Step S31: performing gear center shrinkage difference analysis based on the helix tilt deviation curve to obtain gear shrinkage difference data; Step S32: performing an asymmetry trend analysis on the helical shape deviation curve to obtain asymmetry trend data; Step S33: performing tooth surface comprehensive shrinkage effect identification on the helix total deviation curve to obtain tooth surface comprehensive shrinkage effect data; Step S34: performing deviation change trend analysis on the gear shrinkage difference data, asymmetry trend data and tooth surface comprehensive shrinkage effect data, thereby generating helix deviation trend data; Step S35: mining the nonlinear contraction characteristics of the spiral deviation situation data to obtain the nonlinear contraction law.

6. The method for correcting the helix deviation of a small-modulus plastic cylindrical gear according to claim 1, characterized in that: The specific steps of step S4 are: Step S41: Calculate the gear profile parameters one by one on the gear three-dimensional result model to generate multiple gear profile parameters; Step S42: calculating the environmental shrinkage amount according to the nonlinear shrinkage law to generate the current environmental shrinkage amount; Step S43: performing error compensation calculation on multiple gear tooth profile parameters based on the current environmental shrinkage amount to generate a helical shrinkage error compensation value.

7. The method for correcting the helix deviation of a small-modulus plastic cylindrical gear according to claim 1, characterized in that: The helical line shrinkage error compensation value includes the upper and lower tooth width reducer compensation value, the middle tooth width waist compensation value and the helical line inclination compensation value; the specific steps of step S5 are: Based on the upper and lower tooth width compensation values ​​and the middle tooth width waist compensation value, reverse compensation and fine-tuning processing is performed on the small modulus gear to be tested; Perform helix angle inclination correction on the small modulus gear to be inspected according to the helix line inclination compensation value; Based on the above operation, a corrected small modulus gear is obtained; The helix characteristic parameters of the corrected small-module gear are identified to obtain the corrected gear helix parameters.

8. The method for correcting the helix deviation of a small-modulus plastic cylindrical gear according to claim 1, characterized in that: The specific steps of step S6 are: According to the preset theoretical design gear parameters, the helix accuracy correction calculation is performed on the corrected gear helix parameters to generate the helix accuracy correction deviation value; Based on the preset gear deviation accuracy range, the helix accuracy correction deviation value is corrected and evaluated to obtain a helix deviation correction result; the helix deviation correction result includes a deviation correction pass and a deviation correction failure; When the helical line deviation correction result is a deviation correction failure, repeat the error parameter fine-tuning correction, and perform the correction evaluation again until the helical line deviation correction result is a deviation correction pass; When the helix deviation correction result is qualified, the helix deviation correction operation of the small modulus gear is completed.

9. A device for correcting the helical deviation of a small-modulus plastic cylindrical gear, characterized in that: The method for correcting the helix deviation of a small-modulus plastic cylindrical gear as claimed in claim 1 comprises: An image enhancement module is used to obtain multi-directional images of small-modulus gears to be detected; perform global histogram equalization on the multi-directional images of small-modulus gears to be detected to construct brightness-optimized gear images; The helix deviation module is used to reconstruct the 3D point cloud of the brightness optimized gear image, calculate the overall gear deviation, and generate the helix total deviation curve; The nonlinear shrinkage module is used to analyze the deviation change trend of the total deviation curve of the spiral line and to mine the nonlinear shrinkage characteristics to obtain the nonlinear shrinkage law; The error compensation module is used to calculate the error compensation of the gear three-dimensional result model according to the nonlinear shrinkage law and generate the helical shrinkage error compensation value; An error correction module is used to fine-tune the error parameters of the small-module gear to be inspected based on the helix contraction error compensation value, so as to obtain the corrected gear helix parameters; The accuracy correction evaluation module is used to perform helix accuracy correction evaluation on the corrected gear helix parameters, obtain the helix deviation correction result, and complete the small modulus gear helix deviation correction operation.

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