CN112051323A - Weak magnetic detection method for bonding quality of ceramic matrix composite - Google Patents
Weak magnetic detection method for bonding quality of ceramic matrix composite Download PDFInfo
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Abstract
The invention relates to a weak magnetic detection method for bonding quality of a ceramic matrix composite, which comprises the following steps: step (1): acquiring magnetic induction intensity data of the surface of the ceramic matrix composite by using a fluxgate probe; step (2): carrying out differential processing on the magnetic induction intensity data acquired by each fluxgate probe to obtain a magnetic field gradient value; and (3): constructing a standard normal distribution function with respect to the magnetic field gradient values; and (4): and judging whether the magnetic field gradient value is a defect signal or not by setting a threshold limit of a confidence interval of a standard normal distribution function of the magnetic field gradient value. The method can accurately detect the bonding condition of the ceramic-based bonding surface, and has strong practicability.
Description
Technical Field
The invention relates to the technical field of weak magnetic detection, in particular to a weak magnetic detection method for bonding quality of a ceramic matrix composite.
Background
The silicon carbide ceramic matrix composite is the most widely applied novel ultra-high temperature material in the world today. Compared with other materials, the material has high temperature resistance, oxidation resistance and corrosion resistance; and has higher mechanical properties due to more complicated fracture mechanism under impact load. In recent years, the method is widely applied to various fields such as petrochemical industry, aerospace, national defense industry and the like. The ceramic base and the metal material are bonded by gel in a special proportion, and the gel is used for engine housings of national defense missiles and invisible airplanes and is a heat insulation material with excellent effect.
The nondestructive detection is to detect whether the detected object has defects or non-uniformity by using the characteristics of sound, light, magnetism, electricity and the like of the substance on the premise of not damaging or influencing the use performance of the detected object, and give information such as the size, position, property, quantity and the like of the defects. Compared with destructive detection, nondestructive detection has the following characteristics. The first is non-destructive, because it will not damage the use performance of the detected object when detecting; secondly, the detection is comprehensive, and as the detection is nondestructive, 100% of the comprehensive detection can be carried out on the detected object if necessary, which cannot be achieved by destructive detection; and thirdly, the destructive testing is complete, the destructive testing is generally only suitable for testing raw materials, such as stretching, compression, bending and the like commonly adopted in mechanical engineering, the destructive testing is carried out on the raw materials for manufacturing, and the destructive testing cannot be carried out on finished products and articles unless the finished products and the articles are not ready to be used continuously, and the nondestructive testing does not damage the service performance of the tested object. Therefore, the method not only can carry out the whole-process detection on the raw materials for manufacturing, all the intermediate process links and the final finished products, but also can carry out the detection on the equipment in service.
The common nondestructive testing method comprises the following steps: eddy Current Test (ECT), Radiographic Test (RT), Ultrasonic Test (UT), magnetic particle test (MT), and liquid Penetration Test (PT). Other non-destructive testing methods: acoustic emission inspection (AE), thermographic/infrared (TIR), Leak Test (LT), ac field measurement technique (ACFMT), magnetic flux leakage test (MFL), far field test detection method (RFT), ultrasonic diffraction time difference method (TOFD), and the like.
The eddy current inspection method works on the principle of electromagnetic induction, so that the eddy current inspection method can detect surface defects and near-surface defects of a workpiece. The salient feature of eddy current testing is that it works with conductive materials, not necessarily ferromagnetic materials, but poorly with ferromagnetic materials. Secondly, the smoothness, flatness and boundary of the surface of the workpiece to be detected have great influence on the eddy current, so the eddy current detection method is often used for detecting flaws of non-ferromagnetic workpieces such as copper pipes with regular shapes and smooth surfaces.
The ray detection is one of five major conventions of nondestructive detection, and the principle is that the position and size of a defect are judged by comparing the change of the ray intensity by utilizing the mechanism that the photon of an X ray can penetrate through an object and generate complex physical and chemical actions. The detection rate is high, and the defect imaging is more visual; however, the X-ray is not sensitive to the air layer, and the method cannot always ensure that the interface is detected in a bonding and debonding state.
For ultrasonic detection, when ultrasonic waves propagate in a detected material, the acoustic characteristics of the material and the changes of internal tissues have certain influence on the propagation of the ultrasonic waves, and the technology of knowing the material performance and structural changes by detecting the influence degree and condition of the ultrasonic waves is called ultrasonic detection. The ultrasonic detection method generally includes a transmission method, a pulse reflection method, a tandem method, and the like. The method has the disadvantages that the attenuation of ultrasonic waves in air is fast, and the detection efficiency is low because a sound wave propagation medium such as couplant such as oil or water is generally required during detection. And the ceramic matrix structure is complex, the ultrasonic attenuation is serious, and the debonding detection effect of the ceramic matrix composite material is not good.
The magnetic powder inspection detects the leakage magnetic flux formed at the defect position, is only suitable for ferromagnetic materials, and can form the leakage magnetic flux on the surface of a workpiece only by the defects on the surface and the near surface after the ferromagnetic materials are magnetized. In the magnetic leakage detection, magnetization is a prerequisite for detection, and determines whether a measured object can generate a magnetic field signal to be measured and distinguished, and influences the performance characteristics of the detection signal and the structural characteristics of the measuring device. The leakage flux detection mechanism determines the complexity of the detection device and correspondingly increases the unreliability of the detection system.
The penetration test is a nondestructive testing method for inspecting surface opening defects based on the principle of capillary action. It is often used for surface defect detection, but near-surface defects are difficult to detect. Therefore, the penetration detection is not suitable for the detection of the debonding of the ceramic matrix composite material.
Disclosure of Invention
The invention aims to provide a weak magnetic detection method for the bonding quality of a ceramic matrix composite material, which can accurately detect the bonding quality of a ceramic matrix bonding surface.
The technical scheme adopted by the invention for solving the technical problems is as follows: the method for detecting the bonding quality weak magnetism of the ceramic matrix composite material comprises the following steps:
step (1): acquiring magnetic induction intensity data of the surface of the ceramic matrix composite by using a fluxgate probe;
step (2): carrying out differential processing on the magnetic induction intensity data acquired by each fluxgate probe to obtain a magnetic field gradient value;
and (3): constructing a standard normal distribution function with respect to the magnetic field gradient values;
and (4): and judging whether the magnetic field gradient value is a defect signal or not by setting a threshold limit of a confidence interval of a standard normal distribution function of the magnetic field gradient value.
The step (3) is specifically as follows: constructing a standard normal distribution function for the magnetic field gradient values by the magnetic field gradient values, an average of the magnetic field gradient values, and a standard deviation of the magnetic field gradient values, with the formula:
wherein Δ B represents a magnetic field gradient value, μ represents an average value of the magnetic field gradient values and
σ represents the standard deviation of the magnetic field gradient and
n represents the number of sampling points of the fluxgate probe.
The step (4) is specifically as follows: if the gradient value of the magnetic field intensity exceeds the threshold value limit of a preset confidence interval, indicating that the detection area has defects; and if the magnetic field intensity gradient value is within the threshold value limit of the preset confidence interval, indicating that no defect exists in the detection area.
The step (1) further comprises preprocessing the acquired magnetic induction intensity data by a cubic threshold line method, a segmented threshold line method or an extreme value method.
In the step (2), the difference processing is performed on the magnetic induction intensity data acquired by each fluxgate probe, specifically: and for the magnetic induction intensity data acquired by each fluxgate probe, subtracting the former data from the latter data to perform differential processing.
The magnetic induction data are discrete data.
Advantageous effects
Due to the adoption of the technical scheme, compared with the prior art, the invention has the following advantages and positive effects: the method for detecting the bonding quality of the ceramic matrix composite product by weak magnetism solves the problem of quality detection of the bonding surface of the ceramic matrix, can accurately detect the bonding condition of the ceramic matrix, does not need excitation, can detect in service, and has high detection efficiency and strong practicability.
Drawings
FIG. 1 is a schematic diagram of the detection principle of an embodiment of the present invention;
FIG. 2 is a schematic representation of a host computer interface according to an embodiment of the present invention;
FIG. 3 is a schematic view of a differential geometry according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a standard normal distribution according to an embodiment of the present invention.
Detailed Description
The invention will be further illustrated with reference to the following specific examples. It should be understood that these examples are for illustrative purposes only and are not intended to limit the scope of the present invention. Further, it should be understood that various changes or modifications of the present invention may be made by those skilled in the art after reading the teaching of the present invention, and such equivalents may fall within the scope of the present invention as defined in the appended claims.
The embodiment of the invention relates to a weak magnetic detection method for the bonding quality of a ceramic matrix composite, which also becomes a new research direction for the detection of the bonding quality of the ceramic matrix composite along with the wide use of the ceramic matrix composite. The detection device in the embodiment comprises an upper computer, a signal acquisition board and a fluxgate probe. The main function of the upper computer is to perform mapping, preliminary analysis and storage on the acquired data; the signal acquisition board mainly has the function of converting the electric signal analog quantity acquired by the fluxgate probe into digital quantity to facilitate the processing of an upper computer; the fluxgate probe has the main functions of acquiring magnetic induction intensity data, scanning the ceramic substrate surface along the same direction by holding the fluxgate probe by a detector, converting the acquired magnetic induction intensity signal into an analog electric signal and sending the analog electric signal to the signal acquisition board.
As shown in fig. 1, which is a schematic view of a detection principle of an embodiment of the present invention, fig. 1 shows a fluxgate probe placement and tooling, as shown in fig. 2, which is a schematic view of an upper computer interface of an embodiment of the present invention, 12 signal channels are designed on a signal acquisition board portion of the embodiment of the present invention, and the 12 channels (CH 1-CH 12) are respectively responsible for analyzing and processing data acquired by the 12 fluxgate probes. The 12-channel design also ensures that the detection device can be adapted to different sizes of material.
This embodiment is critical to the analysis and processing of data for ceramic-based bond detection, and requires the separation of both effective defect signals and unwanted noise signals. From the detection experience, it is known that the detected magnetic field signal contains aperiodic electrical noise in the case of uncertainty in the material and instability during operation. Based on this, the present embodiment considers the collected magnetic field signal as one large data sample by means of mathematical statistics and follows a standard normal distribution. When the magnetic field intensity of the ceramic-based bonding surface magnetized by the geomagnetic field is a conventional magnetic signal, the acquired defect signal can be regarded as a small-probability event, and the defect signal can be judged if the signal exceeds a threshold line by setting a threshold of a confidence interval.
In the present embodiment, the difference processing needs to be involved, and the idea of the difference processing is based on differentiation, so before the difference processing is introduced, the principle of differentiation will be explained, specifically as follows:
because the magnetic field signal is a vector signal, the magnetic field intensity perpendicular to the surface direction, namely the normal direction, is collected by the detection device, and in order to better analyze a signal curve, gradient processing is carried out on the magnetic signal in the embodiment. The magnetic field gradient refers to the maximum rate of change of the magnetic field strength in the normal direction, expressed as Δ B, and is given by the formula:
wherein, B represents the magnetic field intensity acquired by the fluxgate probe in real time, and x represents the detection distance.
Differentiation is an effective sharpening processing means, and is defined by the following steps: in a rectangular coordinate system, the function y ═ f (x) is a curve, and the function is continuous and derivable in some interval, Δ x is represented in x0The x increment at this point, then Δ y increment at Δ x can be expressed as:
Δy＝AΔx+o(Δx)
where a is a constant independent of Δ x, and o (Δ x) denotes the higher order infinitesimal of Δ x, then y ═ f (x) is calculated at x ═ x0In some cases, a Δ x may be referred to as the function y ═ f (x), where x ═ x0The differential of (c).
Dividing both sides of the formula Δ y ═ a Δ x + o (Δ x) by Δ x to obtain
When Δ x → 0, formula
It can be written as f' (x) ═ a, so if the function f (x) is at point x0Can be fine, then f (x) at point x0And a ═ f (x), then f (x) is in x0The differential at (a) can also be expressed as dy ═ f' (x) dx.
FIG. 3 is a schematic diagram of the differential geometry of an embodiment of the present invention, the geometric meaning of the differential being for a fixed x0Value, there is a point M (x) on the curve0,y0) When the independent variable x is increased by Δ x, another point N (x) on the corresponding curve can be obtained0+Δx,y0+ Δ y), as can be seen from fig. 3:
MQ＝Δx
QN＝Δy
the crossing point M is a tangent to the curve, and the angle of inclination of the tangent to the x-axis is α, then QP ═ MQ · tan α ═ Δ x · f' (x ═ M · tan ═ M { (x) } n0) I.e., dy is QP.
Based on the above analysis, when Δ x approaches zero, according to the meaning of differentiation, the tangent of the curve at a certain point can be approximated to replace the curve segment of the curve, and a linear function is approximated to replace the nonlinear function, which is mathematically called linearization of the nonlinear function, and is the basic idea method of differential science. The idea method is often adopted in the research of natural science and engineering problems to analyze corresponding problems
The specific method steps in the embodiment are as follows:
step (1): acquiring magnetic induction intensity data of the surface of the ceramic matrix composite by using a fluxgate probe;
step (2): carrying out differential processing on the magnetic induction intensity data acquired by each fluxgate probe to obtain a magnetic field gradient value;
and (3): constructing a standard normal distribution function with respect to the magnetic field gradient values;
and (4): and judging whether the magnetic field gradient value is a defect signal or not by setting a threshold limit of a confidence interval of a standard normal distribution function of the magnetic field gradient value.
The magnetic induction data acquired in this embodiment is discrete data.
Further, the step (1) further comprises preprocessing the collected magnetic induction intensity data by a cubic threshold line method, a segmented threshold line method or an extreme value method, wherein the cubic threshold line method is to remove points exceeding the threshold line on the basis of taking the threshold line by a normal gradient method, then take the threshold line again, judge whether points exceeding the threshold line again exist, repeat for three times, and aims to eliminate the problem that the threshold line is too high due to too large fluctuation of a certain point, so that some tiny defects are easy to miss detection, and the normal gradient method is to take the threshold line by the whole data group; the segmented threshold line method is that a threshold value is taken once every 15 data points, the threshold line is finer, and the purpose of preventing small defects from missing detection is also achieved; the extreme method is used for judging whether defects exist or not by capturing extreme points of the whole data set and judging whether defects exist or not according to the magnitude of the extreme difference, and the extreme method is suitable for being used when defect signals are obvious or defect waveform trends are fixed.
Further, in the step (2), the difference processing is performed on the magnetic induction data acquired by each fluxgate probe, specifically: and for the magnetic induction intensity data acquired by each fluxgate probe, subtracting the former data from the latter data to perform differential processing.
Further, the step (3) is specifically: constructing a standard normal distribution function for the magnetic field gradient values by the magnetic field gradient values, an average of the magnetic field gradient values, and a standard deviation of the magnetic field gradient values, with the formula:
that is, the division of Δ B- μ by σ obeys the normal distribution N (μ, σ)2) Where Δ B represents the magnetic field gradient value, μRepresents the average value of the gradient values of the magnetic field and
σ represents the standard deviation of the magnetic field gradient and
n represents the number of sampling points of the fluxgate probe.
Further, the step (4) is specifically as follows: if the gradient value of the magnetic field intensity exceeds the threshold value limit of a preset confidence interval, indicating that the detection area has defects; and if the magnetic field intensity gradient value is within the threshold value limit of the preset confidence interval, indicating that no defect exists in the detection area.
As shown in fig. 4, in the normal distribution diagram of the embodiment of the present invention, the probability of the magnetic gradient value within the confidence interval (μ -3 σ, μ +3 σ) in the uniform space is 99.7% under the normal distribution with μ -3 σ as the lower threshold limit of the confidence interval and μ +3 σ as the upper threshold limit of the confidence interval. And when the magnetic field strength gradient value exceeds a preset confidence interval (mu-3 sigma, mu +3 sigma), indicating that the detection area has defects, and if the magnetic field strength gradient value is within the preset confidence interval (mu-3 sigma, mu +3 sigma), indicating that the detection area has no defects.
Therefore, the method for detecting the bonding quality of the ceramic matrix composite product by weak magnetism solves the problem of quality detection of the ceramic matrix bonding surface, can accurately detect the bonding condition of the ceramic matrix, and has strong practicability.
Claims (6)
1. A method for detecting the bonding quality weak magnetism of a ceramic matrix composite is characterized by comprising the following steps:
step (1): acquiring magnetic induction intensity data of the surface of the ceramic matrix composite by using a fluxgate probe;
step (2): carrying out differential processing on the magnetic induction intensity data acquired by each fluxgate probe to obtain a magnetic field gradient value;
and (3): constructing a standard normal distribution function with respect to the magnetic field gradient values;
and (4): and judging whether the magnetic field gradient value is a defect signal or not by setting a threshold limit of a confidence interval of a standard normal distribution function of the magnetic field gradient value.
2. The method for detecting the bonding quality weak magnetism of the ceramic matrix composite material according to claim 1, wherein the step (3) is specifically as follows: constructing a standard normal distribution function for the magnetic field gradient values by the magnetic field gradient values, an average of the magnetic field gradient values, and a standard deviation of the magnetic field gradient values, with the formula:
wherein Δ B represents a magnetic field gradient value, μ represents an average value of the magnetic field gradient values and
σ represents the standard deviation of the magnetic field gradient and
n represents the number of sampling points of the fluxgate probe.
3. The method for detecting the bonding quality weak magnetism of the ceramic matrix composite material according to claim 1, wherein the step (4) is specifically as follows: if the gradient value of the magnetic field intensity exceeds the threshold value limit of a preset confidence interval, indicating that the detection area has defects; and if the magnetic field intensity gradient value is within the threshold value limit of the preset confidence interval, indicating that no defect exists in the detection area.
4. The method for detecting the bonding quality of the ceramic matrix composite material according to claim 1, wherein the step (1) further comprises preprocessing the collected magnetic induction intensity data by a cubic threshold line method, a segmented threshold line method or an extreme value method.
5. The method for detecting the bonding quality flux weakening of the ceramic matrix composite material according to claim 1, wherein in the step (2), the difference processing is performed on the magnetic induction intensity data acquired by each fluxgate probe, specifically: and for the magnetic induction intensity data acquired by each fluxgate probe, subtracting the former data from the latter data to perform differential processing.
6. The method for detecting the bonding quality of the ceramic matrix composite material in a weak magnetic field according to claim 1, wherein the magnetic induction data is discrete data.
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| CN113567541A (en) * | 2021-08-10 | 2021-10-29 | 南昌航空大学 | Absolute weak magnetic detection method and detection device |
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| CN113567541A (en) * | 2021-08-10 | 2021-10-29 | 南昌航空大学 | Absolute weak magnetic detection method and detection device |
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Application publication date: 20201208 |