JP7415757B2 - Ultrasonic flaw detection method for round bar materials - Google Patents
Ultrasonic flaw detection method for round bar materials Download PDFInfo
- Publication number
- JP7415757B2 JP7415757B2 JP2020070115A JP2020070115A JP7415757B2 JP 7415757 B2 JP7415757 B2 JP 7415757B2 JP 2020070115 A JP2020070115 A JP 2020070115A JP 2020070115 A JP2020070115 A JP 2020070115A JP 7415757 B2 JP7415757 B2 JP 7415757B2
- Authority
- JP
- Japan
- Prior art keywords
- flaw
- round bar
- data
- flaw echo
- flaws
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000001514 detection method Methods 0.000 title claims description 15
- 239000000463 material Substances 0.000 title claims description 11
- 238000013528 artificial neural network Methods 0.000 claims description 5
- 230000007547 defect Effects 0.000 claims description 5
- 239000002344 surface layer Substances 0.000 claims description 4
- 239000000284 extract Substances 0.000 claims 1
- 239000000523 sample Substances 0.000 description 6
- 238000013527 convolutional neural network Methods 0.000 description 5
- 238000000034 method Methods 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 239000010410 layer Substances 0.000 description 4
- 238000006243 chemical reaction Methods 0.000 description 3
- 102100033592 Calponin-3 Human genes 0.000 description 2
- 101000945410 Homo sapiens Calponin-3 Proteins 0.000 description 2
- JEIPFZHSYJVQDO-UHFFFAOYSA-N iron(III) oxide Inorganic materials O=[Fe]O[Fe]=O JEIPFZHSYJVQDO-UHFFFAOYSA-N 0.000 description 2
- 239000002184 metal Substances 0.000 description 2
- 229910052751 metal Inorganic materials 0.000 description 2
- 241001226779 Royena whyteana Species 0.000 description 1
- 235000018723 Terminalia ivorensis Nutrition 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 230000005284 excitation Effects 0.000 description 1
- 238000010438 heat treatment Methods 0.000 description 1
- 238000011176 pooling Methods 0.000 description 1
- 238000005096 rolling process Methods 0.000 description 1
- 238000002604 ultrasonography Methods 0.000 description 1
- 239000002023 wood Substances 0.000 description 1
Description
本発明は丸棒材の超音波探傷方法に関し、特に表面疵と表面直下の丸棒材内部に生じる表層疵を良好に識別して検出できる超音波探傷方法に関するものである。 The present invention relates to an ultrasonic flaw detection method for round bar materials, and more particularly to an ultrasonic flaw detection method that can satisfactorily distinguish and detect surface flaws and surface flaws occurring inside the round bar material immediately below the surface.
丸棒材の表面近くの疵を探傷する場合には図７に示すように探傷用の超音波ビームＵｂの横波を使用しその屈折角（セクタースキャン角）を４５度程度に設定して行う。しかし、この方法では、丸棒材Ｍの表面に開口する表面疵Ｍ１(図６（１）)と丸棒材Ｍの表面直下の内部に生じる表層疵Ｍ２（図６（２））からの疵エコー信号（図７（１）、（２））がほほ同じ大きさで同じ時間帯に現れることがあるため、往々にして両者を区別することが困難であった。 When detecting flaws near the surface of a round bar, a transverse wave of the ultrasonic beam Ub for flaw detection is used and its refraction angle (sector scan angle) is set to about 45 degrees, as shown in FIG. However, with this method, scratches from surface flaws M1 (FIG. 6 (1)) that open on the surface of the round bar M and surface flaws M2 (FIG. 6 (2)) that occur inside the round bar M just below the surface. Since the echo signals (FIGS. 7(1) and 7(2)) may appear with almost the same size and at the same time, it is often difficult to distinguish between the two.
そこで、例えば特許文献１では異なるセクタースキャン角を設定して、各セクタースキャン角で得られた疵エコー信号の大きさが所定の閾値を超えたときにそれぞれ表面疵あるいは表層疵があるもと判定する探傷方法が開示されている。 For example, in Patent Document 1, different sector scan angles are set, and when the magnitude of the flaw echo signal obtained at each sector scan angle exceeds a predetermined threshold, it is determined that there is a surface flaw or surface flaw. A flaw detection method is disclosed.
しかし、上記従来の方法では、セクタースキャン角を変更して同様の探傷を繰り返す必要があるために探傷に時間を要し、ラインを流れる丸棒材の探傷を迅速に行えないという問題があった。 However, with the above conventional method, it is necessary to repeat the same flaw detection by changing the sector scan angle, which takes time, and there is a problem that flaw detection cannot be performed quickly on round bars flowing on the line. .
そこで、本発明はこのような課題を解決するもので、表面疵と表層疵を迅速かつ確実に判別できる丸棒材の超音波探傷方法を提供することを目的とする。 SUMMARY OF THE INVENTION The present invention is intended to solve these problems, and aims to provide an ultrasonic flaw detection method for round bars that can quickly and reliably distinguish between surface flaws and surface flaws.
上記目的を達成するために、本第１発明では、丸棒材（Ｍ）の表面、および当該表面直下の表層を含む領域で収束する超音波（Ｕｂ）を送信しつつこれを前記丸棒材（Ｍ）の周面に沿う方向で走査し、前記丸棒材（Ｍ）の表面疵（Ｍ１）、ないし表面直下の丸棒材（Ｍ）内部に生じる表層疵（Ｍ２）で反射して戻る反射超音波を受信して疵エコー信号（Ｓa1,Ｓa2）とし、表面疵（Ｍ１）と表層疵（Ｍ２）の疵エコー信号（（Ｓa1,Ｓa2）に対応した各デジタル信号のデータ列からピーク値を示すデータを検出して当該ピーク値を示すデータの前後一定数のデータを抽出して、これらのデータから横軸をデータ位置、縦軸をデータ値としてグラフ化した二次元の疵エコー画像（Ｘ,Ｙ）を生成し、必要数の前記疵エコー画像（Ｘ，Ｙ）を学習データとしてニューラルネットワーク（３）に与えて学習させ、学習済みの前記ニューラルネットワーク（３）に対して新たな前記疵エコー画像（Ｘ，Ｙ）を与えて、当該疵エコー画像（Ｘ，Ｙ）に対応する疵が前記表面疵（Ｍ１）か表層疵（Ｍ２）かを判別させる。 In order to achieve the above object, the first invention transmits ultrasonic waves (Ub) that converge in a region including the surface of the round bar (M) and the surface layer immediately below the surface of the round bar (M), and (M) is scanned in the direction along the circumferential surface of the round bar (M), and is reflected back by the surface flaw (M1) of the round bar (M) or the surface flaw (M2) that occurs inside the round bar (M) just below the surface. The reflected ultrasonic waves are received and converted into flaw echo signals (Sa1, Sa2), and the peak value is calculated from the data string of each digital signal corresponding to the flaw echo signals ((Sa1, Sa2) of surface flaws (M1) and surface flaws (M2). A two-dimensional flaw echo image ( X,Y), the necessary number of said flaw echo images (X,Y) are given to the neural network (3) as learning data for learning, and the trained neural network (3) is given a new A flaw echo image (X, Y) is given to determine whether the flaw corresponding to the flaw echo image (X, Y) is the surface flaw (M1) or the surface flaw (M2).
本第１発明においては、疵エコー画像を作成し、当該疵エコー画像によって予め学習させたニューラルネットワークに、新たな疵エコー画像を与えて当該疵エコー画像に対応する疵が表面疵か表層疵かを判別するようにしたから、表面疵と表層疵を迅速かつ確実に判別することができる。 In the first invention, a flaw echo image is created, and a new flaw echo image is given to a neural network trained in advance using the flaw echo image to determine whether the flaw corresponding to the flaw echo image is a surface flaw or a superficial flaw. Since it is possible to distinguish between surface flaws and superficial flaws, it is possible to quickly and reliably distinguish between surface flaws and surface flaws.
上記カッコ内の符号は、後述する実施形態に記載の具体的手段との対応関係を参考的に示すものである。 The above reference numerals in parentheses indicate for reference the correspondence with specific means described in the embodiments to be described later.
以上のように、本発明の丸棒材の超音波探傷方法によれば、表面疵と表層疵を迅速かつ確実に判別することができる。より具体的には、丸棒材が圧延工程、熱処理工程、曲がり矯正工程を経た状態で、その表面が酸化金属である黒錆で覆われた状態のものを対象とすることができる。黒皮材は最表層に黒錆を備えることから、特に表面疵と表層疵等の判別が困難であるが、本発明の方法を適用することで、表面疵と表層疵を迅速かつ確実に判別することが可能となる。 As described above, according to the ultrasonic flaw detection method for round bar materials of the present invention, surface flaws and surface flaws can be quickly and reliably distinguished. More specifically, the object can be a round bar material that has undergone a rolling process, a heat treatment process, and a bend straightening process, and whose surface is covered with black rust, which is an oxidized metal. Since black bark wood has black rust on the outermost layer, it is particularly difficult to distinguish between surface flaws and surface flaws, but by applying the method of the present invention, surface flaws and surface flaws can be quickly and reliably distinguished. It becomes possible to do so.
なお、以下に説明する実施形態はあくまで一例であり、本発明の要旨を逸脱しない範囲で当業者が行う種々の設計的改良も本発明の範囲に含まれる。 Note that the embodiments described below are merely examples, and various design improvements made by those skilled in the art without departing from the gist of the present invention are also included within the scope of the present invention.
図1には本発明の超音波探傷方法を実施する装置の構成を示す。図１において、金属製丸棒材Ｍの外周面に対向させてフェーズドアレイ探触子１が設けられている。フェーズドアレイ探触子１では、多数の超音波振動子（図示略）が丸棒材Ｍの外周に倣って湾曲する送受信面１ａを形成するように公知の構造で隣接配置されており、隣接する所定数の超音波振動子を、コンピュータを内蔵した制御装置２から出力される所定の時間差を有する励振信号で振動させることによって、本実施形態では略４５度のセクタースキャン角を有し丸棒材の表面およびその直下の表層を含む領域で収束する斜角探傷用超音波たる横波超音波ビームＵｂを生成している。そして振動させる所定数の超音波振動子の範囲を順次移動させることによって、約９０度の角度範囲Ｄで横波超音波ビームＵｂを走査して、この範囲にある表面疵Ｍ１および表層疵Ｍ２からの反射超音波をフェーズドアレイ探触子１で再び受信して、疵エコー信号として制御装置２へ出力している。 FIG. 1 shows the configuration of an apparatus for carrying out the ultrasonic flaw detection method of the present invention. In FIG. 1, a phased array probe 1 is provided facing the outer circumferential surface of a metal round bar M. In the phased array probe 1, a large number of ultrasonic transducers (not shown) are arranged adjacently in a known structure so as to form a transmitting/receiving surface 1a curved along the outer circumference of a round bar M. In this embodiment, by vibrating a predetermined number of ultrasonic transducers with excitation signals having a predetermined time difference output from a control device 2 with a built-in computer, a round bar material having a sector scan angle of approximately 45 degrees is produced. A transverse ultrasonic beam Ub, which is an ultrasonic wave for oblique flaw detection, is generated that converges in a region including the surface and the surface layer immediately below the surface. By sequentially moving the range of a predetermined number of vibrating ultrasonic transducers, the transverse ultrasonic beam Ub is scanned in an angular range D of about 90 degrees, and surface flaws M1 and surface flaws M2 within this range are removed. The reflected ultrasonic waves are received again by the phased array probe 1 and output to the control device 2 as a flaw echo signal.
なお、丸棒材Ｍの全周について表面疵Ｍ１および表層疵Ｍ２を検出する場合には、同様の構成のフェーズドアレイ探触子１を丸棒材Ｍの全周に沿って複数（４つ）設けるか、丸棒材Ｍを回転させ、ないしフェーズドアレイ探触子１を丸棒材Ｍ周りに旋回させる。 In addition, when detecting surface flaws M1 and surface flaws M2 around the entire circumference of the round bar M, a plurality (4) of phased array probes 1 having a similar configuration are installed along the entire circumference of the round bar M. Alternatively, the round bar M may be rotated, or the phased array probe 1 may be rotated around the round bar M.
図２（１）には表面疵があった場合の疵エコー信号Ｓａ1の経時波形を示し、図２（２）には表層疵があった場合の疵エコー信号Ｓａ2の経時波形を示し、これら疵エコー信号Ｓａ1,Ｓａ2は制御装置２内に設けられたＡＤ変換回路（図示略）に入力する。ＡＤ変換回路は疵エコー信号Ｓａ1,Ｓａ2の正負の最大振幅範囲をカバーできる入力レンジを有し、図３に示すような、疵エコー信号Ｓａ1,Ｓａ2はＡＤ変換回路で、所定サンプリング時間毎の振幅に応じた数列（データ列）からなるデジタル信号Ｓｄ（図３）に変換される。なお、図３は８ビットのＡＤ変換回路を使用した場合のデジタル信号Ｓｄの一例である。 Figure 2 (1) shows the waveform of the flaw echo signal Sa1 over time when there is a surface flaw, and Figure 2 (2) shows the waveform of the flaw echo signal Sa2 over time when there is a surface flaw. The echo signals Sa1 and Sa2 are input to an AD conversion circuit (not shown) provided within the control device 2. The AD conversion circuit has an input range that can cover the maximum positive and negative amplitude range of the flaw echo signals Sa1 and Sa2, and as shown in FIG. It is converted into a digital signal Sd (FIG. 3) consisting of a number sequence (data sequence) corresponding to . Note that FIG. 3 is an example of the digital signal Sd when an 8-bit AD conversion circuit is used.
ここで、前述した図７（１）（２）に示した疵エコー信号では、反射強度の波形を絶対値で換算しているため正の波形と負の波形の各々を表しておらず、波形の変化を認識しにくいため疵の判定精度に影響が出てしまう。これに対して、図３に示したデジタル信号Ｓｄでは、絶対値では換算しておらず、信号値が正の疵エコー信号Ｓａ1,Ｓａ2と負の疵エコー信号Ｓａ1,Ｓａ2の双方を反映していることから、図７（１）（２）の疵エコー信号と比較すると波形の変化が認識しやすい。 Here, in the flaw echo signals shown in FIGS. 7(1) and 7(2), the waveform of the reflected intensity is converted into an absolute value, so it does not represent the positive waveform and negative waveform, and the waveform Since it is difficult to recognize changes in the flaws, the accuracy of flaw determination is affected. On the other hand, the digital signal Sd shown in FIG. 3 is not converted into an absolute value, and the signal value reflects both the positive flaw echo signals Sa1, Sa2 and the negative flaw echo signals Sa1, Sa2. Therefore, the change in waveform is easier to recognize when compared with the flaw echo signals in FIGS. 7(1) and 7(2).
制御装置２内では、表面疵と表層疵の疵エコー信号Ｓａ1,Ｓａ2に対応した各デジタル信号のデータ列からピーク値を示すデータを検出して、当該ピークデータの前後各５０個（すなわち１００個）のデータを抽出する。そして、これらのデータから、図４（１）、（２）に示すような、横軸をデータ位置、縦軸をデータ値としてグラフ化した二次元の疵エコー画像Ｘ，Ｙを生成する。なお、疵エコー画像Ｘ，Ｙは図２に示したアナログの疵エコー信号Ｓa1,Ｓa2のＸ´領域、Ｙ´領域に対応している。 In the control device 2, data indicating a peak value is detected from the data string of each digital signal corresponding to the flaw echo signals Sa1 and Sa2 of surface flaws and surface flaws, and 50 data (i.e. 100 data) are detected before and after the peak data. ). From these data, two-dimensional flaw echo images X and Y, which are graphed with the horizontal axis representing the data position and the vertical axis representing the data value, as shown in FIGS. 4(1) and 4(2), are generated. Note that the flaw echo images X and Y correspond to the X' and Y' areas of the analog flaw echo signals Sa1 and Sa2 shown in FIG. 2, respectively.
制御装置２内には、図４に示すような、適当数の畳み込み層３１とプーリング層３２、および全結合層３３を有する公知の構成の畳み込みニューラルネットワーク（ＣＮＮ）３が構成されており、表面疵と表層疵の各疵エコー信号Ｓa1,Ｓa2から生成された上記疵エコー画像Ｘ，ＹをＣＮＮに学習画像として与えて学習させる。 In the control device 2, a convolutional neural network (CNN) 3 having a known configuration including an appropriate number of convolution layers 31, pooling layers 32, and fully connected layers 33 as shown in FIG. 4 is configured. The flaw echo images X and Y generated from the flaw echo signals Sa1 and Sa2 of the flaw and surface flaw are given to the CNN as learning images for learning.
本実施形態では表面疵の学習画像を１４９枚、表層疵の学習画像を１３０枚与えて所定のパラメータ値で学習させた。続いて学習させたＣＮＮ３に、表面疵の新たな疵エコー画像Ｘ，Ｙであるテスト画像を１５４枚、表層疵の新たな疵エコー画像Ｘ，Ｙであるテスト画像を１３２枚与えてそれぞれのテスト正解率を得た。これによると表面疵に対する正解率は９４．８％、表層疵に対する正解率は８８．６％であった。これを表１（ａ）に示す。 In this embodiment, 149 learning images of surface flaws and 130 learning images of surface flaws were provided to perform learning using predetermined parameter values. Next, the trained CNN3 was given 154 test images that are new flaw echo images X and Y of surface flaws, and 132 test images that are new flaw echo images X and Y of surface flaws, and each test was performed. I got the correct answer rate. According to this, the accuracy rate for surface flaws was 94.8%, and the accuracy rate for surface flaws was 88.6%. This is shown in Table 1(a).
これに対して、参考例として、表面疵と表層疵の疵エコー信号に対応した各デジタル信号のデータ列の、ピークデータの前後各５０個をそのまま学習データ列としてＣＮＮ３に与えた場合には、上記学習画像におけるパラメータ値と同一値で学習を繰り返しても、表１（ｂ）に示すように、学習結果の正解率は８２．４％にしかならず、またテストデータ列に対するテスト正解率も表面疵で８３．８％、表層疵で８３．３％と、いずれも疵エコー画像を与えた場合に比してテスト正解率は劣ったものになる。 On the other hand, as a reference example, if 50 pieces of data before and after the peak data of each digital signal data string corresponding to the flaw echo signals of surface flaws and surface layer flaws are directly fed to CNN3 as learning data strings, Even if learning is repeated with the same parameter values as in the above learning image, the correct answer rate of the learning result is only 82.4%, as shown in Table 1 (b), and the test correct answer rate for the test data string also has surface defects. The test accuracy rate was 83.8% for surface defects and 83.3% for superficial defects, both of which were inferior to the case where a defect echo image was provided.
上記実施形態では、制御装置内のコンピュータにデジタル信号のデータ列を取り込んでグラフ化することによって疵エコー画像を得るようにしたが、例えばオシロスコープで得られたアナログの疵エコー信号の必要領域Ｘ´，Ｙ´を写真撮影する等の手段で疵エコー画像を得るようにしても良い。 In the above embodiment, the flaw echo image is obtained by importing the data string of the digital signal into the computer in the control device and graphing it, but for example, the required area , Y' may be photographed to obtain a flaw echo image.
１…フェーズドアレイ探触子、２…制御装置、３…畳み込みニューラルネットワーク、Ｍ…丸棒材、Ｍ１…表面疵、Ｍ２…表層疵、Ｓa1,Ｓa2…疵エコー信号、Ｕｂ…横波超音波ビーム（斜角探傷用超音波）、Ｘ，Ｙ…疵エコー画像。 1... Phased array probe, 2... Control device, 3... Convolutional neural network, M... Round bar, M1... Surface flaw, M2... Surface flaw, Sa1, Sa2... Flaw echo signal, Ub... Transverse wave ultrasonic beam ( Ultrasound for oblique flaw detection), X, Y...Flaw echo image.
Claims (1)
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP2020070115A JP7415757B2 (en) | 2020-04-09 | 2020-04-09 | Ultrasonic flaw detection method for round bar materials |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP2020070115A JP7415757B2 (en) | 2020-04-09 | 2020-04-09 | Ultrasonic flaw detection method for round bar materials |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| JP2021167730A JP2021167730A (en) | 2021-10-21 |
| JP7415757B2 true JP7415757B2 (en) | 2024-01-17 |
Family
ID=78079613
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| JP2020070115A Active JP7415757B2 (en) | 2020-04-09 | 2020-04-09 | Ultrasonic flaw detection method for round bar materials |
Country Status (1)
| Country | Link |
|---|---|
| JP (1) | JP7415757B2 (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2009541765A (en) | 2006-06-30 | 2009-11-26 | ヴイ・アンド・エム・フランス | Non-destructive inspection for pipes, especially during production or in the finished state |
| JP2011506992A (en) | 2007-12-21 | 2011-03-03 | ヴイ・アンド・エム・フランス | Non-destructive inspection for pipes, especially during production or in the finished state |
| JP2012225887A (en) | 2011-04-22 | 2012-11-15 | Daido Steel Co Ltd | Ultrasonic flaw detection method for round bar material |
| JP2013156104A (en) | 2012-01-30 | 2013-08-15 | Hitachi Ltd | Ultrasonic flaw detection apparatus and ultrasonic flaw detection method for welded part |
| JP2016200566A (en) | 2015-04-14 | 2016-12-01 | 積水化学工業株式会社 | Degradation testing method and layer thickness testing method of multilayer body, and degradation testing apparatus and layer thickness testing apparatus of the same |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPS62153744A (en) * | 1985-12-27 | 1987-07-08 | Tokyo Keiki Co Ltd | Probe rotating type ultrasonic flaw detection apparatus |
| JPS62162958A (en) * | 1986-01-13 | 1987-07-18 | Mitsubishi Electric Corp | Ultrasonic flaw detecting method |
| JPS62194454A (en) * | 1986-02-20 | 1987-08-26 | Nippon Steel Corp | Method for inspecting flaw of steel pipe welded part |
| JPH0333652A (en) * | 1989-06-30 | 1991-02-13 | Hitachi Ltd | Ultrasonic inspection method and apparatus |
-
2020
- 2020-04-09 JP JP2020070115A patent/JP7415757B2/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2009541765A (en) | 2006-06-30 | 2009-11-26 | ヴイ・アンド・エム・フランス | Non-destructive inspection for pipes, especially during production or in the finished state |
| JP2011506992A (en) | 2007-12-21 | 2011-03-03 | ヴイ・アンド・エム・フランス | Non-destructive inspection for pipes, especially during production or in the finished state |
| JP2012225887A (en) | 2011-04-22 | 2012-11-15 | Daido Steel Co Ltd | Ultrasonic flaw detection method for round bar material |
| JP2013156104A (en) | 2012-01-30 | 2013-08-15 | Hitachi Ltd | Ultrasonic flaw detection apparatus and ultrasonic flaw detection method for welded part |
| JP2016200566A (en) | 2015-04-14 | 2016-12-01 | 積水化学工業株式会社 | Degradation testing method and layer thickness testing method of multilayer body, and degradation testing apparatus and layer thickness testing apparatus of the same |
Also Published As
| Publication number | Publication date |
|---|---|
| JP2021167730A (en) | 2021-10-21 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| JP5535044B2 (en) | Circuit device for ultrasonic nondestructive testing of subjects | |
| JP5824858B2 (en) | Method and apparatus for imaging structure of welded portion | |
| JP2010527015A5 (en) | ||
| JP4792440B2 (en) | Pipe weld inspection system | |
| US10921291B2 (en) | Method for inspecting a weld seam | |
| CN107356670A (en) | A kind of ultrasonic phase array weld defect detection method based on oblique incidence | |
| JP2004279144A (en) | Ultrasonic inspection method and device | |
| JP6058284B2 (en) | Ultrasonic flaw detection apparatus and method | |
| JP7415757B2 (en) | Ultrasonic flaw detection method for round bar materials | |
| JP6290748B2 (en) | Ultrasonic inspection method and ultrasonic inspection apparatus | |
| CN108663434A (en) | A kind of coarse grain material total focus detection method of phased array supersonic defectoscope | |
| WO2008010711A1 (en) | System and method for measuring on a wall of a pipeline with the aid of at least one ultrasonic beam | |
| KR102014947B1 (en) | Apparatus and method for evaluating effect of dead elements in a phased array ultrasonic probe | |
| Boehm et al. | Advanced crack shape analysis using SAFT | |
| Kapoor et al. | On parameters affecting the sensitivity of ultrasonic testing of tubes: experimental and simulation | |
| JP5456367B2 (en) | Phased array aperture synthesis processing method | |
| US6295677B1 (en) | Method for inspecting liquid filled pipes using magnetostrictive sensors | |
| CN113256566A (en) | Pipeline weld defect identification method | |
| JP6761780B2 (en) | Defect evaluation method | |
| Lukacs et al. | Grating-lobe Suppression through Angular Weighting for Laser Induced Phased Arrays | |
| JP2019174239A (en) | Ultrasonic flaw detection method | |
| JP2023081401A (en) | Ultrasonic flaw detection method for round bar material | |
| JP2021139821A (en) | Ultrasonic flaw detection method for round bar material | |
| Zhang et al. | Ultrasonic flaw recognition by multi-angle phased array data integration | |
| RU2395802C1 (en) | Method of ultrasound control over butt-welded seams |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| A621 | Written request for application examination |
Free format text: JAPANESE INTERMEDIATE CODE: A621 Effective date: 20230215 |
|
| A131 | Notification of reasons for refusal |
Free format text: JAPANESE INTERMEDIATE CODE: A131 Effective date: 20230926 |
|
| A521 | Request for written amendment filed |
Free format text: JAPANESE INTERMEDIATE CODE: A523 Effective date: 20231122 |
|
| TRDD | Decision of grant or rejection written | ||
| A01 | Written decision to grant a patent or to grant a registration (utility model) |
Free format text: JAPANESE INTERMEDIATE CODE: A01 Effective date: 20231205 |
|
| A61 | First payment of annual fees (during grant procedure) |
Free format text: JAPANESE INTERMEDIATE CODE: A61 Effective date: 20231218 |
|
| R150 | Certificate of patent or registration of utility model |
Ref document number: 7415757 Country of ref document: JP Free format text: JAPANESE INTERMEDIATE CODE: R150 |