Magnetic detection method for age hardening of alloy
By combining Barkhausen magnetic detection and filtering with Vickers hardness measurement, a linear fitting equation was established, which solved the problem of non-destructive testing of age hardening and irradiation embrittlement in nuclear reactor pressure vessels, and achieved efficient and accurate assessment of alloy age hardening.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-04
- Publication Date
- 2026-03-10
AI Technical Summary
In the existing technology, the age hardening and irradiation embrittlement problems of nuclear reactor pressure vessels (RPVs) lead to increased material hardness and reduced toughness, affecting the long-term operational safety of RPVs. In addition, traditional testing methods are destructive and lack reliable non-destructive testing means.
A magnetic testing method for age-hardening alloys was adopted. Noise signals were collected by Barkhausen magnetic detection, filtered, and time-domain feature value extracted. Combined with Vickers hardness measurement, a linear fitting equation was established to calculate the predicted age-hardening value. Weighting factors were used for optimization to finally achieve non-destructive evaluation.
This method enables non-destructive testing of age-hardened alloy materials, improving testing accuracy and efficiency while reducing costs. It is applicable to age-hardened testing of various metallic materials and has broad application prospects.
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Figure CN121633242A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of alloy material detection, and particularly relates to a magnetic detection method for age hardening of an alloy. BACKGROUND
[0002] A nuclear reactor pressure vessel (RPV) is one of the core barriers of a nuclear power plant, and the RPV is made of low alloy steel. Age hardening and irradiation embrittlement of the RPV are important factors affecting the long-term operation safety of the RPV. When the RPV is operated at a high temperature and in an irradiation environment, the RPV material hardness increases and the toughness decreases, that is, age hardening and irradiation embrittlement occur, thereby affecting the safety of the RPV. At present, the evaluation of the service damage of the RPV is mainly performed by using a destructive method on an irradiation supervision sample of the RPV, such as a traditional hardness test and a Charpy impact test. However, due to the limited nature of the irradiation supervision sample, it is of great significance to develop a reliable alloy nondestructive detection technology for the long-term operation monitoring of the RPV. SUMMARY
[0003] The technical problem solved by the present application is to provide a magnetic detection method for age hardening of an alloy in view of at least one defect in the related art.
[0004] The technical solution adopted by the present application to solve the technical problem is a magnetic detection method for age hardening of an alloy, comprising the following steps: S1, signal acquisition: age hardening of an original-state alloy to obtain an aged alloy, Barkhausen magnetic detection of the original-state alloy and the aged alloy, respectively, and acquisition of a first noise signal of the original-state alloy and a second noise signal of the aged alloy; S2, signal processing: filtering processing of the first noise signal and the second noise signal, and extraction of time domain characteristic values, wherein the time domain characteristic values include a root mean square and a peak-to-peak value; S3, measurement of a reference hardness: Vickers hardness measurement of the original-state alloy to obtain a measured value of the reference hardness of the original-state alloy; S4, calculation of an age hardening value: calculation of an age hardening prediction value of the aged alloy and calculation of a comprehensive hardening prediction value of the aged alloy based on the time domain characteristic values and the measured value of the reference hardness of the original-state alloy.
[0005] Preferably, in the step S1, the age hardening is isothermal age hardening, and the isothermal age hardening is to heat the original-state alloy at 450-500 DEG C for 1-2000 hours.
[0006] Preferably, in the step S1, the alloy is a low alloy steel, and in the Barkhausen magnetic detection, the excitation frequency is 4-7 Hz, the magnetic field strength is 10-50 A / m, and the sampling rate is 1-3 MHz.
[0007] Preferably, in step S2, the filtering process includes: performing high-pass filtering and low-pass filtering on both the first noise signal and the second noise signal, and then performing signal normalization processing on each.
[0008] Preferably, the cutoff frequency of the high-pass filter is 0.1 kHz to 1 kHz; and / or, the cutoff frequency of the low-pass filter is 100 kHz to 500 kHz.
[0009] Preferably, step S4 includes: first, obtaining the time-domain characteristic values and measured hardness values of alloy standard samples at different aging stages; combining the time-domain characteristic values of the original alloy and the measured hardness value of the reference, establishing a linear fitting equation for the increments of the time-domain characteristic values and the measured hardness values, and obtaining the slope of the linear fitting equation, wherein the increment of the measured hardness value is the difference between the measured hardness value and the measured hardness value of the reference; then, calculating the age-hardening increment of the age-hardening alloy by combining the time-domain characteristic values of the age-hardening alloy, and calculating the predicted age-hardening value of the age-hardening alloy; determining the weighting factor of the predicted age-hardening value, and calculating the comprehensive predicted age-hardening value of the age-hardening alloy.
[0010] Preferably, in step S4, Barkhausen magnetic testing and Vickers hardness measurement are performed on alloy standard samples at different aging stages to obtain the root mean square, peak-to-peak value, and measured hardness values of the alloy standard samples at different aging stages; linear fitting is performed on the increment of the measured hardness value and the root mean square to obtain a first linear equation and a first slope; linear fitting is performed on the increment of the measured hardness value and the peak-to-peak value to obtain a second linear equation and a second slope. Based on the time-domain eigenvalues of the aging alloy, the aging hardening increment of the aging alloy is calculated using equations (1) and (2). The aging hardening increment includes the first aging hardening increment ΔH based on the root mean square. RMS and the second age hardening increment ΔH based on peak-to-peak value Vpp Equations (1) and (2) are expressed as follows: ΔH RMS =P×RMS (1) ΔH Vpp =Q×V pp (2) Where P is the first slope of the first linear equation, Q is the second slope of the second linear equation, RMS is the root mean square of the aging alloy, and V pp This represents the peak-to-peak value of the aging alloy.
[0011] Preferably, in step S4, based on the age-hardening increment of the aging alloy, the predicted age-hardening value of the aging alloy is calculated using equations (3) and (4). The predicted age-hardening value includes a first predicted age-hardening value H based on the root mean square. RMS and the second age hardening prediction value H based on peak-to-peak value Vpp Equations (3) and (4) are expressed as follows: H RMS =H0+ΔH RMS (3) H Vpp =H0+ΔH Vpp (4) Where H0 is the measured reference hardness value of the original alloy, and ΔH RMS For the first age hardening increment, ΔH Vpp This is the second-stage hardening increment.
[0012] Preferably, in step S4, the comprehensive hardening prediction value H of the aging alloy is calculated using equation (5), which is expressed as follows: H = a × H RMS +b×H Vpp (5) Among them, H RMS H is the predicted value for the first age-hardening stage. Vpp , where a is the first weighting factor of the first time-dependent hardening prediction value, and b is the second weighting factor of the second time-dependent hardening prediction value; The first and second weighting factors are determined based on the age hardening prediction and measured hardness values of alloy standard samples at different aging stages. The error between the comprehensive hardening prediction and measured hardness values of the alloy standard samples meets the preset conditions.
[0013] Preferably, in step S4, the first and second age-hardening prediction values of alloy standard samples at different aging stages are obtained, and combined with the measured hardness values of alloy standard samples at different aging stages, a first and second weighting factors are selected. The comprehensive hardening prediction value of the alloy standard sample is calculated according to equation (5) so that the error between the comprehensive hardening prediction value and the measured hardness value of the alloy standard sample meets a preset condition, which is an error of less than ±10%; and / or, The magnetic testing method for age-hardened alloys also includes the following steps: S5. Repeated testing: The aging alloy is tested more than three times to obtain multiple comprehensive hardening prediction values. Outliers are removed and the average value is taken to obtain the final hardening prediction value.
[0014] The beneficial effects of this invention are as follows: The magnetic detection method for age-hardening alloys of this invention is a non-destructive detection method. It uses Barkhausen noise signals to evaluate the hardening of age-hardening alloys, avoiding physical damage to the alloy materials and improving detection efficiency. By combining the root mean square value and peak-to-peak value of time-domain eigenvalues for hardening prediction, the age-hardening state of the alloy materials can be predicted more accurately. The detection method of this invention can be implemented at a lower cost, saving a significant amount of material and labor costs. Attached Figure Description
[0015] The invention will now be further described with reference to the accompanying drawings, in which: Figure 1 This is a flowchart of a magnetic detection method for age-hardened alloys in some embodiments of the present invention. Detailed Implementation
[0016] To provide a clearer understanding of the technical features, objectives, and effects of the present invention, specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.
[0017] It should be noted that the flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.
[0018] like Figure 1 As shown, the magnetic detection method for age-hardened alloys in some embodiments of the present invention includes the following steps: S1. Signal Acquisition: The original alloy is aged to obtain an aged alloy. Barkhausen magnetic detection is performed on the original alloy and the aged alloy respectively, and the first noise signal of the original alloy and the second noise signal of the aged alloy are acquired.
[0019] In some embodiments, the aging treatment is an isothermal aging treatment, which involves holding the original alloy at 450°C to 500°C for 1 to 2000 hours. The isothermal aging temperature can be 450°C, 460°C, 480°C, 490°C, or 500°C, and the holding time can be 1 hour, 50 hours, 100 hours, 500 hours, 1000 hours, or 2000 hours, etc.
[0020] In some embodiments, the alloy is a low-alloy steel (such as steel used in nuclear reactor pressure vessels). In Barkhausen magnetic detection, the excitation frequency is 4 Hz to 7 Hz, the magnetic field strength is 10 A / m to 50 A / m, and the sampling rate is 1 MHz to 3 MHz. Understandably, the excitation frequency can be 4 Hz, 5 Hz, 6 Hz, or 7 Hz, etc., the magnetic field strength can be 10 A / m, 20 A / m, 30 A / m, 40 A / m, or 50 A / m, etc., and the sampling rate can be 1 MHz, 1.5 MHz, 2 MHz, 2.5 MHz, or 3 MHz, etc.
[0021] The magnetic field strength is adjusted to achieve magnetic saturation of the alloy, but the required magnetic field strength for magnetic saturation varies significantly among different alloy materials. From the perspective of magnetic material classification, low-alloy steel belongs to ferromagnetic materials, and its magnetic saturation characteristics are somewhat unique. Due to the presence of numerous alloying elements and unique crystal structure features within low-alloy steel, the magnetic field strength required to achieve magnetic saturation during magnetization is relatively low, typically within the range of 10-50 A / m. However, for other types of magnetic materials, such as pure iron in soft magnetic materials, which has high permeability, the required magnetic field strength for magnetic saturation may be lower than 10 A / m; while for hard magnetic materials such as neodymium iron boron permanent magnets, due to their high coercivity, the required magnetic field strength for magnetic saturation is often much higher than 50 A / m, potentially requiring hundreds or even thousands of A / m. Furthermore, even for the same type of material, the required magnetic field strength for magnetic saturation can vary due to differences in composition ratios, heat treatment processes, and other factors. Therefore, it is necessary to determine the appropriate magnetic field strength based on the specific magnetic properties of the alloy material.
[0022] In step S1, a magnetic Barkhausen detector is used for testing. The sensor moves along the alloy surface at a constant speed (e.g., 1-5 mm / s) to collect at least 10 sets of magnetic Barkhausen noise (MBN) signal data. Each set of signal data has a duration of ≥10 seconds to ensure signal stability.
[0023] S2. Signal Processing: Filter the first and second noise signals, then extract time-domain feature values, including root mean square (RMS) and peak-to-peak (V) values. pp ).
[0024] In step S2, a digital filter is used for filtering. This filtering process includes high-pass filtering and low-pass filtering of both the first and second noise signals, followed by signal normalization. The high-pass filter eliminates low-frequency interference (such as power supply noise), with a cutoff frequency of 0.1 kHz to 1 kHz, which can be selected from 0.1 kHz, 0.3 kHz, 0.5 kHz, 0.8 kHz, or 1 kHz. The low-pass filter suppresses high-frequency noise, with a cutoff frequency of 100 kHz to 500 kHz, which can be selected from 100 kHz, 200 kHz, 300 kHz, 400 kHz, or 500 kHz.
[0025] The core purpose of signal normalization is to eliminate signal amplitude fluctuations caused by factors such as differences in contact pressure between the sensor and the alloy surface, ensuring the comparability of signals under different detection conditions and providing a stable and reliable data foundation for subsequent time-domain feature extraction and age-hardening prediction calculation. In some embodiments, signal normalization includes the following steps: R1. Determine the signal amplitude range: First, obtain the maximum value of the filtered MBN signal (denoted as X). max ) and minimum value (denoted as X) min This determines the amplitude fluctuation range of the signal, which is then used as the basic information input for step R2.
[0026] R2, Normalization Transformation: Based on X obtained in step R1... max and X min Using a linear normalization method, based on the transformation formula, each data point x of the filtered MBN signal is... i Convert to normalized signal value y i The conversion formula is: y i =100× (x i - X min ) / (X max - X min This formula allows the amplitude of the original signal to be mapped to the interval [0, 100].
[0027] R3. Verify the normalization effect: Check the normalized signal to ensure that its amplitude is within the range of [0,100] and that it can retain the fluctuation characteristics and time domain regularity of the original signal, without losing key information due to normalization processing.
[0028] After the above signal normalization processing, regardless of the change in contact pressure between the sensor and the alloy surface, as long as the relative fluctuation characteristics of the signal remain unchanged, the normalized signal can maintain a consistent amplitude range. This effectively eliminates the interference of external factors on the signal amplitude, making the MBN signals from different detections and different alloy samples more comparable, thereby improving the subsequent time-domain eigenvalues RMS and V. pp The accuracy of extraction lays the foundation for the precise calculation of the final comprehensive hardening prediction value.
[0029] In the time-domain eigenvalues, RMS (root mean square value) reflects the overall energy intensity of the signal, and its calculation formula is as follows: Where, x i For discrete signal points, N is the number of sampling points.
[0030] V in the time domain eigenvalues ppPeak-to-peak value represents the maximum fluctuation amplitude of the signal, and is the difference between the highest and lowest points of the signal envelope.
[0031] In step S2, the first noise signal is filtered and then the time-domain feature value is extracted to obtain the root mean square and peak-to-peak value of the original alloy; the second noise signal is filtered and then the time-domain feature value is extracted to obtain the root mean square and peak-to-peak value of the aged alloy.
[0032] S3. Measuring the reference hardness: Perform Vickers hardness measurement on the original alloy to obtain the measured value of the reference hardness of the original alloy.
[0033] In some embodiments, during Vickers hardness measurement, five points are uniformly selected on the surface of the pristine alloy for testing. The test load is 5-30 kgf, and the indentation holding time is 10-15 seconds. The average value of the measured values is taken as the reference hardness measured value H0 of the pristine alloy. The test load can be selected from 5 kgf, 10 kgf, 15 kgf, 20 kgf, or 30 kgf, preferably 10 kgf; the indentation holding time can be selected from 10 seconds, 12 seconds, 13 seconds, 14 seconds, or 15 seconds, etc.
[0034] S4. Calculate the age-hardening value: Based on the time-domain characteristic value and the measured reference hardness value of the original alloy, calculate the age-hardening predicted value of the age-hardening alloy, and calculate the comprehensive hardening predicted value of the age-hardening alloy.
[0035] Specifically, step S4 includes: first, obtaining the time-domain characteristic values and measured hardness values of alloy standard samples at different aging stages; combining the time-domain characteristic values of the original alloy and the measured hardness value of the reference, establishing a linear fitting equation for the increment of the time-domain characteristic values and the measured hardness value (i.e., the difference between the measured hardness value and the measured hardness value of the reference), and obtaining the slope of the linear fitting equation; then, calculating the age-hardening increment of the age-hardening alloy by combining the time-domain characteristic values of the age-hardening alloy, and calculating the predicted age-hardening value of the age-hardening alloy; determining the weighting factor of the predicted age-hardening value, and calculating the comprehensive predicted hardening value of the age-hardening alloy.
[0036] More specifically, firstly, Barkhausen magnetic testing and Vickers hardness measurement were performed on alloy standards at different aging stages to obtain the root mean square, peak-to-peak value, and measured hardness values of the alloy standards at different aging stages; then, a linear fit was performed between the increment of the measured hardness value and the root mean square of the alloy standards to obtain the first linear equation and the first slope; finally, a linear fit was performed between the increment of the measured hardness value and the peak-to-peak value of the alloy standards to obtain the second linear equation and the second slope.
[0037] Furthermore, based on the time-domain eigenvalues of the aging alloy, the aging hardening increment of the aging alloy is calculated using equations (1) and (2). The aging hardening increment includes the first aging hardening increment ΔH based on the root mean square. RMSand the second age hardening increment ΔH based on peak-to-peak value Vpp Equations (1) and (2) are expressed as follows: ΔH RMS =P×RMS (1) ΔH Vpp =Q×V pp (2) Where P is the first slope of the first linear equation, Q is the second slope of the second linear equation, RMS is the root mean square of the aging alloy, and V pp This represents the peak-to-peak value of the aging alloy.
[0038] In equations (1) and (2), ΔH RMS The aging hardening increment is calculated based on the RMS eigenvalues of the MBN signal in the time domain. It reflects the increase in hardness, as manifested by the RMS eigenvalues, caused by changes in the internal structure of the alloy material during the aging process. ΔH Vpp Based on the time-domain eigenvalues V of the MBN signal pp The calculated age hardening increment characterizes the hardening effect achieved through V pp The eigenvalues reflect the increase in hardness of the alloy material during aging. The first and second slopes reflect RMS and V. pp The degree of influence of changes on the age-hardening increment is obtained by fitting actual test data and can reflect RMS and V. pp The linear relationship between the increment of age-hardening.
[0039] Furthermore, based on the age-hardening increment of the aforementioned aging alloy, the predicted age-hardening value of the aging alloy is calculated using equations (3) and (4). The predicted age-hardening value includes the first predicted age-hardening value H based on the root mean square. RMS and the second age hardening prediction value H based on peak-to-peak value Vpp Equations (3) and (4) are expressed as follows: H RMS =H0+ΔH RMS (3) H Vpp =H0+ΔH Vpp (4) Where H0 is the measured reference hardness value of the original alloy, and ΔH RMS For the first age hardening increment, ΔH Vpp This is the second-stage hardening increment.
[0040] In equation (3), H RMS This represents the measured reference hardness value of the original alloy and the ΔH value of the aged alloy. RMSThe obtained age-hardening prediction value combines the baseline hardness and the age-hardening increment determined by the RMS eigenvalue, and is used to predict the hardness after age-hardening from one dimension. In equation (4), H Vpp This represents the measured reference hardness value of the original alloy and the ΔH value of the aged alloy. Vpp Another age-hardening prediction value was obtained, which combines the baseline hardness and the age-hardening increment determined by the Vpp eigenvalue, and was used to predict the hardness after age-hardening from another dimension.
[0041] Furthermore, based on the age-hardening prediction values of the aforementioned age-hardening alloys, the comprehensive hardening prediction value H of the age-hardening alloys is calculated using equation (5), which is expressed as follows: H = a × H RMS +b×H Vpp (5) Among them, H RMS H is the predicted value for the first age-hardening stage. Vpp is the second predicted value for hardening over time, a is the first weighting factor of the first predicted value for hardening over time, and b is the second weighting factor of the second predicted value for hardening over time.
[0042] The first and second weighting factors are determined based on the age hardening prediction and measured hardness values of alloy standard samples at different aging stages. The error between the comprehensive hardening prediction and measured hardness values of the alloy standard samples meets the preset conditions.
[0043] The specific steps for determining the first weighting factor and the second weighting factor are as follows: obtain the first aging hardening prediction value and the second aging hardening prediction value of the alloy standard sample at different aging stages, and combine them with the measured hardness value of the alloy standard sample at different aging stages to select the first weighting factor and the second weighting factor. Calculate the comprehensive hardening prediction value of the alloy standard sample according to formula (5) so that the error between the comprehensive hardening prediction value and the measured hardness value of the alloy standard sample meets the preset condition, which is that the error is less than ±10%.
[0044] The first weighting factor a and the second weighting factor b are determined primarily through the analysis and optimization of a large amount of data (including the predicted values of age hardening of the alloy and the measured values of hardness). The values of the first and second weighting factors are adjusted using methods such as multivariate statistical analysis and least squares to minimize the error between the predicted comprehensive hardening value calculated according to equation (5) and the actual hardness value. In some embodiments, by fitting and optimizing the data, a = 0.55 and b = 0.45 are selected. At this point, the relative error between the calculated predicted comprehensive hardening value and the measured hardness value is small, which meets the requirements for detection accuracy.
[0045] The first weighting factor a and the second weighting factor b need to be adjusted and verified according to specific circumstances. Different alloy materials, different aging treatment conditions, and different testing environments may all affect H. RMS and H Vpp The weights of a and b in the prediction of overall hardening. If the alloy material is changed or the testing conditions are adjusted, the testing data needs to be collected again, and appropriate values for a and b should be determined using the method described above to ensure that the final calculated predicted value H of overall hardening has high accuracy. If the values of a and b are chosen arbitrarily without verification, the predicted results may deviate significantly from the actual situation, reducing the testing accuracy.
[0046] The time-domain eigenvalues RMS and V of the original alloy pp It serves as an important benchmark reference in subsequent calculations, and its main applications are reflected in the following two aspects: 1. Basic data support in the time-hardening prediction model When establishing the age hardening prediction model, i.e., equations (1) to (5), the RMS and V of the original alloy are... pp These are the key foundational data for calibrating the first slope P and the second slope Q. By testing alloy standard samples with known degrees of age hardening, RMS and Vt values are obtained simultaneously in the original state and under different aging conditions. pp Values. The original RMS and V. pp As a "benchmark value," it is compared with the corresponding time-domain characteristic value after aging to reflect the change amplitude of MBN signal characteristics during the aging process. This change amplitude is related to the age hardening increment (ΔH) of the alloy standard sample. When fitting the first and second linear equations through multiple regression analysis, the RMS and V of the original alloy are... pp It is the starting point for calculating the "change" and directly affects the accuracy of parameters P and Q, thereby ensuring that the model can accurately reflect the relationship between signal characteristics and hardening increment.
[0047] 2. Basis for comparison and correction in subsequent testing When testing aged alloys with unknown aging conditions, the RMS and V of the original alloy are... pp It can serve as a reference standard to help determine the reasonableness of test results. For example, if the RMS or V of a certain aging alloy is... pp The values are much lower than in the original state. Combined with the general law of age hardening in alloy materials (aging usually leads to an increase in the time-domain eigenvalues of the signal), this can preliminarily indicate that there may be anomalies in the testing process (such as poor sensor contact, signal interference, etc.), providing a basis for data validity verification. Furthermore, when comparing data from different batches or different testing equipment, the RMS and V values of the original state alloy... pp It can serve as a "calibration benchmark" to reduce the impact of systematic errors on results and ensure consistency in cross-scenario detection.
[0048] In some embodiments, the method for detecting the magnetic properties of age-hardened alloys further includes the following steps: S5. Repeated testing: Test the aging alloy three or more times to obtain multiple comprehensive hardening prediction values, remove outliers and take the average value to obtain the final hardening prediction value H'.
[0049] The removal of outliers includes: first calculating the average of multiple comprehensive hardening predictions (denoted as x). 平均 The mean x and the standard deviation (denoted as s) will be compared with the mean x. 平均 A composite hardening prediction value with a deviation exceeding 2 or 3 times the standard deviation is considered an outlier. i Satisfy |x i -x 平均 If the value exceeds 2s (or 3s), it is considered an outlier, thus effectively identifying values that deviate from the normal fluctuation range. Removing outliers can further improve the reliability and accuracy of age hardening prediction, and the final hardening prediction value obtained is an optimized result of the comprehensive hardening prediction value.
[0050] The magnetic detection method for age-hardened alloys of the present invention has the following significant advantages: 1. Non-destructive assessment: Traditional alloy age hardening testing often employs destructive techniques, such as Vickers hardness testing and Charpy impact testing, which can cause irreversible damage to the material. This invention uses Barkhausen noise signals to assess the hardening of age-hardening alloys, avoiding physical damage to the alloy material and achieving non-destructive testing of alloy age hardening. Multiple tests can be performed on the same alloy sample, facilitating long-term monitoring of the age hardening process of the alloy material.
[0051] 2. Improved prediction accuracy: This invention integrates the time-domain eigenvalues RMS and V of the MBN signal. pp By combining these two time-domain eigenvalues, an age-hardening prediction model was established, which can more accurately predict the age-hardening state of alloy materials. The parameters in the prediction model, namely the first slope P, the second slope Q, the first weighting factor a, and the second weighting factor b, can be adjusted according to the specific characteristics of the alloy material, thereby improving the accuracy of age-hardening prediction and overcoming the problem of bias in prediction based on a single eigenvalue.
[0052] 3. Improved detection efficiency: Compared with traditional detection methods, the detection process of this invention is simpler and faster. The acquisition and analysis of MBN signals can be completed in a short time, greatly shortening the detection cycle and enabling rapid acquisition of age hardening information of materials, thus meeting the need for rapid detection of age hardening of alloys.
[0053] 4. Cost-effectiveness: Due to the versatility, durability, and ease of operation of MBN testing equipment, the magnetic testing method for age-hardened alloys of the present invention can be implemented at a low cost, saving a significant amount of material and labor costs.
[0054] 5. Wide applicability: The magnetic detection method for age hardening of alloys of the present invention is applicable to the age hardening detection of various other metallic materials. By adjusting the prediction model parameters for different alloy materials, the age hardening state of different metallic materials can be effectively evaluated, which has broad application prospects.
[0055] Furthermore, although irradiation embrittlement and age hardening are two different degradation phenomena in alloy materials, they are closely related during the service life of alloys (taking low-alloy steel as an example), mainly in the following aspects: 1. Correlation of Microscopic Mechanisms: Both irradiation embrittlement and age hardening are closely related to changes in the internal microstructure of materials. Irradiation embrittlement occurs when low-alloy steel is irradiated with high-energy particles (such as neutrons), resulting in the formation of numerous point defects (vacancies, interstitial atoms) and defect clusters (such as dislocation loops, precipitates). These defects hinder dislocation movement, leading to decreased toughness and increased brittleness. Age hardening, on the other hand, occurs during long-term high-temperature service when fine second-phase particles (such as carbides, intermetallic compounds) precipitate internally, similarly pinning dislocations and increasing hardness while decreasing plasticity. Although the triggering conditions for these two phenomena differ (irradiation and thermal aging), they ultimately affect material properties by altering the resistance to dislocation movement, exhibiting similar structure-property correlation patterns.
[0056] 2. Synergistic Effect of Performance Changes: Irradiation embrittlement and age hardening have a synergistic effect on material properties. Irradiation not only directly causes embrittlement but may also accelerate the aging process—defects generated by high-energy particle irradiation promote atomic diffusion, making it easier for age-aged precipitates to form or coarsen, indirectly exacerbating age hardening; conversely, precipitates formed during the aging process may also become nucleation centers for irradiation defects, increasing the susceptibility to irradiation embrittlement. Therefore, these two phenomena often occur simultaneously, jointly leading to increased material hardness and decreased toughness, posing a threat to the long-term safety of equipment using low-alloy steel (such as RPV).
[0057] 3. Consistency of Detection Requirements: Due to the correlation between irradiation embrittlement and age hardening in terms of their microscopic mechanisms and performance effects, there are common requirements for their detection. The alloy age hardening detection method of this invention essentially captures changes in the material's microstructure (such as precipitates and defect density) through magnetic Barkhausen noise signals, and these changes are also closely related to irradiation embrittlement. Therefore, the detection method of this invention can not only be used to assess the degree of age hardening, but also provide a reference for indirectly judging the state of irradiation embrittlement—by monitoring changes in material properties such as hardness, the evolution trend of its internal microscopic defects can be inferred, providing a basis for the risk assessment of irradiation embrittlement in nuclear reactor pressure vessels.
[0058] The following examples illustrate this: The magnetic testing method for age hardening of nuclear low-alloy steel A508-Ⅲ, as described in this invention, is used to evaluate the age hardening of the alloy. This magnetic testing method includes the following steps: S1. Signal Acquisition: The original alloy is aged to obtain an aged alloy. Barkhausen magnetic detection is performed on the original alloy and the aged alloy respectively, and the first noise signal of the original alloy and the second noise signal of the aged alloy are acquired.
[0059] First, a sample of nuclear low alloy steel A508-Ⅲ, 20mm×20mm×5mm, was processed as the original alloy. Then, some samples were subjected to aging treatment, which was an isothermal aging treatment, which involved holding the original alloy at 500℃ for 100 hours.
[0060] Furthermore, a magnetic Barkhausen detector was used for testing. The sensor moved along the alloy surface at a constant speed (3 mm / s) to collect at least 10 sets of MBN signal data, with each set lasting ≥10 seconds. The excitation frequency was 6 Hz, the magnetic field strength was 30 A / m, and the sampling rate was 2 MHz.
[0061] S2. Signal processing: Filter the first noise signal and the second noise signal, and then extract the time-domain feature values, which include root mean square and peak-to-peak values.
[0062] Specifically, digital filters are used for filtering, which includes high-pass filtering and low-pass filtering of both the first and second noise signals, followed by signal normalization. The high-pass filter has a cutoff frequency of 0.5 kHz, and the low-pass filter has a cutoff frequency of 300 kHz.
[0063] Furthermore, the signal normalization process includes the following steps: R1. Determine the signal amplitude range: First, obtain the maximum value of the filtered MBN signal (denoted as X).max ) and minimum value (denoted as X) min This is used to determine the amplitude fluctuation range of the signal.
[0064] R2, Normalization Transformation: A linear normalization method is used, based on the transformation formula, to normalize each data point x of the original signal. i Convert to normalized signal value y i The conversion formula is: y i =100× (x i - X min ) / (X max - X min This formula allows the amplitude of the original signal to be mapped to the interval [0, 100].
[0065] R3. Verify the normalization effect: Check the normalized signal to ensure that its amplitude is within the range of [0,100].
[0066] The formula for calculating RMS in the time-domain eigenvalues is: Where, x i For discrete signal points, N is the number of sampling points.
[0067] V in the time domain eigenvalues pp Take the difference between the highest and lowest points of the signal envelope.
[0068] After step S2, the root mean square (RMS) of the pristine alloy was obtained as 12.3 mV·ms. -1 The peak-to-peak value was 45.6 mV; the root mean square value of the aging alloy was 18.7 mV·ms. -1 The peak-to-peak value is 62.1 mV.
[0069] S3. Measuring the reference hardness: Perform Vickers hardness measurement on the original alloy to obtain the measured value of the reference hardness of the original alloy.
[0070] In Vickers hardness measurement, five points are uniformly selected on the surface of the original alloy for testing. The test load is 10 kgf, the indentation holding time is 13 seconds, and the average value of the measured values is taken to obtain the reference hardness measured value of the original alloy, H0 = 215 HV.
[0071] S4. Calculate the age-hardening value: Based on the time-domain characteristic value and the measured reference hardness value of the original alloy, calculate the age-hardening predicted value of the age-hardening alloy, and calculate the comprehensive hardening predicted value of the age-hardening alloy.
[0072] Specifically, step S4 includes: first, obtaining the time-domain characteristic values and measured hardness values of alloy standard samples at different aging stages; combining the time-domain characteristic values of the original alloy and the measured hardness value of the reference, establishing a linear fitting equation for the increment of the time-domain characteristic values and the measured hardness value (i.e., the difference between the measured hardness value and the measured hardness value of the reference), and obtaining the slope of the linear fitting equation; then, calculating the age-hardening increment of the age-hardening alloy by combining the time-domain characteristic values of the age-hardening alloy, and calculating the predicted age-hardening value of the age-hardening alloy; determining the weighting factor of the predicted age-hardening value, and calculating the comprehensive predicted hardening value of the age-hardening alloy.
[0073] More specifically, firstly, Barkhausen magnetic testing and Vickers hardness measurement were performed on alloy standards at different aging stages to obtain the root mean square (RMS), peak-to-peak value, and measured hardness values for the alloy standards at different aging stages. Then, a linear fit was performed between the increment of the measured hardness value and the RMS of the alloy standards to obtain the first linear equation and the first slope P = 1.2 HV / (mV·ms). -1 Linear fitting was performed on the increment of the measured hardness value and the peak-to-peak value of the alloy standard to obtain the second linear equation and the second slope Q=0.8 HV / mV.
[0074] Furthermore, based on the time-domain eigenvalues of the aging alloy, the aging hardening increment of the aging alloy is calculated using equations (1) and (2). The aging hardening increment includes the first aging hardening increment ΔH based on the root mean square. RMS and the second age hardening increment ΔH based on peak-to-peak value Vpp Equations (1) and (2) are expressed as follows: ΔH RMS =P×RMS (1) ΔH Vpp =Q×V pp (2) Where P is the first slope of the first linear equation, Q is the second slope of the second linear equation, RMS is the root mean square of the aging alloy, and V pp This represents the peak-to-peak value of the aging alloy.
[0075] Furthermore, based on the age-hardening increment of the aforementioned aging alloy, the predicted age-hardening value of the aging alloy is calculated using equations (3) and (4). The predicted age-hardening value includes the first predicted age-hardening value H based on the root mean square. RMS and the second age hardening prediction value H based on peak-to-peak value Vpp Equations (3) and (4) are expressed as follows: H RMS =H0+ΔH RMS (3) H Vpp =H0+ΔH Vpp (4) Where H0 is the measured reference hardness value of the original alloy, and ΔH RMS For the first age hardening increment, ΔH Vpp This is the second-stage hardening increment.
[0076] Substituting equations (1) and (2) into equations (3) and (4) respectively, we obtain H. RMS =215 + 1.2 × 18.7 = 237.4 HV, H Vpp =215+0.8×62.1=264.7 HV.
[0077] Furthermore, based on the age-hardening prediction values of the aforementioned age-hardening alloys, the comprehensive hardening prediction value H of the age-hardening alloys is calculated using equation (5), which is expressed as follows: H = a × H RMS +b×H Vpp (5) Among them, H RMS H is the predicted value for the first age-hardening stage. Vpp is the second predicted value for hardening over time, a is the first weighting factor of the first predicted value for hardening over time, and b is the second weighting factor of the second predicted value for hardening over time.
[0078] The first and second weighting factors are determined based on the age-hardening prediction and measured hardness values of alloy standard samples at different aging stages. The error between the comprehensive hardening prediction and the measured hardness value of the alloy standard sample meets the preset condition. Specifically, the first and second age-hardening prediction values of alloy standard samples at different aging stages are first obtained, and combined with the measured hardness values of alloy standard samples at different aging stages, the first and second weighting factors are selected. The comprehensive hardening prediction value of the alloy standard sample is calculated according to formula (5) so that the error between the comprehensive hardening prediction value and the measured hardness value of the alloy standard sample meets the preset condition, which is that the error is less than ±10%.
[0079] In this embodiment, by fitting and optimizing the data, we select a=0.55 and b=0.45, and substitute them into equation (5) to calculate the comprehensive hardening prediction value H=0.55×237.4+0.45×264.7=249.7 HV.
[0080] Prediction result verification: The Vickers hardness test is performed on the aging alloy to obtain the measured hardness value. The relative error between the measured value and the comprehensive hardening prediction value or the final hardening prediction value is calculated. The allowable range of the relative error needs to be determined comprehensively in combination with the accuracy requirements of the testing scenario, industry standards and actual application needs, and should be controlled within ±10%.
[0081] The hardness of the age-hardening alloy of the present invention was measured to be 253 HV by Vickers hardness measurement, with a relative error of 1.3% compared with the comprehensive hardening prediction value. This indicates that the magnetic detection method for age-hardening of the alloy of the present invention has high accuracy, and proves the effectiveness of the age-hardening prediction model of the present invention.
[0082] It is understood that the above embodiments only illustrate preferred embodiments of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can freely combine the above technical features without departing from the concept of the present invention, and can also make several modifications and improvements, all of which fall within the protection scope of the present invention. Therefore, all equivalent transformations and modifications made with respect to the scope of the claims of the present invention should fall within the scope of the claims of the present invention.
Claims
1. A method of magnetic detection of age hardening of an alloy, characterized in that, The method comprises the following steps: S1, collecting signals: aging the original-state alloy to obtain an aged alloy, respectively performing Barkhausen magnetic detection on the original-state alloy and the aged alloy, collecting a first noise signal of the original-state alloy and a second noise signal of the aged alloy; S2, signal processing: performing filtering processing on the first noise signal and the second noise signal, and then extracting time domain characteristic values, wherein the time domain characteristic values comprise a root mean square and a peak-to-peak value; S3, measuring a reference hardness: performing Vickers hardness measurement on the original-state alloy to obtain a measured value of the reference hardness of the original-state alloy; S4, calculating an aging hardening value: based on the time domain characteristic values and the measured value of the reference hardness of the original-state alloy, calculating a predicted value of the aging hardening of the aged alloy, and calculating a predicted value of the comprehensive hardening of the aged alloy.
2. The method of claim 1, wherein the alloy is aged. In the S1 step, the aging treatment is isothermal aging treatment, and the isothermal aging treatment is to heat the original-state alloy at 450-500 DEG C for 1-2000 hours.
3. The method of claim 1, wherein the alloy is aged. In the S1 step, the alloy is a low-alloy steel, and in the Barkhausen magnetic detection, the excitation frequency is 4-7 Hz, the magnetic field strength is 10-50 A / m, and the sampling rate is 1-3 MHz.
4. The method of claim 1, wherein the alloy is aged. In the S2 step, the filtering processing comprises: performing high-pass filtering processing and low-pass filtering processing on the first noise signal and the second noise signal, and then performing signal normalization processing on the first noise signal and the second noise signal, respectively.
5. The method of claim 4, wherein the alloy is aged. The cut-off frequency of the high-pass filtering processing is 0.1-1 kHz; and / or, the cut-off frequency of the low-pass filtering processing is 100-500 kHz.
6. The method of claim 1, wherein the alloy is aged. The S4 step comprises: first, obtaining the time domain characteristic values and the measured values of hardness of alloy samples at different aging stages, combining the time domain characteristic values and the measured value of the reference hardness of the original-state alloy, establishing a linear fitting equation of the increments of the time domain characteristic values and the measured values of hardness, and obtaining the slope of the linear fitting equation, wherein the increment of the measured value of hardness is the difference between the measured value of hardness and the measured value of the reference hardness; then, combining the time domain characteristic values of the aged alloy to calculate the aging hardening increment of the aged alloy, and calculating the predicted value of the aging hardening of the aged alloy; determining the weight factor of the predicted value of the aging hardening, and calculating the predicted value of the comprehensive hardening of the aged alloy.
7. The method of claim 6, wherein the alloy is aged. In the S4 step, the Barkhausen magnetic detection and Vickers hardness measurement are performed on the alloy samples at different aging stages to obtain the root mean square, the peak-to-peak value and the measured value of hardness of the alloy samples at different aging stages; the increments of the measured values of hardness and the root mean squares of the alloy samples are linearly fitted to obtain a first linear equation and a first slope; the increments of the measured values of hardness and the peak-to-peak values of the alloy samples are linearly fitted to obtain a second linear equation and a second slope; Based on the time domain characteristic value of the aging alloy, an aging hardening increment of the aging alloy is calculated by using formula (1) and formula (2), the aging hardening increment includes a first aging hardening increment ΔH based on a root mean square and a second aging hardening increment ΔH based on a peak-to-peak value RMS , the formula (1) and the formula (2) are expressed as follows: Vpp ΔH RMS = P x RMS (1) ΔH Vpp = Q x V pp (2) where P is a first slope of the first linear equation, Q is the second slope of the second linear equation, RMS is a root mean square of the age alloy, V pp is a peak to peak value of the age alloy.
8. The method of claim 7, wherein the alloy is aged. In the S4 step, based on the age hardening increment of the age alloy, the age hardening prediction value of the age alloy is calculated by using formula (3), formula (4), the age hardening prediction value includes a first age hardening prediction value H RMS based on the root mean square and a second age hardening prediction value H Vpp based on the peak to peak value, the formula (3), the formula (4) are as follows: (3) H = H0+ H1+ H2+ H3+ H4+ H5+ H6+ H7+ H8+ H9+ H10+ H11+ H12+ H13+ H14+ H15+ H16+ H17+ H18+ H19+ H20+ H21+ H22+ H23+ H24+ H25+ H26+ H27+ H28+ H29+ H30+ H31+ H32+ H33+ H34+ H35+ H36+ H37+ H38+ H39+ H40+ H41 H RMS = H0+ ΔH RMS (3) H Vpp = H0+ ΔH Vpp (4) where H0 is the measured reference hardness of the as-cast alloy, AH RMS is the first age hardening increment, AH Vpp is the second age hardening increment.
9. The method of claim 8, wherein the alloy is aged. In the S4 step, the predicted value of the comprehensive hardening of the aged alloy H is calculated by using formula (5), and the formula (5) is as follows: H = a x H RMS + b x H Vpp (5) where H RMS is the first age hardening prediction, H Vpp is the second age hardening prediction, a is a first weighting factor for the first age hardening prediction, and b is a second weighting factor for the second age hardening prediction; The first weight factor and the second weight factor are determined according to the aging hardening prediction value and the hardness measured value of the alloy standard sample at different aging stages, and the error between the comprehensive hardening prediction value and the hardness measured value of the alloy standard sample satisfies a preset condition.
10. The method of claim 9, wherein the alloy is aged. In the S4 step, the first aging hardening prediction value and the second aging hardening prediction value of the alloy standard sample at different aging stages are obtained, and the first weight factor and the second weight factor are selected in combination with the hardness measured value of the alloy standard sample at different aging stages, and the comprehensive hardening prediction value of the alloy standard sample is calculated according to the formula (5), so that the error between the comprehensive hardening prediction value and the hardness measured value of the alloy standard sample satisfies a preset condition, and the preset condition is that the error is less than ±10%; And / or, The magnetic detection method of the aging hardening of the alloy further comprises the following steps: S5, repeated detection: the aging alloy is detected more than three times, a plurality of comprehensive hardening prediction values are obtained, abnormal values are removed and an average value is taken to obtain a final hardening prediction value.