JP6775233B2 - Material diagnostic method - Google Patents

Description

The present invention is non-destructive regarding the types, amounts, depth distributions, etc. of microstructures such as precipitates and voids generated in steel materials such as stainless steel due to radiation irradiation, thermal aging, plastic deformation, etc. Material to be diagnosed.

Conventionally, electron microscopes and ultrasonic waves have been used as a method for quantifying and diagnosing the type, amount of generation, depth distribution, etc. of microstructure changes such as precipitates and voids generated in the above-mentioned steel materials such as stainless steel. Etc. are adopted.

The former method using an electron microscope is a destructive method for quantifying the amount of microstructure near the surface by cutting out a sample from a material and observing it with an electron microscope, and when quantifying the depth distribution of microstructure. Has problems such as the need to cut out a large number of samples.

On the other hand, the latter method using ultrasonic waves is a non-destructive method for quantifying and diagnosing the microstructure generated in the material by irradiating the material with ultrasonic waves, and is similar to the method using an electron microscope. This is preferable because it is not necessary to cut out a large number of samples.

The outline of the measurement method using this ultrasonic wave is schematically shown in FIG. As shown in FIG. 1, when an ultrasonic wave with a thick arrow to the right is incident on a material, a signal (bottom wave) reflected from the bottom surface of the material indicated by the thick arrow to the left and a signal reflected from the inside of the material indicated by the thin arrow. Signal (backscattered wave) can be acquired.

At this time, if microstructures such as voids, dislocations, and precipitates are generated in the material, reflection from the microstructure is generated and added to the backscattered wave, so that the backscattered wave intensity is increased. On the other hand, the bottom wave intensity changes. Therefore, if these intensities can be measured, the occurrence of microstructure can be known.

In the case of microstructures of several tens of μm or more, the ultrasonic intensity directly reflected and scattered from these microstructures is sufficiently large, so that the amount of backscattered wave intensity change is large, and material diagnostic measurement using conventional ultrasonic waves is performed. Even if the method is used, the microstructure of the material can be sufficiently quantified.

However, in the case of a minute microstructure, specifically, a microstructure of several tens of μm or less, the amount of change in backscattered wave intensity is very large, about one-thousandth of the backscattered wave intensity in the crystal grains. Due to its small size, scattering by crystal grains is dominant. For this reason, it is difficult to identify the change in backscattered wave intensity due to the generation of microstructure as a clear signal, and it is difficult to quantify these minute microstructure defects by conventional material diagnostic measurement using ultrasonic waves. It was.

In addition, it is necessary to quantify the depth distribution of the generated microstructure, and a method of quantifying the depth distribution from the obtained ultrasonic data by using wavelet transform or the like has been attempted (for example, Patent Documents). 1), but the error was large and it was difficult to quantify.

For this reason, various methods for quantifying minute microstructures using ultrasonic waves have been proposed (for example, Patent Documents 2 to 4).

JP-A-2002-303608 Japanese Unexamined Patent Publication No. 2003-294880 Japanese Unexamined Patent Publication No. 2008-261765 Japanese Unexamined Patent Publication No. 2009-281846

However, since these methods are basically methods for evaluating ultrasonic waves directly scattered by microstructures, it has not yet been sufficient to accurately quantify microstructures of several tens of μm or less. ..

Therefore, in view of the above-mentioned problems of the prior art, the present invention is non-destructive and sufficiently accurate in quantifying the type, amount of generation, depth distribution, etc. of even a minute microstructure of about several tens of μm or less. An object of the present invention is to provide a material diagnostic method capable of diagnosing.

(1) The present inventor has been diligently studying the solution to the above problems, and when microstructures such as precipitates such as carbides, voids, dislocations, and phase transformations occur in the material, the above-mentioned ultrasonic signal intensity We paid attention to the fact that the grain physical properties (volume, density, sound velocity, etc.) also change according to the change.

That is, for example, when voids are generated in the crystal grains as a microstructure, the volume of the crystal grains increases with the generation of the voids, so that the density decreases. Further, as the voids are generated, the Young's modulus decreases, so that the speed of sound propagating through the crystal grains decreases (see FIG. 2).

On the other hand, when microstructure is generated, the ultrasonic signal intensity changes. For example, the signal waveform when microstructure is not generated as shown in FIG. 3A can be changed to the signal waveform shown in FIG. 3B. , The signal waveform has an increased backscattered wave intensity.

The present inventor has conventionally diagnosed the microstructure by the change in the backscattered wave intensity of the ultrasonic wave directly reflected from the microstructure, and therefore it is difficult to diagnose the minute microstructure. Focusing on the relationship between the change in the grain quality value and the change in the ultrasonic signal intensity, we conducted a diligent study.

As a result, the generation of microstructure causes changes in bottom wave and backscattered wave intensities as described above, but as shown in FIG. 4, changes in wave height and frequency distribution in backscattered waves cause microstructure in the depth direction. While it is related to the distribution, it was found that the change in sound velocity and the change in wave height and frequency distribution in the bottom wave are related to the average value of material characteristic changes in the depth direction. The frequency distribution of the bottom wave and the backscattered wave described above can be calculated by frequency-converting the time change of the ultrasonic signal waveform. Further, in the above, the "mean value of material property change" is a signal that the bottom wave reciprocates inside the material (see FIG. 1), and utilizes the fact that it is influenced by the microstructure inside the material through which ultrasonic waves have passed. Then, the change in material properties is shown as an average value from the change in sound velocity and attenuation coefficient, and the change in wave height and frequency distribution in the bottom wave.

As a result of the examination, it was found that by paying attention to the change in the ultrasonic signal due to the change in the physical properties of the crystal grains, even a minute microstructure can be easily and accurately diagnosed.

That is, when ultrasonic waves are incident on the material to obtain an ultrasonic waveform as shown in the upper part of FIG. 5, for example, the first bottom wave and the second bottom wave of the obtained ultrasonic waveform are frequency-converted. Therefore, the frequency distributions of the first bottom wave and the second bottom wave shown in the lower part of FIG. 5 are calculated, and by using the amplitudes P ₁ (arbitrary unit) and P ₂ (arbitrary unit) at a predetermined frequency, (Equation 1). The attenuation coefficient α (neper / m) shown in is obtained. In (Equation 1), d is the material thickness (m).

The present inventor is a material (polycrystal) when it is a polycrystal like the material targeted by the present invention and its crystal grain size is much smaller than the incident ultrasonic wavelength (Rayleigh scattering region). It was noted that the above-mentioned attenuation coefficient α can be expressed by an equation with the crystal grain physical property value as a variable as shown in (Equation 2) below, as the attenuation coefficient α _L of the longitudinal wave incident in).

In (Equation 2), since the longitudinal wave attenuation coefficient α _L is inversely proportional to the square of the density ρ, which is the value of the longitudinal wave physical properties, and the eighth power of the longitudinal wave sound velocity ν _L , the crystal grain properties change. For example, it can be seen that even if the change in physical property values such as the ultrasonic sound velocity due to the generation of minute microstructures is small, the longitudinal wave attenuation coefficient α _L appears as a large change. Further, since the longitudinal wave attenuation coefficient α _L is proportional to the crystal grain volume V, it can be seen that the crystal grain volume V also appears as a large change in response to a large change in the longitudinal wave attenuation coefficient α _L. .. That is, a small change in the physical property value due to the generation of a minute microstructure can be regarded as a large change in the volume of the crystal grain.

Based on the above findings, the present inventor has come to the conclusion that the amount of microstructure generated in the material can be easily quantified by using the following method.

That is, first, the attenuation coefficient is calculated for each frequency using (Equation 1) from the bottom wave that is reflected from the material by injecting ultrasonic waves into the material without the generation of microstructure. Then, using the density and sound velocity separately obtained by the method shown below and the attenuation coefficient calculated above, the volume of crystal grains before the generation of microstructure is calculated based on (Equation 2). Keep it. At this time, the volume of the crystal grains has a constant value regardless of the frequency. Then, ultrasonic waves are incident on the material to be diagnosed, the attenuation coefficient is calculated for each frequency in the same manner as described above, and then the volume of crystal grains after the generation of microstructure is calculated.

The calculated difference (volume change amount) between the volume of the crystal grains before the generation of the microstructure and the volume of the crystal grains after the generation of the microstructure is the volume change accompanying the generation of the microstructure, so this value. Can be regarded as the amount of microstructure generated, and by applying the above method, the amount of microstructure generated can be quantified.

As shown in FIG. 4, the longitudinal wave sound velocity can be obtained from the time from the incident of the ultrasonic wave incident on the material having the thickness d to the appearance of the first bottom wave by using the ultrasonic waveform. .. The density may be obtained in advance, but it may be calculated from the reference material or a literature value may be used. Further, the acoustic anisotropy may be calculated in advance from the reference material, but literature values may be used. Further, the longitudinal wave sound velocity and the transverse wave sound velocity can be calculated from the following known formulas using, for example, Poisson's ratio, Young's modulus, and density.

In the above, using the same material, the attenuation coefficient is used for each frequency using (Equation 1) from the bottom wave that is reflected from the material by injecting ultrasonic waves before and after the generation of microstructure (initial state). However, instead of quantifying the occurrence of microstructure using the same material in this way, a material composed of the same type of material as the material to be diagnosed and in a state where no microstructure is generated is prepared separately. Then, ultrasonic waves may be incident on this to perform the same measurement, and the obtained data may be used as data for a material in which no microstructure is generated.

Further, instead of the above-mentioned data of the material in which the microstructure is not generated, ultrasonic waves are applied to the diagnostic material in which the microstructure has already been generated over time and the amount of the microstructure generated is known in advance. The same measurement may be performed after incident, and the obtained data may be used. In this case, the time change of the amount of microstructure generated in the diagnostic material can be captured.

The first to third techniques related to the present invention are techniques based on the above findings.

That is, the first technique related to the present invention is
It is a material diagnostic method that non-destructively diagnoses the microstructure generated in the material using ultrasonic waves.
Utilizing the fact that changes in the physical properties of crystal grains caused by microstructures affect ultrasonic scattering, the amount of change in microstructure can be determined by capturing changes in ultrasonic scattering due to crystal grains from bottom waves and backscattered waves. It is a material diagnostic method characterized by quantification.

In addition, the second technique related to the present invention is
It is a material diagnostic method that non-destructively diagnoses the microstructure generated in the material using ultrasonic waves.
From the frequency distribution of the first bottom wave and the second bottom wave created by frequency-converting the ultrasonic waveform after obtaining an ultrasonic waveform by injecting ultrasonic waves into the material without the generation of microstructure. Initial stage in which the attenuation coefficient for each frequency is calculated, the density of the material and the sound velocity of the ultrasonic wave are acquired, and the volume of crystal grains is calculated from the obtained attenuation coefficient, the density of the material and the sound velocity of the ultrasonic wave. Crystal grain volume calculation process and
After obtaining an ultrasonic waveform by injecting ultrasonic waves onto the material to be diagnosed, a step of calculating the volume of the grain to be diagnosed and a step of calculating the volume of the grain to be diagnosed are performed in the same manner as in the initial grain volume calculation step.
The microstructure generation amount quantification step for quantifying the microstructure generation amount by obtaining the volume change amount of the crystal grains from each volume obtained in the initial crystal grain volume calculation step and the diagnosis target crystal grain volume calculation step. It is a material diagnostic method characterized by being provided.

In addition, the third technique related to the present invention is
It is a material diagnostic method that non-destructively diagnoses the microstructure generated in the material using ultrasonic waves.
The material to be diagnosed in the initial state, the material to be diagnosed that is similar to the material to be diagnosed and has no microstructure, or the material to be diagnosed in which the amount of microstructure generated is known in advance. After obtaining an ultrasonic waveform by injecting sound, the attenuation coefficient for each frequency is calculated from the frequency distribution of the first bottom wave and the second bottom wave created by frequency-converting the ultrasonic waveform, and further. A first crystal grain volume calculation step of acquiring the density of the material and the sound velocity of the ultrasonic wave and calculating the volume of the crystal grain from the obtained attenuation coefficient, the density of the material and the sound velocity of the ultrasonic wave.
After ultrasonic waves are incident on the material to be diagnosed to obtain an ultrasonic waveform, the volume of the crystal grains to be diagnosed is calculated in the same manner as in the first crystal grain volume calculation step. Calculation process and
Microstructure generation amount quantification to quantify the amount of microstructure generated by obtaining the volume change amount of crystal grains from each volume obtained in the first crystal grain volume calculation step and the second crystal grain volume calculation step. It is a material diagnosis method characterized by having a process.

In the second technique and the third technique related to the present invention, the volume of crystal grains is directly obtained from the attenuation coefficient, and the amount of microstructure generated is quantified from the change in the volume of crystal grains. It is also possible to assume the amount of structure generated, that is, the amount of change in the volume of crystal grains, and create an attenuation coefficient and frequency distribution by (Equation 2) based on that assumption. Then, by changing the assumption of the amount of microstructure generated in various ways and repeating the collation with the frequency distribution obtained from the diagnostic material, the amount of microstructure generated can be quantified more easily.

At this time, the amount of change in the speed of sound can be obtained from the ultrasonic signal from the material in which the microstructure is generated, but the quantification that does not hinder practical use even if the value obtained from the past knowledge is used. Can be done.

(2) Next, the quantification of the depth distribution of minute microstructures generated in the material will be described.

By applying the above method, the amount of microstructure generated is quantified, but since microstructures such as precipitates and voids have a depth distribution, not only the amount generated but also the depth distribution It needs to be quantified.

The present inventor paid attention to the backscattered wave of ultrasonic waves in quantifying the depth distribution of this microstructure. That is, since the bottom wave is a signal affected by all the inside of the material, it is possible to calculate the average physical property value of the material that has passed and reflected. On the other hand, since the backscattered wave is a weak signal but is a signal reflected from a certain depth, it has information on the depth distribution and it is considered that the depth distribution can be calculated. It was.

The backscattered wave of ultrasonic waves is defined by (Equation 3) shown below, and has an attenuation coefficient α as a variable. Then, as described above, since the change in the attenuation coefficient appears as a large change even if the change in the grain physical property value is small, the backscattered wave also changes greatly, and even if it is a minute microstructure, its depth It is possible to quantify the distribution.

Specifically, first, the backscattered wave region of the ultrasonic waveform acquired by injecting ultrasonic waves into the material to be diagnosed is divided into sections for each depth, and frequency conversion is performed in each section. Calculate the frequency distribution of backscattered waves in each section.

Next, assuming the depth distribution of the microstructure in the material, the frequency distribution of the backscattered wave is calculated based on (Equation 3) for each same section.

At this time, the frequency distribution of the backscattered wave is calculated by using the differential cross section (dγ / dΩ) _{L, π} of the solid angle Ω of the cross section γ in each section of the assumed depth distribution. Here, L indicates a longitudinal wave and π indicates a reflection direction. Then, the differential cross section of the crystal grains is calculated by (Equation 4). Since the grain physical property value is also included as a variable in this (Equation 4), the backscattered wave calculated by the (Equation 3) changes further greatly due to the change in the grain physical property value.

Next, the frequency distribution of the backscattered wave calculated based on the depth distribution of the microstructure in the assumed material is compared with the frequency distribution of the backscattered wave obtained above. Then, the matching is repeated by changing various assumptions until the two are exactly matched.

By combining the frequency distribution of the backscattered wave calculated based on the assumption with the frequency distribution of the backscattered wave obtained from the diagnosis target in this way, the depth distribution of the microstructure can be easily quantified. ..

In addition, the above-mentioned quantification of the depth distribution of the microstructure is the integrated intensity area ratio of the frequency distribution of the backscattered wave calculated based on the depth distribution of the microstructure in the assumed material and the obtained backscattered wave. It can also be performed by collating the integrated intensity area ratio of the frequency distribution of each backscattered wave calculated from the frequency distribution of the above, changing various assumptions, and repeating the collation until the two are exactly matched.

The fourth technique and the fifth technique related to the present invention are techniques based on the above findings.

That is, the fourth technique related to the present invention is
It is a material diagnostic method that non-destructively diagnoses the microstructure generated in the material using ultrasonic waves.
The backscattered wave region of the ultrasonic waveform acquired by injecting ultrasonic waves into the material to be diagnosed whose amount of microstructure is known is divided into sections having a predetermined width in the depth direction of the material, and each of them is divided. A diagnostic target frequency distribution calculation process that calculates the frequency distribution of backscattered waves using frequency conversion for each section, and
Assuming the depth distribution of the microstructure, the depth distribution of the microstructure is determined by collating the frequency distribution of the backscattered waves calculated for each section in the same section as the frequency distribution calculation step of the diagnosis target. It is a material diagnostic method characterized by including a microstructure depth distribution quantification step for quantifying the microstructure depth distribution in a material.

In addition, the fifth technique related to the present invention is
It is a material diagnostic method that non-destructively diagnoses the microstructure generated in the material using ultrasonic waves.
The backscattered wave region of the ultrasonic waveform acquired by injecting ultrasonic waves into the material to be diagnosed whose amount of microstructure is known is divided into sections having a predetermined width in the depth direction of the material, and each of them is divided. The process of calculating the integrated intensity area ratio of the frequency distribution of the backscattered wave using frequency conversion for each section, and the process of calculating the integrated intensity area ratio of the frequency distribution to be diagnosed.
Assuming the depth distribution of the microstructure, the depth of the microstructure is compared with the integrated intensity area ratio of the frequency distribution of the backscattered wave calculated for each section in the same section as the step of calculating the integrated intensity area ratio of the frequency distribution to be diagnosed. The material diagnosis method is characterized by including a microstructure depth distribution quantification step for quantifying the microstructure depth distribution in the material to be diagnosed by determining the bulk distribution.

(3) Next, the quantification of the depth distribution of the minute microstructure due to the dislocation structure generated in the material will be described.

In addition to the voids and precipitates described above, the microstructure also includes a microstructure with a dislocation structure, and in the case of a microstructure with a dislocation structure, it is necessary to obtain the depth distribution of the dislocation density.

Here, the present inventor has found that, unlike precipitates and voids, the rearscattered structure does not scatter ultrasonic waves and only absorbs them, and the absorption of ultrasonic waves reduces backscattered waves. It was noted that the ultrasonic absorption by the tissue, that is, the attenuation α (ω) can be expressed by the following (Equation 5).

In (Equation 5), μ is the rigidity, b is the burger spectrum, and C is the tension of dislocations, which are obtained from the lattice constant, density, rigidity, and Poisson's ratio by the following equations, respectively.

Further, since it is difficult to measure the average dislocation length L, it is obtained from the lattice constant and the reference in advance. The natural frequency ω ₀ of dislocations is determined by the average dislocation length L as shown in the following equation, but is known to be on the order of GHz.

Then, the present inventor has come to the conclusion that the depth distribution of the dislocation density in the material can be easily quantified by using the following method.

That is, first, ultrasonic waves are incident on a material whose dislocation density and crystal grain size are known in advance, and the frequency distribution of backscattered waves caused by crystal grains is calculated at each depth.

Then, the depth distribution of the dislocation density in the material is assumed. Backscattered waves caused by grain grains are absorbed and attenuated by dislocations present in the ultrasonic stroke. The degree of attenuation can be determined using (Equation 5). From the above, the backscattered wave frequency distribution is calculated at each depth.

That is, since the amount of decrease in the backscatter wave frequency distribution obtained from the two backscatter wave frequency distributions can be regarded as the amount of absorption of ultrasonic waves due to the rearrangement, the frequency distribution of the backscatter waves calculated based on this assumption. And the frequency distribution of the backscattered wave obtained above are collated. Then, the dislocation depth distribution in the material can be quantified by repeating the collation by changing various assumptions until the two are exactly matched.

Further, in the same manner as described above, the integrated intensity area ratio of the frequency distribution of the backscattered wave calculated based on the assumption and the integrated frequency distribution of each backscattered wave calculated from the obtained frequency distribution of the backscattered wave. The backscatter depth distribution in the material can also be quantified by collating with the strength-area ratio, changing various assumptions and repeating the collation until the two match accurately.

The sixth technique and the seventh technique related to the present invention are techniques based on the above findings.

That is, the sixth technique related to the present invention is
It is a material diagnostic method that non-destructively diagnoses the microstructure generated in the material using ultrasonic waves.
The backscattered wave region of the ultrasonic waveform obtained by injecting ultrasonic waves into the material to be diagnosed whose transposition density and crystal grain size are known is divided into sections having a predetermined width in the depth direction of the material. A diagnostic target frequency distribution calculation process that calculates the frequency distribution of backscattered waves using frequency conversion in each section, and
Assuming the depth distribution of the microstructure, the matching frequency distribution calculation step of calculating the frequency distribution for each section same as the diagnosis target frequency distribution data calculation step,
The depth distribution of the microstructure in the material is determined from the decrease amount of each frequency distribution obtained in the frequency distribution creation step for diagnosis and the frequency distribution calculation step for collation, and the determined microstructure depth distribution is assumed. The material diagnostic method is characterized by comprising a step of quantifying the dislocation depth distribution of the microstructure in the material to be diagnosed.

In addition, the seventh technique related to the present invention is
It is a material diagnostic method that non-destructively diagnoses the microstructure generated in the material using ultrasonic waves.
The backscattered wave region of the ultrasonic waveform obtained by injecting ultrasonic waves into the material to be diagnosed whose transposition density and crystal grain size are known is divided into sections having a predetermined width in the depth direction of the material. The process of calculating the integrated intensity area ratio of the frequency distribution of the backscattered wave using frequency conversion in each section, and the process of calculating the integrated intensity area ratio of the frequency distribution to be diagnosed.
Assuming the depth distribution of the microstructure, calculate the integrated intensity area ratio of the frequency distribution for each section same as the data calculation process of the integrated intensity area ratio of the frequency distribution to be diagnosed. Process and
The depth distribution of the microstructure in the material is determined from the integrated intensity area ratio of each frequency distribution obtained in the integrated intensity area ratio creation step of the frequency distribution to be diagnosed and the integrated intensity area ratio calculation step of the matching frequency distribution. , A material diagnosis characterized by comprising a microstructure dislocation depth distribution quantification step for quantifying the dislocation depth distribution of the microstructure in the material to be diagnosed based on the determined microstructure depth distribution assumption. The method.

(4) Next, a case where a plurality of types of minute microstructures are generated in the material will be described.

The microstructures such as the above-mentioned precipitates, voids, and dislocation structures are not always generated individually, and a plurality of microstructures may be generated at the same time.

Therefore, the present inventor examined to determine the type of the generated microstructure, and then estimate and quantify each microstructure.

As a result, each of the above-mentioned sound velocity, attenuation coefficient, bottom wave frequency distribution, and backward scattered wave frequency distribution of the ultrasonic wave is used as an index, and each index obtained by injecting ultrasonic wave into the material to be diagnosed is used. A material to be diagnosed in the initial state (a state in which no microstructure is generated), a material similar to the material to be diagnosed in a state in which no microstructure is generated, and a state in which the amount of microstructure generated is known in advance. When ultrasonic waves are incident on any of the materials to be diagnosed and analysis is performed in consideration of each index acquired in advance and the usage history of the material, these indexes are characteristic depending on the type of microstructure. Since it appears, it was found that microstructures such as precipitates, voids, and rearranged structures can be easily discriminated. Then, in the case of one index, for example, two microstructures of a dislocation structure and a precipitate (carbide), such discrimination can be performed by simply using the speed of sound as an index. I understood.

Here, "considering the usage history of the material" means considering the usage environment such as the usage temperature, the irradiation amount, and the usage time.

The eighth technique related to the present invention has been made based on the above findings.
It is a material diagnostic method that non-destructively diagnoses the microstructure generated in the material using ultrasonic waves.
Sound velocity, attenuation coefficient, bottom wave frequency distribution, low frequency side wave height, high frequency side wave height, frequency distribution of backward scattered wave, and integrated intensity of frequency of backward scattered wave obtained by injecting ultrasonic waves into the material. Using one or more indicators selected from the area ratio,
The material to be diagnosed in the initial state, the material to be diagnosed that is similar to the material to be diagnosed and has no microstructure, or the material to be diagnosed in which the amount of microstructure generated is known in advance. Based on the change between the acquired reference material data and the material data to be diagnosed obtained at the time of diagnosis, and the usage history of the material.
It is a material diagnostic method characterized by discriminating the type of microstructure generated.

By applying each of the diagnostic methods of the first to seventh techniques described above, the amount of occurrence and the depth distribution can be estimated or quantified for each microstructure defect determined by the present technique.

(5) As described above, the present inventor does not diagnose the microstructure by focusing only on the change in the backscattered wave intensity of the ultrasonic signal waveform as in the conventional case, but deposits such as carbides and voids. By analyzing changes in crystal grain physical properties (volume, density, sound velocity, etc.) due to the generation of microstructures such as dislocations and phase transformations, even minute microstructures can be easily and accurately diagnosed. I found that I could do it.

That is, the ninth technique related to the present invention is
It is a material diagnostic method that non-destructively diagnoses the microstructure generated in the material using ultrasonic waves.
The material to be diagnosed in the initial state, the material to be diagnosed that is similar to the material to be diagnosed and has no microstructure, or the material to be diagnosed in which the amount of microstructure generated is known in advance. The first data acquisition step of acquiring the ultrasonic waveform and the crystal grain property value of the material by injecting a sound wave, and
It is provided with a second data acquisition step of acquiring ultrasonic waves and crystal grain property values of the material by injecting ultrasonic waves into the material to be diagnosed.
The microstructure generated in the material is diagnosed by evaluating the ultrasonic waveform in consideration of the changes in the crystal grain property values obtained in the first data acquisition step and the second data acquisition step. This is a material diagnostic method characterized by the above.

Further, the tenth technique related to the present invention is
The material diagnostic method according to a ninth technique, wherein the microstructure generated in the material is a microstructure of any one or more of precipitates, voids, dislocations, and phase transformations.

The present invention has been made based on the above-mentioned techniques, and the invention according to claim 1 is the invention.
In grains with one or more microstructures of precipitates, voids, dislocations and phase transformations, in the material based on the fact that changes in backscattered waves are related to the microstructure distribution in the depth direction. It is a material diagnosis method that non-destructively quantifies and diagnoses the microstructure generated in the above using ultrasonic waves.
The backscattered wave region of the ultrasonic waveform acquired by injecting ultrasonic waves into the material to be diagnosed whose amount of microstructure is known is divided into sections having a predetermined width in the depth direction of the material, and each of them is divided. The process of calculating the integrated intensity area ratio of the frequency distribution of the backscattered wave using frequency conversion for each section, and the process of calculating the integrated intensity area ratio of the frequency distribution to be diagnosed.
Assuming the depth distribution of the microstructure, the depth of the microstructure is compared with the integrated intensity area ratio of the frequency distribution of the backscattered wave calculated for each section in the same section as the step of calculating the integrated intensity area ratio of the frequency distribution to be diagnosed. The material diagnosis method is characterized by including a microstructure depth distribution quantification step for quantifying the microstructure depth distribution in the material to be diagnosed by determining the bulk distribution.

The invention according to claim 2
In grains with one or more microstructures of precipitates, voids, dislocations and phase transformations, in the material based on the fact that changes in backscattered waves are related to the microstructure distribution in the depth direction. It is a material diagnosis method that non-destructively quantifies and diagnoses the microstructure generated in the above using ultrasonic waves.
The backscattered wave region of the ultrasonic waveform obtained by injecting ultrasonic waves into the material to be diagnosed whose transposition density and crystal grain size are known is divided into sections having a predetermined width in the depth direction of the material. The process of calculating the integrated intensity area ratio of the frequency distribution of the backscattered wave using frequency conversion in each section, and the process of calculating the integrated intensity area ratio of the frequency distribution to be diagnosed.
Assuming the depth distribution of the microstructure, calculate the integrated intensity area ratio of the frequency distribution for each section same as the data calculation process of the integrated intensity area ratio of the frequency distribution to be diagnosed. Process and
The depth distribution of the microstructure in the material is determined from the integrated intensity area ratio of each frequency distribution obtained in the integrated intensity area ratio creation step of the frequency distribution to be diagnosed and the integrated intensity area ratio calculation step of the matching frequency distribution. , A material diagnosis characterized by comprising a microstructure dislocation depth distribution quantification step for quantifying the dislocation depth distribution of the microstructure in the material to be diagnosed based on the determined microstructure depth distribution assumption. The method.

According to the present invention, even if the microstructure is as small as several tens of μm or less with respect to the material, its type, amount generated, depth distribution, etc. are non-destructively quantified and evaluated with sufficient accuracy. A material diagnostic method that can be diagnosed can be provided.

It is a figure which shows typically the material diagnosis measurement method by ultrasonic wave. It is a figure explaining the change of the physical property value of a crystal grain when a void occurs. It is a figure which shows an example of the waveform of the ultrasonic signal wave observed when the ultrasonic wave is incident on the material. It is a figure explaining the relationship between the waveform of an ultrasonic signal wave, the microstructure distribution and the material property. It is a figure explaining the method of calculating the attenuation coefficient based on the frequency distribution of the bottom wave. It is a figure which shows the frequency distribution of the bottom wave and the backscattered wave at the time of 1.3% void swirling. It is a figure which shows the frequency distribution of the bottom wave and the backscatter wave when it is densified by 0.6% by the precipitation of carbides. It is a figure which shows the frequency distribution of the bottom wave and the backscatter wave when the dislocation density increases. It is a figure which shows the depth distribution of void swelling. It is a figure which shows the frequency distribution of the backscattered wave at each depth when there is a depth distribution of void swelling. It is a figure which shows the integral intensity area ratio of the frequency distribution of the backscattered wave at each depth when the void swelling is uniformly distributed. It is a figure which shows the division method which divides the material to be examined in the depth direction in one Embodiment of this invention. It is a figure which shows the integral intensity area ratio of the frequency distribution of the backscattered wave at each depth when there is a depth distribution in void swelling. It is a figure which shows the result of having compared the integrated intensity area ratio of the backscattered wave frequency distribution of an irradiated material and an unirradiated material. It is a figure which shows the result of having compared the experimental value of the integral intensity area ratio of the backscattered wave frequency distribution of an irradiation material with the theoretical calculation. It is a figure explaining the discrimination method when microstructures occur at the same time.

1. 1. Quantitative evaluation of the amount of microstructure generated in the case of uniform distribution The following is a quantitative evaluation of the amount of microstructure generated in the case of uniformly distributed microstructure for a stainless steel hexagonal column material with a thickness of 52.23 mm. Regarding how to do it.

(1) Acquisition of ultrasonic data of a material without microstructure generation First, ultrasonic waves with a peak frequency of 10 MHz are incident on a material without microstructure generation (unirradiated archive material) to obtain ultrasonic waves. The waveform was acquired.

(2) Acquisition of ultrasonic data of the material in which the microstructure is generated Focusing on the change in the physical property value of the crystal grains (here, the change in sound velocity is used) due to the generation of the assumed microstructure, based on the amount of change in the damping coefficient. The frequency distribution was calculated. Hereinafter, specific calculation examples will be shown for each of the cases of voids, precipitates, and microstructures generated by dislocations.

(A) In the case of microstructure generated by voids Assuming that the microstructure is microstructure generated by void swelling of 1.3% swelling, the attenuation coefficient α _L for each frequency is obtained using (Equation 2). , The frequency distribution of the bottom wave was calculated. Based on past findings, the amount of change in sound velocity due to the occurrence of void swelling with swelling of 1.3% was set to 0.7%.

The calculation result is shown in FIG. Note that FIG. 6 shows the frequency distribution (a) of the bottom wave and the frequency distribution (b) of the backscattered wave at a depth of 36 to 50 mm from the viewpoint of the average value of material properties and the microstructure depth distribution. There is. In FIG. 6, the solid line is the unirradiated archive material in which no microstructure is generated, and the broken line is the 1.3% swelling material.

From FIG. 6, it can be seen that the data when there is no microstructure (solid line) and the data when there is microstructure (broken line) can be clearly distinguished.

(B) In the case of the microstructure generated by the precipitate As the microstructure, assuming the microstructure generated by 0.6% densification by the carbide precipitation, the frequency distribution of the bottom wave and the frequency distribution of the bottom wave are used by using the same method as above. The frequency distribution of the backscattered wave was calculated. Based on past findings, the amount of change in sound velocity due to 0.6% densification due to carbide precipitation was set to 2%.

The calculation result is shown in FIG. Note that FIG. 7 shows the frequency distribution (a) of the bottom wave and the frequency distribution (b) of the backscattered wave at a depth of 36 to 50 mm from the same viewpoint as described above. In FIG. 7, the solid line is the unirradiated archive material in which no microstructure is generated, and the broken line is the material in which carbides are deposited.

From FIG. 7, it can be seen that the data when there is no microstructure (solid line) and the data when there is microstructure (broken line) can be clearly distinguished.

(C) In the case of microstructure generated by dislocation As the microstructure, a microstructure generated by an increase in dislocation density (10 ¹⁵ m / m ³ ) is assumed, and each ω is used using (Equation 3) and (Equation 5). The damping coefficient for each was obtained, and the frequency distribution of the bottom wave and the backscattered wave was calculated with the average dislocation length as a parameter, paying attention to the change in the damping coefficient due to the change in the physical properties of the crystal grains.

The calculation result is shown in FIG. Note that FIG. 8 shows the frequency distribution (a) of the bottom wave and the frequency distribution (b) of the backscattered wave at a depth of 25 to 39 mm from the same viewpoint as described above. In FIG. 8, the solid line is the frequency distribution obtained from the unirradiated archive material in which no microstructure is generated, and the other lines show the average dislocation length due to the increased dislocation density of 50 nm (broken line) and 100 nm (dotted line). ), 150 nm (dashed line), and 200 nm (dashed line).

From FIG. 8, the data when no microstructure is generated (solid line) and the data when microstructure is developed (other lines) can be clearly distinguished, and further, the difference in the average dislocation length also causes. It can be seen that they can be clearly distinguished.

(3) Consideration From the results in each of the above cases, paying attention to the change in the physical property value of the crystal grain due to the generation of the microstructure, by reflecting the physical property value of the crystal grain using each of the above formulas, a minute microstructure can be obtained. It can be seen that even if there is, it is possible to clearly distinguish them.

Then, by applying this method and creating many frequency distributions in advance by changing each assumption in each case in advance, the material to be diagnosed is collated with the frequency distribution obtained by irradiating the material with ultrasonic waves. By doing so, it becomes possible to easily quantitatively evaluate the microstructure.

In the above, the quantitative evaluation of the microstructure was performed using the same material and the time-lapse data of the material in the state where no microstructure was generated (initial state).

However, instead of quantitatively evaluating the microstructure using the same material, the data measured using the same type of unirradiated archive material may be used as the data of the unirradiated archive material to quantitatively evaluate the microstructure. ..

2. Quantitative evaluation of the depth distribution of the microstructure The following describes (A) quantitative evaluation using the backscattered wave frequency distribution, and (B) backward regarding the method of quantitatively evaluating the depth distribution of voids having a depth distribution. It will be described in order of quantitative evaluation using the integrated intensity area ratio of the scattered wave frequency distribution.

(A) Quantitative evaluation using backscattered wave frequency distribution (1) Acquisition of ultrasonic data of material without microstructure First, the thickness of the hexagonal column made of stainless steel was 52.23 mm. An ultrasonic wave with a peak frequency of 10 MHz is incident on a material (unirradiated archive material) in a state where no microstructure is generated, an ultrasonic wave waveform is acquired, a section is 14 mm wide as a backscattered wave calculation region, and a depth is 16 The frequency distribution of backscattered waves at -30 mm and 36-50 mm was calculated.

(2) Target materials Two types of materials with the following microstructures were assumed as the materials to be diagnosed.
(A) Material in which voids with 1.3% swelling are uniformly distributed (b) Voids with 0.7% swelling at both ends of 16 mm width and swelling with a width of 20.23 mm at the center 2. Material with 25% voids (total width 52.23 mm, average swelling 1.3%) (see FIG. 9)

(3) Acquisition of frequency distribution of backscattered waves For each material to be diagnosed, backscattering at depths of 16-30 mm and 36-50 mm with a section of 14 mm width as the backscattered wave calculation region using equation (4). The frequency distribution of the wave was calculated.

The calculation result is shown in FIG. In FIG. 10, (a) shows the frequency distribution of the backscattered wave at a depth of 16-30 mm, (b) shows the frequency distribution of the backscattered wave at a depth of 35-50 mm, and the solid line shows the unirradiated archive material and the broken line. Is a uniform void, and the alternate long and short dash line is a frequency distribution obtained from a distributed void.

From FIG. 10, in both (a) and (b), a material without microstructure generation (solid line), a material with uniform voids (broken line), and a material with distributed voids are generated. It can be seen that each of the (dotted chain lines) is clearly distinguishable.

In this way, by paying attention to the change in the physical property value of the crystal grains due to the generation of the microstructure and applying the above formula, it is possible to clearly distinguish the microstructure having a depth distribution.

Then, if this method is applied to change the assumptions of microstructures such as voids in advance and the frequency distribution of backscattered waves for each section is created in advance, the material to be diagnosed is irradiated with ultrasonic waves. By collating with the frequency distribution for each section obtained above, it is possible to easily quantitatively evaluate the depth distribution of microstructures such as voids.

The present inventor has confirmed that the quantitative evaluation of the depth distribution in the dislocation having the depth distribution can be performed in the same manner as described above.

(B) Quantitative evaluation using the integrated intensity area ratio of the backscattered wave frequency distribution (1) Materials in which voids are uniformly distributed First, voids are uniformly distributed as materials to be diagnosed. Assuming the generated material, the integrated intensity area ratio of the backscattered wave frequency distribution was calculated.

Specifically, assuming a material in which voids are uniformly distributed in unirradiated archive material with 0%, 1%, 2%, 3%, and 5% swelling, the depth of each material is assumed. The integrated intensity of the backscattered wave frequency distribution was obtained by changing the depth from the material surface at a width of 12 mm. For example, the calculation at a depth of 42 mm with 0% swelling integrated a frequency of 6-9 MHz in the unirradiated archive material.

The calculation result is shown in FIG. In FIG. 11, the horizontal axis is the depth (mm) from the material surface. The vertical axis is the integrated intensity area ratio of the backscattered wave frequency distribution in which the integrated intensity of the backscattered wave frequency distribution at a depth width of 12 mm is standardized at a depth of 18 mm.

From FIG. 11, it can be confirmed that when the voids are uniformly distributed, the integrated intensity area ratio of the backscattered wave frequency distribution is changed by swelling.

(2) Material in which voids are generated with a distribution Next, as a material to be diagnosed, voids are generated in the swelling distribution shown in Table 1 in the regions (a) to (c) shown in FIG. Assuming four types of materials, the integrated intensity area ratio of the backscattered wave frequency distribution was obtained for each material in the same manner as described above. As shown in Table 1, the average swelling of these materials is 1%, and the average void amount is the same.

The calculation result is shown in FIG. From FIG. 13, it can be confirmed that the integrated intensity area ratio of the backscattered wave frequency distribution differs depending on the void distribution even though the average void amount in the entire material is the same.

(3) Comparison of experimental values obtained from the irradiated material with the unirradiated material Next, the backscattered wave frequency distribution between the unirradiated archive material without the generation of microstructure and the irradiated material with the generation of microstructure. The integrated intensity area ratio of was obtained and compared. The results are shown in FIG.

From FIG. 14, it can be seen that the integrated intensity area ratio of the backscattered wave frequency distribution changes from that of the unirradiated archive material because microstructure is generated in the irradiated material. It can be seen that this change is different from the change in void swelling in the uniform distribution shown in FIG. 11, and exhibits a behavior close to that of (b) region 3% swelling shown in FIG.

Next, the irradiation material was quantitatively evaluated for the depth distribution of the microstructure change obtained in FIG. Specifically, the results calculated by allocating the void swelling distribution shown in Table 2 for each depth from the surface and using (Equations 1 to 5) are shown as theoretical values. The total length of the irradiation material is 52.2 mm. The results are shown in FIG.

As shown in FIG. 15, there is a good agreement between the theoretical value and the amount of void swelling actually obtained from the density measurement result.

The depth distribution of void swelling shown in Table 2 is in excellent agreement with the depth distribution obtained from the density measurement result of the irradiation material and the depth distribution obtained from the void observation result by TEM, and the method of the present invention is found. It was confirmed that by applying the above, it is possible to easily quantitatively evaluate the depth distribution of microstructures such as voids.

The present inventor has confirmed that the quantitative evaluation of the depth distribution in the dislocation structure and the precipitate having the depth distribution can be performed in the same manner as described above.

3. 3. Discrimination when microstructures exist at the same time The following relates to a method for discriminating individual microstructures when microstructures exist at the same time.

(1) Assumption It was assumed that the microstructure was generated by first growing the dislocation structure, then precipitating carbides, and finally void swelling.

(2) Index As the index, four indexes were used: the speed of sound, the backscattered wave peak frequency, and the wave heights of 7 MHz (low frequency side) and 13 MHz (high frequency side).

(3) Acquisition of ultrasonic data After calculating the frequency distribution obtained when ultrasonic waves are incident on the material in the state where no microstructure is generated and in the state where each of the above microstructures is generated, each of the above The index was calculated, and the relative change was calculated based on the value in the state where no microstructure was generated.

The measurement result is shown in FIG. In FIG. 16, ◯ is the wave height of the backscattered wave peak frequency, □ is the wave height of 7 MHz, ■ is the wave height of 13 MHz, and Δ is the speed of sound.

From FIG. 16, it can be seen that the relative change in the speed of sound is not so large, while the relative change in the other indicators is large. Then, it can be clearly classified that the arrangement of each data is different depending on the existence state of the microstructure, and it can be seen that the microstructure can be discriminated.

In this way, by paying attention to the change in the physical property value of the crystal grain due to the generation of the microstructure, the relative change of the index obtained from the frequency distribution of the backscattered wave can be increased. Then, by paying attention to the changes in these indexes, it becomes possible to discriminate the microstructures that occur at the same time.

Although the present invention has been described above based on the embodiments, the present invention is not limited to the above embodiments. Various modifications can be made to the above embodiments within the same and equivalent scope as the present invention.

Claims (2)

In grains with one or more microstructures of precipitates, voids, dislocations and phase transformations, in the material based on the fact that changes in backscattered waves are related to the microstructure distribution in the depth direction. It is a material diagnosis method that non-destructively quantifies and diagnoses the microstructure generated in the above using ultrasonic waves.
The backscattered wave region of the ultrasonic waveform acquired by injecting ultrasonic waves into the material to be diagnosed whose amount of microstructure is known is divided into sections having a predetermined width in the depth direction of the material, and each of them is divided. The process of calculating the integrated intensity area ratio of the frequency distribution of the backscattered wave using frequency conversion for each section, and the process of calculating the integrated intensity area ratio of the frequency distribution to be diagnosed.
Assuming the depth distribution of the microstructure, the depth of the microstructure is compared with the integrated intensity area ratio of the frequency distribution of the backscattered wave calculated for each section in the same section as the step of calculating the integrated intensity area ratio of the frequency distribution to be diagnosed. A material diagnosis method comprising a microstructure depth distribution quantification step for quantifying the microstructure depth distribution in a material to be diagnosed by determining the bulk distribution. In grains with one or more microstructures of precipitates, voids, dislocations and phase transformations, in the material based on the fact that changes in backscattered waves are related to the microstructure distribution in the depth direction. It is a material diagnosis method that non-destructively quantifies and diagnoses the microstructure generated in the above using ultrasonic waves.
The backscattered wave region of the ultrasonic waveform obtained by injecting ultrasonic waves into the material to be diagnosed whose transposition density and crystal grain size are known is divided into sections having a predetermined width in the depth direction of the material. The process of calculating the integrated intensity area ratio of the frequency distribution of the backscattered wave using frequency conversion in each section, and the process of calculating the integrated intensity area ratio of the frequency distribution to be diagnosed.
Assuming the depth distribution of the microstructure, calculate the integrated intensity area ratio of the frequency distribution for each section same as the data calculation process of the integrated intensity area ratio of the frequency distribution to be diagnosed. Process and
The depth distribution of the microstructure in the material is determined from the integrated intensity area ratio of each frequency distribution obtained in the integrated intensity area ratio creation step of the frequency distribution to be diagnosed and the integrated intensity area ratio calculation step of the matching frequency distribution. , A material diagnosis characterized by comprising a microstructure dislocation depth distribution quantification step for quantifying the dislocation depth distribution of the microstructure in the material to be diagnosed based on the determined microstructure depth distribution assumption. Method.

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