Effective tear path length calculation method

Abstract

The invention relates to the technical field of tensile test samples, in particular to an effective tear path length calculation method, which comprises the following steps of: 1, performing a tensile test on an original sample; step 2, rearranging the torn samples; 3, acquiring image information of the rearranged samples; 4, performing contour extraction on the image information of the sample; and 5, calculating the effective tearing path length leff, thereby realizing accurate calculation of the effective tearing path length.

Description

Technical Field

[0001] The invention relates to the technical field of tensile test samples, and in particular to a method for calculating an effective tear path length. Background Art

[0002] In traditional tensile tests, the tear strength and maximum elongation of the sample are the two most commonly used indicators of concern. To a certain extent, they can reflect the toughness and durability of the material. High tear strength means that the material is not easy to tear when subjected to concentrated stress, while high maximum elongation means that the material can withstand large deformation when stretched and is not easy to break. In addition, the area of ​​the tear fracture is also one of the important indicators for evaluating the tear resistance of the material, which reflects the difficulty of the tear propagating in the material. The calculation of the tear fracture area is usually the product of the tear path length and the sample thickness.

[0003] At present, for the calculation of the tear fracture area, such as the method for automatically measuring the percentage of shear area of ​​the section of a drop weight tear test with publication number CN111754563A, the fracture image is first collected, and then the fracture area is calculated. However, after the sample is stretched and torn, the tearing path is irregular, and the fracture is not flat, but uneven. The collected fracture image is two-dimensional (the collected image can be understood as a fracture cross section), which cannot truly reflect the tearing path of the fracture. Based on this, the calculated fracture area has a large deviation, and the accuracy is low when performing subsequent toughness and durability evaluations. Summary of the invention

[0004] The technical problem to be solved by the present invention is to overcome the deficiencies of the prior art and provide a method for calculating the effective tear path length.

[0005] The technical solution adopted by the present invention to solve the technical problem is: the effective tear path length calculation method includes the following steps:

[0006] Step 1: Perform tensile test on the original sample;

[0007] Step 2: Rearrange the torn samples;

[0008] Step 3: Obtain image information of the rearranged sample;

[0009] Step 4: Extract the contour of the sample image information;

[0010] Step 5: Calculate the effective tear path length l eff .

[0011] In the step 1, the original sample has an original incision, and the length of the original incision is recorded as l0;

[0012] The original sample was placed in a tensile machine for tensile testing.

[0013] The step 2 includes the following sub-steps:

[0014] 2-1: The two parts of the original sample after tearing are recorded as fragment A and fragment B respectively;

[0015] 2-2: Rearrange fragment A and fragment B;

[0016] 2-3: Let the length of the rearranged sample be d.

[0017] In 2-2, when fragment A and fragment B are rearranged, the following rules are followed:

[0018] The ends of segment A and segment B facing away from the tearing path are spliced ​​together and aligned.

[0019] In the step 3, the rearranged sample is placed on a pure color background to perform image acquisition;

[0020] The color of the pure color background is different from the sample color, so that the subsequent contour extraction is convenient.

[0021] The step 4 includes the following sub-steps:

[0022] 4-1: Convert the acquired image into a grayscale image;

[0023] 4-2: Convert the grayscale image into a binary image;

[0024] 4-3: Determine the sample contour based on the binary image;

[0025] 4-4: Calculate the perimeter S of the sample contour.

[0026] In 4-4, the perimeter S of the sample contour is calculated based on the cv2.arcLength() function. For a closed contour, cv2.arcLength() adds the Euclidean distance between each adjacent point (pixel) on the contour to calculate the total perimeter.

[0027] The 4-4 comprises the following sub-steps:

[0028] 4-4-1: Determine a calibration object whose size is known, and calculate the pixel size corresponding to the size of the calibration object;

[0029] 4-4-2: The size of a unit pixel is the quotient of the size of the calibration object and the pixel corresponding to the size of the calibration object;

[0030] 4-4-3: Determine the pixels corresponding to the sample contour;

[0031] 4-4-4: The perimeter S of the sample contour is the product of the pixel size of the sample contour and the size of the unit pixel.

[0032] In order to convert pixels to actual sizes, this application requires a reference object of known size (usually called a calibration object), such as a ruler or an object of known size. Through this known object, the actual size of each pixel can be calculated. Further, assuming that the actual width of the calibration object is D actual , and the pixel corresponding to the width of the calibration object in the image is D pixel pixels, then the physical size of each pixel (that is, the size of the unit pixel) is

[0033] The perimeter S of the sample contour is calculated according to the following formula:

[0034]

[0035] Among them, S pixel The total pixels corresponding to the perimeter of the sample contour calculated by the cv2.arcLength() function.

[0036] The effective tearing path length l in step 5 eff The calculation formula is:

[0037]

[0038] Among them, l is the total length of the tear path, l0 is the original cut length, S is the perimeter of the sample contour, and d is the length of the rearranged sample.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] This application realizes accurate calculation of effective tear path length and improves calculation accuracy by rearranging the torn samples, collecting images from a top-down angle, and accurately calculating the sample contour. Finally, the sample contour perimeter, sample length, and original incision length are used to infer the tear path length, thereby achieving accurate calculation of effective tear path length and improving calculation accuracy. When calculating the fracture area, the effective fracture area can be determined based on the product of the calculated effective tear path length and the sample thickness, thereby improving the accuracy of subsequent evaluation of sample toughness and durability. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0042] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] Reference Figure 1 , the effective tear path length calculation method includes the following steps:

[0044] Step 1: Perform tensile test on the original sample;

[0045] In the step 1, the original sample has an original incision, and the length of the original incision is recorded as l0;

[0046] The original sample was placed in a tensile machine for tensile testing.

[0047] Step 2: rearrange the torn samples; the step 2 includes the following sub-steps:

[0048] 2-1: The two parts of the original sample after tearing are recorded as fragment A and fragment B respectively;

[0049] 2-2: Rearrange fragment A and fragment B; in 2-2, when fragment A and fragment B are rearranged, the following rules are followed:

[0050] The ends of segment A and segment B facing away from the tearing path are spliced ​​together and aligned.

[0051] 2-3: Let the length of the rearranged sample be d.

[0052] Step 3: Obtain image information of the rearranged sample; in the step 3, the rearranged sample is placed on a pure color background to perform image acquisition;

[0053] The color of the pure color background is different from the sample color, so that the subsequent contour extraction is convenient.

[0054] Step 4: Extract the contour of the sample image information; Step 4 includes the following sub-steps:

[0055] 4-1: Convert the acquired image into a grayscale image;

[0056] 4-2: Convert the grayscale image into a binary image;

[0057] 4-3: Determine the sample contour based on the binary image;

[0058] 4-4: Calculate the perimeter S of the sample contour.

[0059] In 4-4, the perimeter S of the sample contour is calculated based on the cv2.arcLength() function. For a closed contour, cv2.arcLength() adds the Euclidean distance between each adjacent point (pixel) on the contour to calculate the total perimeter.

[0060] The 4-4 comprises the following sub-steps:

[0061] 4-4-1: Determine a calibration object whose size is known, and calculate the pixel size corresponding to the size of the calibration object;

[0062] 4-4-2: The size of a unit pixel is the quotient of the size of the calibration object and the pixel corresponding to the size of the calibration object;

[0063] 4-4-3: Determine the pixels corresponding to the sample contour;

[0064] 4-4-4: The perimeter S of the sample contour is the product of the pixel size of the sample contour and the size of the unit pixel.

[0065] In order to convert pixels to actual sizes, this application requires a reference object of known size (usually called a calibration object), such as a ruler or an object of known size. Through this known object, the actual size of each pixel can be calculated. Further, assuming that the actual width of the calibration object is D actual , and the pixel corresponding to the width of the calibration object in the image is D pixel pixels, then the physical size of each pixel (that is, the size of the unit pixel) is

[0066] The perimeter S of the sample contour is calculated according to the following formula:

[0067]

[0068] Among them, S pixel The total pixels corresponding to the perimeter of the sample contour calculated by the cv2.arcLength() function.

[0069] Step 5: Calculate the effective tear path length l eff .

[0070] The effective tearing path length l in step 5 eff The calculation formula is:

[0071]

[0072] Among them, l is the total length of the tear path, l0 is the original cut length, S is the perimeter of the sample contour, and d is the length of the rearranged sample.

[0073] Basically, the effective tear path calculated by this method reflects the actual situation of the sample fracture (the concave-convexity of the fracture cross section). Then, the product of the effective tear path and the sample thickness is the actual area of ​​the fracture. The fracture area calculated in this way has a higher accuracy in evaluating the toughness and durability of the sample.

[0074] In this application, the folder containing the target tear sample photo set is placed in the current working directory;

[0075] Then place the effective tear path length calculation script tear_traditional_prediction.py in the current working directory, open it with a text editor and modify the value of the target_images_dir variable to the name of the folder containing the target tear sample photo set (for example: N098_100C), and at the same time, modify the value of the original_csv_path variable to point to the original tear data (*.csv) file exported by the tensile machine, and finally save the script.

[0076] Call the command prompt program (CMD) in the current working directory, and run conda activateopencv in the CMD window to start the working environment required by the script.

[0077] Run the python tear_traditional_prediction.py command in the CMD window to run the script and process the target photo set.

[0078] The final calculation results (including indicators such as effective tear path length) are saved as *_rev.csv file.

[0079] The above descriptions are only optional embodiments of the present invention, and are not intended to limit the patent scope of the present invention. All equivalent structural changes made using the contents of the present invention specification under the inventive concept of the present invention, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present invention.

Claims

1. A method for calculating effective tear path length, characterized in that: The steps include: Step 1: Perform tensile test on the original sample; Step 2: Rearrange the torn samples; Step 3: Obtain image information of the rearranged sample; Step 4: Extract the contour of the sample image information; Step 5: Calculate the effective tear path length l eff .

2. The effective tear path length calculation method according to claim 1, characterized in that: In the step 1, the original sample has an original incision, and the length of the original incision is recorded as l0; The original sample was placed in a tensile testing machine for tensile testing.

3. The method for calculating the effective tear path length according to claim 2, characterized in that: The step 2 includes the following sub-steps: 2-1: The two parts of the original sample after tearing are recorded as fragment A and fragment B respectively; 2-2: Rearrange fragment A and fragment B; 2-3: Let the length of the rearranged sample be d.

4. The method for calculating the effective tear path length according to claim 3, characterized in that: In 2-2, when fragment A and fragment B are rearranged, the following rules are followed: The ends of segment A and segment B facing away from the tearing path are spliced ​​together and aligned.

5. The method for calculating the effective tear path length according to claim 1, characterized in that: In the step 3, the rearranged sample is placed on a pure color background to perform image acquisition; The solid background has a color that is different from the sample color.

6. The method for calculating the effective tear path length according to claim 3, characterized in that: The step 4 includes the following sub-steps: 4-1: Convert the acquired image into a grayscale image; 4-2: Convert the grayscale image into a binary image; 4-3: Determine the sample contour based on the binary image; 4-4: Calculate the perimeter S of the sample contour.

7. The method for calculating the effective tear path length according to claim 6, characterized in that: In 4-4, the perimeter S of the sample contour is calculated based on the cv.arcLength() function.

8. The method for calculating the effective tear path length according to claim 6 or 7, characterized in that: The 4-4 comprises the following sub-steps: 4-4-1: Determine a calibration object whose size is known, and calculate the pixel size corresponding to the size of the calibration object; 4-4-2: The size of a unit pixel is the quotient of the size of the calibration object and the pixel corresponding to the size of the calibration object; 4-4-3: Determine the pixels corresponding to the sample contour; 4-4-4: The perimeter S of the sample contour is the product of the pixel size of the sample contour and the size of the unit pixel.

9. The method for calculating the effective tear path length according to claim 8, characterized in that: The calibration object is a ruler.

10. The method for calculating the effective tear path length according to claim 8, characterized in that: The effective tearing path length l in step 5 eff The calculation formula is: Among them, l is the total length of the tear path, l0 is the original cut length, S is the perimeter of the sample contour, and d is the length of the rearranged sample.

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