CN113325084B - Method for detecting mechanical property of ferromagnetic material based on sound velocity effect - Google Patents
Method for detecting mechanical property of ferromagnetic material based on sound velocity effect Download PDFInfo
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Abstract
The invention discloses a method for detecting the mechanical property of a ferromagnetic material based on the sound velocity effect, which comprises the steps of exciting ultrasonic waves by using an electromagnetic ultrasonic transducer based on the magnetostrictive effect, constructing a GRNN neural network according to the characteristic that the mechanical property of the ferromagnetic material is closely related to the wave velocity and the magnetostrictive coefficient but lacks of the ferromagnetic material, taking the characteristic value point and the wave velocity related to the magnetostrictive coefficient as network input, and taking the yield strength, the tensile strength and the elongation of the material as output to predict the mechanical property of the material. The accuracy of the prediction result with the relative error less than 10% can reach 100%.
Description
Technical Field
The invention relates to the technical field of nondestructive testing, in particular to a method for detecting the mechanical property of a ferromagnetic material based on the sound velocity effect.
Background
At present, the mechanical property detection method of the material is mainly destructive detection, and a material testing machine is used for measuring the tensile strength, the elongation percentage and the like of the material. Such methods are expensive and only sample detection can be achieved. Therefore, it is necessary to find suitable non-destructive testing techniques to determine the mechanical property parameters of the material.
The nondestructive inspection is to detect or measure a physical quantity change due to the presence of a defect or local unevenness in an object to be inspected by a predetermined means without damaging the object to be inspected such as a structure to be inspected or a product, to judge whether or not there is a defect in the internal structure or the surface of the object to be inspected, and to judge physical properties such as structural integrity, continuity, safety and reliability of the object to be inspected. Nondestructive testing has gained high attention and rapid development in the fields of energy, machinery, railways and the like due to the advantages of non-destructiveness, reliability, safety and the like, and is a technology playing a very key role in the aspects of quality control, raw material saving, process improvement and the like.
Disclosure of Invention
The invention aims to solve the technical problem of providing a method for detecting the mechanical property of a ferromagnetic material based on the sound velocity effect aiming at the defects involved in the background technology.
The invention adopts the following technical scheme to solve the technical problems
A method for detecting the mechanical property of a ferromagnetic material based on the sound velocity effect comprises the following steps:
step 1), taking N ferromagnetic material samples, wherein N is a natural number greater than 1, and for each ferromagnetic material sample:
step 1.1), placing the ferromagnetic material sample piece in a static magnetic field, increasing the strength of the static magnetic field from 0 to a preset maximum magnetic field strength threshold according to a preset magnetic field strength step length, and for each strength of the static magnetic field:
step 1.1.1), exciting ultrasonic waves at one end of a ferromagnetic material sample piece, and receiving the ultrasonic waves at the other end, wherein the intensity of the excited ultrasonic waves is a preset ultrasonic intensity threshold value;
step 1.1.2), collecting electromagnetic ultrasonic signals of a ferromagnetic material sample piece at a receiving end of the ferromagnetic material sample piece;
step 1.1.3), for the acquired electromagnetic ultrasonic signals:
step 1.1.3.1), carrying out low-pass filtering processing on the acquired electromagnetic ultrasonic signals according to a Matlab Butterworth filter function, and only preserving envelope signals; the method comprises the steps that a received signal is subjected to primary band-pass filtering, and the center frequency of a passband is a preset excitation frequency threshold; dividing the signals subjected to band-pass filtering into two paths of signals, wherein one path of signals is subjected to primary multiplication with a reference signal, and the other path of signals is subjected to primary multiplication with the signals subjected to 90-degree phase shift with the reference signal; the reference signal frequency is equal to a preset excitation frequency threshold value;
1.1.3.2), respectively passing the two paths of signals after multiplication through a low-pass filter to obtain an I path signal and a Q path signal after orthogonal demodulation, completing demodulation, filtering the alternating signals, keeping the envelope with definite rising edges, and extracting envelope signals;
step 1.1.3.3), obtaining the time difference between the transmitting signal and the receiving signal in the envelope signal through a Matlab programΔ tAnd the distance between the ultrasonic wave transmitting end and the ultrasonic wave receiving endd；
Step 1.1.3.4), calculating the wave velocity of the current ultrasonic wavev=d/Δt；
Step 1.2), drawing a curve of the electromagnetic ultrasonic signal amplitude of the ferromagnetic material sample piece at the receiving end of the ferromagnetic material sample piece along with the change of the static magnetic field intensity;
step 1.3), extracting 5 characteristic value points from the curve as the characteristic value points of the ferromagnetic material sample piece under the ultrasonic wave: difference between two peak currentsΔI p Difference between two valley currentsΔI v Peak to peak valueΔE p Area ofSAnd slopek；
Step 1.4), obtaining the yield strength, tensile strength and elongation of the ferromagnetic material sample piece;
step 2), taking the characteristic value points of the ferromagnetic material sample piece under the ultrasonic wave and the wave speed of the ultrasonic wave as input, and taking the yield strength, the tensile strength and the elongation of the ferromagnetic material sample piece as output to construct a GRNN neural network;
step 3), taking the data obtained in the step 1) as a training set, and training the constructed GRNN neural network by adopting Matlab to obtain a trained GRNN neural network;
step 4), if the ferromagnetic material to be detected needs to be detected, the mechanical property detection is carried out:
step 4.1), exciting ultrasonic waves at one end of a ferromagnetic material sample to be detected, receiving the ultrasonic waves at the other end, collecting characteristic value points of the ferromagnetic material under the ultrasonic waves and the wave speed of the ultrasonic waves, wherein the intensity of the excited ultrasonic waves is a preset ultrasonic wave intensity threshold value;
and 4.2) inputting the characteristic value points of the detected ferromagnetic material under the ultrasonic waves and the wave speed of the ultrasonic waves into the trained GRNN neural network to obtain the yield strength, the tensile strength and the elongation of the detected ferromagnetic material.
Compared with the prior art, the invention adopting the technical scheme has the following technical effects:
the invention utilizes the electromagnetic ultrasonic transducer based on the magnetostrictive effect to excite ultrasonic waves, and carries out nondestructive detection on the mechanical property of the ferromagnetic material according to the characteristic that the mechanical property of the ferromagnetic material is closely related to the magnetostrictive coefficient and the wave speed, and the accuracy rate of the prediction result with the relative error less than 10 percent can reach 100 percent.
Drawings
FIG. 1 is a schematic diagram of a detection system for detecting mechanical properties using wave velocity according to the present invention;
FIG. 2 is a schematic diagram of quadrature demodulation of a signal;
fig. 3 is a structural diagram of a constructed GRNN neural network.
Detailed Description
The technical scheme of the invention is further explained in detail by combining the attached drawings:
the present invention may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art. In the drawings, components are exaggerated for clarity.
The invention discloses a method for detecting the mechanical property of a ferromagnetic material based on the sound velocity effect, which comprises the following steps:
step 1), taking N ferromagnetic material samples, wherein N is a natural number greater than 1, and for each ferromagnetic material sample:
step 1.1), placing the ferromagnetic material sample piece in a static magnetic field, increasing the strength of the static magnetic field from 0 to a preset maximum magnetic field strength threshold according to a preset magnetic field strength step length, and for each strength of the static magnetic field:
step 1.1.1), exciting ultrasonic waves at one end of a ferromagnetic material sample piece, and receiving the ultrasonic waves at the other end, wherein the intensity of the excited ultrasonic waves is a preset ultrasonic intensity threshold value;
step 1.1.2), collecting electromagnetic ultrasonic signals of a ferromagnetic material sample piece at a receiving end of the ferromagnetic material sample piece;
step 1.1.3), for the acquired electromagnetic ultrasonic signals:
step 1.1.3.1), carrying out low-pass filtering processing on the acquired electromagnetic ultrasonic signals according to a Matlab Butterworth filter function, and only preserving envelope signals; the method comprises the steps that a received signal is subjected to primary band-pass filtering, and the center frequency of a passband is a preset excitation frequency threshold; dividing the signals subjected to band-pass filtering into two paths of signals, wherein one path of signals is subjected to primary multiplication with a reference signal, and the other path of signals is subjected to primary multiplication with the signals subjected to 90-degree phase shift with the reference signal; the reference signal frequency is equal to a preset excitation frequency threshold value;
1.1.3.2), respectively passing the two paths of signals after multiplication through a low-pass filter to obtain an I path signal and a Q path signal after orthogonal demodulation, completing demodulation, filtering the alternating signals, keeping the envelope with definite rising edges, and extracting envelope signals;
step 1.1.3.3), obtaining the time difference between the transmitting signal and the receiving signal in the envelope signal through a Matlab programΔ tAnd the distance between the ultrasonic wave transmitting end and the ultrasonic wave receiving endd；
Step 1.1.3.4), calculating the wave velocity of the current ultrasonic wavev=d/Δt；
Step 1.2), drawing a curve of the electromagnetic ultrasonic signal amplitude of the ferromagnetic material sample piece at the receiving end of the ferromagnetic material sample piece along with the change of the static magnetic field intensity;
step 1.3), extracting 5 characteristic value points from the curve as the characteristic value points of the ferromagnetic material sample piece under the ultrasonic wave: difference between two peak currentsΔI p Difference between two valley currentsΔI v Peak to peak valueΔE p Area ofSAnd slopek；
Step 1.4), obtaining the yield strength, tensile strength and elongation of the ferromagnetic material sample piece;
step 2), taking the characteristic value points of the ferromagnetic material sample piece under the ultrasonic wave and the wave speed of the ultrasonic wave as input, and taking the yield strength, the tensile strength and the elongation of the ferromagnetic material sample piece as output to construct a GRNN neural network;
step 3), taking the data obtained in the step 1) as a training set, and training the constructed GRNN neural network by adopting Matlab to obtain a trained GRNN neural network;
step 4), if the ferromagnetic material to be detected needs to be detected, the mechanical property detection is carried out:
step 4.1), exciting ultrasonic waves at one end of a ferromagnetic material sample to be detected, receiving the ultrasonic waves at the other end, collecting characteristic value points of the ferromagnetic material under the ultrasonic waves and the wave speed of the ultrasonic waves, wherein the intensity of the excited ultrasonic waves is a preset ultrasonic wave intensity threshold value;
and 4.2) inputting the characteristic value points of the detected ferromagnetic material under the ultrasonic waves and the wave speed of the ultrasonic waves into the trained GRNN neural network to obtain the yield strength, the tensile strength and the elongation of the detected ferromagnetic material.
The detection device for detecting mechanical properties by using wave velocity is shown in fig. 1 and comprises a transmitting coil, a receiving coil, a transmitting circuit module, a receiving circuit module, an electromagnet, a permanent magnet and a signal acquisition and processing module. Firstly, a transmitting probe is matched with a static magnetic field provided by an electromagnet to excite SH ultrasonic signals, the electromagnetic field signals are converted into ultrasonic signals, the ultrasonic signals are transmitted in a plate, vibration is collected by a receiving probe, the ultrasonic signals are converted into electric signals, and the electric signals are amplified, filtered and output through a receiving circuit.
Changing the static magnetic field intensity of the transmitting end, collecting the peak value of the received signal, drawing the curve of the peak value of the received signal changing with the static magnetic field, and extracting the current difference between the two peak valuesΔI p Difference between two valley currentsΔI v Peak to peak valueΔE p Area ofSAnd slopekAs a feature value point.
The received signal is processed by demodulation filtering, and the demodulation block diagram is as shown in fig. 2, and the propagation time of the ultrasonic wave from transmission to reception is measured in the envelope of the received signal, the distance between the transmitting and receiving probes is measured, and the distance-to-time ratio is calculated. Calculating the time difference between the transmitted and received signals in the envelopeΔtMeasuring the distance between the transmitting probe and the receiving probedWave velocityv=d/Δt。
The GRNN neural network is constructed, the characteristic value points and the wave velocity of the material are used as network input, the yield strength, the tensile strength and the elongation rate of the material are used as output, the mechanical property of the material is predicted, and the accuracy rate of the prediction result with the relative error of less than 10% can reach 100%.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The above-mentioned embodiments, objects, technical solutions and advantages of the present invention are further described in detail, it should be understood that the above-mentioned embodiments are only illustrative of the present invention and are not intended to limit the present invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (1)
1. A method for detecting the mechanical property of a ferromagnetic material based on the sound velocity effect is characterized by comprising the following steps:
step 1), taking N ferromagnetic material samples, wherein N is a natural number greater than 1, and for each ferromagnetic material sample:
step 1.1), placing the ferromagnetic material sample piece in a static magnetic field, increasing the strength of the static magnetic field from 0 to a preset maximum magnetic field strength threshold according to a preset magnetic field strength step length, and for each strength of the static magnetic field:
step 1.1.1), exciting ultrasonic waves at one end of a ferromagnetic material sample piece, and receiving the ultrasonic waves at the other end, wherein the intensity of the excited ultrasonic waves is a preset ultrasonic intensity threshold value;
step 1.1.2), collecting electromagnetic ultrasonic signals of a ferromagnetic material sample piece at a receiving end of the ferromagnetic material sample piece;
step 1.1.3), for the acquired electromagnetic ultrasonic signals:
step 1.1.3.1), carrying out low-pass filtering processing on the acquired electromagnetic ultrasonic signals according to a Matlab Butterworth filter function, and only preserving envelope signals; the method comprises the steps that a received signal is subjected to primary band-pass filtering, and the center frequency of a passband is a preset excitation frequency threshold; dividing the signals subjected to band-pass filtering into two paths of signals, wherein one path of signals is subjected to primary multiplication with a reference signal, and the other path of signals is subjected to primary multiplication with the signals subjected to 90-degree phase shift with the reference signal; the reference signal frequency is equal to a preset excitation frequency threshold value;
1.1.3.2), respectively passing the two paths of signals after multiplication through a low-pass filter to obtain an I path signal and a Q path signal after orthogonal demodulation, completing demodulation, filtering the alternating signals, keeping the envelope with definite rising edges, and extracting envelope signals;
step 1.1.3.3), obtaining the time difference between the transmitting signal and the receiving signal in the envelope signal through a Matlab programΔtAnd the distance between the ultrasonic wave transmitting end and the ultrasonic wave receiving endd；
Step 1.1.3.4), calculating the wave velocity of the current ultrasonic wavev=d/Δt；
Step 1.2), drawing a curve of the electromagnetic ultrasonic signal amplitude of the ferromagnetic material sample piece at the receiving end of the ferromagnetic material sample piece along with the change of the static magnetic field intensity;
step 1.3), extracting 5 characteristic value points from the curve as the characteristic value points of the ferromagnetic material sample piece under the ultrasonic wave: difference between two peak currentsΔI p Difference between two valley currentsΔI v Peak to peak valueΔE p Area ofSAnd slopek；
Step 1.4), obtaining the yield strength, tensile strength and elongation of the ferromagnetic material sample piece;
step 2), taking the characteristic value points of the ferromagnetic material sample piece under the ultrasonic wave and the wave speed of the ultrasonic wave as input, and taking the yield strength, the tensile strength and the elongation of the ferromagnetic material sample piece as output to construct a GRNN neural network;
step 3), taking the data obtained in the step 1) as a training set, and training the constructed GRNN neural network by adopting Matlab to obtain a trained GRNN neural network;
step 4), if the ferromagnetic material to be detected needs to be detected, the mechanical property detection is carried out:
step 4.1), exciting ultrasonic waves at one end of a ferromagnetic material sample to be detected, receiving the ultrasonic waves at the other end, collecting characteristic value points of the ferromagnetic material under the ultrasonic waves and the wave speed of the ultrasonic waves, wherein the intensity of the excited ultrasonic waves is a preset ultrasonic wave intensity threshold value;
and 4.2) inputting the characteristic value points of the detected ferromagnetic material under the ultrasonic waves and the wave speed of the ultrasonic waves into the trained GRNN neural network to obtain the yield strength, the tensile strength and the elongation of the detected ferromagnetic material.
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|---|---|---|---|---|
| SU1527564A1 (en) * | 1988-04-05 | 1989-12-07 | Предприятие П/Я А-1575 | Apparatus for inspecting mechanical properties of articles of ferromagnetic materials |
| CN108760117A (en) * | 2018-03-09 | 2018-11-06 | 南京航空航天大学 | The method that electromagnetic acoustic based on magnetostrictive effect measures plate stress |
| CN108896649A (en) * | 2018-04-28 | 2018-11-27 | 南京航空航天大学 | Method for estimating yield strength of ferromagnetic material based on electromagnetic ultrasound |
| CN110471010A (en) * | 2019-09-18 | 2019-11-19 | 北京工业大学 | The SH of ferrimagnet Magnetostrictive curve0Mode supersonic guide-wave measurement method |
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| SU1527564A1 (en) * | 1988-04-05 | 1989-12-07 | Предприятие П/Я А-1575 | Apparatus for inspecting mechanical properties of articles of ferromagnetic materials |
| CN108760117A (en) * | 2018-03-09 | 2018-11-06 | 南京航空航天大学 | The method that electromagnetic acoustic based on magnetostrictive effect measures plate stress |
| CN108896649A (en) * | 2018-04-28 | 2018-11-27 | 南京航空航天大学 | Method for estimating yield strength of ferromagnetic material based on electromagnetic ultrasound |
| CN110471010A (en) * | 2019-09-18 | 2019-11-19 | 北京工业大学 | The SH of ferrimagnet Magnetostrictive curve0Mode supersonic guide-wave measurement method |
Non-Patent Citations (2)
| Title |
|---|
| Method of measuring the mechanical properties of ferromagnetic materials based on magnetostrictive EMAT characteristic parameters;Ping Wang 等;《Measurement》;20200723;第168卷;第1-11页 * |
| 基于电磁超声的构件机械特性测试方法;韩笑 等;《2018远东无损检测新技术论坛》;20181231;第63-68页 * |
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