A dynamic magnetization multi-effect fusion method for detecting thermal aging damage degree of heat-resistant steel

Abstract

The present invention discloses a method for detecting the degree of thermal aging damage of heat-resistant steel by integrating dynamic magnetization and multiple effects, and relates to the technical field of detecting the degree of thermal aging damage of heat-resistant steel. The method comprises obtaining sensitive magnetic parameters of the heat-resistant steel to be detected; the sensitive magnetic parameters comprise the third harmonic amplitude A3 of the tangential magnetic field intensity signal, the sum of the high-order harmonic amplitudes UHS, the distortion factor K, the peak width corresponding to the maximum amplitude Mmax of the 75% MBN of the magnetic Barkhausen noise signal, and the peak width corresponding to the maximum amplitude Umax of the 75% incremental magnetic permeability signal; the sensitive magnetic parameters of the heat-resistant steel to be detected are input into a thermal aging damage degree assessment model to obtain the degree of thermal aging damage; the thermal aging damage degree assessment model is a model established based on the sensitive magnetic parameters and the degree of thermal aging damage of heat-resistant steel. The present invention can realize industrial on-site, in-situ, fast, accurate and non-destructive detection of the degree of thermal aging damage of heat-resistant steel.

Description

Technical Field

[0001] The invention relates to the technical field of heat-resistant steel thermal aging damage degree detection, and in particular to a heat-resistant steel thermal aging damage degree detection method integrating dynamic magnetization multiple effects. Background Art

[0002] Heat-resistant steel is widely used in key parts such as generator sets and steam pipes in large power plants. Due to long-term service in high temperature and high pressure environment, the internal microstructure of heat-resistant steel changes, resulting in a decrease in the mechanical properties of the material, that is, heat-resistant steel thermal aging damage occurs, which may cause serious safety accidents. Therefore, it is necessary to monitor the degree of thermal aging damage of heat-resistant steel materials.

[0003] At present, the main evaluation methods for heat aging damage of heat-resistant steel are destructive testing, ultrasonic testing and eddy current testing. Destructive testing requires cutting a part of the material from the tested part to test the mechanical properties or observe the metallographic microstructure, but this method is destructive to the tested part; although the ultrasonic testing method can achieve non-destructive testing, it requires a coupling agent and cannot achieve online high-temperature testing; the eddy current testing method does not require a coupling agent and can achieve non-destructive testing, but this method is limited by the skin depth and has limited detection capabilities. Summary of the invention

[0004] The purpose of the present invention is to provide a dynamic magnetization multi-effect fusion heat-resistant steel thermal aging damage degree detection method, which can use a simple process to achieve rapid, accurate and non-destructive detection of the heat-resistant steel thermal aging damage degree.

[0005] To achieve the above object, the present invention provides the following solutions:

[0006] The present invention provides a method for detecting the degree of thermal aging damage of heat-resistant steel by integrating dynamic magnetization and multiple effects, comprising:

[0007] The heat-resistant steel to be tested is subjected to dynamic magnetization multi-effect fusion detection to obtain sensitive magnetic parameters of the heat-resistant steel to be tested; the sensitive magnetic parameters include the third harmonic amplitude A3 of the tangential magnetic field intensity signal, the sum of the higher harmonic amplitudes UHS, the distortion factor K, the peak width corresponding to the 75% MBN maximum amplitude Mmax of the magnetic Barkhausen noise signal, and the peak width corresponding to the 75% incremental magnetic permeability signal maximum amplitude Umax.

[0008] The sensitive magnetic parameters of the heat-resistant steel to be tested are input into a thermal aging damage degree assessment model to obtain the thermal aging damage degree; the thermal aging damage degree assessment model is a model established based on the sensitive magnetic parameters and the thermal aging damage degree of the heat-resistant steel.

[0009] Optionally, the establishment of the thermal aging damage degree assessment model specifically includes:

[0010] The degree of thermal aging damage of several heat-resistant steel samples is obtained; the degree of thermal aging damage includes: mechanical properties and metallographic microstructure statistical learning characteristics; the mechanical properties include: hardness and strength; the metallographic microstructure statistical learning characteristics are the proportion of precipitation phase area and the number of precipitation phases obtained by metallographic image analysis.

[0011] Dynamic magnetization multi-effect fusion detection is carried out on each heat-resistant steel sample to obtain the sensitive magnetic parameters of each heat-resistant steel sample.

[0012] Taking the sensitive magnetic parameters of each heat-resistant steel sample as input and the thermal aging damage degree corresponding to the sensitive magnetic parameters of each heat-resistant steel sample as output, the machine learning algorithm is trained to obtain a heat-resistant steel thermal aging damage degree assessment model.

[0013] Optionally, obtaining the degree of thermal aging damage of a plurality of heat-resistant steel samples specifically includes:

[0014] Mechanical property tests and metallographic microstructure tests were carried out on several heat-resistant steel samples to obtain the mechanical property and metallographic microstructure statistical learning characteristics of each heat-resistant steel sample.

[0015] Based on the mechanical properties and metallographic microstructure statistical learning characteristics, a thermal aging damage degree grading level sequence of heat-resistant steel samples is established; the thermal aging damage degree grading level sequence includes the thermal aging damage degree of each heat-resistant steel sample.

[0016] Optionally, the machine learning algorithm is any one of random forest, support vector machine, gradient boosted tree regression and ridge regression.

[0017] Optionally, obtaining sensitive magnetic parameters of the heat-resistant steel to be tested specifically includes:

[0018] A dynamic magnetization multiple-effect fusion number detection instrument is used to perform dynamic magnetization multiple-effect fusion detection on the heat-resistant steel to be detected, so as to obtain one or more sensitive magnetic parameters of the heat-resistant steel to be detected.

[0019] Optionally, the dynamic magnetization multi-effect fusion number detection instrument includes: a control module, a waveform generation module, an excitation module, an acquisition and processing module and a host computer module.

[0020] The control module is connected to the waveform generation module, the acquisition and processing module and the host computer module respectively, and is used to generate control waveform instructions, data acquisition and processing instructions, and transmit the acquired and processed data to the host computer module.

[0021] The waveform generating module is used to receive the control waveform instruction and generate an excitation signal.

[0022] The excitation module is connected to the waveform generation module, and is used to excite and amplify the excitation signal to obtain an amplified excitation signal.

[0023] The acquisition and processing module is connected to the excitation module, and is used to receive the data acquisition and processing instructions, acquire the amplified excitation signal, and process the acquired amplified excitation signal to obtain processed data.

[0024] The host computer module is used to display the processed data and save the processed data.

[0025] Optionally, the control module is an FPGA chip.

[0026] Optionally, the excitation module includes: a high-frequency excitation amplifier and a low-frequency excitation amplifier.

[0027] The high-frequency excitation amplifier is used to perform high-frequency excitation constant-amplitude amplification on the excitation signal to obtain an amplified high-frequency excitation signal.

[0028] The low-frequency excitation amplifier is used to perform low-frequency excitation constant-amplitude amplification on the excitation signal to obtain an amplified low-frequency excitation signal.

[0029] Optionally, the acquisition and processing module includes: a sampling circuit, an amplification circuit and an AD conversion unit; the amplification circuit includes: a circuit incremental permeability amplification circuit, a tangential magnetic field intensity amplification circuit and a magnetic Barkhausen amplification circuit.

[0030] The sampling circuit is used to collect the amplified excitation signal to obtain a collected signal;

[0031] The circuit incremental magnetic permeability amplifier circuit is used to amplify the collected signal to obtain an incremental magnetic permeability signal.

[0032] The tangential magnetic field strength amplification circuit is used to amplify the collected signal to obtain a tangential magnetic field strength signal.

[0033] The magnetic Barkhausen amplifier circuit is used to amplify the collected signal to obtain a magnetic Barkhausen signal.

[0034] The AD conversion unit is used to perform AD conversion on the incremental magnetic permeability signal, the tangential magnetic field intensity signal and the magnetic Barkhausen signal to obtain converted signals.

[0035] Optionally, the low-frequency excitation amplifier is further used to generate a low-frequency excitation reference signal, and transmit the low-frequency excitation reference signal to the AD conversion unit.

[0036] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0037] The present invention provides a method for detecting the degree of thermal aging damage of heat-resistant steel by means of dynamic magnetization multi-effect fusion, by obtaining the sensitive magnetic parameters of the heat-resistant steel to be detected; the sensitive magnetic parameters include the third harmonic amplitude A3 of the tangential magnetic field intensity signal, the sum of the higher harmonic amplitudes UHS, the distortion factor K, the peak width corresponding to the maximum amplitude Mmax of the 75% MBN of the magnetic Barkhausen noise signal, and the peak width corresponding to the maximum amplitude Umax of the 75% incremental magnetic permeability signal; by inputting the sensitive magnetic parameters into the thermal aging damage degree assessment model, the degree of thermal aging damage can be obtained. The present invention uses dynamic magnetization multi-effect fusion to determine the degree of thermal aging damage of heat-resistant steel, and can realize industrial on-site, in-situ, fast, accurate and non-destructive detection of the degree of thermal aging damage of heat-resistant steel. Compared with the related art, it avoids damage to the components to be tested, does not require coupling agents, and solves the problem of limited detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0039] Figure 1 A schematic flow chart of a method for detecting the degree of thermal aging damage of heat-resistant steel by integrating dynamic magnetization and multiple effects is provided in accordance with an embodiment of the present invention.

[0040] Figure 2 A schematic diagram of establishing a grading sequence of thermal aging damage degrees of a sample provided by an embodiment of the present invention.

[0041] FIG. 3 is a schematic diagram of measuring sensitive magnetic parameters provided by an embodiment of the present invention.

[0042] Figure 4 A schematic diagram of establishing a thermal aging damage degree assessment model provided by an embodiment of the present invention.

[0043] Figure 5 A schematic diagram of measuring the degree of thermal aging damage provided in one embodiment of the present invention.

[0044] Figure 6 A schematic diagram of the structure of a dynamic magnetization multi-effect fusion number detection instrument provided in one embodiment of the present invention.

[0045] Figure 7 A schematic diagram of the structure of a computer device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and understandable, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0048] In an exemplary embodiment, Figure 1 As shown, a method for detecting the degree of thermal aging damage of heat-resistant steel by integrating dynamic magnetization and multiple effects is provided, comprising the following steps S1 to S2. Among them:

[0049] Step S1, obtaining sensitive magnetic parameters of the heat-resistant steel to be tested; the sensitive magnetic parameters include the third harmonic amplitude A3 of the tangential magnetic field intensity signal, the sum of the higher harmonic amplitudes UHS, the distortion factor K, the peak width corresponding to the 75% MBN maximum amplitude Mmax of the magnetic Barkhausen noise signal, and the peak width corresponding to the 75% incremental permeability signal maximum amplitude Umax.

[0050] Step S2, inputting the sensitive magnetic parameters of the heat-resistant steel to be tested into a thermal aging damage degree assessment model to obtain the thermal aging damage degree; the thermal aging damage degree assessment model is a model established based on the sensitive magnetic parameters and the thermal aging damage degree of the heat-resistant steel.

[0051] By implementing the above steps S1 to S2, the degree of thermal aging damage of heat-resistant steel can be measured quickly, accurately, non-destructively and without blind spots through simple procedures. This innovation can effectively solve the problems of existing methods for evaluating the degree of thermal aging damage of heat-resistant steel, such as high cost, long evaluation cycle, high destructiveness of sampling and difficulty in repairing workpieces.

[0052] In another exemplary embodiment of the present invention, in step S2, the establishment of the thermal aging damage degree assessment model specifically includes:

[0053] S21: Obtain the degree of thermal aging damage of several heat-resistant steel samples; the degree of thermal aging damage includes: mechanical properties and metallographic microstructure statistical learning characteristics; the mechanical properties include: hardness and strength; the metallographic microstructure statistical learning characteristics are the area ratio of precipitated phases and the number of precipitated phases obtained by metallographic image analysis.

[0054] S22: Perform dynamic magnetization multi-effect fusion detection on each heat-resistant steel sample to obtain the sensitive magnetic parameters of each heat-resistant steel sample.

[0055] S23: Taking the sensitive magnetic parameters of each heat-resistant steel sample as input and the thermal aging damage degree corresponding to the sensitive magnetic parameters of each heat-resistant steel sample as output, the machine learning algorithm is trained to obtain a heat-resistant steel thermal aging damage degree assessment model.

[0056] In another exemplary embodiment of the present invention, in step S21, the following steps are specifically included:

[0057] S211: Perform mechanical property tests and metallographic microstructure tests on a number of heat-resistant steel samples to obtain mechanical property and metallographic microstructure statistical learning features of each heat-resistant steel sample.

[0058] S212: Based on the mechanical properties and metallographic microstructure statistical learning characteristics, a thermal aging damage degree classification level sequence of heat-resistant steel samples is established; the thermal aging damage degree classification level sequence includes the thermal aging damage degree of each heat-resistant steel sample.

[0059] In step S21, the same material heat-resistant steel is used to make corresponding test specimens for mechanical properties and observe metallographic structure, and obtain mechanical properties and metallographic microstructure statistical learning characteristics; the mechanical properties include hardness and strength, and the statistical characteristics are the area ratio of precipitated phases and the number of precipitated phases obtained by metallographic analysis; according to the mechanical properties and metallographic microstructure statistical learning characteristics of the heat-resistant steel sample, a graded sequence of thermal aging damage degree of the sample is established. The specific process is as follows: Figure 2 shown.

[0060] In step S22, a dynamic magnetization multi-effect fusion number detection instrument is used to perform dynamic magnetization multi-effect fusion detection on each heat-resistant steel sample to obtain one or more sensitive magnetic parameters, wherein the sensitive magnetic parameters include the third harmonic amplitude A3 of the tangential magnetic field intensity signal, the sum of the high-order harmonic amplitudes UHS, the distortion factor K, the peak width corresponding to the maximum amplitude Mmax of the 75% MBN of the magnetic Barkhausen noise signal, and the peak width corresponding to the maximum amplitude Umax of the 75% incremental permeability signal. The experimentally measured signal is shown in FIG3 (wherein, Figure 3a is the magnetic Barkhausen noise signal, Figure 3b is the tangential magnetic field strength signal, Figure 3c is the incremental magnetic permeability signal. Figure 3 (a) shows the change of the magnetic Barkhausen noise signal of the P91 steel test block from thermal aging level 1 to thermal aging level 4. Figure 3 (b) shows the tangential magnetic field strength signal under a certain thermal aging state. The tangential magnetic field strength signal consists of a test waveform, a basic waveform and a harmonic waveform. Figure 3 (c) shows the change of the incremental magnetic permeability signal of the P91 steel test block from thermal aging level 1 to thermal aging level 4. Through analysis, the characteristic parameters of these signals are extracted respectively).

[0061] In another exemplary embodiment of the present invention, the machine learning algorithm is any one of random forest, support vector machine, gradient boosted tree regression and ridge regression.

[0062] The specific process of using machine learning algorithms to train the evaluation model between sensitive magnetic parameters and the degree of thermal aging damage of heat-resistant steel is as follows: Figure 4 shown.

[0063] Through the above-mentioned machine learning algorithm, the degree of thermal aging damage can be determined.

[0064] The unknown heat-resistant steel sample to be tested is subjected to dynamic magnetization multi-effect fusion detection to obtain sensitive magnetic parameters, which are input into the established thermal aging damage degree assessment model to carry out the thermal aging damage degree determination of the unknown sample to be tested. The specific process is as follows Figure 5 shown.

[0065] The present invention provides an algorithm case to establish an evaluation model for sensitive magnetic parameters and heat aging loss degree of heat-resistant steel. The algorithm is a random forest algorithm, and the specific process is as follows:

[0066] Specific implementation case: Dynamic magnetization multi-effect fusion based on BP neural network algorithm evaluates the degree of thermal aging damage, takes sensitive magnetic parameters as input, and the degree of thermal aging damage of heat-resistant steel as output, and uses random forest algorithm for training to obtain a good prediction model for the degree of thermal aging damage of heat-resistant steel. The training and prediction process of the model can be calculated and processed using MATLAB software to obtain a prediction model for the degree of thermal aging damage of heat-resistant steel.

[0067] The random forest algorithm is used to predict the degree of thermal aging damage of heat-resistant steel using the MATLAB programming language and the random forest function library that comes with MATLAB. First, the sensitive magnetic parameters are imported using the xlsread function. The sensitive magnetic parameters are the input feature parameters of the model, that is, the X feature matrix, and the degree of thermal aging damage of heat-resistant steel is the output prediction target of the prediction model, that is, the Y target vector. Then, the randperm and res functions are used to shuffle the data set and divide the data set into a training set and a test set. The training set is used to train a good prediction model, and the test set is used to verify the accuracy and stability of the prediction model. Then, the training set and the test set are normalized, and the processed training data set is input into the TreeBagger function, and the relevant random forest model parameters such as the number of decision trees, the minimum number of leaves, classification regression, etc. are set.

[0068] The random forest regression model randomly extracts samples and features, builds multiple unrelated decision trees, and obtains prediction results in parallel. Each decision tree can get a prediction result based on the extracted samples and features, and the regression prediction result of the entire forest is obtained by averaging all the results. The specific process is as follows:

[0069] (1) Extract a portion of samples from the original training set with replacement to form a sub-sample set, so that each decision tree is trained on different samples to increase sample diversity.

[0070] (2) If the feature dimension of each sample is M, then specify a constant m<M, randomly select m feature subsets from the M features, and select the best one from these m feature subsets each time the tree splits.

[0071] (3) Use the sample set and feature subset selected in (1) and (2) to train a decision tree model.

[0072] (4) Repeat (2) and (3) multiple times to generate multiple decision trees. Each decision tree is obtained through different sample sets and feature subsets, and there are certain differences between them.

[0073] (5) Random forest obtains the final result by averaging the prediction results of each tree.

[0074] It can be seen that the dynamic magnetization multi-effect fusion heat aging damage degree detection method of heat-resistant steel of the present invention has at least the following beneficial effects compared with the related art:

[0075] 1) Since the dynamic magnetization multi-effect fusion detection only requires the instrument to use the sensor probe for detection, it is only necessary to remove the surface oxide layer and paint layer of the workpiece surface. Therefore, the method of the present invention can be used for on-site detection without destroying the workpiece.

[0076] 2) After the preliminary work is completed, the existing heat-resistant steel parts in service can be quickly tested.

[0077] 3) The process is simple. Currently, the determination of the degree of thermal aging damage of heat-resistant steel requires metallographic and mechanical property testing, and then the sample is graded with the help of standards. This process is long and destructive, and real-time monitoring is not possible. However, magnetic multi-parameter determination can be performed after the training of the prediction model for the degree of thermal aging damage of heat-resistant steel is completed.

[0078] 4) No need for chemical agents such as coupling agents. Currently, there is a method of using ultrasound to rate the degree of thermal aging damage of heat-resistant steel, but ultrasound requires coupling agents for detection and has blind spots, while magnetic multi-parameter detection does not require coupling agents and has no detection blind spots.

[0079] The present invention also provides an application scenario, which applies the above-mentioned dynamic magnetization multi-effect fusion heat-resistant steel thermal aging damage degree detection method. Specifically: The dynamic magnetization multi-effect fusion heat-resistant steel thermal aging damage degree detection method provided in this embodiment can be applied in the heat-resistant steel thermal aging damage degree detection scenario. The heat-resistant steel thermal aging damage degree detection scenario includes the sensitive magnetic parameter acquisition link of the heat-resistant steel to be detected; the damage degree detection link; the sensitive magnetic parameters of the heat-resistant steel to be detected are input into the thermal aging damage degree assessment model to obtain the thermal aging damage degree.

[0080] In another exemplary embodiment of the present invention, obtaining the sensitive magnetic parameters of the heat-resistant steel to be tested specifically includes:

[0081] A dynamic magnetization multiple-effect fusion number detection instrument is used to perform dynamic magnetization multiple-effect fusion detection on the heat-resistant steel to be detected, so as to obtain one or more sensitive magnetic parameters of the heat-resistant steel to be detected.

[0082] like Figure 6 As shown, the dynamic magnetization multi-effect fusion number detection instrument includes: a control module, a waveform generation module, an excitation module, a collection and processing module and a host computer module.

[0083] The control module is connected to the waveform generation module, the acquisition and processing module and the host computer module respectively, and is used to generate control waveform instructions, data acquisition and processing instructions, and transmit the acquired and processed data to the host computer module.

[0084] The waveform generating module is used to receive the control waveform instruction and generate an excitation signal.

[0085] The excitation module is connected to the waveform generation module and is used to excite and amplify the excitation signal to obtain an amplified excitation signal. The excitation module is connected to the waveform generator and the sensor, and the waveform generator receives the control module instruction to generate a waveform for power amplification for the sensor excitation magnetic field; it is divided into a high-frequency excitation constant amplitude amplifier circuit and a low-frequency excitation constant amplitude amplifier circuit, which are used to amplify the excitation signal to achieve the magnetic field size required for magnetization.

[0086] The acquisition and processing module is connected to the excitation module, and is used to receive the data acquisition and processing instructions, acquire the amplified excitation signal, and process the acquired amplified excitation signal to obtain processed data. The acquisition and processing module is mainly composed of a sampling circuit and an amplifying circuit, which connects the sensor and the control module, processes the signal acquired by the sensor, and transmits it to the control module; in addition, the sampling circuit acquires the magnetic signal and the amplifying circuit amplifies it.

[0087] The host computer module is used to display and save the processed data. The host computer module can display the magnetic signal waveform and each characteristic parameter value of the magnetic signal, and can save them.

[0088] As an optional implementation, the control module is a FPGA chip.

[0089] As an optional implementation, the excitation module includes: a high-frequency excitation amplifier and a low-frequency excitation amplifier.

[0090] The high-frequency excitation amplifier is used to perform high-frequency excitation constant-amplitude amplification on the excitation signal to obtain an amplified high-frequency excitation signal.

[0091] The low-frequency excitation amplifier is used to perform low-frequency excitation constant-amplitude amplification on the excitation signal to obtain an amplified low-frequency excitation signal.

[0092] As an optional implementation, the acquisition and processing module includes: a sampling circuit, an amplification circuit and an AD conversion unit; the amplification circuit includes: a circuit incremental permeability amplification circuit, a tangential magnetic field strength amplification circuit and a magnetic Barkhausen amplification circuit.

[0093] The sampling circuit is used to collect the amplified excitation signal to obtain a collected signal.

[0094] The circuit incremental magnetic permeability amplifier circuit is used to amplify the collected signal to obtain an incremental magnetic permeability signal (MIP signal).

[0095] The tangential magnetic field intensity amplifying circuit is used to amplify the collected signal to obtain a tangential magnetic field intensity signal (TMF signal).

[0096] The magnetic Barkhausen amplifier circuit is used to amplify the collected signal to obtain a magnetic Barkhausen signal (MBN signal).

[0097] The AD conversion unit is used to perform AD conversion on the incremental magnetic permeability signal, the tangential magnetic field intensity signal and the magnetic Barkhausen signal to obtain converted signals.

[0098] As an optional implementation manner, the low-frequency excitation amplifier is further used to generate a low-frequency excitation reference signal, and transmit the low-frequency excitation reference signal to the AD conversion unit.

[0099] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 7As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store sensitive magnetic parameter data of the heat-resistant steel to be detected. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a dynamic magnetization multi-effect fusion heat aging damage degree detection method for heat-resistant steel is realized.

[0100] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the scheme of the present invention, and does not constitute a limitation on the computer device to which the scheme of the present invention is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0101] In an exemplary embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the above-mentioned method embodiments are implemented when the processor executes the computer program.

[0102] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the above-mentioned method embodiments are implemented.

[0103] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the above-mentioned method embodiments are implemented.

[0104] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in the present invention are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0105] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0106] The database involved in each embodiment provided by the present invention may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided by the present invention may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0107] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0108] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A dynamic magnetization multi-effect fusion heat aging damage degree detection method for heat-resistant steel, characterized in that: The dynamic magnetization multi-effect fusion heat aging damage degree detection method for heat-resistant steel comprises: Acquire sensitive magnetic parameters of the heat-resistant steel to be tested; the sensitive magnetic parameters include the third harmonic amplitude A3 of the tangential magnetic field intensity signal, the sum of the higher harmonic amplitudes UHS, the distortion factor K, the peak width corresponding to the 75% MBN maximum amplitude Mmax of the magnetic Barkhausen noise signal, and the peak width corresponding to the 75% incremental permeability signal maximum amplitude Umax; Inputting the sensitive magnetic parameters of the heat-resistant steel to be tested into a thermal aging damage degree assessment model to obtain the thermal aging damage degree; the thermal aging damage degree assessment model is a model established based on the sensitive magnetic parameters and the thermal aging damage degree of the heat-resistant steel; The establishment of the thermal aging damage degree assessment model specifically includes: Obtaining the degree of thermal aging damage of several heat-resistant steel samples; the degree of thermal aging damage includes: mechanical properties and metallographic microstructure statistical learning characteristics; the mechanical properties include: hardness and strength; the metallographic microstructure statistical learning characteristics are the proportion of precipitation phase area and the number of precipitation phases obtained by metallographic image analysis; Perform dynamic magnetization multi-effect fusion detection on each heat-resistant steel sample to obtain the sensitive magnetic parameters of each heat-resistant steel sample; Taking the sensitive magnetic parameters of each heat-resistant steel sample as input and the thermal aging damage degree corresponding to the sensitive magnetic parameters of each heat-resistant steel sample as output, the machine learning algorithm is trained to obtain a heat-resistant steel thermal aging damage degree assessment model.

2. According to claim 1, a dynamic magnetization multi-effect fusion heat aging damage degree detection method for heat-resistant steel is characterized in that: The step of obtaining the degree of thermal aging damage of a plurality of heat-resistant steel samples specifically includes: Conduct mechanical property tests and metallographic microstructure tests on several heat-resistant steel samples to obtain the mechanical property and metallographic microstructure statistical learning characteristics of each heat-resistant steel sample; Based on the mechanical properties and metallographic microstructure statistical learning characteristics, a thermal aging damage degree grading level sequence of heat-resistant steel samples is established; the thermal aging damage degree grading level sequence includes the thermal aging damage degree of each heat-resistant steel sample.

3. The method for detecting the degree of thermal aging damage of heat-resistant steel by dynamic magnetization multi-effect fusion according to claim 1 is characterized in that: The machine learning algorithm is any one of random forest, support vector machine, gradient boosted tree regression and ridge regression.

4. The method for detecting the degree of thermal aging damage of heat-resistant steel by dynamic magnetization multi-effect fusion according to claim 1 is characterized in that: Obtain the sensitive magnetic parameters of the heat-resistant steel to be tested, including: A dynamic magnetization multiple-effect fusion number detection instrument is used to perform dynamic magnetization multiple-effect fusion detection on the heat-resistant steel to be detected, so as to obtain one or more sensitive magnetic parameters of the heat-resistant steel to be detected.

5. The method for detecting the degree of thermal aging damage of heat-resistant steel by integrating dynamic magnetization and multiple effects according to claim 4 is characterized in that: The dynamic magnetization multi-effect fusion number detection instrument comprises: a control module, a waveform generation module, an excitation module, a collection and processing module and a host computer module; The control module is connected to the waveform generation module, the acquisition and processing module and the host computer module respectively, and is used to generate control waveform instructions, data acquisition and processing instructions, and transmit the acquired and processed data to the host computer module; The waveform generating module is used to receive the control waveform instruction and generate an excitation signal; The excitation module is connected to the waveform generation module and is used to excite and amplify the excitation signal to obtain an amplified excitation signal; The acquisition and processing module is connected to the excitation module, and is used to receive the data acquisition and processing instructions, acquire the amplified excitation signal, and process the acquired amplified excitation signal to obtain processed data; The host computer module is used to display the processed data and save the processed data.

6. The method for detecting the degree of thermal aging damage of heat-resistant steel by integrating dynamic magnetization and multiple effects according to claim 5 is characterized in that: The control module is an FPGA chip.

7. The method for detecting the degree of thermal aging damage of heat-resistant steel by integrating dynamic magnetization and multiple effects according to claim 6 is characterized in that: The excitation module includes: a high-frequency excitation amplifier and a low-frequency excitation amplifier; The high-frequency excitation amplifier is used to perform high-frequency excitation constant-amplification amplification on the excitation signal to obtain an amplified high-frequency excitation signal; The low-frequency excitation amplifier is used to perform low-frequency excitation constant-amplitude amplification on the excitation signal to obtain an amplified low-frequency excitation signal.

8. The method for detecting the degree of thermal aging damage of heat-resistant steel by integrating dynamic magnetization and multiple effects according to claim 7 is characterized in that: The acquisition and processing module includes: a sampling circuit, an amplification circuit and an AD conversion unit; the amplification circuit includes: a circuit incremental permeability amplification circuit, a tangential magnetic field intensity amplification circuit and a magnetic Barkhausen amplification circuit; The sampling circuit is used to collect the amplified excitation signal to obtain a collected signal; The circuit incremental permeability amplifier circuit is used to amplify the collected signal to obtain an incremental permeability signal; The tangential magnetic field strength amplification circuit is used to amplify the collected signal to obtain a tangential magnetic field strength signal; The magnetic Barkhausen amplifier circuit is used to amplify the collected signal to obtain a magnetic Barkhausen signal; The AD conversion unit is used to perform AD conversion on the incremental magnetic permeability signal, the tangential magnetic field intensity signal and the magnetic Barkhausen signal to obtain converted signals.

9. A dynamic magnetization multi-effect fusion heat aging damage degree detection method for heat-resistant steel according to claim 8, characterized in that: The low-frequency excitation amplifier is further used to generate a low-frequency excitation reference signal and transmit the low-frequency excitation reference signal to the AD conversion unit.

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