JPH0875709A - Method and device for identifying defect type by ultrasonic flaw detection - Google Patents

Abstract

(57) [Summary] [Purpose] Defect types can be identified with high accuracy with a small amount of features,
(EN) Provided are a defect type discrimination method by ultrasonic flaw detection and a device used for carrying out the method, in which the discrimination processing time is short. [Structure] A waveform feature amount extraction circuit 7 extracts a peak amplitude P _A and supplies it to a first flaw type determination circuit 9 and also obtains a dominant frequency band W _L , which is then determined by a second flaw type determination circuit.
Give to 10. The controller 3 supplies the scanning information of the scanner 2 to the signal processing device 8, and the signal processing device 8 calculates the defect area S _K, and uses it to calculate the first defect type determination circuit 9 and the second defect type. It is given to the seed determination circuit 10. The first flaw type determining circuit 9 determines whether the defect is a cracking defect or an inclusion based on the peak amplitude P _A and the defect area S _K , and when it is determined to be an inclusion. , Second
The defect type determination circuit 10 determines the type of the inclusion based on the dominant frequency band W _L and the defect area S _K , and records the determination result on the disk 11.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a method of discriminating a defect type on the basis of the result of ultrasonic flaw detection and an apparatus used for carrying out the method.

[0002]

2. Description of the Related Art As a conventional defect type discriminating method by ultrasonic flaw detection, there is a method described in JP-A-53-143386. This is because ultrasonic waves are incident on the material to be detected from the probe so that the incident angles are in the range of 0 ° to 45 ° and 45 ° to 90 °, and the echo is received. And the difference signal between the standard signal and the standard signal, respectively. on the other hand,
The feature amount of the signal interval and the signal duration is calculated for both received signals, and the comparison area in the difference signal is specified based on the calculated feature amount. Then, based on the waveform of the difference signal in the comparison area and a predetermined reference waveform, it is determined whether or not the defect is due to inclusions.

However, in the method described above, ultrasonic waves must be incident on the material to be inspected at two or more incident angles in order to obtain the result of defect type discrimination with high accuracy, and it takes a long time for flaw detection. There was a problem.

In order to solve this problem, Japanese Patent Application No. 5-17
The method described in 1117 is proposed. This is because ultrasonic waves are incident on a flaw-detecting material at a single incident angle from a probe movably arranged on the flaw-detecting material such as a steel plate, and an echo is received, and the received signal has a predetermined S / N ratio. If it exceeds, it is judged as a defect. When it is determined that there is a defect, a total of six types of feature quantities including the center frequency of the frequency band of the received signal, the peak frequency, the peak signal strength, the signal duration, the received signal rising time, and the falling time are obtained. Further, the area of the defect is obtained based on a plurality of received signals obtained in the moving order of the probe. Then, the obtained feature amount and defect area are given to the neuro circuit to determine the defect type. As a result, even if the flaw detection is performed at one incident angle, the defect type can be accurately determined, and the flaw detection time is shortened.

[0005]

However, in the above-mentioned method, since the number of feature amounts required for discrimination is large,
There has been a problem that the processing time of the discrimination processing becomes long and the throughput of the discrimination processing is low. The present invention has been made in view of the above problems, and an object thereof is to determine a defect type based on the maximum signal intensity of a received signal of an echo related to a defect, a dominant frequency band, and the area of the defect. Accordingly, a defect type can be accurately determined with a small amount of features, the processing time of the determination process can be shortened, and a defect type determination method by ultrasonic flaw detection with high throughput of the determination process and an apparatus used for the implementation are provided. To do.

[0006]

According to a first aspect of the present invention, there is provided a defect type discrimination method using ultrasonic flaw detection, wherein ultrasonic waves are transmitted from the probe to the flaw detection material at a predetermined cycle while moving the probe or the flaw detection material. In the method of detecting the defect in the material to be inspected by receiving each echo and receiving the echo, and determining the defect type based on a plurality of received signals of the echo related to the defect, it is an area having a predetermined intensity or more of each received signal. Calculate the area of the defect based on the defect width, extract the maximum signal strength of the received signal related to the defect,
The defect area, maximum signal strength, and whether the defect is a cracking defect or an inclusion is determined based on a preset threshold value, and further, frequency analysis of the received signal relating to the maximum signal strength is performed, Based on the result, a dominant frequency band having a relatively high signal strength is obtained, and when it is determined that the inclusion is an inclusion, the inclusion type is determined based on the area of the defect, the dominant frequency band, and a preset threshold value. Is characterized in that

In the defect type identification apparatus using ultrasonic flaw detection according to the second aspect of the present invention, while moving the probe or the flaw-detecting material, ultrasonic waves are incident on the flaw-detecting material from the probe at a predetermined cycle, and each echo is generated. In an apparatus for detecting a defect in a material to be inspected by receiving, and for determining the defect type based on a plurality of received signals of echoes related to the defect, based on a defect width which is an area having a predetermined intensity or more of each received signal, A means for calculating the area of the defect, a means for extracting the maximum signal intensity of the received signal relating to the defect, and whether the defect is a cracking defect based on the area of the defect, the maximum signal intensity, and a preset threshold value. Means for determining whether it is an inclusion, further means for performing a frequency analysis of the received signal relating to the maximum signal strength, and means for obtaining a dominant frequency band in which the signal strength is relatively high based on the result , As an inclusion If another has been the area of the defect,
It is characterized by comprising means for discriminating the type of inclusion based on a dominant frequency band and a preset threshold value.

[0008]

FIG. 3 is a graph showing the results of time-dependent measurement of the received signal intensity of the echo of the ultrasonic wave incident on the material to be inspected, where the vertical axis shows the received signal intensity and the horizontal axis shows the time. . As shown in FIG. 2, the received signal strength has a plurality of peaks having a predetermined value or more, which indicate defects. The width of the defect can be obtained by a region having a signal intensity equal to or higher than a predetermined value, and the defect width can be calculated by integrating the width of the defect based on a plurality of received signals obtained in the moving order of the probe or the material to be inspected. Find the area. The following relationship exists between the defect area and the maximum peak (peak amplitude) of the received signal strength related to the defect.

FIG. 4 is a graph showing the relationship between the peak amplitude and the defect area in crack defects and inclusion defects. In the figure, the solid line indicates crack defects and the broken line indicates inclusions. The intensity of the received signal described above is proportional to the sound pressure reflectance r _p of the ultrasonic wave incident on the flaw detection target material, which is obtained by the following equation (1). r _p = (Z ₂ −Z ₁ ) / (Z ₁ + Z ₂ ) ... (1) Z = ρc where Z: acoustic impedance Z ₁ : acoustic impedance of the first substance Z ₂ : acoustic impedance of the second substance ρ: Density c: speed of sound

The relationship between the acoustic impedance of a crack defect having a space in the defect, the acoustic impedance of an inclusion having no space, and the acoustic impedance of, for example, a steel material as the flaw detection material is expressed by the following equation (2). Is. Inclusions> Steels >>>> Air (2) Further, the sound pressure reflectance from steel to air and from steel to inclusions is expressed by the following equation (3). (Steel material → air) >> (Steel material → inclusions) (3) Therefore, the peak amplitudes of cracking defects and inclusion defects in the same area are cracking defects >> inclusions, as shown in FIG. Then, when the received signal related to the defect has a peak amplitude of a predetermined value or more, it is determined to be a crack defect.

On the other hand, when it is determined that it is not a cracking defect but an inclusion, the type of inclusion is determined as follows.

The types of inclusions mainly include alumina-based inclusions and manganese-based inclusions. These inclusions have different flaws, the former is scattered in clusters, and the latter is not clustered. Therefore, when ultrasonic waves of the required frequency band are incident on the flaw-detected object and its echo is detected, alumina-based inclusions have many high-frequency components, and manganese-based inclusions have many components that spread normally in the low-frequency region. .

FIG. 5 is a graph showing an example of frequency analysis of a received signal of an echo. The vertical axis shows the received signal strength and the horizontal axis shows the frequency. The frequency band of the received signal of the echo is divided into, for example, every 10 MHz according to the frequency of the ultrasonic wave, and the region A, the region B, and
... In this example, as shown in FIG. 5, the peak of the signal intensity is in the region B, and the portion showing the signal intensity next to the peak is in the wide region including the regions C, D, and E.
Then, the dominant frequency band is obtained by integrating the signal strength in each area and rendering the maximum area of the respective values. That is, the dominant frequency band is the region having the maximum value in the area obtained for each region described above. The dominant frequency band obtained in this way is the region D, and as is clear from FIG. 5, it represents the central frequency band in the received signal.

FIG. 6 is a graph showing the relationship between the defect area of inclusions and the dominant frequency band. In the figure, the solid line indicates alumina-based inclusions and the broken line indicates manganese-based inclusions. As is clear from FIG. 6, in the dominant frequency band in the same area, the frequency of alumina inclusions is higher than that of manganese inclusions. Therefore, the dominant frequency band is obtained based on the result of the frequency analysis of the received signal, and if the dominant frequency band is equal to or higher than a predetermined frequency, it is an alumina-based inclusion, and if not, a manganese-based inclusion. It is determined to be a thing.

[0015]

DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention will be specifically described below with reference to the drawings showing the embodiments thereof. FIG. 1 is a block diagram showing the configuration of an apparatus used for implementing the present invention, and in the figure, 1 is a probe for transmitting and receiving ultrasonic waves. The probe 1 is attached to a scanner 2 which scans the flaw-detecting material T so as to face the flaw-detecting material T such as a steel plate at a predetermined angle, for example, 90 °.
The operation is controlled by the controller 3.

While the scanner 2 scans the material T to be inspected, echoes of ultrasonic waves which are incident on the material T to be inspected from the probe 1 at a predetermined cycle are received by the probe 1 and received by the pulsar receiver 4. Converted to a signal. This received signal is given to the analog / digital (A / D) converter 5, where it is converted into a digital signal and given to the waveform sampling circuit 6. The reference S / N ratio is preset in the waveform sampling circuit 6, and the waveform sampling circuit 6 uses the S / N ratio of the signal given from the A / D converter 5.
When the / N ratio is higher than the reference S / N ratio, the signal is sampled.
save.

The signals sampled and stored in the waveform sampling circuit 6 are given to the waveform feature amount extraction circuit 7 and the signal processing device 8. The waveform feature amount extraction circuit 7 updates the peak value of the intensity of the given signal when it is larger than the peak value stored previously, and peaks the peak value stored when flaw detection of the defect is completed. The predominant frequency band W _L is obtained based on the result of frequency analysis of the signal related to the peak amplitude P _A by the fast Fourier transform, while giving it as the amplitude P _A to the first flaw type determination circuit 9. It is given to the second flaw type judging circuit 10.
On the other hand, the signal processing device 8 includes the controller 3 to the scanner 2
Scanning information is given, and the signal processing device 8 corrects the intensity of the signal given from the waveform sampling circuit 6, and then the defect area S based on the correction signal and the scanning information.
_K is calculated and given to the first flaw type determining circuit 9 and the second flaw type determining circuit 10.

The first flaw type determining circuit 9 is based on the peak amplitude P _A given from the waveform feature amount extracting circuit 7 and the defect area S _K given from the signal processing device 8 and the defect is a cracking defect. It is discriminated whether or not it is an inclusion, and the discrimination result is recorded on the disk 11. When it is determined by the first flaw type determination circuit 9 that it is an inclusion, the second
The defect type determination circuit 10 determines the inclusion type based on the dominant frequency band W _L given from the waveform feature amount extraction circuit 7 and the defect area S _K given from the signal processing device 8, and the determination result Is recorded on the disk 11.

In the present embodiment, the probe is scanned by the scanner, but the present invention is not limited to this, and it goes without saying that the material to be detected may be transferred. .

FIG. 2 is a flow chart showing a procedure for discriminating a defect type by the apparatus shown in FIG. When the received signal relating to the defect is read from the waveform sampling circuit 6 and the scanning information is read from the controller 3 (step S1), the signal processing device 8 calculates the area S _K of the defect based on the information (step S2). Further, the waveform feature amount extraction circuit 7 extracts the peak amplitude P _A by updating the peak value of the received signal strength (step S3), and also performs the frequency analysis of the received signal related to the peak amplitude P _A. , The dominant frequency band W _L is obtained based on the result (step S4).

The first flaw type determination circuit 9 determines the area S of the defect.
It is determined whether or not the peak amplitude P _A within _K is greater than or equal to a predetermined value (step S5), and if it is greater than or equal to the predetermined value, it is determined that the defect is a cracking defect (step S6). On the other hand, if it is not more than the predetermined value,
The second flaw type judging circuit 10 judges whether or not the dominant frequency band W _L in the area S _K of the defect is equal to or higher than a predetermined frequency (step S7). Is determined to be an alumina-based inclusion (step S8), and is determined to be a manganese-based inclusion if the frequency is not higher than a predetermined frequency (step S9).

Next, the result of discriminating defects using the present invention will be described. Regarding the plurality of defects occurring in the steel sheet of the following materials, it is determined whether they are cracking defects or inclusions, and if they are inclusions, it is further determined whether they are Al ₂ O ₃ or MnS. The defective part was cut, the defect type was determined based on observation with a microscope, etc., and the hit rate for determining the defect type was calculated. As a result, the accuracy of discrimination of crack defects and inclusions was about 80%, and the accuracy of discrimination of Al ₂ O ₃ and MnS was 90% or more. In addition, the time required for this determination was about half that of the method using the conventional neuro circuit.

[0023]

As described above in detail, in the present invention, the defect type is discriminated on the basis of the defect area, the maximum received signal strength, and the dominant frequency band of the received signal. The present invention has excellent effects such that the determination can be performed, the feature amount required for the defect type determination is small, the processing time of the determination processing is shortened, and the throughput of the determination processing is high.

[Brief description of drawings]

FIG. 1 is a block diagram showing the configuration of an apparatus used for implementing the present invention.

FIG. 2 is a flowchart showing a procedure for discriminating defect types by the apparatus shown in FIG.

FIG. 3 is a graph showing a result of time-dependent measurement of received signal strength of an echo of an ultrasonic wave incident on a flaw detection target material.

FIG. 4 is a graph showing a relationship between a peak amplitude and a defect area in a crack defect and an inclusion defect.

FIG. 5 is a graph showing an example of frequency analysis of echo reception signals.

FIG. 6 is a graph showing the relationship between the defect area of inclusions and the dominant frequency band.

[Explanation of symbols]

 DESCRIPTION OF SYMBOLS 1 probe 2 scanner 3 controller 4 pulser receiver 6 waveform sampling circuit 7 waveform characteristic amount extraction circuit 8 signal processing device 9 first flaw type determination circuit 10 second flaw type determination circuit

Claims (2)

[Claims] 1. An ultrasonic wave is incident on a flaw-detected material from the probe at a predetermined cycle while moving the probe or the flaw-detected material, and each echo is received to detect a defect in the flaw-detected material, In a method for determining the defect type based on a plurality of received signals of echoes related to a defect, the area of the defect is calculated based on the defect width which is a region having a predetermined intensity or more of each received signal, and the received signal related to the defect is calculated. The maximum signal intensity of the defect is extracted, and it is determined whether the defect is a cracking defect or an inclusion based on the area of the defect, the maximum signal intensity, and a preset threshold value. The frequency analysis of the received signal is performed, and based on the result, the dominant frequency band having a relatively high signal strength is obtained, and when it is determined to be an inclusion, the area of the defect, the dominant frequency band, and the Intervene based on the set threshold Defect type determination method according to the ultrasonic flaw detection, characterized in that to determine the species. 2. An ultrasonic wave is incident on the flaw-detected material from the probe at a predetermined cycle while moving the probe or the flaw-detected material, and each echo is received to detect a defect in the flaw-detected material, In a device for determining the defect type based on a plurality of received signals of echoes related to a defect, a means for calculating the area of the defect based on the defect width which is a region having a predetermined intensity or more of each received signal, and Means for extracting the maximum signal intensity of the received signal, means for determining whether the defect is a cracking defect or an inclusion based on the area of the defect, the maximum signal intensity, and a preset threshold value, Means for performing frequency analysis of the received signal relating to the maximum signal strength, means for obtaining a dominant frequency band in which the signal strength is relatively high based on the result, and the defect if it is determined to be an inclusion Area, dominant frequency band And a means for discriminating the type of inclusions based on a preset threshold value, and a defect type discriminating apparatus by ultrasonic flaw detection.