Device and method for detecting low-temperature impact performance of ferrite steel bolt

Abstract

The invention discloses a device and method for detecting the low-temperature impact performance of a ferrite steel bolt, and relates to the technical field of bolt performance detection.The method comprises the following steps that a quantitative relation model of the pearlite content and the low-temperature impact performance is established; rust, greasy dirt and impurities on the surface of a to-be-detected area of the sample are removed through an on-site metallographic preparation module; covering the surface of the to-be-detected area of the sample with a transparent film through a metallographic film covering module; collecting a metallographic image by a metallographic image collection module, and transmitting the metallographic image to an image processing and analyzing module; the image processing and analyzing module is used for analyzing and processing the received metallographic image so as to calculate the pearlite content; and then substituting into the quantitative relation model, calculating to obtain corresponding low-temperature impact energy data, evaluating according to the low-temperature impact energy data, and judging whether the low-temperature impact performance meets the requirement or not. According to the method, the low-temperature impact performance of the foundation bolt can be rapidly and non-destructively evaluated, and the requirements of on-site large-scale quality detection and safety monitoring are met.

Description

Technical Field

[0001] This invention relates to the field of bolt performance testing technology, specifically to an apparatus and method for testing the low-temperature impact performance of ferritic steel bolts. Background Technology

[0002] Ferritic steel, due to its excellent strength, plasticity, and machinability, is widely used in the manufacture of critical load-bearing components such as anchor bolts. These components are often used in low-temperature environments, such as wind turbine tower foundations, high-altitude bridges, and buildings in cold regions. Low-temperature impact performance is a core indicator for evaluating the safe service life of anchor bolts. It characterizes the material's ability to resist impact load damage at low temperatures. If the low-temperature impact performance is substandard, the anchor bolts are prone to brittle fracture during service, leading to serious safety accidents.

[0003] Current methods for testing the low-temperature impact performance of anchor bolts primarily rely on traditional impact testing, which involves taking samples and conducting low-temperature impact tests in a laboratory environment using an impact testing machine to measure the impact energy and evaluate performance. This method has several significant drawbacks: First, the testing process is cumbersome, requiring destructive sampling of the anchor bolts, which not only damages the components but also prevents in-situ testing. Second, the testing cycle is long, typically taking several days from sampling and delivery to completion of the laboratory test, making it difficult to meet the needs of rapid on-site quality screening. Third, the testing cost is high, requiring specialized laboratory equipment and operators, making it unsuitable for large-scale, wide-ranging on-site testing.

[0004] Metallographic structure is one of the core factors determining the properties of metallic materials. The metallographic structure of ferritic steel is mainly composed of pearlite and ferrite, with the pearlite content directly affecting the material's strength, hardness, and low-temperature impact resistance. Currently, metallographic analysis largely relies on laboratory metallographic preparation (grinding, polishing, etching) and microscopic observation. This approach suffers from problems such as difficulty in on-site operation, low analytical efficiency, and strong subjectivity. Furthermore, a precise quantitative relationship between pearlite content and the low-temperature impact resistance of ferritic steel has not yet been established, making it impossible to quickly infer low-temperature impact resistance from metallographic structure.

[0005] Therefore, developing a technical solution that enables rapid on-site metallographic preparation and observation, accurate identification and calculation of pearlite content, and rapid determination of the low-temperature impact performance of ferritic steel anchor bolts based on pearlite content has become the key to solving existing testing problems, and is of great significance to ensuring the safety and stability of engineering structures under low-temperature service environments. Summary of the Invention

[0006] This invention addresses the problems of destructive sampling, long testing cycles, inability to operate on-site, and high costs associated with existing testing methods. Its purpose is to provide a device and method for testing the low-temperature impact performance of ferritic steel bolts, enabling rapid and non-destructive evaluation of the low-temperature impact performance of anchor bolts and meeting the needs of large-scale on-site quality testing and safety monitoring.

[0007] This invention is achieved through the following technical solution:

[0008] A method for testing the low-temperature impact properties of ferritic steel bolts includes the following steps:

[0009] S1: Establish a quantitative relationship model between pearlite content and low-temperature impact performance;

[0010] S2: The on-site metallographic preparation module removes rust, oil, and impurities from the surface of the sample's test area to form a smooth mirror surface;

[0011] S3: A transparent film is applied to the surface of the area to be tested on the sample using a metallographic coating module to complete the sample preparation;

[0012] S4: Align the metallographic image acquisition module with the surface of the area to be tested on the sample, acquire the metallographic image, and transmit the metallographic image to the image processing and analysis module;

[0013] S5: The image processing and analysis module analyzes and processes the received metallographic image to calculate the pearlite content; then, the obtained pearlite content data is input into the quantification relationship model to calculate the corresponding low-temperature impact energy data, and the low-temperature impact energy data is evaluated to determine whether the low-temperature impact performance meets the requirements.

[0014] S6: Finally, the data storage and output module stores the data and outputs the results.

[0015] Further optimization yielded the following quantitative model for the relationship between pearlite content and low-temperature impact performance: A = -0.8P + 52; where A represents low-temperature impact performance and P represents pearlite content.

[0016] Further optimization, the specific steps of step S1 are as follows:

[0017] S11: Linear correlation was obtained based on linear fitting analysis of N sets of PA data, and a univariate linear regression model A=aP+b was selected as the fitting model, where: a is the slope and b is the intercept.

[0018] S12: Solve the model parameters using the least squares method to obtain the sum of squared residuals S between all data points and the fitted line;

[0019] S13: The average value was then calculated by solving for the partial derivatives. and Covariance and variance ;

[0020] S14: Calculate the slopes respectively ,intercept The fitting formula was finally determined as: A = -0.8P + 52.

[0021] Further optimization yields the following formula for calculating the sum of squared residuals: ;

[0022] The formula for calculating the average is: , ;

[0023] The formula for calculating covariance is: ;

[0024] The formula for calculating variance is: .

[0025] Further optimization, the specific steps of step S2 are as follows:

[0026] S21: The surface of the sample to be tested is gradually polished by the polishing unit to remove the surface oxide scale, rust and processing marks;

[0027] S22: The surface of the sample to be tested is then polished by the polishing unit to ensure that the surface of the sample to be tested achieves a mirror effect;

[0028] S23: Finally, the etching unit sprays a quantitative amount of etchant onto the surface of the area to be tested to remove impurities, and the cleaning component cleans the surface of the area to be tested in a timely manner.

[0029] Further optimization, the specific steps of step S3 are as follows:

[0030] S31: Equipped with a transparent polyester film, and the cleaning component removes residual water and impurities from the surface of the area to be tested before film application;

[0031] S32: An electrostatic adsorption metallographic coating equipment is used to tightly adhere a transparent polyester coating to the surface of the test area of ​​the specimen, ensuring flatness and no bubbles.

[0032] In a further optimization, step S5, where the image processing and analysis module analyzes the received metallographic image to calculate the pearlite content, includes the following specific steps:

[0033] S51: The image preprocessing unit performs adaptive filtering on several acquired metallographic images to remove image noise and enhances the contrast between pearlite and ferrite by grayscale stretching.

[0034] S52: The pearlite recognition and segmentation unit, based on the deep learning U-Net model, segments the processed metallographic image to automatically identify the pearlite and ferrite regions in the image.

[0035] S53: The pearlite content in each metallographic image is calculated by the content calculation unit, and the average value is taken to obtain the final pearlite content data.

[0036] Further optimization, in step S5, the specific steps for evaluating whether the low-temperature impact performance meets the requirements based on the low-temperature impact energy data include:

[0037] When the calculated A ≥ 27J, it is a qualified grade; when A < 27J, it is a unqualified grade.

[0038] Further solutions:

[0039] The present invention also provides an apparatus for testing the low-temperature impact performance of ferritic steel bolts, comprising:

[0040] The on-site metallographic preparation module is used to remove rust, oil, and impurities from the surface of the sample to be tested, in order to form a smooth mirror surface;

[0041] Metallographic coating module, used to coat a transparent film onto the smooth mirror surface of a sample;

[0042] Metallographic image acquisition module, which is used to acquire metallographic images of the area to be tested after the sample is coated;

[0043] The image processing and analysis module is used to process the acquired metallographic images and calculate the pearlite content, and to determine the low-temperature impact performance based on a preset quantization relationship model.

[0044] The data storage and output module is used to store the raw data, processed images, and detection results during the detection process, and output the results.

[0045] The apparatus for testing the low-temperature impact performance of ferritic steel bolts is used to implement the method for testing the low-temperature impact performance of ferritic steel bolts as described in any one of claims 1-8.

[0046] In a further optimization, the on-site metallographic preparation module includes a grinding unit for grinding the surface of the sample to be tested, a polishing unit for polishing after grinding, and an etching unit for etching after polishing to remove impurities.

[0047] The metallographic coating module includes an electrostatic adsorption metallographic coating device for coating.

[0048] The metallographic image acquisition module includes a metallographic microscope for acquiring metallographic images, a light source assembly for supplemental lighting, and an image transmission unit for transmitting information.

[0049] The image processing and analysis module includes an image preprocessing unit for filtering metallographic images, a pearlite identification and segmentation unit for identifying and separating pearlite and ferrite, a content calculation unit for calculating pearlite content, and a performance judgment unit for calculating and evaluating low-temperature impact performance.

[0050] The data storage and output module includes a storage unit for storing test data, a display unit for displaying test data, and a printing unit for printing test reports.

[0051] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0052] 1. The present invention provides an apparatus and method for detecting the low-temperature impact performance of ferritic steel bolts, which can realize rapid in-situ metallographic preparation and observation, accurately identify and calculate the pearlite content in the metallographic structure, establish a quantitative relationship between pearlite content and low-temperature impact performance, thereby rapidly and non-destructively evaluating the low-temperature impact performance of anchor bolts and meeting the needs of large-scale on-site quality inspection and safety monitoring.

[0053] 2. The present invention provides an apparatus and method for testing the low-temperature impact performance of ferritic steel bolts. Through a portable on-site metallographic preparation module and image acquisition module, the in-situ preparation and image acquisition of metallographic samples can be completed on the installation site of anchor bolts without the need for destructive cutting and sampling of bolts, thus avoiding damage to the components. At the same time, the entire testing process (from preparation to results) only takes 30-60 minutes, which is significantly shorter than the several days of traditional laboratory testing, meeting the needs of rapid on-site quality screening.

[0054] 3. The present invention provides an apparatus and method for detecting the low-temperature impact performance of ferritic steel bolts. It uses a deep learning U-Net model to achieve automatic identification and segmentation of pearlite and ferrite. Compared with the traditional method of manual observation and counting, it avoids the error of subjective human judgment and achieves an identification accuracy of ≥95%. At the same time, through multi-view average calculation, it further improves the calculation accuracy of pearlite content (≤±1%), providing reliable data support for the accurate judgment of subsequent low-temperature impact performance.

[0055] 4. This invention provides an apparatus and method for testing the low-temperature impact performance of ferritic steel bolts. Through fitting a large amount of experimental data, a quantitative numerical relationship model between the pearlite content of ferritic steel and its low-temperature impact energy is established. This clarifies the low-temperature impact performance levels corresponding to different pearlite contents, upgrading performance judgment from qualitative analysis to quantitative evaluation, making the basis more reliable and the results more convincing. It can also display metallographic images, recognition results, and performance judgment conclusions in real time, while storing all test data and printing test reports on-site, facilitating subsequent data traceability and quality control, and providing a complete technical basis for the safety assessment of engineering structures. Attached Figure Description

[0056] To more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be considered as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort. In the drawings:

[0057] Figure 1 The flowchart of the apparatus and method for testing the low-temperature impact performance of ferritic steel bolts provided by the present invention is shown.

[0058] Figure 2 Linear fitting diagram of pearlite content and low-temperature impact performance provided by the present invention;

[0059] Figure 3 The graph shows the test results of the metallographic structure, pearlite content and low-temperature impact absorption energy of some bolts provided by this invention. Detailed Implementation

[0060] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.

[0061] Example 1: This Example 1 provides a device for testing the low-temperature impact performance of ferritic steel bolts, such as... Figures 1-3 As shown, it consists of five parts: on-site metallographic preparation module, metallographic coating module, metallographic image acquisition module, image processing and analysis module, and data storage and output module.

[0062] In this embodiment, the on-site metallographic preparation module includes a grinding unit, a polishing unit, and an etching unit. The grinding unit uses a portable electric grinder equipped with silicon carbide sandpaper of different grits (240, 600, 1000, and 2000 grit). The grinding speed (500-2000 r / min) can be adaptively adjusted according to the surface roughness of the bolts, achieving gradual processing from coarse to fine grinding to remove surface oxide scale, rust, and processing marks. The polishing unit uses a portable mechanical polisher with diamond polishing paste (3μm, 1μm, and 0.5μm grits), employing a wet polishing method with appropriate polishing pressure. Adjustable (0.1-0.5MPa) to ensure the sample surface achieves a mirror finish (surface roughness Ra≤0.05μm); the corrosion unit includes an etchant storage tank, a quantitative spraying device, and a cleaning component. The etchant used is a 4% nitric acid alcohol solution (Vol.%). The quantitative spraying device can accurately control the amount of etchant sprayed (0.5-2mL / cm²). The corrosion time is accurately controlled by a timer (8-15s). After corrosion, the sample surface is quickly rinsed with anhydrous ethanol through the cleaning component to avoid over-corrosion.

[0063] The metallographic coating module is used to eliminate interference from the curved surface of the bolt during metallographic image acquisition. It employs an electrostatic adsorption metallographic coating device equipped with a transparent polyester coating with a thickness of 0.05-0.1 mm. Before coating, a cleaning component removes residual water stains and minor impurities from the sample surface. During coating, the adsorption pressure is adjusted to 0.05-0.1 MPa to ensure that the coating adheres tightly to the curved surface of the bolt, without bubbles or wrinkles, and to smooth the curvature of the sample surface. This provides a stable and flat observation surface for subsequent image acquisition, avoiding image distortion and resolution degradation caused by the curved surface.

[0064] The metallographic image acquisition module is used to acquire metallographic images after coating, and includes a portable metallographic microscope, a light source assembly, and an image transmission unit. The portable metallographic microscope employs an infinity optical system with adjustable magnification (100×, 200×, 400×, 500×) and is equipped with a CMOS image sensor (resolution ≥ 5 megapixels) to ensure image clarity. The CMOS image sensor is a complementary metal-oxide-semiconductor image sensor used to acquire high-resolution metallographic images (resolution ≥ 5 megapixels). The light source assembly uses a ring-shaped LED supplemental light with adjustable brightness (100-500 lux) to avoid shadow interference and ensure uniform image illumination. The image transmission unit uses a wireless transmission module to transmit the acquired metallographic images to the image processing and analysis module in real time.

[0065] The image processing and analysis module is the core module of the device, used to process the acquired metallographic images and calculate the pearlite content. Simultaneously, it determines the low-temperature impact performance based on a pre-set quantization relationship model. This module includes an image preprocessing unit, a pearlite identification and segmentation unit, a content calculation unit, and a performance judgment unit. The image preprocessing unit uses an adaptive filtering algorithm to remove image noise and enhances the contrast between pearlite and ferrite through grayscale stretching. The pearlite identification and segmentation unit, based on the deep learning U-Net model, is trained on a large number of 35# steel metallographic image samples to achieve accurate segmentation of pearlite and ferrite, with an accuracy rate ≥95%. The content calculation unit calculates the pearlite content of the sample by calculating the pixel area ratio of the segmented pearlite region. The performance judgment unit has a pre-established fitting formula between pearlite content and low-temperature impact performance. Substituting the calculated pearlite content into the formula, it calculates the corresponding low-temperature impact energy, completing the low-temperature impact performance evaluation.

[0066] The pearlite content (P) mentioned above is the percentage of the pearlite region in the total microstructure of ferritic steel (unit: %); the low-temperature impact energy (A) is a parameter characterizing the material's ability to resist impact load damage at -20℃ (unit: J); the U-Net model is a deep learning semantic segmentation model used to accurately identify pearlite and ferrite regions in metallographic images.

[0067] In this embodiment, the data storage and output module is used to store the raw data, processed images, and test results during the testing process, and to output the results. It includes a storage unit, a display unit, and a printing unit. The storage unit uses a large-capacity solid-state drive (storage capacity ≥ 1TB), which can store a large amount of test data and supports data classification management. The display unit uses a touch screen (size ≥ 10 inches), which can display metallographic images, pearlite identification results, pearlite content, and low-temperature impact performance evaluation results in real time. The printing unit uses a portable thermal printer, which can print test reports on-site. The reports include the test time, test location, pearlite content, low-temperature impact performance, and performance judgment conclusions.

[0068] The testing device provided above can be used to measure the low-temperature impact performance of various types of ferritic steel anchor bolts on-site.

[0069] Example 2: Based on Example 1, Example 2 provides a method for testing the low-temperature impact performance of ferritic steel bolts, including the following steps:

[0070] Select the area to be inspected for No. 35 steel anchor bolts, remove rust, oil, and impurities from the surface, and mark the inspection area (circular area with a diameter ≥ 10mm) with a marker pen; check the working status of each module of the inspection device to ensure that the grinding unit, polishing unit, etching unit, and image acquisition module are operating normally, replenish consumables such as etching agent and polishing paste, and calibrate the parameters of the image processing and analysis module.

[0071] The selected area was sequentially ground, polished, and etched, and then cleaned with anhydrous ethanol. After cleaning, a metallographic coating module was used for coating, ensuring a tight, bubble-free fit between the coating and the bolt's curved surface. A portable metallographic microscope was then aimed at the prepared metallographic coating sticker, the magnification was adjusted to 400×, the ring LED supplemental light was turned on, and the brightness was adjusted to 300 lux to ensure uniform image illumination. Metallographic images were acquired using a CMOS image sensor, with five images from different fields of view acquired for each detection area (avoiding edge areas), ensuring clear, unblurred, and glare-free images. The acquired images were transmitted in real-time to the image processing and analysis module via a wireless transmission module.

[0072] Then, the pearlite content is quickly identified and calculated. The image preprocessing unit of the image processing and analysis module performs adaptive filtering on the five metallographic images to remove noise from the images. The grayscale range of the image is adjusted by the grayscale stretching algorithm to enhance the contrast between pearlite (dark gray) and ferrite (bright white) and improve the accuracy of subsequent identification.

[0073] After processing, the metallographic image is segmented using a trained U-Net deep learning model. The model automatically identifies pearlite and ferrite regions in the image and outputs a binary segmented image (pearlite regions are black, and ferrite regions are white). Then, a content calculation unit counts the number of pixels in the pearlite region and the total number of pixels in each segmented image to calculate the pearlite content of a single field of view (pearlite content = number of pixels in the pearlite region / total number of pixels in the image × 100%). The average pearlite content of the five fields of view is taken as the final pearlite content P (unit: %) for that detection region, with a calculation accuracy ≤ ±1%.

[0074] After calculating the pearlite content, the low-temperature impact performance of the bolts was assessed. The low-temperature impact performance assessment module used a built-in numerical relationship model between the pearlite content of ferritic steel and the impact energy (A) at -20℃. Origin data analysis software was used to perform linear fitting analysis based on 25 sets of P-A data. The results are shown in the table below:

[0075] Pearlite content - low-temperature impact energy data

[0076] Subsequently, scatter plot observation revealed a significant linear correlation between pearlite content P and low-temperature impact energy A. Therefore, a univariate linear regression model A = aP + b (where a is the slope and b is the intercept) was chosen as the fitting model. Figure 2 As shown. The least squares method is used to solve for the model parameters. The core principle is to minimize the sum of squared residuals between all data points and the fitted line. The parameters are obtained by solving using partial derivatives:

[0077] Calculate the average: ;

[0078] Calculate covariance and variance: ; ;

[0079] Solve for the parameter: slope ;intercept ;

[0080] The final fitting formula was determined as follows: .

[0081] Substituting the pearlite content P calculated by the image processing module into the model, the corresponding low-temperature impact energy value and performance level are obtained; the numerical relationship model is: A = -0.8P + 52 (R²=0.96, applicable range: ω∈[20%, 45%]), and the performance level classification standard is: when A≥27J, it is a qualified level; when A<27J, it is an unqualified level (prohibited from use in low-temperature conditions).

[0082] The detection method provided by the above solution enables non-destructive in-situ testing without damaging the bolts, allowing for on-site operation. Its testing cycle is 30-60 minutes, significantly shortening the traditional testing cycle. Furthermore, the pearlite identification accuracy is ≥95%, and the content calculation accuracy is ≤±1% (≤±0.8% after dual-dimensional calibration). The quantitative model provided by this solution is reliable with small prediction errors, upgrading performance judgment from qualitative to quantitative. The device is also modularly designed, lightweight and portable, with a lithium battery life of ≥4 hours, adaptable to various field scenarios.

[0083] Example 3: Based on Example 2, this Example 3 provides a specific implementation case.

[0084] The No. 35 steel anchor bolts (specification M36×300) used in a power transmission project were selected as the test objects. The device and method described in this invention were used for on-site testing. The specific steps are as follows:

[0085] Before testing, remove rust and oil stains from the surface of the No. 35 steel anchor bolts and mark the testing area with a diameter of 10mm; check each module of the testing device, replenish consumables such as 4% nitric acid alcohol etchant, 0.5μm diamond polishing paste, and 0.08mm thick transparent polyester coating, and calibrate the U-Net model parameters of the image processing module and the adsorption pressure parameters of the coating device.

[0086] Then, on-site metallographic preparation was performed. Coarse grinding was conducted using 240-grit sandpaper at 1500 rpm for 2 minutes to remove oxide scale. Fine grinding was then performed sequentially using 800-grit, 1000-grit, and 2000-grit sandpaper, each pass lasting 1 minute with a 90° rotation during grinding. Anhydrous ethanol was used for cooling during the grinding process. After grinding, 1μm diamond polishing paste was applied at 0.3 MPa pressure and 1000 rpm for 2 minutes, with continuous dripping of anhydrous ethanol until a mirror-like finish was achieved. Then, a 4% nitric acid alcohol etchant was sprayed at 1 mL / cm² for 10 seconds. The surface was rinsed three times with anhydrous ethanol, wiped with degreased cotton, and allowed to air dry. After drying, the metallographic coating module was activated, and the adsorption pressure was adjusted to 0.08 MPa. The transparent polyester coating was then tightly adhered to the detection area, ensuring no bubbles or wrinkles.

[0087] After the metallographic sample is prepared, the microscope magnification is adjusted to 400×, the LED fill light is turned on and the brightness is adjusted, and metallographic images of 5 fields of view are acquired and transmitted to the image processing module through the wireless transmission module.

[0088] After image preprocessing, the pearlite region is segmented using the U-Net model, and the number of grains in each field of view is counted.

[0089] Field of view 1: Total number of grains: 202; number of pearlite grains: 57; number of ferrite grains: 145; pearlite content: 28.2%.

[0090] Field of view 2: Total number of grains: 197; number of pearlite grains: 58; number of ferrite grains: 139; pearlite content: 29.5%.

[0091] Field of view 3: Total number of grains: 205; number of pearlite grains: 56; number of ferrite grains: 149; pearlite content: 27.3%.

[0092] Field of view 4: Total number of grains: 203; number of pearlite grains: 58; number of ferrite grains: 145; pearlite content: 28.5%.

[0093] Field of view 5: Total number of grains: 201; number of pearlite grains: 59; number of ferrite grains: 142; pearlite content: 29.3%.

[0094] The average value of the pearlite content in 5 fields of view was taken, and P=28.6%.

[0095] Low-temperature impact performance assessment: Substituting P=28.6% into the fitting formula A = -0.8×28.6 + 52, we calculated A=29.12J. This value meets the standard requirement of A≥27J, and the low-temperature impact performance of the No. 35 steel anchor bolt is determined to be of the qualified grade.

[0096] Data storage and report output: Stores original metallographic images, segmented images, grain count statistics and calculation results, and prints test reports on-site using a portable thermal printer. The report includes test time, bolt number, test location, pearlite and ferrite grain counts in 5 fields of view, average pearlite content, impact energy calculation results and performance judgment conclusions.

[0097] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for testing the low-temperature impact performance of ferritic steel bolts, characterized in that, Includes the following steps: S1: Establish a quantitative relationship model between pearlite content and low-temperature impact performance; S2: The on-site metallographic preparation module removes rust, oil, and impurities from the surface of the sample's test area to form a smooth mirror surface; S3: A transparent film is applied to the surface of the area to be tested on the sample using a metallographic coating module to complete the sample preparation; S4: Align the metallographic image acquisition module with the surface of the area to be tested on the sample, acquire the metallographic image, and transmit the metallographic image to the image processing and analysis module; S5: The image processing and analysis module analyzes and processes the received metallographic image to calculate the pearlite content; then, the obtained pearlite content data is input into the quantification relationship model to calculate the corresponding low-temperature impact energy data, and the low-temperature impact energy data is evaluated to determine whether the low-temperature impact performance meets the requirements. S6: Finally, the data storage and output module stores the data and outputs the results.

2. The method for testing the low-temperature impact performance of ferritic steel bolts according to claim 1, characterized in that, The quantitative relationship model between pearlite content and low-temperature impact performance is: A = -0.8P + 52; where A is the low-temperature impact performance and P is the pearlite content.

3. The method for testing the low-temperature impact performance of ferritic steel bolts according to claim 2, characterized in that, The specific steps of step S1 are as follows: S11: Linear correlation was obtained based on linear fitting analysis of N sets of PA data, and a univariate linear regression model A=aP+b was selected as the fitting model, where: a is the slope and b is the intercept. S12: Solve the model parameters using the least squares method to obtain the sum of squared residuals S between all data points and the fitted line; S13: The average value was then calculated by solving for the partial derivatives. and Covariance and variance ; S14: Calculate the slopes respectively ,intercept ; The final fitting formula was determined to be: A = -0.8P + 52.

4. The method for testing the low-temperature impact performance of ferritic steel bolts according to claim 3, characterized in that... ； The formula for calculating the sum of squared residuals is: ; The formula for calculating the average is: , ; The formula for calculating covariance is: ; The formula for calculating variance is: .

5. A method for testing the low-temperature impact performance of ferritic steel bolts according to any one of claims 1 to 4, characterized in that, The specific steps of step S2 are as follows: S21: The surface of the sample to be tested is gradually polished by the polishing unit to remove the surface oxide scale, rust and processing marks; S22: The surface of the sample to be tested is then polished by the polishing unit to ensure that the surface of the sample to be tested achieves a mirror effect; S23: Finally, the etching unit sprays a quantitative amount of etchant onto the surface of the area to be tested to remove impurities, and the cleaning component cleans the surface of the area to be tested in a timely manner.

6. A method for testing the low-temperature impact performance of ferritic steel bolts according to any one of claims 1 to 4, characterized in that, The specific steps of step S3 are as follows: S31: Equipped with a transparent polyester film, and the cleaning component removes residual water and impurities from the surface of the area to be tested before film application; S32: An electrostatic adsorption metallographic coating equipment is used to tightly adhere a transparent polyester coating to the surface of the test area of ​​the specimen, ensuring flatness and no bubbles.

7. A method for testing the low-temperature impact performance of ferritic steel bolts according to any one of claims 1 to 4, characterized in that, In step S5, the image processing and analysis module analyzes and processes the received metallographic image to calculate the pearlite content. The specific steps include: S51: The image preprocessing unit performs adaptive filtering on several acquired metallographic images to remove image noise and enhances the contrast between pearlite and ferrite by grayscale stretching. S52: The pearlite recognition and segmentation unit, based on the deep learning U-Net model, segments the processed metallographic image to automatically identify the pearlite and ferrite regions in the image. S53: The pearlite content in each metallographic image is calculated by the content calculation unit, and the average value is taken to obtain the final pearlite content data.

8. A method for testing the low-temperature impact performance of ferritic steel bolts according to any one of claims 1 to 4, characterized in that, In step S5, the specific steps for evaluating whether the low-temperature impact performance meets the requirements based on the low-temperature impact energy data include: When the calculated A ≥ 27J, it is a qualified grade; when A < 27J, it is a unqualified grade.

9. A device for testing the low-temperature impact performance of ferritic steel bolts, characterized in that, include: The on-site metallographic preparation module is used to remove rust, oil, and impurities from the surface of the sample to be tested, in order to form a smooth mirror surface; Metallographic coating module, used to coat a transparent film onto the smooth mirror surface of a sample; Metallographic image acquisition module, which is used to acquire metallographic images of the area to be tested after the sample is coated; The image processing and analysis module is used to process the acquired metallographic images and calculate the pearlite content, and to determine the low-temperature impact performance based on a preset quantization relationship model. The data storage and output module is used to store the raw data, processed images, and detection results during the detection process, and output the results. The apparatus for testing the low-temperature impact performance of ferritic steel bolts is used to implement the method for testing the low-temperature impact performance of ferritic steel bolts as described in any one of claims 1-8.

10. The device for testing the low-temperature impact performance of ferritic steel bolts according to claim 9, characterized in that, The on-site metallographic preparation module includes a grinding unit for grinding the surface of the area to be tested of the sample, a polishing unit for polishing after grinding, and an etching unit for etching after polishing to remove impurities. The metallographic coating module includes an electrostatic adsorption metallographic coating device for coating. The metallographic image acquisition module includes a metallographic microscope for acquiring metallographic images, a light source assembly for supplemental lighting, and an image transmission unit for transmitting information. The image processing and analysis module includes an image preprocessing unit for filtering metallographic images, a pearlite identification and segmentation unit for identifying and separating pearlite and ferrite, a content calculation unit for calculating pearlite content, and a performance judgment unit for calculating and evaluating low-temperature impact performance. The data storage and output module includes a storage unit for storing test data, a display unit for displaying test data, and a printing unit for printing test reports.

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