Young modulus measurement method using blade edge sub-pixel level tracking technology

Abstract

The invention relates to the technical field of physical experiment measurement, and discloses a Young modulus measurement method by utilizing a blade edge sub-pixel level tracking technology, which comprises the following steps of: 1, constructing an experiment device and inputting experiment parameters; step 2, acquiring an image sequence and preprocessing the image sequence; step 3, carrying out pixel equivalent calibration and selecting a region of interest; 4, displacement is extracted through edge detection and robust fitting, specifically, a horizontally-arranged cutting edge is fixed to the position of a lower chuck of the metal wire, a camera is arranged to directly face the edge of the cutting edge, and the focal length is kept fixed. Through a core algorithm combining edge detection, Hough transform and robust regression, sub-pixel-level precise positioning of the edge of the blade is realized, subjective interference caused by manual reading and manual calibration is effectively avoided, the accuracy and stability of a measurement result are remarkably improved, and meanwhile, by means of the suppression effect of the algorithm on noise points, the accuracy and stability of the measurement result are improved. And the resistance to environmental interference in the measurement process is enhanced.

Description

Technical Field

[0001] This invention relates to the field of physical experimental measurement technology, specifically to a method for measuring Young's modulus using sub-pixel-level tracking technology of a blade edge. Background Technology

[0002] In the field of physics experiments and the measurement of mechanical parameters of materials, Young's modulus, as a key parameter characterizing the elastic deformation properties of materials, has always been a focus of related research and teaching practice in terms of measurement accuracy and ease of operation. Among them, the measurement of Young's modulus of metal wire is one of the core contents of university physics experiments. Currently, the methods for measuring the Young's modulus of metal wires widely used in industry and teaching mainly include two categories: the optical lever method and the digital microscope method (manual calibration). The optical lever method uses a microscope scale to magnify the minute elongation of the metal wire to achieve measurement. This method requires the construction of a specific optical path, the adjustment process of which is complex and requires a high level of operational proficiency. At the same time, the measurement process is easily affected by environmental vibrations, the magnification itself has significant systematic errors, and the experimenter will also have additional errors due to parallax when reading the data. Subsequent data processing requires a lot of mechanical calculations, which not only increases the burden on the experimenter but also easily leads to calculation errors. The digital microscope method (manual calibration) uses a digital microscope to photograph the micrometer to achieve digital measurement, but its equipment is expensive. It requires a dedicated digital microscope and a computer to form a "one machine, one computer" configuration. During the experiment, after each addition or subtraction of weights, the experimenter needs to manually click on the calibration feature point on the computer screen to record the pixel position. The operation process is cumbersome and the experimental pace is tight. The manual clicking calibration method also introduces subjective errors and cannot achieve automated batch processing of the measurement process, making it difficult to meet the needs of efficient and accurate measurement.

[0003] Given the shortcomings of existing technologies in terms of ease of operation, measurement accuracy, equipment cost, and automation, there is an urgent need to develop a Young's modulus measurement method that can automatically extract minute displacements of metal wires, eliminate human reading errors, lower the barrier to equipment use, and automatically generate experimental reports. This would compensate for the deficiencies of existing technologies and improve measurement efficiency and the reliability of results. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method for measuring Young's modulus using sub-pixel-level tracking technology of the blade edge, thus solving the problems mentioned in the background section.

[0005] To achieve the above objectives, the present invention provides a method for measuring Young's modulus using sub-pixel-level tracking technology of a cutting edge, comprising the following steps: Step 1: Set up the experimental setup and input the experimental parameters; Step 2: Acquire image sequences and perform preprocessing; Step 3: Perform pixel equivalent calibration and select the region of interest; Step 4: Extract displacement through edge detection and robust fitting; Step 5: Calculate the data and solve for Young's modulus; Step 6: Perform linear fitting and automatically generate an experimental report.

[0006] By sequentially executing the steps of experimental setup and parameter input, image sequence acquisition and preprocessing, pixel equivalent calibration and region of interest selection, edge detection and robust fitting displacement extraction, data calculation and Young's modulus solution, linear fitting and automatic generation of experimental reports, sub-pixel-level precise positioning of the blade edge is achieved, effectively avoiding subjective errors caused by manual operation and improving the accuracy and stability of measurement results. Furthermore, the fully automated process simplifies the experimental operation, reduces the mechanical workload of experimental personnel, and shortens the experimental cycle. It eliminates the need for expensive specialized equipment; only a common high-resolution camera and a modified, simple experimental setup are required to complete the measurement, lowering experimental costs and promotion barriers. Additionally, linear fitting verifies the reliability of measurement results and automatically generates standardized experimental reports, further enhancing the convenience and professionalism of the experiment and comprehensively addressing the shortcomings of existing measurement methods in terms of accuracy, efficiency, and cost.

[0007] Preferably, the experimental setup in step one involves fixing a horizontally placed blade and a vertically placed ruler at the lower clamp position of the metal wire, setting the camera to face the edge of the blade and keeping the focal length fixed; the input experimental parameters include the local latitude, the measurement result of the effective length of the metal wire, the diameter of the metal wire measured by the micrometer, and the zero-point error.

[0008] The above technical solution provides a stable and clear observation target for accurately capturing minute deformations of the metal wire. The fixed focal length ensures the consistency of image acquisition, and the comprehensive and accurate parameter input lays a reliable data foundation for subsequent Young's modulus calculation and error correction, ensuring the rigor of the measurement process.

[0009] Preferably, the image sequence acquired in step two includes a calibration image, an image of the weight-adding process, and an image of the weight-removing process, with a total of 19 images; preprocessing includes sorting the images by filename number, and if there is camera distortion, loading the camera intrinsic parameter matrix for distortion correction.

[0010] The above technical solutions fully cover the entire process of experimental loading and unloading, providing comprehensive sample support for subsequent data processing. Sequential sorting ensures the logical coherence of data processing, while camera distortion correction eliminates image distortion caused by equipment defects, ensuring the accuracy of subsequent edge detection and localization.

[0011] Preferably, in step three, the pixel equivalent calibration specifically involves the user selecting two points at a known distance on the steel ruler in the image, and the system calculating the scale; the region of interest selection specifically involves the user selecting a region containing the blade edge on the first image, and subsequent image processing is performed only within this region.

[0012] The above technical solution enables accurate conversion between pixels and actual physical distance, providing a key basis for precise calculation of displacement. The processing method of focusing on the region of interest effectively eliminates interference from background irrelevant information, while significantly reducing the amount of data processing and improving overall processing efficiency.

[0013] Preferably, in step four, edge detection uses the Canny operator to extract image edge features, and double thresholds are used to ensure edge connectivity. Specifically, edge extraction is achieved using the edge operator.

[0014] The above technical solution can accurately identify the key features of the blade edge. The setting of dual thresholds effectively avoids the occurrence of edge breakage or false edges, ensuring the integrity and continuity of the edge contour, and providing high-quality basic data for subsequent linear positioning and precise positioning.

[0015] Preferably, in step four, the coarse positioning of the straight line uses Hough transform to detect straight line segments in the edge, and a specific scoring mechanism is used to filter out long and horizontal line segments to lock the blade edge. The calculation formula for the scoring mechanism is as follows: Where L is the length of the line segment and angle is the angle between the line segment and the horizontal direction. Line segments with higher scores are selected first as the straight line segments corresponding to the edge of the target blade.

[0016] The above technical solution efficiently filters out effective straight line segments related to the blade edge from complex edge features, effectively eliminating invalid line segments caused by interference factors such as environmental noise and scratches on the blade surface, quickly locking the target area, and narrowing the range and improving efficiency for subsequent sub-pixel level fine positioning.

[0017] Preferably, the sub-pixel-level precise localization in step four specifically involves first converting the image to grayscale and cropping it according to the region of interest (ROI), then extracting the set of edge pixels near the Hough line, and using a robust regression algorithm to fit the pixels. The fitting equation is as follows: Finally, the intersection of the vertical line at the center of the region of interest and the fitted line is calculated as the precise vertical position of the blade. This process can effectively eliminate the interference of noise (outliers) such as dust and scratches on the blade surface, achieving sub-pixel level positioning accuracy.

[0018] The above technical solution breaks through the accuracy limitation of pixel-level positioning. The robust regression algorithm can effectively resist the interference of abnormal pixels and achieve ultra-precise positioning of the blade edge, thereby accurately capturing the tiny elongation of the metal wire and providing core guarantee for the high-precision calculation of Young's modulus.

[0019] Preferably, in step five, the average pixel displacement corresponding to each unit increase in mass of the metal wire is calculated using the successive difference method, and then converted into the actual elongation ΔL using the scale obtained in step three. Young's modulus is then calculated using relevant parameters. The formula for calculating Young's modulus is as follows: F is derived from the mass of the weight and the gravitational acceleration g automatically calculated based on the local latitude, A is the cross-sectional area of ​​the metal wire, and L is the effective length of the metal wire.

[0020] The above technical solution makes full use of the data collected throughout the process, effectively offsetting the impact of systematic and random errors, improving the accuracy of displacement calculation, and achieving a precise conversion from pixel displacement to actual physical elongation by combining the previously calibrated scale, thus ensuring the reliability of Young's modulus calculation results.

[0021] Preferably, in step five, the Type A and Type B uncertainties of the directly measured quantities (wire diameter d, effective wire length L, actual elongation ΔL) are automatically calculated, and the relative uncertainty of Young's modulus E is synthesized according to the uncertainty synthesis rules.

[0022] The above technical solution comprehensively considers various sources of error in the measurement process, scientifically evaluates the reliability of the measurement results, provides a quantitative basis for the accuracy of the experimental results, improves the scientificity and rigor of the entire measurement method, and meets the requirements of professional experiments for data integrity and reliability.

[0023] Preferably, in step six, linear fitting is performed based on the stress and strain data to generate a fitting curve. The slope of the fitting line is the Young's modulus of the metal wire. The measurement data, intermediate calculation results, uncertainty analysis, and fitting curve are automatically filled into a preset PDF template using a PDF engine to generate the final experimental report.

[0024] Through the above technical solutions, linear fitting intuitively presents the correspondence between stress and strain, further verifying the rationality of the Young's modulus results. The automated report generation function eliminates the tedious process of manually organizing data, drawing charts, and writing reports, greatly improving experimental efficiency while ensuring the standardization and completeness of experimental reports.

[0025] This invention provides a method for measuring Young's modulus using sub-pixel-level tracking technology at the edge of a cutting tool. It offers the following advantages: 1. This measurement method achieves sub-pixel-level precise positioning of the blade edge by combining core algorithms of edge detection, Hough transform and robust regression. It effectively avoids subjective interference caused by manual reading and calibration, significantly improving the accuracy and stability of the measurement results. At the same time, thanks to the algorithm's noise suppression effect, it enhances the resistance of the measurement process to environmental interference, ensuring that reliable data can still be obtained under normal experimental conditions.

[0026] 2. This invention automates the entire process from image acquisition and data processing to report generation, eliminating the need for manual intervention in tedious mechanical calculations and data organization. It significantly simplifies experimental procedures, reduces the operational burden and learning costs for experimenters, and shortens the overall experimental cycle, making the experimental process more efficient and convenient. It is suitable for large-scale teaching experiments or batch measurement scenarios.

[0027] 3. This invention does not rely on expensive dedicated digital microscope equipment. It only requires a regular high-resolution camera and an algorithm to complete the measurement. Moreover, the experimental device is simple to modify. It only requires the addition of a blade and a ruler to the existing equipment, which significantly reduces the cost of experimental equipment and makes it easy to promote and apply in various laboratories, thus expanding the applicability and popularity of this measurement method. Attached Figure Description

[0028] Figure 1 This is the overall flowchart of the present invention; Figure 2 This is a schematic diagram of the Young's modulus of the metal wire of the present invention. Detailed Implementation

[0029] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0030] Please see the appendix Figure 1 - Appendix Figure 2 This invention provides a method for measuring Young's modulus using sub-pixel-level tracking technology at the edge of a cutting tool, comprising the following steps: Step 1: Set up the experimental setup and input the experimental parameters; Fix a horizontally placed blade and a vertically placed ruler at the lower clamp position of the wire.

[0031] Position the camera (such as a regular high-resolution mobile phone or USB camera) directly at the edge of the blade, keeping the focus fixed.

[0032] Input experimental parameters through the human-computer interaction interface (GUI): local latitude ( The effective length of the metal wire (L), the diameter (d) measured by the micrometer screw gauge, and the zero-point error are all measured.

[0033] Innovation: The system automatically calculates the local gravitational acceleration g based on the input latitude, using the following formula: .

[0034] Step 2: Acquire and preprocess the image sequence; the acquired image sequence includes calibration images, images of the weight addition process, and images of the weight removal process, with a total of 19 images; the preprocessing includes sorting the images by file name number, and if there is camera distortion, loading the camera intrinsic parameter matrix for distortion correction.

[0035] Step 3: Perform pixel equivalent calibration and select the region of interest. Pixel equivalent calibration involves the user selecting two points at a known distance on the steel ruler in the image, and the system calculates the scale. The region of interest selection involves the user selecting a region containing the edge of the blade on the first image, and subsequent image processing is performed only within this region.

[0036] Step 4: Extract displacement through edge detection and robust fitting; For each acquired and preprocessed image sequence, the following automated processing flow is executed sequentially to achieve accurate displacement extraction: Grayscale conversion and ROI cropping: First, the color image is converted to grayscale to reduce the amount of data processing and highlight edge features; then, the grayscale image is cropped according to the region of interest (ROI) selected by the user in step three, retaining only the core area containing the blade edge, eliminating background irrelevant information interference, and improving processing efficiency.

[0037] Edge detection: The Canny operator is used to extract image edge features, specifically through the edge(crop,'Canny',[0.1,0.3]) function. By setting dual thresholds (low threshold 0.1, high threshold 0.3), the connectivity of the edge contour is ensured, avoiding edge breaks or false edges, and providing high-quality basic data for subsequent line localization.

[0038] Coarse Line Localization: The Hough Transform is used to detect straight line segments in the edge, and a specific scoring mechanism is used to filter out valid line segments that match the blade edge. The scoring mechanism formula is score=0.7*L+0.3*(20-angle), where L is the line segment length and angle is the angle between the line segment and the horizontal direction. Line segments with high scores, long lengths, and horizontal orientations are prioritized, effectively eliminating invalid line segments caused by interference factors such as environmental noise and scratches on the blade surface, and quickly locating the straight line area corresponding to the blade edge.

[0039] Subpixel-level precise localization: Extract the set of edge pixels near the Hough line, and use a robust regression algorithm (code calls the robustfit function) to fit the pixels, with the fitting equation being x=ay+b; this algorithm can effectively resist the interference of abnormal pixels (noise), break through the accuracy limitation of pixel-level localization, and achieve subpixel-level precise fitting; finally, calculate the intersection point of the vertical line at the center of the region of interest and the fitted line, and take this intersection point as the precise vertical position of the blade at that moment, completing the displacement feature extraction of a single image.

[0040] Step 5: Calculate the data and solve for Young's modulus; Based on the vertical position data of the cutting edge extracted in step four, and combined with experimental parameters and calibration results, the calculation and error analysis of Young's modulus are completed through the following process: Actual elongation conversion: The vertical position data of the blade corresponding to all images during loading and unloading are processed using the successive difference method to calculate the average pixel displacement corresponding to each unit increase in mass (e.g., 5 kg) of the metal wire; then, using the scale obtained in step three, the average pixel displacement is converted into the actual elongation ΔL (unit: mm) of the metal wire. The successive difference method effectively offsets the influence of systematic and random errors, improving the accuracy of displacement calculation.

[0041] The core calculation of Young's modulus is based on Hooke's law and the definition of Young's modulus, E=(F·L) / (A·ΔL), where: F is the tension in the metal wire, which is obtained by multiplying the mass of the weight by the gravitational acceleration g (calculated automatically based on the local latitude input in step one, using the formula g=9.7803(1+0.0053024sin²φ-0.000005sin²2φ), where φ is the local latitude); L is the effective length of the metal wire input in step one; A is the cross-sectional area of ​​the metal wire, which is calculated from the wire diameter d input in step one using A=π(d / 2)².

[0042] Uncertainty assessment: The system automatically calculates the Type A uncertainty (derived from the statistical distribution of the measurement data) and Type B uncertainty (derived from factors such as the accuracy of the measuring instrument and environmental influence) of the directly measured quantities (diameter d of the metal wire, effective length L of the metal wire, and actual elongation ΔL). Then, according to the uncertainty synthesis rules, it calculates and synthesizes the relative uncertainty of Young's modulus E, providing a quantitative assessment basis for the reliability of the experimental results.

[0043] Step Six: Perform linear fitting and automatically generate an experimental report. The average pixel displacement corresponding to each unit increase in the mass of the metal wire is calculated using the successive difference method, then converted to actual elongation using a scale bar, and Young's modulus is calculated based on relevant parameters. Type A and Type B uncertainties of the directly measured quantities are automatically calculated, and the relative uncertainty of Young's modulus is synthesized. Linear fitting is performed based on stress and strain data to generate a fitting curve, further verifying Young's modulus (slope). The measurement data, intermediate calculation results, uncertainty analysis, and fitting curve are automatically filled into a preset PDF template using a PDF engine to generate the final experimental report.

[0044] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for measuring Young's modulus using sub-pixel-level tracking technology of a cutting edge, characterized in that, Includes the following steps: Step 1: Set up the experimental setup and input the experimental parameters; Step 2: Acquire image sequences and perform preprocessing; Step 3: Perform pixel equivalent calibration and select the region of interest; Step 4: Extract displacement through edge detection and robust fitting; Step 5: Calculate the data and solve for Young's modulus; Step 6: Perform linear fitting and automatically generate an experimental report.

2. The method for measuring Young's modulus using sub-pixel-level tracking technology at the blade edge according to claim 1, characterized in that, In step one, the experimental setup is constructed by fixing a horizontally placed blade and a vertically placed ruler at the lower clamp position of the metal wire, setting the camera to face the edge of the blade and keeping the focal length fixed; the input experimental parameters include the local latitude, the measurement result of the effective length of the metal wire, the diameter of the metal wire measured by the micrometer, and the zero-point error.

3. The method for measuring Young's modulus using sub-pixel-level tracking technology at the blade edge according to claim 1, characterized in that, The image sequence acquired in step two includes calibration images, images of the weight addition process, and images of the weight removal process, totaling 19 images. Preprocessing includes sorting the images by filename number, and if camera distortion exists, loading the camera intrinsic parameter matrix for distortion correction.

4. The method for measuring Young's modulus using sub-pixel-level tracking technology at the blade edge according to claim 1, characterized in that, In step three, the pixel equivalent calibration specifically involves the user selecting two points at a known distance on the steel ruler in the image, and the system calculating the scale. The region of interest selection specifically involves the user selecting a region containing the edge of the blade on the first image, and subsequent image processing is performed only within this region, which greatly improves the efficiency of digital image processing and effectively reduces the requirements for computer hardware.

5. The method for measuring Young's modulus using sub-pixel-level tracking technology at the blade edge according to claim 1, characterized in that, In step four, the coarse positioning of the straight line uses Hough transform to detect straight line segments in the edge, and a specific scoring mechanism is used to filter out long and horizontal line segments to lock the blade edge. The calculation formula for the scoring mechanism is as follows: Where L is the length of the line segment and angle is the angle between the line segment and the horizontal direction. Line segments with higher scores are selected first as the straight line segments corresponding to the edge of the target blade.

6. The method for measuring Young's modulus using sub-pixel-level tracking technology at the blade edge according to claim 1, characterized in that, In step four, edge detection uses the Canny operator to extract image edge features, and double thresholds are used to ensure edge connectivity. Specifically, edge extraction is achieved using the edge operator.

7. The method for measuring Young's modulus using sub-pixel-level tracking technology at the blade edge according to claim 1, characterized in that, The sub-pixel-level precise localization in step four specifically involves first converting the image to grayscale and cropping it according to the region of interest (ROI). Then, the edge pixel set near the Hough line is extracted, and a robust regression algorithm is used to fit the pixel data. The fitting equation is as follows: Finally, the intersection of the vertical line at the center of the region of interest and the fitted line is calculated as the precise vertical position of the blade. This process can effectively eliminate the interference of noise (outliers) such as dust and scratches on the blade surface, achieving sub-pixel level positioning accuracy.

8. The method for measuring Young's modulus using sub-pixel-level tracking technology at the blade edge according to claim 1, characterized in that, In step five, the average pixel displacement corresponding to each unit increase in mass of the metal wire is calculated using the successive difference method. Then, the scale obtained in step three is used to convert this displacement into the actual elongation ΔL. Combined with relevant parameters, Young's modulus is calculated. The formula for calculating Young's modulus is: F is derived from the mass of the weight and the gravitational acceleration g automatically calculated based on the local latitude, A is the cross-sectional area of ​​the metal wire, and L is the effective length of the metal wire.

9. The method for measuring Young's modulus using sub-pixel-level tracking technology at the blade edge according to claim 1, characterized in that, In step five, the Type A and Type B uncertainties of the directly measured quantities (wire diameter d, effective wire length L, actual elongation ΔL) are automatically calculated, and the relative uncertainty of Young's modulus E is synthesized according to the uncertainty synthesis rules.

10. The method for measuring Young's modulus using sub-pixel-level tracking technology at the blade edge according to claim 1, characterized in that, In step six, linear fitting is performed based on the stress and strain data to generate a fitting curve. The slope of the fitting line is the Young's modulus of the metal wire. The measurement data, intermediate calculation results, uncertainty analysis and fitting curve are automatically filled into the preset PDF template using the PDF engine to generate the final experimental report.

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