CN103543201B - Axial workpiece flaw identification method for radial ultrasonic automatic flaw detection - Google Patents
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Abstract
Axial workpiece flaw identification method for radial ultrasonic automatic flaw detection, belongs to technical field of ultrasonic automatic flaw detection.Axial workpiece flaw identification method for radial ultrasonic automatic flaw detection adopts and makes determining defects curve, ultrasonic helical scanning, defect preliminary judgement, defect characteristic identification, calculates characteristic distance between defect, removes impulsive noise and merge defect set.In axial workpiece radial ultrasonic automatic flaw detection process, sometimes because extraneous noise interference is too strong, although take conventional filtering method, but still there will be the impulsive noise also higher than defect waves, cause automatic crack detection system that impulse noise signal is mistaken for defect, for this situation, present invention achieves to repeated detection to defect carry out feature each other and compare, extract effectively real defect information, be applicable to the quantitative detection of solid shaft class inside parts defect, have better effects for the robotization and accuracy improving axial workpiece flaw detection.
Description
Technical field
The invention belongs to technical field of ultrasonic automatic flaw detection, particularly a kind of axial workpiece flaw identification method for radial ultrasonic automatic flaw detection.
Background technology
Along with developing rapidly of China's industrial intelligent, intellectuality serves critical effect in the performance boost of commercial unit.Axial workpiece is as revolving part, be widely used in big machinery device, namely axletree such as rail traffic vehicles belongs to axial workpiece, axial workpiece is in operational process, generally, under being all in the alternate load state of mechanical periodicity, axletree before dispatching from the factory and running clearance all need arrangement to carry out carrying out flaw detection, and along with the fast development of transportation by railroad, the axletree quantity detected is needed also to get more and more, traditional craft flaw detection can not meet current current demand, therefore the automatic flaw detection that can realize axletree is needed to detect, and along with more and more higher to the requirement of flaw detection operation automation degree, needing can be in real time in testing process, determine the quantitative result of defect exactly.
All focus on towards robotization and intelligentized future development in axial workpiece ultrasound examination field both at home and abroad at present, and develop corresponding application product, the AURA automatic ultrasonic Wheel set detecting system such as developed by German Fu Langhuofei Non-Destructive Testing research institute (IZFP) and TEG, patent: " automated ultrasonic flaw detecting device for track traffic vehicle axles " publication number: CN101614703A, all adopt the defects detection program of robotization, in use, repeat its transmission ultrasound wave carries out Scanning Detction to axletree, axletree remains a constant speed rotation status in flaw detection process, in different operating environments, the ground connection performance of equipment is different, if grounding requirement is bad, the noise signal that easy generation is larger, even the wave height of jam-to-signal defect is taller, the ultrasonic signal simultaneously detected when axletree is in different position and postures due to same defect also can be different, easy generation error detection or by same defect as multiple defect, and be unfavorable for the accurate quantitative analysis to defect, also need the details of the mode determination defect by manually multiple spy.
Summary of the invention
The object of this invention is to provide operable defect identification method in a kind of axial workpiece radial ultrasonic automatic flaw detection process.
Technical scheme of the present invention is as follows:
Axial workpiece flaw identification method for radial ultrasonic automatic flaw detection, the method comprises the steps:
Step 1: make determining defects curve: first select diameter to be d
0test block axle, described test block axle comprises at least three and is respectively h along the test block Axial and radial direction degree of depth
1, h
2..., h
ndemarcation flat-bottom hole, aperture is Φ, adopts identical ultrasonic probe, sound path that yield value measurement that coupling lift-off Distance geometry is identical obtains each demarcation flat-bottom hole is respectively x
1, x
2..., x
n, wave height is respectively E
1, E
2..., E
n, adopt the method for curve to obtain defect criterion curve, n be more than or equal to 3 positive integer;
Step 2: ultrasonic helical scanning: make to wait that visiting axial workpiece at the uniform velocity rotates around axle center, ultrasonic probe is being waited to visit Axle Surface in axial direction uniform motion, receive ultrasonic signal by ultrasonic signal collecting unit repeat its transmission, described at the uniform velocity angular velocity of rotation ω <2 Φ f/kd, wherein, f is the repetition frequency that described ultrasonic probe repeat its transmission receives ultrasonic signal, and k is that defect repeats detection times, d=d
0for waiting the diameter visiting axial workpiece;
Step 3: defect preliminary judgement: utilize defect preliminary judgement unit, adopt the defect criterion curve that step 1 makes, preliminary judgement is carried out to all ultrasound echo signals collected by ultrasonic signal collecting unit, the defective waveform signal all wave height being exceeded to defect criterion curve carries out record, the defect waves number of signals be recorded to is N, and to wherein each defect waves signal record, it gathers the distance L of the center probe positional distance axial end in moment
iwith the anglec of rotation θ of axial workpiece
i, wherein i is the positive integer being less than or equal to N;
Step 4: defect characteristic identification: the peak-peak E recording each defect waves signal
i1with the height E of defect waves center position defect criterion curve
i2, then calculate Defect Equivalent A
i=20log
10(E
i1/ E
i2), the sound path of recording defect ripple signal is x
i, then calculate the radial location R of defect in axial workpiece
i=| d-x
i|/2, the distance L of center probe positional distance axial end
i, the anglec of rotation θ of axial workpiece
i, preserving gained information is defect characteristic vector α
i=(L
i, A
i, R
i, θ
i), wherein 0<i≤N;
Step 5: calculate the characteristic distance between defect: by defect characteristic counter to the N number of defect characteristic vector α preserved in step 4
i(0<i≤N) calculates the characteristic distance between any two proper vectors, C (i, j)=(Δ L
(i, j), Δ A
(i, j), Δ R
(i, j), Δ θ
(i, j)), wherein Δ L
(i, j)=| L
i-L
j|, Δ A
(i, j)=| A
i-A
j|, Δ R
(i, j)=| R
i-R
j|, Δ θ
(i, j)=| sin (θ
i-θ
j) |, (0<i≤N, 0<j≤N, i ≠ j);
Step 6: remove impulsive noise: establish L
0for defect characteristic distance resemble threshold value, A
0for Defect Equivalent similar threshold value, R
0for the radial location similar threshold value of defect in axial workpiece, θ
0for anglec of rotation similar threshold value, utilize impulse noise mitigation unit block (15) by all defect proper vector α
i(0<i≤N) according to following rule classification to different set Q
kin (0<k≤M), wherein M is the set number that can classify: for arbitrary collection Q
k, any two defect characteristics vector a in this set
iwith a
jbetween characteristic distance should meet Δ L simultaneously
(i, j)≤ L
0, Δ A
(i, j)≤ A
0, Δ R
(i, j)≤ R
0with Δ θ
(i, j)≤ θ
0defect characteristic vector be categorized into M set Q
kin (0<k≤M), remove and do not meet Δ L with other N-1 that record any defect characteristic vectors
(i, j)≤ L
0, Δ A
(i, j)≤ A
0, Δ R
(i, j)≤ R
0or Δ θ
(i, j)≤ θ
0defect characteristic vector;
Step 7: merge defect set: by defect set merging unit (16), to M the set Q calculated from step 6
kthe merger result of maximum flaw indication as set is worth according to Defect Equivalent, the kth namely recognized an effective flaw indication in (0<k≤M).
Described test block axle comprises: at least one degree of depth is less than d
0the demarcation flat-bottom hole of/2, at least one degree of depth is greater than d
0the demarcation flat-bottom hole of/2.
Ultrasound wave original signal is read in this signal gathering unit by described ultrasonic signal collecting unit, and filtering process is carried out to collected ultrasonic signal, this filter processing method adopts the combination of one or more methods in middle position value filtering method, digital averaging filtering method, correlation filtering method, bandpass filtering method, low pass filtering method, high-pass filtering method or wavelet filtering method.
Described defect criterion curve adopts conic fitting to judge, and line, logarithmic curve-fitting judge that line, segmentation conic fitting judge that line, segmentation logarithmic curve-fitting judge that line, segmented linear section judge that line or spline curve fitting judge the one in line.
The present invention compared with prior art, has the following advantages and high-lighting effect:
The present invention adopts ultrasonic waveform collector, defect preliminary judgement unit, defect characteristic recognition unit, defect characteristic metrics calculation unit, impulse noise mitigation unit block and defect set merging unit etc., comprehensively achieve the defect automatic ration identification of ultrasonic inspection, the method is by calculating the equivalent size of defect, and to repeated detection to defect between carry out aspect ratio pair mutually, realize the accurate identification to defect, impulse noise mitigation is to the interference of defect inspection system, and the automatic mensuration realized Defect Equivalent size, this method is applicable to the quantitative detection of the inherent vice of solid axial workpiece, better effects is had for the robotization and accuracy that improve axial workpiece flaw detection.
Accompanying drawing explanation
Fig. 1 is the process flow diagram of axial workpiece flaw identification method for radial ultrasonic automatic flaw detection of the present invention.
Fig. 2 is defect criterion curve plotting method schematic diagram in axial workpiece radial ultrasonic detection defects automatic identifying method step 1 of the present invention.
Fig. 3 is the schematic diagram of Defect Equivalent size quantivative approach in axial workpiece radial ultrasonic detection defects automatic identifying method step 3 of the present invention.
Fig. 4 is the schematic diagram of defect characteristic dimension definitions in axial workpiece radial ultrasonic detection defects automatic identifying method step 3 of the present invention.
Table 1 is a kind of embodiment of one group of DAC curve post point parameter described in axial workpiece radial ultrasonic detection defects automatic identifying method step 1 of the present invention.
Table 2 is the one group of embodiment of the defect characteristic information identified in axial workpiece radial ultrasonic detection defects automatic identifying method step 3 of the present invention.
Table 3 is the defect characteristic information that table 2 illustrated embodiment identifies according to step 4.
Table 4 is defect characteristic range information set that table 3 illustrated embodiment calculates according to step 5.
Table 5 adopts step 6, the defect numbering merged according to table 4 illustrated embodiment and the set of correspondence thereof.
In Fig. 1 to Fig. 4:
11-ultrasonic signal collecting unit; 12-defect preliminary judgement unit;
13-defect characteristic counter; 14-impulse noise suppressor;
15-defect set merging device;
2-test block axle; 21-demarcates flat-bottom hole;
22-ultrasonic probe.
Embodiment
The content of concrete structure of the present invention, principle of work is further described below in conjunction with drawings and Examples.
An embodiment for axial workpiece flaw identification method for radial ultrasonic automatic flaw detection, as shown in Figure 1, comprises the steps:
Step 1: make determining defects curve: first select diameter to be d
0test block axle (2), described test block axle (2) comprises at least three and is respectively h along test block axle (2) the radial direction degree of depth
1, h
2..., h
ndemarcation flat-bottom hole (21), aperture is Φ, adopts identical ultrasonic probe, sound path that yield value measurement that coupling lift-off Distance geometry is identical obtains each demarcation flat-bottom hole (21) is respectively x
1, x
2..., x
n, wave height is respectively E
1, E
2..., E
n, adopt the method for curve to obtain defect criterion curve, n be more than or equal to 3 positive integer; Test block shaft diameter in the present embodiment is 160mm, three flat-bottom hole diameters are 3mm, three flat-bottom hole degree of depth are respectively: 135mm, 80mm, 35mm, corresponding sound path is exactly 25mm respectively, 80mm and 125mm, the DAC curve post fixed point recorded is as shown in table 1, adopts logarithmic curve E=aln (x)+b to make least square fitting, obtains defect criterion logarithmic curve: E=-68.39log
10(x)+187.74.
Table 1
Step 2: ultrasonic helical scanning: make to wait that visiting axial workpiece at the uniform velocity rotates around axle center, ultrasonic probe is being waited to visit Axle Surface in axial direction uniform motion, receive ultrasonic signal by ultrasonic signal collecting unit 11 repeat its transmission, described at the uniform velocity angular velocity of rotation ω <2 Φ f/kd, wherein, f is the repetition frequency that described ultrasonic probe repeat its transmission receives ultrasonic signal, and k is that defect repeats detection times, d=d
0for waiting the diameter visiting axial workpiece; In the present embodiment, Φ is 3mm, k is 3 times, and repetition frequency f is 200Ez, and axle diameters d is 150mm to the maximum, uniform rotation speed omega <2.67rad/s, and namely rotating speed should be less than 51 revs/min.
Step 3: defect preliminary judgement: utilize defect preliminary judgement unit 12, adopt the defect criterion curve that step 1 makes, preliminary judgement is carried out to all ultrasound echo signals collected by ultrasonic signal collecting unit 11, the defective waveform signal all wave height being exceeded to defect criterion curve carries out record, the defect waves number of signals be recorded to is N, and to wherein each defect waves signal record, it gathers the distance L of the center probe positional distance axial end in moment
iwith the anglec of rotation θ of axial workpiece
i, wherein i is the positive integer being less than or equal to N; In the present embodiment, the preliminary defect noise signal recorded and status information as shown in table 2.
Table 2
Step 4: defect characteristic identification: the peak-peak E recording each defect waves signal
i1with the height E of defect waves center position defect criterion curve
i2, then calculate Defect Equivalent A
i=20log
10(E
i1/ E
i2), the sound path of recording defect ripple signal is x
i, then calculate the radial location R of defect in axial workpiece
i=| d-x
i|/2, the distance L of center probe positional distance axial end
i, the anglec of rotation θ of axial workpiece
i, preserving gained information is defect characteristic vector α
i=(L
i, A
i, R
i, θ
i), wherein 0<i≤N; In the present embodiment, the defect characteristic information of defect information shown in table 2 is as shown in table 3.
Table 3
Step 5: calculate the characteristic distance between defect: by the N number of defect characteristic vector α preserved in defect characteristic counter 14 pairs of steps 4
i(0<i≤N) calculates the characteristic distance between any two proper vectors, C (i, j)=(Δ L
(i, j), Δ A
(i, j), Δ R
(i, j), Δ θ
(i, j)), wherein Δ L
(i, j)=| L
i-L
j|, Δ A
(i, j)=| A
i-A
j|, Δ R
(i, j)=| R
i-R
j|, Δ θ
(i, j)=| sin (θ
i-θ
j) |, (0<i≤N, 0<j≤N, i ≠ j); Characteristic distance result of calculation between each defect characteristic vector in the present embodiment is as shown in table 4;
Table 4
Step 6: remove impulsive noise: establish L
0for defect characteristic distance resemble threshold value, A
0for Defect Equivalent similar threshold value, R
0for the radial location similar threshold value of defect in axial workpiece, θ
0for anglec of rotation similar threshold value, utilize impulse noise mitigation unit block (15) by all defect proper vector α
i(0<i≤N) according to following rule classification to different set Q
kin (0<k≤M), wherein M is the set number that can classify: for arbitrary collection Q
k, any two defect characteristics vector a in this set
iwith a
jbetween characteristic distance meet Δ L
(i, j)≤ L
0, Δ A
(i, j)≤ A
0, Δ R
(i, j)≤ R
0, Δ θ
(i, j)≤ θ
0be categorized into M set Q
kin (0<k≤M), remove and all do not meet Δ L with other N-1 that record any defect characteristic vectors
(i, j)≤ L
0, Δ A
(i, j)≤ A
0, Δ R
(i, j)≤ R
0with Δ θ
(i, j)≤ θ
0defect characteristic vector; In the present embodiment, L is set
0=2mm, A
0=0.15dB, R
0=2mm, θ
0=0.15, do not meet Δ L in the C of set shown in removal table 4
(i, j)≤ 2mm, Δ A
(i, j)≤ 0.15dB, Δ R
(i, j)≤ 2mm, Δ θ
(i, j)the element of≤0.15, be labeled as "Yes" in removed element " whether removing " row in table 4, the rubidium marking of reservation is "No"; A is set
0=0.15dB, R
0=2mm, θ
0=0.15, be defect set Q as shown in table 5 by classification of defects
k, the defect element be wherein comprised in identity set is denoted as identical set number, as shown in table 5, and the flaw indication of the present embodiment has 1 defect set.
Table 5
Step 7: merge defect set: by defect set merging unit 16, to M the set Q calculated from step 6
kthe merger result of maximum flaw indication as set is worth according to Defect Equivalent, the kth namely recognized an effective flaw indication in (0<k≤M).In the present embodiment, be that the defect in 1 merges into 1 defect by defect packet number, this defect characteristic information is as shown in table 6.
Table 6
Described test block axle 2 comprises: at least one degree of depth is less than d
0the demarcation flat-bottom hole 21 of/2, at least one degree of depth is greater than d
0the demarcation flat-bottom hole 21 of/2; In the present embodiment, the diameter of test block axle is 160mm, and the degree of depth on three flat-bottom hole distance surfaces is respectively 30mm, 80mm, 120mm.
Ultrasound wave original signal is read in this signal gathering unit by described ultrasonic signal collecting unit, and filtering process is carried out to collected ultrasonic signal, this filter processing method adopts the combination of one or more methods in middle position value filtering method, digital averaging filtering method, correlation filtering method, bandpass filtering method, low pass filtering method, high-pass filtering method or wavelet filtering method.In the present embodiment, have employed bandpass filtering method.
Described defect criterion curve adopts conic fitting to judge, and line, logarithmic curve-fitting judge that line, segmentation conic fitting judge that line, segmentation logarithmic curve-fitting judge that line, segmented linear section judge that line or spline curve fitting judge the one in line.In the present embodiment, line is as defect criterion curve to adopt logarithmic curve-fitting to judge.
The present invention adopts and makes determining defects curve, ultrasonic helical scanning, defect preliminary judgement, defect characteristic identification, calculate characteristic distance between defect, remove impulsive noise and merge the steps such as defect set, by calculating the equivalent size of defect, and to repeated detection to defect between carry out aspect ratio pair mutually, realize the accurate identification to defect, impulse noise mitigation is to the interference of defect inspection system, extract effective flaw indication, and the automatic mensuration realized Defect Equivalent size, this method is applicable to the quantitative detection of the inherent vice of solid axial workpiece, certain effect is had for the robotization and accuracy that improve axial workpiece flaw detection.
Claims (4)
1. an axial workpiece flaw identification method for radial ultrasonic automatic flaw detection, is characterized in that, comprises the steps:
Step 1: make determining defects curve: first select diameter to be d
0test block axle (2), described test block axle (2) comprises at least three and is respectively h along test block axle (2) the radial direction degree of depth
1, h
2..., h
ndemarcation flat-bottom hole (21), aperture is Φ, adopts identical ultrasonic probe, sound path that yield value measurement that coupling lift-off Distance geometry is identical obtains each demarcation flat-bottom hole (21) is respectively x
1, x
2..., x
n, wave height is respectively E
1, E
2..., E
n, adopt the method for curve to obtain defect criterion curve, n be more than or equal to 3 positive integer;
Step 2: ultrasonic helical scanning: make to wait that visiting axial workpiece at the uniform velocity rotates around axle center, ultrasonic probe is being waited to visit Axle Surface in axial direction uniform motion, receive ultrasonic signal by ultrasonic signal collecting unit (11) repeat its transmission, described at the uniform velocity angular velocity of rotation ω <2 Φ f/kd, wherein, f is the repetition frequency that described ultrasonic probe repeat its transmission receives ultrasonic signal, and k is that defect repeats detection times, d=d
0for waiting the diameter visiting axial workpiece;
Step 3: defect preliminary judgement: utilize defect preliminary judgement unit (12), adopt the defect criterion curve that step 1 makes, preliminary judgement is carried out to all ultrasound echo signals collected by ultrasonic signal collecting unit (11), the defective waveform signal all wave height being exceeded to defect criterion curve carries out record, the defect waves number of signals be recorded to is N, and to wherein each defect waves signal record, it gathers the distance L of the center probe positional distance axial end in moment
iwith the anglec of rotation θ of axial workpiece
i, wherein i is the positive integer being less than or equal to N;
Step 4: defect characteristic identification: the peak-peak E recording each defect waves signal
i1with the height E of defect waves center position defect criterion curve
i2, then calculate Defect Equivalent A
i=20log
10(E
i1/ E
i2), the sound path of recording defect ripple signal is x
i, then calculate the radial location R of defect in axial workpiece
i=| d-x
i|/2, the distance L of center probe positional distance axial end
i, the anglec of rotation θ of axial workpiece
i, preserving gained information is defect characteristic vector α
i=(L
i, A
i, R
i, θ
i), wherein 0<i≤N;
Step 5: calculate characteristic distance between defect: by defect characteristic counter (13) to the N number of defect characteristic vector α preserved in step 4
i(0<i≤N) calculates the characteristic distance between any two proper vectors, C (i, j)=(Δ L
(i, j), Δ A
(i, j), Δ R
(i, j), Δ θ
(i, j)), wherein Δ L
(i, j)=| L
i-L
j|, Δ A
(i, j)=| A
i-A
j|, Δ R
(i, j)=| R
i-R
j|, Δ θ
(i, j)=| sin (θ
i-θ
j) |, (0<i≤N, 0<j≤N, i ≠ j);
Step 6: remove impulsive noise: establish L
0for defect characteristic distance resemble threshold value, A
0for Defect Equivalent similar threshold value, R
0for the radial location similar threshold value of defect in axial workpiece, θ
0for anglec of rotation similar threshold value, utilize impulse noise mitigation unit block (14) by all defect proper vector α
i(0<i≤N) according to following rule classification to different set Q
kin (0<k≤M), wherein M is the set number that can classify: for arbitrary collection Q
k, any two defect characteristics vector a in this set
iwith a
jbetween characteristic distance should meet Δ L simultaneously
(i, j)≤ L
0, Δ A
(i, j)≤ A
0, Δ R
(i, j)≤ R
0with Δ θ
(i, j)≤ θ
0defect characteristic vector be categorized into M set Q
kin (0<k≤M), remove and do not meet Δ L with other N-1 that record any defect characteristic vectors
(i, j)≤ L
0, Δ A
(i, j)≤ A
0, Δ R
(i, j)≤ R
0or Δ θ
(i, j)≤ θ
0defect characteristic vector;
Step 7: merge defect set: by defect set merging unit (15), to M the set Q calculated from step 6
kthe merger result of maximum flaw indication as set is worth according to Defect Equivalent, the kth namely recognized an effective flaw indication in (0<k≤M).
2. a kind of axial workpiece flaw identification method for radial ultrasonic automatic flaw detection as claimed in claim 1, is characterized in that: described test block axle (2) comprising: at least one degree of depth is less than d
0the demarcation flat-bottom hole (21) of/2, at least one degree of depth is greater than d
0the demarcation flat-bottom hole (21) of/2.
3. a kind of axial workpiece flaw identification method for radial ultrasonic automatic flaw detection as claimed in claim 1, it is characterized in that: ultrasound wave original signal is read in this signal gathering unit by described ultrasonic signal collecting unit (11), and filtering process is carried out to collected ultrasonic signal, this filter processing method adopts the combination of one or more methods in middle position value filtering method, digital averaging filtering method, correlation filtering method, bandpass filtering method, low pass filtering method, high-pass filtering method or wavelet filtering method.
4. a kind of axial workpiece flaw identification method for radial ultrasonic automatic flaw detection as claimed in claim 1, is characterized in that: described defect criterion curve adopts conic fitting judgement line, logarithmic curve-fitting judges that line, segmentation conic fitting judge that line, segmentation logarithmic curve-fitting judge that line, segmented linear section judge the one in line or spline curve fitting judgement line.
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| CN110006995B (en) * | 2019-04-10 | 2022-11-15 | 上海船舶工艺研究所(中国船舶集团有限公司第十一研究所) | Phased array ultrasonic detection method for welding seam of metal thick plate with gradient acoustic velocity non-uniformity |
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