CN113588794B - Ultrasonic measurement method for defect size of polycrystalline material - Google Patents

Ultrasonic measurement method for defect size of polycrystalline material Download PDF

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CN113588794B
CN113588794B CN202111139539.1A CN202111139539A CN113588794B CN 113588794 B CN113588794 B CN 113588794B CN 202111139539 A CN202111139539 A CN 202111139539A CN 113588794 B CN113588794 B CN 113588794B
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李雄兵
宋永锋
倪培君
史亦韦
郑孟
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Abstract

The invention discloses an ultrasonic measurement method for the defect size of a polycrystalline material, which comprises the following steps: s1, establishing Rayleigh distribution of grain noise of the polycrystalline material and Rice distribution of amplitude of coherent defect echo between the grain noise and the defect echo based on a back scattering response model; s2, constructing a limit distribution model of the defect echo amplitude according to the Rayleigh distribution and the Laisi distribution, and calculating a confidence upper limit and a confidence lower limit according to the inverse cumulative distribution; and S3, acquiring interval estimation of the defect size of the polycrystalline material according to the coherent defect echo amplitude value measured by the ultrasonic measurement model and the C scanning experiment and the confidence upper limit and the confidence lower limit obtained in the step S2. The invention can effectively measure the size of the tiny defect in the environment with low signal-to-noise ratio.

Description

Ultrasonic measurement method for defect size of polycrystalline material
Technical Field
The invention relates to the field of ultrasonic measurement of material defect size, in particular to an ultrasonic measurement method of the defect size of a polycrystalline material.
Background
At present, the common mechanical engineering materials mainly comprise polycrystalline materials, such as iron-based alloys, aluminum-based alloys, copper-based alloys, nickel-based alloys and the like, and are widely applied to a plurality of important fields of aerospace, weaponry, ocean platforms and the like. However, the materials inevitably have tiny defects in the smelting and machining processes. In particular, the present invention relates to a micro defect which is a defect having a size of not more than 0.4mm but larger than the average grain size of the polycrystalline material. The fatigue resistance of the polycrystalline material is reduced due to the tiny defects, and potential hidden danger is formed on the fatigue life of the material; the micro defects easily induce the nucleation of cracks during the use of the product, and the continuous growth of the cracks can cause insufficient strength and finally cause fracture accidents.
According to the cumulative damage model, effective measurement of the micro-defect size is the basis for fatigue life prediction. The method for detecting and quantifying the defects of the polycrystalline material mainly comprises visual measurement, permeability measurement, magnetic powder measurement, eddy current measurement, ray measurement, ultrasonic measurement and the like. Visual inspection and infiltration methods can only measure surface open defects, magnetic powder and eddy current methods can measure near surface defects but cannot measure internal defects. The ray method is capable of detecting minute defects inside the material, but also requires that the workpiece not be too thick for the radiation to penetrate, and in addition the radiation may be harmful to the health of the inspector. The ultrasonic method can detect surface and near-surface defects and internal defects, and is harmless to human bodies, so that the ultrasonic method is commonly used for detecting defects and quantifying the sizes of the defects.
The current ultrasonic quantitative method for the defect size mainly comprises a-6 dB length measurement method and a Distance Amplitude Correction (DAC) curve method. The-6 dB length measurement method is only applicable when the defect size is larger than the wavelength, even the size is larger than the diameter of the probe, and the micro defect below 0.4mm is difficult to quantify. The DAC curve method mainly considers the influence of the defect buried depth on the defect echo amplitude, can carry out size quantification on the defects smaller than the diameter of the probe, and is suitable for the defects of sub-wavelength. However, the DAC curve method does not take into account the defect size quantification error caused by grain noise in the ultrasound signal.
However, when the ultrasonic wave propagates in the polycrystalline material, the ultrasonic wave is inevitably influenced by grain scattering, and the grain noise in the received signal causes the distortion of the defect echo, which finally leads to the failure of the existing ultrasonic quantitative characterization method. Structural noise is due to the material microstructure and cannot be removed using simultaneous averaging techniques for electrical noise. For most polycrystalline materials, structural noise, also referred to as grain noise, is caused by ultrasonic backscattering due to small acoustic impedance differences at grain boundaries. When grain noise is present, it will add or cancel coherently with the defect echo, thereby randomly affecting the amplitude of the coherent defect echo. This effect is particularly pronounced when the defect size is close to the average grain size and so the DAC curve method will fail.
The background description provided herein is for the purpose of generally presenting the context of the disclosure. Unless otherwise indicated herein, the material described in this section is not prior art to the claims in this application and is not admitted to be prior art by inclusion in this section.
Disclosure of Invention
In order to solve the technical problem, the invention provides an ultrasonic measurement method for the defect size of a polycrystalline material, which comprises the following steps:
s1, establishing Rayleigh distribution of grain noise of the polycrystalline material and Rice distribution of amplitude of coherent defect echo between the grain noise and the defect echo based on a back scattering response model;
s2, constructing a limit distribution model of the defect echo amplitude according to the Rayleigh distribution and the Laisi distribution, and calculating a confidence upper limit and a confidence lower limit according to the inverse cumulative distribution;
and S3, acquiring interval estimation of the defect size of the polycrystalline material according to the coherent defect echo amplitude value measured by the ultrasonic measurement model and the C scanning experiment and the confidence upper limit and the confidence lower limit obtained in the step S2.
Specifically, step S3 specifically includes: and constructing a relation between the defect size and the defect echo amplitude according to an ultrasonic measurement model, obtaining point estimation according to the relation between the defect size and the defect echo amplitude and a coherent defect echo amplitude measured by a C scanning experiment, and obtaining two end points of interval estimation according to an inverse function of the relation between the defect size and the defect echo amplitude and the upper and lower confidence limits.
Specifically, the step S2 specifically includes: and establishing a three-section limit density function taking a zero point as a limit according to the Rayleigh distribution and the Rice distribution, and respectively acquiring the mathematical expectation, the upper confidence limit and the lower confidence limit according to the three-section limit density function.
Specifically, the step S1 specifically includes: the rayleigh distribution is constructed based on a zero-mean normal distribution of grain noise.
Specifically, the step S1 includes:
s11, assuming that the grain noise in the RF sampling mode is spatially in accordance with the normal distribution of zero mean, the cumulative distribution function of the grain noise amplitude in the envelope sampling mode is
Figure 947528DEST_PATH_IMAGE001
(1)
In the formula
Figure 975527DEST_PATH_IMAGE002
Is composed of
Figure 812902DEST_PATH_IMAGE003
The magnitude of the grain noise at a time instant,
Figure 799312DEST_PATH_IMAGE004
is the standard deviation;
s12, according to the back scattering response model, when the water immersion ultrasonic C scanning system works in the longitudinal wave-longitudinal wave mode, the standard deviation in the step S11 is
Figure 105660DEST_PATH_IMAGE005
(2)
In the formula
Figure 202929DEST_PATH_IMAGE006
Is a calibration parameter of the ultrasonic measurement system,
Figure 691548DEST_PATH_IMAGE007
is the spatial correlation coefficient of the microstructure of the polycrystalline material,
Figure 724226DEST_PATH_IMAGE008
is the average grain radius，
Figure 642504DEST_PATH_IMAGE009
Is the scattering intensity of a polycrystalline material,
Figure 136939DEST_PATH_IMAGE010
is the integral of the ultrasonic field in the polycrystalline material,
Figure 496376DEST_PATH_IMAGE011
is the longitudinal wave attenuation coefficient of the polycrystalline material;
s13, assuming that the highest wave of coherent defect echo between grain noise and defect echo is located
Figure 824589DEST_PATH_IMAGE012
Time of day, maximum amplitude of coherent defect echo
Figure 89217DEST_PATH_IMAGE013
Obey rice distribution
Figure 793868DEST_PATH_IMAGE014
(3)
In the formula
Figure 211074DEST_PATH_IMAGE015
Is a 1 st order markan Q function,
Figure 710189DEST_PATH_IMAGE016
in order to be the amplitude of the defect echo,
Figure 462113DEST_PATH_IMAGE017
is a time of day
Figure 439296DEST_PATH_IMAGE018
Get
Figure 976588DEST_PATH_IMAGE019
Standard deviation of time; wherein the 1 st order Markan Q function is
Figure 646603DEST_PATH_IMAGE020
(4)
In the formula
Figure 891683DEST_PATH_IMAGE021
A first type of modified bessel function of zero order.
Specifically, the step S2 specifically includes:
s21, assuming that the highest wave amplitude value of coherent defect echo obtained by actual measurement is
Figure 406978DEST_PATH_IMAGE022
Defining a limit distribution function of the amplitude of the defect echo according to the Rayleigh distribution and the Rice distribution in the step S1 as
Figure 798776DEST_PATH_IMAGE023
(5)
Wherein envelope amplitude coincidence of defect echo is specified
Figure 639693DEST_PATH_IMAGE024
I.e. its lower bound is zero; so that the corresponding limiting density function is
Figure 366210DEST_PATH_IMAGE025
(6)
In the formula
Figure 950775DEST_PATH_IMAGE026
A first type of modified Bessel function that is first order;
s22, calculating the mathematical expected value of the amplitude of the defect echo according to the limit density function
Figure 993817DEST_PATH_IMAGE027
(7)
The infinite integral in the formula can be cut off in numerical calculation, and numerical integration is carried out;
s23, calculating the confidence upper limit and the confidence lower limit of the defect echo amplitude value according to the limit density function
Figure 130269DEST_PATH_IMAGE028
(8)
Figure 953869DEST_PATH_IMAGE029
(9)
Inverse cumulative distribution function in formula
Figure 217491DEST_PATH_IMAGE030
The calculation may be performed by an interpolation algorithm.
Specifically, the step S3 specifically includes:
s31, assuming the transverse hole as the detection equivalent of the defect size, the ultrasonic measurement model is
Figure 442936DEST_PATH_IMAGE031
(10)
In the formula
Figure 750290DEST_PATH_IMAGE032
Is the equivalent radius of the defect size,
Figure 326764DEST_PATH_IMAGE033
as a system function of the ultrasonic measurement system,
Figure 128498DEST_PATH_IMAGE034
in order to be the angular frequency of the frequency,
Figure 208450DEST_PATH_IMAGE035
the number of longitudinal waves is the number of longitudinal waves,
Figure 952284DEST_PATH_IMAGE036
is the surface area of the ultrasound probe,
Figure 750475DEST_PATH_IMAGE037
and
Figure 90321DEST_PATH_IMAGE038
the density and longitudinal wave sound velocity of the polycrystalline material,
Figure 290358DEST_PATH_IMAGE039
and
Figure 205094DEST_PATH_IMAGE040
respectively the density and the sound velocity of the coupling fluid,
Figure 225002DEST_PATH_IMAGE041
the length of the transverse hole is the same as the length of the transverse hole,
Figure 368539DEST_PATH_IMAGE042
is the amplitude of the sound field at the cross-hole defect,
Figure 423082DEST_PATH_IMAGE043
is the position of the defect of the transverse hole,
Figure 508719DEST_PATH_IMAGE044
far field defect scattering amplitude for transverse holes, operator
Figure 281503DEST_PATH_IMAGE045
Representing an inverse fourier transform;
s32, through the ultrasonic measurement model in S31, the relation between the defect size and the defect echo amplitude can be established, namely the DAC curve is
Figure 228730DEST_PATH_IMAGE046
(11)
Operator in formula
Figure 872201DEST_PATH_IMAGE047
Representing a hilbert transform;
s33, sweeping by CDetermining the actual coherent defect echo of the defect, i.e. giving the amplitude of the highest wave
Figure 128739DEST_PATH_IMAGE048
Further, the point at which the defect size under the influence of grain noise can be estimated by using the formula (11) in S32 is
Figure 123240DEST_PATH_IMAGE049
(12)
In the formula
Figure 874158DEST_PATH_IMAGE050
Is the equivalent diameter of the defect size,
Figure 903294DEST_PATH_IMAGE051
the inverse function of the formula (11) can be solved by an interpolation method; and the interval of the defect size is estimated as
Figure 59295DEST_PATH_IMAGE052
(13)
Figure 806671DEST_PATH_IMAGE053
(14)
In the formula
Figure 95701DEST_PATH_IMAGE054
And
Figure 979343DEST_PATH_IMAGE055
is the upper confidence limit for the defect diameter and radius,
Figure 312104DEST_PATH_IMAGE056
and
Figure 281197DEST_PATH_IMAGE057
is the lower confidence limit for the defect diameter and radius.
In a second aspect, another embodiment of the present invention discloses a method for ultrasonic measurement of a defect size of a polycrystalline material, comprising the steps of:
s10, obtaining coherent defect echo amplitude of the polycrystalline material based on an ultrasonic measurement model and a C-scan experiment;
s20, obtaining interval estimation of the defect size of the polycrystalline material according to the coherent defect echo amplitude of the polycrystalline material, the upper confidence limit and the lower confidence limit; the upper confidence limit and the lower confidence limit are three-section limit density functions which are established according to Rayleigh distribution of polycrystalline material grain noise and Rice distribution of coherent defect echo amplitude between the grain noise and the defect echo and take a zero point as a limit, and mathematical expectations, the upper confidence limit and the lower confidence limit are respectively obtained according to the three-section limit density functions; and two end points of the interval estimation are respectively obtained according to an inverse function of the relation between the defect size and the defect echo amplitude, and the upper limit and the lower limit of confidence.
In the embodiment, the rayleigh distribution of the grain noise is calculated by the ultrasonic backscattering model and the microstructure parameters of the polycrystalline material; describing coherent defect echo amplitude between grain noise and defect echo by using a Rice distribution; and establishing a limit distribution model of the defect echo amplitude through Rayleigh distribution and Rice distribution, and obtaining an upper confidence limit and a lower confidence limit according to the limit distribution model of the curve echo, thereby realizing point estimation and interval estimation of the defect size of the polycrystalline material. The defect size quantification error caused by grain noise in an ultrasonic signal is not considered in the traditional DAC curve method, and the method of the embodiment can effectively measure the size of a tiny defect and uncertainty of the tiny defect in the environment with a low signal-to-noise ratio. In addition, the boundary distribution model established in this embodiment is obtained by calculating the probability density in three segments with a zero point as a boundary.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic diagram of a system for ultrasonic measurement of defect size of polycrystalline material according to the present invention;
FIG. 2 is a flow chart of a method for ultrasonic measurement of defect size of polycrystalline material according to the present invention;
FIG. 3 is a design diagram of a test block containing a micro transverse hole defect according to the present invention;
FIG. 4 is a DAC graph based on an ultrasonic measurement model in the present invention;
FIG. 5 is a C-scan image of a test block containing a micro transverse hole defect according to the present invention;
FIG. 6 is a boundary probability density distribution diagram of defect echo amplitudes in accordance with the present invention;
FIG. 7 is a schematic view of another method of ultrasonic measurement of defect size of polycrystalline material according to the present invention;
FIG. 8 is a schematic structural diagram of an ultrasonic measurement apparatus for measuring a defect size of a polycrystalline material according to the present invention.
The reference numbers 1 are as follows:
1-industrial personal computer, 2-high-speed data acquisition card, 3-ultrasonic instrument, 4-ultrasonic longitudinal wave probe, 5-motion control card, 6-control circuit, 7-five-degree-of-freedom motion platform, 8-probe frame, 9-tested block, 10-water tank and 11-purified water.
Detailed Description
Example one
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments that can be derived by one of ordinary skill in the art from the embodiments given herein are intended to be within the scope of the present invention.
Referring to fig. 1, fig. 1 is a schematic structural diagram of an ultrasonic measurement system for measuring a defect size of a polycrystalline material according to the present embodiment, the ultrasonic measurement system comprising: the industrial personal computer 1 is used for controlling bottom hardware and operation; the high-speed data acquisition card 2 is used for acquiring an ultrasonic A signal; an ultrasound apparatus 3, said ultrasound apparatus 3 being for exciting and receiving ultrasound probe signals; an ultrasonic longitudinal wave probe 4, wherein the ultrasonic longitudinal wave probe 4 is used for transmitting and receiving ultrasonic waves; the motion control card 5 is used for controlling a motion platform control circuit through an upper computer; a control circuit 6, the control circuit 6 being used to operate the motion platform; the five-degree-of-freedom motion platform 7 comprises three degrees of freedom in x, y and z directions and two degrees of freedom rotating around the x and y directions; the probe frame 8 is used for connecting the motion platform and the ultrasonic probe; a test block 9, wherein the test block 9 is a 304 stainless steel test block; the water tank 10 is used for bearing the motion platform, the test block and the coupling liquid; a pure water 11, the pure water 11 serving as a coupling liquid for ultrasonic wave propagation.
In the present example, the five-degree-of-freedom motion platform 7 is a five-degree-of-freedom motion platform produced by Shanghai Liangyi electromechanical Co., Ltd; the high-speed data acquisition card 2 adopts a PCI-9852 digital acquisition card of Taiwan Linghua; the ultrasonic instrument 3 adopts a DPR300 type ultrasonic pulse generator/receiver of JSR; the ultrasonic longitudinal wave probe 4 adopts a GE BENCHMARK 864-360 IS/5.0/1.0 type high-resolution water immersion ultrasonic focusing probe, and the central frequency IS 5 MHz; metallographic analysis was performed using a two-pan grinding and polishing machine model MetaServ 250 from Buehler and a metallographic microscope model BX53M from Olympus.
This example uses a 304 stainless steel test block containing micro-defects as an example to illustrate the ultrasonic measurement method of the micro-defect size of the polycrystalline material of this example. In the embodiment, a tested block 9 is fixed in a water tank 10 filled with purified water 11, an ultrasonic focusing probe 4 is connected with a five-degree-of-freedom motion platform 7 through a probe frame 8, the pose of the ultrasonic focusing probe 4 in the water tank 10 is adjusted, the ultrasonic focusing probe 4 is excited through an ultrasonic pulse generator/receiver 3 (namely an ultrasonic instrument), the motion control card 5 arranged on an industrial personal computer 1 is connected with a control circuit to control the five-degree-of-freedom motion platform 7 to do zigzag scanning motion, original ultrasonic C scanning data output by the ultrasonic instrument 3 is acquired and stored by a high-speed data acquisition card 2 on the industrial personal computer 1, and finally, further analysis and defect size measurement are carried out on the industrial personal computer 1.
Referring to fig. 2, fig. 2 is a flow chart of a method for ultrasonic measurement of a defect size of a polycrystalline material according to the present embodiment, which discloses a method for ultrasonic measurement of a defect size of a polycrystalline material, comprising the steps of:
s1, establishing Rayleigh distribution of grain noise of the polycrystalline material and Rice distribution of amplitude of coherent defect echo between the grain noise and the defect echo based on a back scattering response model;
the specific step S1 includes: construction based on a zero-mean normal distribution of grain noise
Specifically, the step S1 includes the following steps:
s11, assuming that the grain noise in the RF sampling mode is spatially in accordance with the normal distribution of zero mean, the cumulative distribution function of the grain noise amplitude in the envelope sampling mode is
Figure 373918DEST_PATH_IMAGE058
(1)
In the formula
Figure 112067DEST_PATH_IMAGE059
Is composed of
Figure 881309DEST_PATH_IMAGE060
The magnitude of the grain noise at a time instant,
Figure 337698DEST_PATH_IMAGE061
is the standard deviation;
s12, according to the back scattering response model, when the water immersion ultrasonic C scanning system works in the longitudinal wave-longitudinal wave mode, the standard deviation in the step S11 is
Figure 765268DEST_PATH_IMAGE062
(2)
In the formula
Figure 748137DEST_PATH_IMAGE063
Is a calibration parameter of the ultrasonic measurement system,
Figure 563646DEST_PATH_IMAGE064
is the spatial correlation coefficient of the microstructure of the polycrystalline material,
Figure 117118DEST_PATH_IMAGE065
is the average grain radius of the grains,
Figure 676275DEST_PATH_IMAGE066
is the scattering intensity of a polycrystalline material,
Figure 513650DEST_PATH_IMAGE067
is the integral of the ultrasonic field in the polycrystalline material,
Figure 500061DEST_PATH_IMAGE068
is the longitudinal wave attenuation coefficient of the polycrystalline material;
s13, assuming that the highest wave of coherent defect echo between grain noise and defect echo is located
Figure 806408DEST_PATH_IMAGE069
Time of day, maximum amplitude of coherent defect echo
Figure 903677DEST_PATH_IMAGE070
Obey rice distribution
Figure 595559DEST_PATH_IMAGE071
(3)
In the formula
Figure 752871DEST_PATH_IMAGE072
Is a 1 st order markan Q function,
Figure 280935DEST_PATH_IMAGE073
being defect echoesThe amplitude of the amplitude is,
Figure 181895DEST_PATH_IMAGE074
is a time of day
Figure 993862DEST_PATH_IMAGE075
Get
Figure 322075DEST_PATH_IMAGE076
Standard deviation of time; wherein the 1 st order Markan Q function is:
Figure 337436DEST_PATH_IMAGE077
(4)
in the formula
Figure 510928DEST_PATH_IMAGE078
A first type of modified Bessel function of zero order;
s2, constructing a limit distribution model of the defect echo amplitude according to the Rayleigh distribution and the Laisi distribution, and calculating a confidence upper limit and a confidence lower limit according to the inverse cumulative distribution;
the specific step S2 further includes: and establishing a three-section limit density function taking a zero point as a limit according to the Rayleigh distribution and the Rice distribution, and respectively acquiring the mathematical expectation, the upper confidence limit and the lower confidence limit according to the three-section limit density function.
The specific step S2 includes the following steps:
s21, assuming that the highest wave amplitude value of coherent defect echo obtained by actual measurement is
Figure 177402DEST_PATH_IMAGE079
Defining a limit distribution function of the amplitude of the defect echo according to the Rayleigh distribution and the Rice distribution in the step S1 as
Figure 942095DEST_PATH_IMAGE080
(5)
Envelope amplitude indicator for defining defect echo in formula (5)Combination of Chinese herbs
Figure 444752DEST_PATH_IMAGE081
I.e. its lower bound is zero; so that the corresponding limiting density function is
Figure 687515DEST_PATH_IMAGE082
(6)
In the formula
Figure 479933DEST_PATH_IMAGE083
A first type of modified Bessel function that is first order;
the formula (6) is characterized in that: and calculating the probability density by taking the zero point as a boundary and dividing into three sections, wherein the boundary distribution function and the boundary density function jointly form a boundary distribution model of the defect echo amplitude, and the calculation of the probability density by taking the zero point as a boundary and dividing into three sections shows that the probability of the defect echo amplitude is zero even if the coherent defect echo amplitude is not zero.
S22, calculating the mathematical expected value of the amplitude of the defect echo according to the limit density function
Figure 149949DEST_PATH_IMAGE084
(7)
The infinite integral in the formula (7) can be truncated in numerical calculation, and numerical integration is performed;
s23, calculating the confidence upper limit and the confidence lower limit of the defect echo amplitude value according to the limit density function
Figure 874322DEST_PATH_IMAGE085
(8)
Figure 920776DEST_PATH_IMAGE086
(9)
Inverse cumulative distribution function in formula
Figure 296262DEST_PATH_IMAGE087
The calculation can be performed by an interpolation algorithm;
and S3, acquiring interval estimation of the defect size of the polycrystalline material according to the coherent defect echo amplitude value measured by the ultrasonic measurement model and the C scanning experiment and the confidence upper limit and the confidence lower limit obtained in the step S2.
The specific step S3 further includes: and constructing a relation between the defect size and the defect echo amplitude according to an ultrasonic measurement model, obtaining point estimation according to the relation between the defect size and the defect echo amplitude and a coherent defect echo amplitude measured by a C scanning experiment, and obtaining two end points of interval estimation according to an inverse function of the relation between the defect size and the defect echo amplitude and the upper and lower confidence limits.
Specifically, the step S3 includes the following steps:
s31, assuming the transverse hole as the detection equivalent of the defect size, the ultrasonic measurement model is
Figure 402759DEST_PATH_IMAGE088
(10)
In the formula
Figure 348849DEST_PATH_IMAGE089
Is the equivalent radius of the defect size,
Figure 933414DEST_PATH_IMAGE090
as a system function of the ultrasonic measurement system,
Figure 428986DEST_PATH_IMAGE091
in order to be the angular frequency of the frequency,
Figure 440805DEST_PATH_IMAGE092
the number of longitudinal waves is the number of longitudinal waves,
Figure 202087DEST_PATH_IMAGE093
is the surface area of the ultrasound probe,
Figure 449398DEST_PATH_IMAGE094
and
Figure 940422DEST_PATH_IMAGE095
the density and longitudinal wave sound velocity of the polycrystalline material,
Figure 998508DEST_PATH_IMAGE096
and
Figure 309404DEST_PATH_IMAGE097
respectively the density and the sound velocity of the coupling fluid,
Figure 360405DEST_PATH_IMAGE098
the length of the transverse hole is the same as the length of the transverse hole,
Figure 705936DEST_PATH_IMAGE099
is the amplitude of the sound field at the cross-hole defect,
Figure 934923DEST_PATH_IMAGE100
is the position of the defect of the transverse hole,
Figure 733115DEST_PATH_IMAGE101
far field defect scattering amplitude for transverse holes, operator
Figure 587807DEST_PATH_IMAGE102
Representing an inverse fourier transform;
s32, through the ultrasonic measurement model in S31, the relation between the defect size and the defect echo amplitude can be established, namely the DAC curve is
Figure 787844DEST_PATH_IMAGE103
(11)
Operator in formula
Figure 187733DEST_PATH_IMAGE104
Representing a hilbert transform;
s33, determining the actual coherent defect echo of the defect by C-scan experiment, i.e. the amplitude of the given maximum wave
Figure 473221DEST_PATH_IMAGE105
Further, the point at which the defect size under the influence of grain noise can be estimated by using the formula (11) in S32 is
Figure 131604DEST_PATH_IMAGE106
(12)
In the formula
Figure 186148DEST_PATH_IMAGE107
Is the equivalent diameter of the defect size,
Figure 756937DEST_PATH_IMAGE108
the inverse function of the formula (11) can be solved by an interpolation method; and the interval of the defect size is estimated as
Figure 264142DEST_PATH_IMAGE109
(13)
Figure 726216DEST_PATH_IMAGE110
(14)
In the formula
Figure 635266DEST_PATH_IMAGE111
And
Figure 642537DEST_PATH_IMAGE112
is the upper confidence limit for the defect diameter and radius,
Figure 637038DEST_PATH_IMAGE113
and
Figure 201722DEST_PATH_IMAGE114
is the lower confidence limit for the defect diameter and radius.
In the embodiment, the rayleigh distribution of the grain noise is calculated by the ultrasonic backscattering model and the microstructure parameters of the polycrystalline material; describing coherent defect echo amplitude between grain noise and defect echo by using a Rice distribution; and establishing a limit distribution model of the defect echo amplitude through Rayleigh distribution and Rice distribution, and obtaining an upper confidence limit and a lower confidence limit according to the limit distribution model of the curve echo, thereby realizing point estimation and interval estimation of the defect size of the polycrystalline material. The defect size quantification error caused by grain noise in an ultrasonic signal is not considered in the traditional DAC curve method, and the method of the embodiment can effectively measure the size of a tiny defect and uncertainty of the tiny defect in the environment with a low signal-to-noise ratio. In addition, the boundary distribution model established in this embodiment is obtained by calculating the probability density in three segments with a zero point as a boundary.
Referring to fig. 3, fig. 3 is a schematic diagram of a test block containing a micro transverse hole defect in the present invention. In this example, the measurement method of this example is described by using a test piece of coarse-grained 304 stainless steel with 0.2 and 0.4mm cross holes, which is manufactured according to the design drawing by Shandong XX die Co. The diameters and the burial depths of 10 micro transverse hole defects marked in the figure 4 are shown in the table 1, and the lengths of the transverse holes are 10 +/-0.1 mm. It should also be noted that the mean grain size of the batch of stock material as determined by metallographic analysis prior to processing of the test block was 135. + -.9 μm, and was included in the macrocrystalline stainless steel material.
TABLE 1 dimensional Specifications for micro transverse hole defects
Figure 965279DEST_PATH_IMAGE115
In the implementation process, the theoretical model established in step S1 needs to be assigned first, and particularly, the standard deviation of the grain noise needs to be calculated in step S12, wherein the spatial correlation coefficient of the microstructure of the polycrystalline material
Figure 143451DEST_PATH_IMAGE116
And average grain radius
Figure 625248DEST_PATH_IMAGE117
Correlation, as can be seen from the results of metallographic analysis
Figure 163545DEST_PATH_IMAGE117
The thickness was 67.5. mu.m. Generating a limit distribution model according to the step S2, storing the limit distribution model into an internal memory of the industrial personal computer, and waiting for the calling of the step S3; then, establishing DAC curves under different equivalent sizes by utilizing the steps S31 and S32 in the step S3, wherein the DAC curves are stored in an internal memory of the industrial personal computer for waiting to be called as shown in FIG. 4; finally, a C-scan experiment is performed on the coarse-grain stainless steel test block, the C-scan experiment is shown in FIG. 5, and then step S33 is adopted to quantify 10 transverse hole defects, wherein defect D2 is taken as an example, and FIG. 6 is a limit probability density distribution diagram of echo amplitude.
Confidence set in defect size quantification
Figure 47188DEST_PATH_IMAGE118
99.73%, dimensional measurements for each defect were obtained as shown in Table 2
TABLE 2 Defect size quantification results and comparison with conventional DAC curve method
Figure 396260DEST_PATH_IMAGE119
The point estimation result of the defect size in this embodiment is substantially the same as that obtained by the conventional DAC curve method, but the embodiment can further realize interval estimation. Meanwhile, for small defects in coarse-grained materials, the error of the traditional DAC curve method is often larger. Taking the transverse hole E as an example, the relative error of the conventional method can be as high as-35%, and the confidence limit is 0.39mm, which is effective in consideration of the machining size of the transverse hole being 0.03 mm. Therefore, the method of the embodiment provides a technical means for effectively measuring the size and the uncertainty of the micro defect under the effect of grain scattering, and has good application prospect and popularization value.
Example two
Referring to fig. 7, fig. 7 is a flow chart illustrating a method for ultrasonic measurement of defect size of polycrystalline material according to the present embodiment, which discloses a method for ultrasonic measurement of defect size of polycrystalline material, comprising the following steps:
s10, obtaining coherent defect echo amplitude of the polycrystalline material based on an ultrasonic measurement model and a C-scan experiment;
the system for ultrasonic measurement of defect size of polycrystalline material in this embodiment refers to the system of the first embodiment, and is not repeated in this embodiment. This example uses a 304 stainless steel test block containing micro-defects as an example to illustrate the ultrasonic measurement method of the micro-defect size of the polycrystalline material of this example. In the embodiment, a tested block 9 is fixed in a water tank 10 filled with purified water 11, an ultrasonic focusing probe 4 is connected with a five-degree-of-freedom motion platform 7 through a probe frame 8, the pose of the ultrasonic focusing probe 4 in the water tank 10 is adjusted, the ultrasonic focusing probe 4 is excited through an ultrasonic pulse generator/receiver 3 (namely an ultrasonic instrument), the motion control card 5 arranged on an industrial personal computer 1 is connected with a control circuit to control the five-degree-of-freedom motion platform 7 to do zigzag scanning motion, original ultrasonic C scanning data output by the ultrasonic instrument 3 is acquired and stored by a high-speed data acquisition card 2 on the industrial personal computer 1, and finally, further analysis and defect size measurement are carried out on the industrial personal computer 1.
S20, obtaining interval estimation of the defect size of the polycrystalline material according to the coherent defect echo amplitude of the polycrystalline material, the upper confidence limit and the lower confidence limit; the upper confidence limit and the lower confidence limit are three-section limit density functions which are established according to Rayleigh distribution of polycrystalline material grain noise and Rice distribution of coherent defect echo amplitude between the grain noise and the defect echo and take a zero point as a limit, and mathematical expectations, the upper confidence limit and the lower confidence limit are respectively obtained according to the three-section limit density functions; and two end points of the interval estimation are respectively obtained according to an inverse function of the relation between the defect size and the defect echo amplitude, and the upper limit and the lower limit of confidence.
The upper confidence limit and the lower confidence limit are three-section limit density functions which are established according to Rayleigh distribution of polycrystalline material grain noise and Rice distribution of coherent defect echo amplitude between the grain noise and the defect echo and take a zero point as a limit, and mathematical expectations, the upper confidence limit and the lower confidence limit are respectively obtained according to the three-section limit density functions, and the method comprises the following steps of:
s1, establishing Rayleigh distribution of grain noise of the polycrystalline material and Rice distribution of amplitude of coherent defect echo between the grain noise and the defect echo based on a back scattering response model;
the specific step S1 includes: construction based on a zero-mean normal distribution of grain noise
Specifically, the step S1 includes the following steps:
s11, assuming that the grain noise in the RF sampling mode is spatially in accordance with the normal distribution of zero mean, the cumulative distribution function of the grain noise amplitude in the envelope sampling mode is
Figure 365353DEST_PATH_IMAGE120
(1)
In the formula
Figure 707342DEST_PATH_IMAGE121
Is composed of
Figure 445491DEST_PATH_IMAGE122
The magnitude of the grain noise at a time instant,
Figure 762203DEST_PATH_IMAGE123
is the standard deviation;
s12, according to the back scattering response model, when the water immersion ultrasonic C scanning system works in the longitudinal wave-longitudinal wave mode, the standard deviation in the step S11 is
Figure 343226DEST_PATH_IMAGE124
(2)
In the formula
Figure 98692DEST_PATH_IMAGE125
Is a calibration parameter of the ultrasonic measurement system,
Figure 832293DEST_PATH_IMAGE126
is the spatial correlation coefficient of the microstructure of the polycrystalline material,
Figure 382223DEST_PATH_IMAGE127
is the average grain radius of the grains,
Figure 450542DEST_PATH_IMAGE128
is the scattering intensity of a polycrystalline material,
Figure 9699DEST_PATH_IMAGE129
is the integral of the ultrasonic field in the polycrystalline material,
Figure 597806DEST_PATH_IMAGE130
is the longitudinal wave attenuation coefficient of the polycrystalline material;
s13, assuming that the highest wave of coherent defect echo between grain noise and defect echo is located
Figure 584217DEST_PATH_IMAGE131
Time of day, maximum amplitude of coherent defect echo
Figure 874253DEST_PATH_IMAGE132
Obey rice distribution
Figure 971522DEST_PATH_IMAGE133
(3)
In the formula
Figure 679715DEST_PATH_IMAGE134
Is a 1 st order markan Q function,
Figure 571447DEST_PATH_IMAGE135
in order to be the amplitude of the defect echo,
Figure 614359DEST_PATH_IMAGE136
is a time of day
Figure 249739DEST_PATH_IMAGE137
Get
Figure 812439DEST_PATH_IMAGE138
Standard deviation of time; wherein the 1 st order Markan Q function is:
Figure 140652DEST_PATH_IMAGE139
(4)
in the formula
Figure 405280DEST_PATH_IMAGE140
A first type of modified Bessel function of zero order;
s2, constructing a limit distribution model of the defect echo amplitude according to the Rayleigh distribution and the Laisi distribution, and calculating a confidence upper limit and a confidence lower limit according to the inverse cumulative distribution;
the specific step S2 further includes: and establishing a three-section limit density function taking a zero point as a limit according to the Rayleigh distribution and the Rice distribution, and respectively acquiring the mathematical expectation, the upper confidence limit and the lower confidence limit according to the three-section limit density function.
The specific step S2 includes the following steps:
s21, assuming that the highest wave amplitude value of coherent defect echo obtained by actual measurement is
Figure 844352DEST_PATH_IMAGE141
Defining a limit distribution function of the amplitude of the defect echo according to the Rayleigh distribution and the Rice distribution in the step S1 as
Figure 261558DEST_PATH_IMAGE142
(5)
Envelope amplitude coincidence of defect echo specified in equation (5)
Figure 760672DEST_PATH_IMAGE143
I.e. its lower bound is zero; so that the corresponding limiting density function is
Figure 518456DEST_PATH_IMAGE144
(6)
In the formula
Figure 761218DEST_PATH_IMAGE145
A first type of modified Bessel function that is first order;
the formula (6) is characterized in that: calculating probability density in three sections by taking a zero point as a boundary, and simultaneously forming a boundary distribution model of the defect echo amplitude by a boundary distribution function and a boundary density function;
s22, calculating the mathematical expected value of the amplitude of the defect echo according to the limit density function
Figure 32931DEST_PATH_IMAGE146
(7)
The infinite integral in the formula (7) can be truncated in numerical calculation, and numerical integration is performed;
s23, calculating the confidence upper limit and the confidence lower limit of the defect echo amplitude value according to the limit density function
Figure 702947DEST_PATH_IMAGE147
(8)
Figure 942167DEST_PATH_IMAGE148
(9)
Inverse cumulative distribution function in formula
Figure 988620DEST_PATH_IMAGE149
The calculation can be performed by an interpolation algorithm;
the two end points of the interval estimation are obtained according to the inverse function of the relation between the defect size and the defect echo amplitude and the upper limit and the lower limit of confidence respectively, and the method comprises the following steps:
and S3, acquiring interval estimation of the defect size of the polycrystalline material according to the coherent defect echo amplitude value measured by the ultrasonic measurement model and the C scanning experiment and the confidence upper limit and the confidence lower limit obtained in the step S2.
The specific step S3 further includes: and constructing a relation between the defect size and the defect echo amplitude according to an ultrasonic measurement model, obtaining point estimation according to the relation between the defect size and the defect echo amplitude and a coherent defect echo amplitude measured by a C scanning experiment, and obtaining two end points of interval estimation according to an inverse function of the relation between the defect size and the defect echo amplitude and the upper and lower confidence limits.
Specifically, the step S3 includes the following steps:
s31, assuming the transverse hole as the detection equivalent of the defect size, the ultrasonic measurement model is
Figure 911577DEST_PATH_IMAGE150
(10)
In the formula
Figure 877128DEST_PATH_IMAGE151
Is the equivalent radius of the defect size,
Figure 479010DEST_PATH_IMAGE152
as a system function of the ultrasonic measurement system,
Figure 938942DEST_PATH_IMAGE153
in order to be the angular frequency of the frequency,
Figure 309880DEST_PATH_IMAGE154
the number of longitudinal waves is the number of longitudinal waves,
Figure 446332DEST_PATH_IMAGE155
is the surface area of the ultrasound probe,
Figure 738773DEST_PATH_IMAGE156
and
Figure 533554DEST_PATH_IMAGE157
of polycrystalline materialThe density and the longitudinal wave sound velocity,
Figure 758999DEST_PATH_IMAGE158
and
Figure 331932DEST_PATH_IMAGE159
respectively the density and the sound velocity of the coupling fluid,
Figure 642827DEST_PATH_IMAGE160
the length of the transverse hole is the same as the length of the transverse hole,
Figure 444561DEST_PATH_IMAGE161
is the amplitude of the sound field at the cross-hole defect,
Figure 790092DEST_PATH_IMAGE162
is the position of the defect of the transverse hole,
Figure 268347DEST_PATH_IMAGE163
far field defect scattering amplitude for transverse holes, operator
Figure 66538DEST_PATH_IMAGE164
Representing an inverse fourier transform;
s32, through the ultrasonic measurement model in S31, the relation between the defect size and the defect echo amplitude can be established, namely the DAC curve is
Figure 671963DEST_PATH_IMAGE165
(11)
Operator in formula
Figure 872000DEST_PATH_IMAGE166
Representing a hilbert transform;
s33, determining the actual coherent defect echo of the defect by C-scan experiment, i.e. the amplitude of the given maximum wave
Figure 521156DEST_PATH_IMAGE167
Further, the influence of grain noise can be realized by the formula (11) in S32The point of the defect size is estimated as
Figure 806644DEST_PATH_IMAGE168
(12)
In the formula
Figure 950181DEST_PATH_IMAGE169
Is the equivalent diameter of the defect size,
Figure 4724DEST_PATH_IMAGE170
the inverse function of the formula (11) can be solved by an interpolation method; and the interval of the defect size is estimated as
Figure 90361DEST_PATH_IMAGE171
(13)
Figure 863145DEST_PATH_IMAGE172
(14)
In the formula
Figure 544793DEST_PATH_IMAGE173
And
Figure 453843DEST_PATH_IMAGE174
is the upper confidence limit for the defect diameter and radius,
Figure 438943DEST_PATH_IMAGE175
and
Figure 699023DEST_PATH_IMAGE176
is the lower confidence limit for the defect diameter and radius.
The experimental data of this embodiment refers to the experimental data of the first embodiment, and this embodiment is not described in detail.
In the embodiment, the Rayleigh distribution of the grain noise is calculated according to an ultrasonic backscattering model and microstructure parameters of the polycrystalline material; describing coherent defect echo amplitude between grain noise and defect echo by using a Rice distribution; and establishing a limit distribution model of the defect echo amplitude through Rayleigh distribution and Rice distribution, and obtaining an upper confidence limit and a lower confidence limit according to the limit distribution model of the curve echo, thereby realizing point estimation and interval estimation of the defect size of the polycrystalline material. The defect size quantification error caused by grain noise in an ultrasonic signal is not considered in the traditional DAC curve method, and the method of the embodiment can effectively measure the size of a tiny defect and uncertainty of the tiny defect in the environment with a low signal-to-noise ratio. In addition, the boundary distribution model established in this embodiment is obtained by calculating the probability density in three segments with a zero point as a boundary.
EXAMPLE III
Referring to fig. 8, fig. 8 is a schematic structural diagram of an ultrasonic measurement apparatus for measuring a defect size of a polycrystalline material according to the present embodiment. An ultrasonic measurement apparatus 20 of the embodiment for measuring the defect size of a polycrystalline material comprises a processor 21, a memory 22 and a computer program stored in the memory 22 and executable on the processor 21. The processor 21, when executing the computer program, performs the steps of the above-described embodiment of a method for ultrasonic measurement based on the size of a defect in a polycrystalline material. Alternatively, the processor 21 implements the functions of the modules/units in the above-described device embodiments when executing the computer program.
Illustratively, the computer program may be divided into one or more modules/units, which are stored in the memory 22 and executed by the processor 21 to accomplish the present invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions for describing the execution of the computer program in the ultrasonic measuring device 20 for measuring the size of a defect in the one polycrystalline material. For example, the computer program may be divided into the modules in the second embodiment, and please refer to the working process of the ultrasonic measurement apparatus for measuring the defect size of a polycrystalline material described in the foregoing embodiment for specific functions of each module, which is not described herein again.
The ultrasonic measurement device 20 for measuring the defect size of the polycrystalline material can include, but is not limited to, a processor 21 and a memory 22. It will be understood by those skilled in the art that the schematic diagram is merely illustrative of an ultrasonic measuring device 20 for a polycrystalline material defect size and is not intended to limit an ultrasonic measuring device 20 for a polycrystalline material defect size, and may include more or fewer components than shown, or some components in combination, or different components, for example, the ultrasonic measuring device 20 for a polycrystalline material defect size may also include input and output devices, network access devices, buses, etc.
The Processor 21 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, etc. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, and the processor 21 is the control center of the ultrasonic measuring device 20 for defect sizes of one polycrystalline material and is connected to the various parts of the ultrasonic measuring device 20 for defect sizes of the entire one polycrystalline material by various interfaces and lines.
The memory 22 may be used to store the computer programs and/or modules, and the processor 21 may implement the functions of the ultrasonic measuring device 20 for measuring the defect size of the polycrystalline material by operating or executing the computer programs and/or modules stored in the memory 22 and invoking the data stored in the memory 22. The memory 22 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. In addition, the memory 22 may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
Wherein, the integrated module/unit of the ultrasonic measuring device 20 for measuring the defect size of the polycrystalline material can be stored in a computer readable storage medium if the module/unit is realized in the form of a software functional unit and is sold or used as a stand-alone product. Based on such understanding, all or part of the flow of the method according to the above embodiments may be implemented by a computer program, which may be stored in a computer readable storage medium and used by the processor 21 to implement the steps of the above embodiments of the method. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
It should be noted that the above-described device embodiments are merely illustrative, where the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. In addition, in the drawings of the embodiment of the apparatus provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, and may be specifically implemented as one or more communication buses or signal lines. One of ordinary skill in the art can understand and implement the method without creative effort.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

Claims (5)

1. An ultrasonic measurement method for the defect size of a polycrystalline material comprises the following steps:
s1, establishing Rayleigh distribution of grain noise of the polycrystalline material and Rice distribution of amplitude of coherent defect echo between the grain noise and the defect echo based on a back scattering response model;
s2, constructing a limit distribution model of the defect echo amplitude according to the Rayleigh distribution and the Laisi distribution, and calculating a confidence upper limit and a confidence lower limit according to the inverse cumulative distribution; the step S2 specifically includes: establishing a three-section limit density function with a zero point as a limit according to the Rayleigh distribution and the Rice distribution, and respectively acquiring a mathematical expectation, the upper confidence limit and the lower confidence limit according to the three-section limit density function;
s3, obtaining interval estimation of the defect size of the polycrystalline material according to the coherent defect echo amplitude value measured by the ultrasonic measurement model and the C scanning experiment and the confidence upper limit and the confidence lower limit obtained in the step S2; the step S3 specifically includes: and constructing a relation between the defect size and the defect echo amplitude according to an ultrasonic measurement model, obtaining point estimation according to the relation between the defect size and the defect echo amplitude and a coherent defect echo amplitude measured by a C scanning experiment, and obtaining two end points of interval estimation according to an inverse function of the relation between the defect size and the defect echo amplitude and the upper and lower confidence limits.
2. The method according to claim 1, wherein the step S1 specifically comprises: the rayleigh distribution is constructed based on a zero-mean normal distribution of grain noise.
3. The method according to claim 2, wherein the step S1 includes:
s11, assuming that the grain noise in the RF sampling mode is spatially in accordance with the normal distribution of zero mean, the cumulative distribution function of the grain noise amplitude in the envelope sampling mode is
Figure DEST_PATH_IMAGE001
(1)
In the formula
Figure 271895DEST_PATH_IMAGE002
Is composed of
Figure DEST_PATH_IMAGE003
The magnitude of the grain noise at a time instant,
Figure 558519DEST_PATH_IMAGE004
is the standard deviation;
s12, according to the back scattering response model, when the water immersion ultrasonic C scanning system works in the longitudinal wave-longitudinal wave mode, the standard deviation in the step S11 is
Figure DEST_PATH_IMAGE005
(2)
In the formula
Figure 479071DEST_PATH_IMAGE006
Is a calibration parameter of the ultrasonic measurement system,
Figure DEST_PATH_IMAGE007
is the spatial correlation coefficient of the microstructure of the polycrystalline material，
Figure 592783DEST_PATH_IMAGE008
Is the average grain radius of the grains,
Figure DEST_PATH_IMAGE009
is the scattering intensity of a polycrystalline material,
Figure 333206DEST_PATH_IMAGE010
is the integral of the ultrasonic field in the polycrystalline material,
Figure DEST_PATH_IMAGE011
is the longitudinal wave attenuation coefficient of the polycrystalline material;
s13, assuming that the highest wave of coherent defect echo between grain noise and defect echo is located
Figure 954680DEST_PATH_IMAGE012
Time of day, maximum amplitude of coherent defect echo
Figure DEST_PATH_IMAGE013
Obey rice distribution
Figure 700044DEST_PATH_IMAGE014
(3)
In the formula
Figure DEST_PATH_IMAGE015
Is a 1 st order markan Q function,
Figure 217613DEST_PATH_IMAGE016
in order to be the amplitude of the defect echo,
Figure DEST_PATH_IMAGE017
is a time of day
Figure 710911DEST_PATH_IMAGE018
Get
Figure DEST_PATH_IMAGE019
Standard deviation of time; wherein the 1 st order Markan Q function is
Figure 840804DEST_PATH_IMAGE020
(4)
In the formula
Figure DEST_PATH_IMAGE021
A first type of modified bessel function of zero order.
4. The method according to claim 3, wherein the step S2 is specifically:
s21, assuming that the highest wave amplitude value of coherent defect echo obtained by actual measurement is
Figure 470368DEST_PATH_IMAGE022
Defining a limit distribution function of the amplitude of the defect echo according to the Rayleigh distribution and the Rice distribution in the step S1 as
Figure DEST_PATH_IMAGE023
(5)
Wherein envelope amplitude coincidence of defect echo is specified
Figure 158838DEST_PATH_IMAGE024
I.e. its lower bound is zero; so that the corresponding limiting density function is
Figure DEST_PATH_IMAGE025
(6)
In the formula
Figure 375318DEST_PATH_IMAGE026
Modified Bessel of the first kindA function;
s22, calculating the mathematical expected value of the amplitude of the defect echo according to the limit density function
Figure DEST_PATH_IMAGE027
(7) The infinite integral in the formula can be cut off in numerical calculation, and numerical integration is carried out;
s23, calculating the confidence upper limit and the confidence lower limit of the defect echo amplitude value according to the limit density function
Figure 807437DEST_PATH_IMAGE028
(8)
Figure DEST_PATH_IMAGE029
(9)
Inverse cumulative distribution function in formula
Figure 760349DEST_PATH_IMAGE030
Can be calculated by an interpolation algorithm, wherein
Figure DEST_PATH_IMAGE031
Is the confidence level.
5. The method according to claim 4, wherein the step S3 is specifically:
s31, assuming the transverse hole as the detection equivalent of the defect size, the ultrasonic measurement model is
Figure 885300DEST_PATH_IMAGE032
(10)
In the formula
Figure DEST_PATH_IMAGE033
Is the equivalent radius of the defect size,
Figure 57918DEST_PATH_IMAGE034
as a system function of the ultrasonic measurement system,
Figure DEST_PATH_IMAGE035
in order to be the angular frequency of the frequency,
Figure 293727DEST_PATH_IMAGE036
the number of longitudinal waves is the number of longitudinal waves,
Figure DEST_PATH_IMAGE037
is the surface area of the ultrasound probe,
Figure 632304DEST_PATH_IMAGE038
and
Figure DEST_PATH_IMAGE039
the density and longitudinal wave sound velocity of the polycrystalline material,
Figure 662577DEST_PATH_IMAGE040
and
Figure DEST_PATH_IMAGE041
respectively the density and the sound velocity of the coupling fluid,
Figure 322491DEST_PATH_IMAGE042
the length of the transverse hole is the same as the length of the transverse hole,
Figure DEST_PATH_IMAGE043
is the amplitude of the sound field at the cross-hole defect,
Figure 96412DEST_PATH_IMAGE044
is the position of the defect of the transverse hole,
Figure DEST_PATH_IMAGE045
far field defect scattering amplitude for transverse holes, operator
Figure 23917DEST_PATH_IMAGE046
Representing an inverse fourier transform;
s32, through the ultrasonic measurement model in S31, the relation between the defect size and the defect echo amplitude can be established, namely the DAC curve is
Figure DEST_PATH_IMAGE047
(11)
Operator in formula
Figure 490670DEST_PATH_IMAGE048
Representing a hilbert transform;
s33, determining the actual coherent defect echo of the defect by C-scan experiment, i.e. the amplitude of the given maximum wave
Figure DEST_PATH_IMAGE049
Further, the point at which the defect size under the influence of grain noise can be estimated by using the formula (11) in S32 is
Figure 637880DEST_PATH_IMAGE050
(12)
In the formula
Figure DEST_PATH_IMAGE051
Is the equivalent diameter of the defect size,
Figure 949913DEST_PATH_IMAGE052
the inverse function of the formula (11) can be solved by an interpolation method; and the interval of the defect size is estimated as
Figure DEST_PATH_IMAGE053
(13)
Figure 997503DEST_PATH_IMAGE054
(14)
In the formula
Figure DEST_PATH_IMAGE055
And
Figure 124904DEST_PATH_IMAGE056
is the upper confidence limit for the defect diameter and radius,
Figure DEST_PATH_IMAGE057
and
Figure 257945DEST_PATH_IMAGE058
is the lower confidence limit for the defect diameter and radius.
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Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107037131A (en) * 2017-05-04 2017-08-11 中南大学 A kind of tiny flaw supersonic detection method theoretical based on the extreme value distribution
CN113325079A (en) * 2021-06-24 2021-08-31 上海交通大学设计研究总院有限公司 Concrete crack absolute size quantitative detection method based on Rayleigh wave energy attenuation

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107037131A (en) * 2017-05-04 2017-08-11 中南大学 A kind of tiny flaw supersonic detection method theoretical based on the extreme value distribution
CN113325079A (en) * 2021-06-24 2021-08-31 上海交通大学设计研究总院有限公司 Concrete crack absolute size quantitative detection method based on Rayleigh wave energy attenuation

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
Enhanced ultrasonic detection of near-surface flaws using transverse-wave backscatter;Yuantian Huang et al;《Ultrasonics》;20190528;第98卷;全文 *
Flaw detection with ultrasonic backscatter signal envelopes;Yongfeng Song et al;《J. Acoust. Soc. Am.》;20190228;第145卷(第2期);全文 *
激光瑞利波的时间依赖性探测表面缺陷深度;刘辉等;《激光与红外》;20170630;第47卷(第6期);全文 *

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