Heat-resistant steel performance degradation evaluation method based on magnetic characteristic parameters

Abstract

The invention discloses a heat-resistant steel performance degradation evaluation method based on magnetic characteristic parameters, which comprises the following steps: heating a plurality of heat-resistant steel test blocks to obtain a plurality of heat-resistant steel test blocks with different heating durations; judging the thermal aging degree of the material according to the mechanical property and microstructure change of the heat-resistant steel test block, and establishing a test block thermal aging grade judgment standard; detecting the test blocks by using a micro-magnetic detection instrument to obtain an optimal magnetic characteristic parameter; establishing a mapping relation between the magnetic characteristic parameter and the heat aging degree of the heat-resistant steel; detecting a heat-resistant steel test block with an unknown thermal aging grade to obtain a corresponding magnetic characteristic parameter; and inputting the obtained magnetic characteristic parameters into the established mapping relation for evaluating the performance degradation of the heat-resistant steel, and judging the thermal aging grade of the test block. According to the method, rapid, accurate and nondestructive evaluation of the thermal aging degree of the heat-resistant steel is realized, and the problems of high cost, long evaluation period and damage to workpieces of the existing thermal aging evaluation method of the heat-resistant steel part can be effectively solved.

Description

Technical Field

[0001] The present invention relates to the technical field of evaluating the performance deterioration of heat-resistant steel, and particularly to a method for evaluating the performance deterioration of heat-resistant steel based on magnetic characteristic parameters. Background Art

[0002] Heat-resistant steel is widely used in industrial equipment such as thermal power plants and chemical plants under high-temperature and high-pressure environments, especially in key components such as boilers and steam pipelines. However, with the increase of service time, the performance of the material will deteriorate, mainly manifested as phenomena such as high-temperature creep, oxidation corrosion, and thermal fatigue. These deterioration phenomena will cause a decrease in important mechanical properties such as the hardness and strength of the material, thereby affecting the safe operation of the equipment.

[0003] At present, mechanical property testing and microscopic metallographic techniques are often used to detect the thermal aging degree of heat-resistant steel. However, sampling is required, which is destructive and extremely inefficient. Existing non-destructive evaluation techniques, such as ultrasonic testing, although can be carried out without damaging the workpiece, usually require a coupling agent for detection, and have high requirements for the surface of the workpiece to be detected. For workpieces under complex working conditions, it is easy to cause inaccurate detection results. Therefore, it is particularly important to develop an efficient, accurate and non-destructive method for evaluating the thermal aging degree of heat-resistant steel. Summary of the Invention

[0004] In order to overcome the deficiencies of the prior art, the present invention provides a method for evaluating the performance deterioration of heat-resistant steel based on magnetic characteristic parameters. By establishing the mapping relationship between the magnetic characteristic parameters and the thermal aging degree of heat-resistant steel, the thermal aging grade of the heat-resistant steel to be tested can be judged, and rapid, accurate and non-destructive detection and evaluation of the thermal aging state of the heat-resistant steel during operation or on-site can be realized.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] The present invention provides a method for evaluating the performance deterioration of heat-resistant steel based on magnetic characteristic parameters, including the following steps:

[0007] Step 1: Obtain heat-resistant steel specimens with different heating durations;

[0008] Step 2: Test the mechanical properties of heat-resistant steel with different heating durations and observe the changes in its microscopic organizational structure. According to the mechanical properties and the changes in the microscopic organizational structure, establish a judgment standard for the thermal aging grade of heat-resistant steel;

[0009] Step 3: Use a micro-magnetic detection instrument to detect the heat-resistant steel specimens with the determined thermal aging grade, and obtain the optimal magnetic characteristic parameters;

[0010] Step 4: Establish the mapping relationship between the magnetic characteristic parameters and the thermal aging degree of heat-resistant steel;

[0011] Step 5: Detect the heat-resistant steel test block with unknown heat aging level to obtain the corresponding magnetic characteristic parameters;

[0012] Step 6: Input the obtained magnetic characteristic parameters into the established mapping relationship to determine the heat aging level of the test block.

[0013] Furthermore, the specific steps for establishing the heat aging level determination standard for heat-resistant steel in Step 2 are as follows:

[0014] (a) Cut parts of the test blocks from multiple heat-resistant steel test blocks for preparing metallographic test blocks and mechanical property test blocks respectively. Among them, use wire cutting to remove the oxidized and decarburized layer on the surface of the metallographic test block generated by high temperature;

[0015] (b) Divide the heat aging degree of the material according to the metallographic microscopy and mechanical property test results in (a). The specific process is: Observe the changes in metallographic microstructure and combine with the changes in material mechanical properties to divide the heat aging degree of the material.

[0016] Furthermore, the specific steps for obtaining the optimal magnetic characteristic parameters in Step 3 are as follows:

[0017] (a) Calculate the magnetic characteristic parameters sensitive to the heat aging degree of heat-resistant steel, that is, screen out the magnetic characteristic parameters with the strongest correlation with the heat aging degree of heat-resistant steel;

[0018] (b) Calculate the correlation between magnetic characteristic parameters and remove some redundant magnetic characteristic parameters.

[0019] Furthermore, for the step of inputting the obtained magnetic characteristic parameters into the established mapping relationship in Step 6 to determine the heat aging level of the test block, the specific steps are as follows:

[0020] (a) Input the magnetic characteristic parameters obtained from the unknown heat aging detection into the established mapping relationship to obtain the corresponding mechanical property value of the material;

[0021] (b) Compare according to the established heat aging level standard for heat-resistant steel and the mechanical property value of the material predicted by the mapping relationship to judge the heat aging level of the heat-resistant steel test block with unknown heat aging level.

[0022] Furthermore, for the mapping relationship between magnetic characteristic parameters and the heat aging degree of heat-resistant steel established in Step 4, the mapping relationship is established based on a suitable algorithm.

[0023] Furthermore, the heat aging levels of the test block include: ultra-light heat aging, light heat aging, moderate heat aging, severe heat aging, and complete heat aging.

[0024] The present invention has the following technical effects:

[0025] (1) By evaluating the mechanical properties of materials and the changes in the microstructure, a heat-resistant steel thermal aging grade is established, providing a new method for the establishment of the heat-resistant steel thermal aging grade.

[0026] (2) The mapping relationship established using magnetic characteristic parameters in the present invention is used to evaluate the degree of heat-resistant steel thermal aging. Compared with traditional destructive testing and single-parameter evaluation, the accuracy of predicting the thermal aging grade is higher.

[0027] (3) The method for evaluating the performance degradation of heat-resistant steel based on magnetic characteristic parameters proposed in the present invention has the characteristics of accuracy, rapidity, and non-destruction, and can be applied to the thermal aging detection of equipment in on-site service, which has important value. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required to be used in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0029] Figure 1 It is a flowchart of the method of the present invention.

[0030] Figure 2 It is a graph of the change in mechanical properties of P91 steel material at different heating times in the specific embodiment of the present invention. (a) Hardness change; (b) Tensile strength change; (c) Yield strength; (d) Impact energy change.

[0031] Figure 3 It is a metallographic change diagram of P91 steel material at different heating times in the specific embodiment of the present invention. (a) Grade 1 thermal aging (50h); (b) Grade 2 thermal aging (100h); (c) Grade 3 thermal aging (200h); (d) Grade 4 thermal aging (400h); (e) Grade 5 thermal aging (600h);

[0032] Figure 4 It is a table of the judgment criteria for the thermal aging grade of P91 steel material in the specific embodiment of the present invention.

[0033] Figure 5 It is the effect of predicting the hardness of P91 steel based on the mapping relationship established by the Gaussian process regression algorithm in the specific embodiment of the present invention. (a) Hardness prediction fitting diagram; (b) Prediction results of the hardness test set.

[0034] Figure 6 It is the prediction result of the hardness test set of the test block with a heating duration of 600 hours in the specific embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0035] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. The heat-resistant steel material in the specific implementation case is taken as P91 steel as an example for illustration, but it is not limited to only one material of P91 steel.

[0036] As Figure 1 shown is a flowchart of a method for evaluating the degradation of the performance of heat-resistant steel based on magnetic characteristic parameters of the present invention:

[0037] Step 1: Heat treatment is performed on multiple P91 steel specimens to obtain multiple P91 steel specimens with different heating durations. In the present invention, high temperature is used to accelerate the thermal aging of P91 steel material. Nine P91 steel specimens with different degrees of thermal aging are obtained by heating for 0h, 20h, 50h, 100h, 200h, 300h, 400h, 500h, and 600h respectively.

[0038] Step 2: Test the mechanical properties of the P91 steel after heat treatment and observe its metallographic microstructure. According to the changes in mechanical properties and metallographic microstructure, establish a judgment standard for the thermal aging grade of P91 steel. Cut parts of the specimens from multiple P91 steel specimens respectively for preparing metallographic specimens and mechanical property test specimens. Among them, the oxide decarburization layer generated on the surface of the metallographic specimens by high temperature is removed by wire cutting. The mechanical property specimens include hardness, strength, and impact energy test specimens. Then, divide the degree of thermal aging of the material according to the test results of metallographic microstructure and mechanical properties. The specific process is as follows: Observe the changes in microscopic metallography combined with the changes in material hardness and yield strength to divide the degree of thermal aging of the material. The changes in the mechanical properties of P91 steel during testing are as Figure 2 shown.

[0039] As Figure 3 shown is the change in the metallographic microstructure of some P91 steel specimens during testing. It can be analyzed from the figure that when the P91 steel material is in the 1st grade of thermal aging, a small part of the martensite structure in its microstructure decreases, and a small amount of Lvaes phase and M 23 C 6 carbide black precipitation phase are generated; during the 2nd grade of thermal aging, a large amount of the martensite structure in the microstructure decreases, and the precipitation phase increases; during the 3rd grade of thermal aging, only a small amount of martensite structure remains, and the black precipitation phase increases significantly; in the metallographic diagrams of the 4th and 5th grades of thermal aging, the martensite structure has completely disappeared, and the black precipitation phase continues to increase. Through the above changes in mechanical properties and metallographic microstructure, a judgment standard for the thermal aging grade of P91 steel is divided, as Figure 4 shown.

[0040] Step 3: Use a micro-magnetic detection instrument to detect the P91 steel specimens with the determined thermal aging grade, and obtain the magnetic characteristic parameters of the corresponding specimens based on the ReliefF algorithm as the training database of the Gaussian process regression prediction model. The specific steps are as follows:

[0041] (a) Perform magnetic multi-parameter detection on the 9 specimens with different thermal aging in Step 1. Each specimen is detected 10 times to obtain 90 groups of data. Take the collected magnetic characteristic parameters as a sample set, select sample R from the sample set, and select k nearest neighbor samples from the samples of the same class as R and denote them as H j , and select k nearest neighbor samples from the samples of different classes from R and denote them as M j (C). And repeat the execution m times;

[0042] (b) Calculate the weights:

[0043]

[0044] In the formula, A is the feature, P(C) is the probability of the occurrence of the samples of the C-th class, Class(R) is the class label of sample R, and diff(A, R 1 , R 2 ) is the distance between sample R 1 and R 2 on feature A. Use the RelirfF algorithm to analyze the relationship between the features and the target, select the magnetic characteristic parameters with the top importance rankings, calculate the correlation coefficients between these magnetic characteristic parameters, remove the redundant features, and leave the magnetic characteristic parameters finally used for inputting into the Gaussian process regression model. The magnetic characteristic parameters selected here for evaluating the material hardness are: K, A 5 , A 7 , M r and other parameters.

[0045] Step 4: Use these data to train and establish a Gaussian process regression model. The specific steps are as follows:

[0046] (a) The joint prior distribution of the observed value Y and the predicted value y′ follows a Gaussian distribution. The Gaussian process is defined by the mean function and the kernel function, and it satisfies the following formula:

[0047]

[0048] In the formula, k(x, x) = (k ij ) is the nth-order covariance matrix, where k ij represents the correlation between x i and x j ; x = (x 1 , x 2 ..., x n ) are the magnetic characteristic parameters obtained by the ReliefF algorithm; x′ is the predicted input data, and I is the nth-order identity matrix.

[0049] (b) Obtain the posterior distribution of the predicted value y′ from the conditional probability distribution formula, that is, obtain the Gaussian process regression model of the predicted value:

[0050]

[0051] (c) Use the data in the training set to train the Gaussian process regression model. When the input of the test set is known, based on the prior distribution of the known predicted values, the mean and covariance can be obtained as follows:

[0052]

[0053] (d) Calculate the mean function and covariance function of the training set according to the above method, and establish a Gaussian regression process model based on the magnetic characteristic parameters. As Figure 5 The figure shows the result of predicting the hardness of P91 steel by the Gaussian process regression model. It can be found that the hardness prediction fitting coefficient is 0.99, and the prediction error (root mean square error) of the test set is 1.3 HB, and the prediction effect of the model is excellent.

[0054] Step Five: Detect the P91 steel specimen with unknown thermal aging grade to obtain the corresponding magnetic characteristic parameters. Select the specimen with a heating duration of 600 h and conduct 4 detections to obtain four groups of data. And obtain the magnetic characteristic parameters according to the content of Step Three.

[0055] Step Six: Input the magnetic characteristic parameters obtained in Step Five into the established Gaussian process regression prediction model to determine the thermal aging grade of the specimen. As Figure 6 The figure shows the hardness prediction result of the specimen with a heating duration of 600 h selected in Step Four. It can be found that the predicted value is very close to the true value, and the prediction error is extremely small. According to the P91 steel thermal aging grade determination standard formulated in Step Two, it can be judged that the specimen is in the 5th grade of thermal aging.

[0056] The above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for evaluating the performance degradation of heat-resistant steel based on magnetic characteristic parameters, characterized in that: The following steps are involved: Step 1, obtaining a plurality of heat-resistant steel test blocks with different heating times; Step 2: Test the mechanical properties of heat-resistant steel at different heating times and observe the changes in its microstructure. According to the mechanical properties and microstructure changes, establish a standard for determining the thermal aging grade of heat-resistant steel. Step 3: Use a micromagnetic testing instrument to test the heat-resistant steel test block with a determined thermal aging grade to obtain the optimal magnetic property parameters; Step 4: Establishing a mapping relationship between magnetic property parameters and thermal aging degree of heat-resistant steel; Step 5: Testing a heat-resistant steel test block of unknown thermal aging grade to obtain corresponding magnetic property parameters; Step 6: Input the obtained magnetic property parameters into the established mapping relationship to determine the thermal aging level of the test block.

2. A method for evaluating heat-resistant steel performance degradation based on magnetic characteristic parameters according to claim 1, characterized in that: The specific steps of establishing the heat aging grade judgment standard for heat-resistant steel as described in step 2 are as follows: (a) cutting some test blocks from a plurality of heat-resistant steel test blocks for preparing metallographic test blocks and mechanical properties test blocks, wherein wire cutting is used to remove the oxidative decarburization layer generated by high temperature on the surface of the metallographic test blocks; (b) The degree of thermal aging of the material is divided according to the metallographic microstructure and mechanical property results tested in (a). The specific process is: observe the microscopic metallographic changes and divide the degree of thermal aging of the material in combination with the changes in the mechanical properties of the material.

3. The method for evaluating heat-resistant steel performance degradation based on magnetic characteristic parameters according to claim 1, characterized in that: The specific steps of obtaining the optimal magnetic property parameters described in step 3 are as follows: (a) calculating the magnetic property parameters that are sensitive to the degree of thermal aging of heat-resistant steel, that is, screening out the magnetic property parameters that are most correlated with the degree of thermal aging of heat-resistant steel; (b) Calculate the correlation between the magnetic property parameters and remove some redundant magnetic property parameters.

4. The method for evaluating heat-resistant steel performance degradation based on magnetic characteristic parameters according to claim 1, characterized in that: The step six of inputting the obtained magnetic property parameters into the established mapping relationship to determine the thermal aging grade of the test block is specifically as follows: (a) Input the magnetic property parameters obtained from the unknown thermal aging test into the established mapping relationship to obtain the corresponding mechanical properties of the material; (b) The thermal aging grade of the heat-resistant steel specimen with unknown thermal aging grade is determined by comparing the material mechanical properties predicted by the established thermal aging grade standard for heat-resistant steel and the mapping relationship.

5. The method for evaluating heat-resistant steel performance degradation based on magnetic characteristic parameters according to claim 1, characterized in that: In the step 4, a mapping relationship between the magnetic property parameters and the thermal aging degree of the heat-resistant steel is established, and the mapping relationship is established based on a suitable algorithm.

6. The method for evaluating heat-resistant steel performance degradation based on magnetic characteristic parameters according to claim 1, characterized in that: The heat aging grades of the test block include: ultra-mild heat aging, mild heat aging, moderate heat aging, severe heat aging and complete heat aging.

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