CN115979138B - Nodular cast iron pipe wall thickness measuring method based on laser ultrasonic and parameter optimization VMD - Google Patents
Nodular cast iron pipe wall thickness measuring method based on laser ultrasonic and parameter optimization VMD Download PDFInfo
- Publication number
- CN115979138B CN115979138B CN202211222655.4A CN202211222655A CN115979138B CN 115979138 B CN115979138 B CN 115979138B CN 202211222655 A CN202211222655 A CN 202211222655A CN 115979138 B CN115979138 B CN 115979138B
- Authority
- CN
- China
- Prior art keywords
- cast iron
- iron pipe
- laser ultrasonic
- laser
- fitting
- 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
- 229910001141 Ductile iron Inorganic materials 0.000 title claims abstract description 59
- 238000000034 method Methods 0.000 title claims abstract description 26
- 238000005457 optimization Methods 0.000 title claims abstract description 10
- 239000000463 material Substances 0.000 claims abstract description 11
- 238000004088 simulation Methods 0.000 claims abstract description 8
- 238000012360 testing method Methods 0.000 claims abstract description 4
- 239000002245 particle Substances 0.000 claims description 29
- 230000005284 excitation Effects 0.000 claims description 20
- 238000000354 decomposition reaction Methods 0.000 claims description 15
- 238000001914 filtration Methods 0.000 claims description 7
- 239000007787 solid Substances 0.000 claims description 7
- 238000002604 ultrasonography Methods 0.000 claims description 7
- 238000004422 calculation algorithm Methods 0.000 claims description 6
- 238000005070 sampling Methods 0.000 claims description 5
- 230000001133 acceleration Effects 0.000 claims description 3
- 238000004458 analytical method Methods 0.000 claims description 3
- 238000003384 imaging method Methods 0.000 claims description 3
- 238000010606 normalization Methods 0.000 claims description 3
- 238000010183 spectrum analysis Methods 0.000 claims description 3
- 238000009826 distribution Methods 0.000 claims description 2
- 238000012163 sequencing technique Methods 0.000 claims 1
- 238000012545 processing Methods 0.000 abstract description 5
- 238000005259 measurement Methods 0.000 abstract description 4
- 230000009286 beneficial effect Effects 0.000 abstract description 3
- 230000003746 surface roughness Effects 0.000 abstract description 2
- 238000001514 detection method Methods 0.000 description 10
- 238000002474 experimental method Methods 0.000 description 5
- 229910001018 Cast iron Inorganic materials 0.000 description 4
- 238000010586 diagram Methods 0.000 description 3
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 description 2
- 230000003044 adaptive effect Effects 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000003672 processing method Methods 0.000 description 2
- 229910000831 Steel Inorganic materials 0.000 description 1
- 230000006978 adaptation Effects 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000005266 casting Methods 0.000 description 1
- 238000012512 characterization method Methods 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 230000002950 deficient Effects 0.000 description 1
- 238000003745 diagnosis Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 229910052742 iron Inorganic materials 0.000 description 1
- 230000001678 irradiating effect Effects 0.000 description 1
- 230000031700 light absorption Effects 0.000 description 1
- 238000005065 mining Methods 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000009659 non-destructive testing Methods 0.000 description 1
- 230000003252 repetitive effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000035939 shock Effects 0.000 description 1
- 239000002893 slag Substances 0.000 description 1
- 239000010959 steel Substances 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Abstract
The application provides a nodular cast iron pipe wall thickness measuring method based on laser ultrasonic and parameter optimization VMD, which takes a plurality of nodular cast iron pipes with different thicknesses as samples for test, and sequentially collects laser ultrasonic signals at different positions of the nodular cast iron pipes with different thicknesses through a laser ultrasonic system; noise reduction processing is carried out on the acquired signals; performing curve fitting on the squares of the arrival time and the arrival position of the longitudinal wave to obtain a fitted curve; establishing a simulation model through COMSOL software, fitting the square of the time of the interferometer light beam at the moment of arrival of the signal longitudinal wave and the square of the laser scanning point position to obtain the wave speed of the longitudinal wave and the thickness of the material, and verifying the fitting curve obtained in the step S3; and calculating the thickness of the nodular cast iron pipe by using the verified fitting curve. The method is beneficial to solving the problem of low signal-to-noise ratio caused by surface roughness, thereby improving thickness measurement accuracy.
Description
Technical Field
The application belongs to the technical field of nondestructive testing, and particularly relates to a nodular cast iron pipe wall thickness measuring method based on laser ultrasonic and parameter optimization VMD.
Background
The ductile cast iron pipe has the nature of iron and the performance of steel, and is mainly used for water supply, gas transmission, oil transmission and the like of municipal and industrial and mining enterprises. However, dimensional failure often occurs during the production of ball-milled cast iron pipes. And uneven wall thickness or over-thin part of the tube can easily cause local bearing capacity reduction, which can affect the service life of the cast iron tube and have certain potential safety hazards. The wall thickness of the spheroidal graphite cast iron pipe is difficult to measure except for two ends of the pipe, so that the wall thickness of the spheroidal graphite cast iron pipe in the production process needs to be detected in real time, but due to the special shape and the rough surface of the tubular casting, in addition, in the ultrasonic nondestructive detection of the wall thickness and slag inclusion process of the centrifugal spheroidal graphite cast iron pipe, people hope that the detection station is as far forward as possible, so that defective products can be detected as early as possible, the subsequent processing is reduced, and the cost is reduced. But the temperature of the pipe wall is not reduced to room temperature before the station is located. Conventional contact approaches are difficult to achieve. The laser ultrasonic wave is a multi-mode ultrasonic wave excited on the surface of the material by using pulse laser, so that nondestructive detection and evaluation of internal defects on the surface of the material can be realized. Compared with the conventional ultrasonic and phased array ultrasonic, the non-contact excitation and receiving can be realized.
The ultrasonic signal excited by the laser has higher frequency, and the laser with high repetition frequency can realize rapid detection. Laser ultrasound is widely used in the fields of material property characterization, defect detection, thickness detection and the like. However, in the thickness measurement of spheroidal graphite cast iron, no report about laser ultrasound is currently available. The exploratory study herein is therefore based on a method of measuring the wall thickness of spheroidal graphite cast iron walls by laser ultrasound. The method comprises the steps of fixing an interferometer on one side of a material, axially scanning a laser excitation source along the cast iron pipe on the other side, measuring the arrival time of longitudinal waves at different positions, and fitting the wave speed of the longitudinal waves and the wall thickness of the cast iron pipe. Aiming at the rough surface of the nodular cast iron pipe, the VMD with optimized parameters is applied to ultrasonic signals for noise reduction. The research can provide guidance for developing application type equipment based on the real-time online monitoring and evaluation of the ductile cast iron pipe.
Disclosure of Invention
The application aims to: in order to solve the problems, the application provides a method for measuring the wall thickness of a spheroidal graphite cast iron pipe, which comprises the steps of firstly scanning spheroidal graphite cast iron pipes with different thicknesses (about 4mm and 6 mm) by using a laser ultrasonic system; then, performing low-pass filtering and VMD noise reduction processing of parameter optimization on the acquired multiple groups of signals; fitting the squares of the time and position of arrival of the longitudinal wave; and finally, calculating the thickness of the nodular cast iron pipe by using the fitted curve.
The technical scheme is as follows: the nodular cast iron pipe wall thickness measuring method based on laser ultrasonic and parameter optimization VMD comprises the following steps:
s1: taking a plurality of ductile cast iron pipes with different thicknesses as samples for test, and sequentially collecting laser ultrasonic signals at different positions of the ductile cast iron pipes with different thicknesses through a laser ultrasonic system;
s2: noise reduction processing is carried out on the acquired signals;
s3: performing curve fitting on the squares of the arrival time and the arrival position of the longitudinal wave to obtain a fitted curve;
s4: and (3) establishing a simulation model through COMSOL software, fitting the square of the time of the interferometer light beam at the arrival time of the signal longitudinal wave and the square of the laser scanning point position to obtain the wave speed of the longitudinal wave and the thickness of the material, and verifying the fitting curve obtained in the step (S3).
Preferably, the laser ultrasound system in step S1 mainly includes: the system comprises a solid laser, an interferometer, a scanning digital galvanometer, an oscilloscope card, laser ultrasonic scanning software and a singlechip control circuit; firstly, fixing a light beam of an interferometer at an outer side central point of a nodular cast iron pipe sample, focusing a solid laser generated laser beam at an inner side central point of the nodular cast iron pipe sample through a scanning digital galvanometer, keeping the light beam of the interferometer and the laser beam of the solid laser on the same horizontal line, taking the central point of the nodular cast iron pipe sample as a starting point, then enabling the laser beam to scan at intervals along the y axis of the nodular cast iron pipe through the deflection of the scanning digital galvanometer, and collecting signals of a plurality of scanning points as original signals to perform noise reduction treatment.
Preferably, the noise reduction processing method for the acquired signal in S2 includes,
preliminary treatment: respectively carrying out spectrum analysis on each original signal, then carrying out 0-10MHz low-pass filtering on the original signals through a Fir1 filter, and then judging the frequency band in which the energy of the filtered signals is concentrated through a Wigner-Ville time-frequency distribution map;
the particle swarm algorithm obtains the optimal position [ K, alpha ] and the optimal fitness value of the particles:
(1) Setting the number n=20 of initial population particles, the iteration times m=50, K= [2-10], and alpha= [500-5000], and taking the [ K, alpha ] combination as the position of the initial population particles; wherein alpha is an acquisition penalty factor, and K is a modal decomposition component;
(2) VMD signal analysis is carried out at different particle positions, and fitness function values f (K, alpha) are calculated;
(3) Updating the individual optimal value and the global optimal value according to the size of the fitness function value;
(4) Updating the speed and position of the particles according to the following formula;
wherein ω is an inertial weight factor, c 1 And c 2 Is the acceleration factor, r 1 And r 2 Is [0,1]Random numbers in between; v (V) i (t)
For the current particle velocity, x i (t) is the current particle position, P best (t) is an individual extremum, pg (t) is a population global extremum; (6) Until reaching the set iteration number m=50, calculating and ending, and outputting the optimal position [ k, alpha ] of the particles]And an optimal fitness value.
Inputting K and alpha of the optimal position of the particle into a VMD algorithm, decomposing each original signal to obtain a plurality of IMF components, recombining the plurality of IMF components, and taking each recombined signal as a noise-reduced signal.
Preferably, the method for constructing the fitness function f (K, α) is as follows:
a correlation coefficient c (K, a) between the original signal and the reconstructed signal is obtained,
where y is the original signal, μ K Representing IMF components, x being the sum of the individual IMF components, d being a sequence of L length, which may be x or y, u and σ representing the mean and variance of the signal, respectively; simultaneously constructing a combination factor b (K, alpha),
wherein a is 1 And a 2 As normalization factor, a 1 ＝1/max(β i )；a 2 ＝1/max(δ i )；ω＝{ω 1 ,ω 2 …,ω i -the center frequency of the different IMF components, fs being the sampling frequency of the signal;β i is the difference of central frequencies delta i Is the maximum and minimum modal center frequency difference;
representing the fitness function as
f(K,α)＝c(K,α)+b(K,α)。
Preferably, B-scan imaging is carried out on the signals after noise reduction, the arrival time of the longitudinal wave at different excitation positions is calculated, and fitting is carried out on the arrival time of the longitudinal wave and the square of the position to obtain a fitting curve.
Preferably, in step S4, the built model center is taken as the origin of coordinates, the excitation distance center is xmm, the model thickness is dmm, signals of a plurality of scanning points are collected as original signals to perform noise reduction treatment, the propagation path length of ultrasonic longitudinal waves received by the interferometer light beam is smm, and the velocity of bulk longitudinal waves in the material is V L m/s, the time that the longitudinal wave takes to reach the interferometer beam is ts, and the following relationship exists among the physical quantities:
s 2 ＝d 2 +x 2
s＝V L ×t
the laser excitation position x is known, the arrival time of the longitudinal wave can be obtained by ultrasonic signals received by an interferometer, and the arrival time is obtained by fitting x 2 And t 2 And fitting a curve, and verifying the fitting curve obtained by the test.
Preferably, calculating the thickness of the ductile cast iron pipe by using the fitted curve; and obtaining the intercept and the slope of the fitting curve, wherein the intercept and the slope correspond to the wave speed of the longitudinal wave and the thickness of the nodular cast iron pipe respectively.
The beneficial effects are that: compared with the prior art, the application has the following advantages:
1. the application provides a VMD signal processing method with optimal noise parameters, which is beneficial to solving the problem of low signal-to-noise ratio caused by surface roughness, thereby improving thickness measurement accuracy.
2. The thickness measuring method provided by the application can provide a reference for the high-temperature on-line thickness measurement of the nodular cast iron pipe.
Drawings
FIG. 1 (a) is a diagram showing the variation of the correlation coefficient c at different K and alpha values;
FIG. 1 (b) is a diagram showing the variation of the center frequency difference βi between K and α;
FIG. 1 (c) is a schematic diagram showing the variation of the maximum and minimum modal center frequency differences δi between different K and α values;
FIG. 2 is a flow chart of an experimental scheme in the present application;
FIG. 3 illustrates excitation and reception point locations in accordance with the present application;
FIG. 4 (a) is a laser ultrasonic signal obtained experimentally in the present application;
FIG. 4 (b) is a signal after noise reduction of the experimental optimal VMD in the present application;
FIG. 5 illustrates the IMF components after decomposition by the VMD method of the present application;
fig. 6 is a flow chart of the present application.
Detailed Description
The present application is further illustrated in the accompanying drawings and detailed description which are to be understood as being merely illustrative of the application and not limiting of its scope, and various modifications of the application, which are equivalent to those skilled in the art upon reading the application, will fall within the scope of the application as defined in the appended claims.
The application provides a method for measuring the wall thickness of a spheroidal graphite cast iron pipe by laser ultrasonic and parameter optimization VMD, which comprises the steps of taking spheroidal graphite cast iron pipe samples with the thickness of 4mm and 6mm as detection objects, preparing spheroidal graphite cast iron pipe samples with different thicknesses, constructing a laser ultrasonic experiment table, scanning spheroidal graphite cast iron pipes with different thicknesses (about 4mm and 6 mm) by using a laser ultrasonic system, scanning the spheroidal graphite cast iron pipes with the distance of 6mm, and collecting signals at all excitation positions of the spheroidal graphite cast iron pipes with different thicknesses. And carrying out spectrum analysis on the signals acquired by the experiment, then carrying out 0-10MHz low-pass filtering on the experimental signals through a Fir1 filter, carrying out 0-10MHz low-pass filtering on the signals obtained by the laser ultrasonic system, carrying out VMD decomposition on the signals after the low-pass filtering, constructing a fitness function suitable for the method, optimizing VMD parameters in each decomposition process by utilizing particle swarms, and recombining the signals after the decomposition to realize noise reduction.
The noise parameter optimal variation pattern decomposition (OVMD) algorithm proposed in this study is as follows:
setting the number n=20 of initial population, the iteration times m=50, K= [2-10], alpha= [500-5000], and taking the [ k, alpha ] combination as the position of the initial population; VMD decomposition is carried out at different particle positions, and fitness value f (k, alpha) is calculated; updating the individual optimal value and the global optimal value according to the fitness;
the velocity and position of the particles are updated according to the following equation.
Where ω is the inertial weight factor, c1 and c2 are acceleration factors, and r1 and r2 are random numbers between [0,1 ]. Vi (t) is the current particle speed, xi (t) is the current particle position, pbest (t) is the individual extremum, and Pg (t) is the population global extremum; and (3) until the set iteration times m=50 are reached, calculating and ending, and outputting the optimal position [ k, alpha ] of the particles and the optimal fitness value.
Wherein the fitness function f (k, α) is constructed as follows:
it is well known that the larger the modal correlation, the better the signal decomposition effect. To fully express the modal correlation, we propose the correlation coefficient c (K, α) between the original signal and the reconstructed signal generated by the modal sum as follows:
where y is the original signal, μK represents the IMF component, x is the sum of the individual IMF components, d is a sequence of L length, and can be x or y, u and σ represent the mean and variance of the signal, respectively.
VMD decomposition of different combination parameters [ K, α ] is performed on defect-free signals of the ductile cast iron pipe with the thickness of 4mm, and as shown in fig. 1 (a), the larger the correlation coefficient c is, the smaller the correlation coefficient is as the value of K is increased, so that the larger the value of K and the smaller the value of α are, the larger the correlation can be obtained. The greater the number of IMF components, the greater the ability of the signal to reconstruct the original signal, and the smaller the alpha value, the more accurate the reconstructed signal. But larger IMF components increase the computational effort and produce an excessive resolution of the signal, producing a repetitive component. The smaller the alpha value, the narrower the band of IMF components. Too narrow a band may result in loss of signal information. Therefore, a combination factor is proposed that defines the frequency band parameters and defines the number of IMF components. The following formula is shown:
wherein a1 and a2 are normalization factors, a1=1/max (βi); a2 =1/max (δi); ω= { ω1, ω2 …, ωi } represents the center frequency of the different IMF component, and fs is the sampling frequency of the signal.
It can be seen from fig. 1 (b) that as the K value increases, βi gradually decreases, and as the α value increases, βi exhibits a nonlinear relationship, so that the adjacent mode center frequency difference βi can limit the K to be too large, and from fig. 1 (c), it can be seen that as the K value increases, δi gradually increases, as the α value increases, δi also gradually increases, so that the maximum and minimum mode center frequency differences δi can limit the α to be too small.
The fitness function may be expressed as:
f(K,α)＝c(K,α)+b(K,α)
obtaining longitudinal wave arrival moments at different excitation positions by finite element simulation and experiments; and fitting the squares of the time and location of arrival of the longitudinal wave.
And obtaining the intercept and the slope of the fitting curve, wherein the intercept and the slope correspond to the wave speed of the longitudinal wave and the thickness of the nodular cast iron pipe respectively.
Firstly, a mode of simultaneously irradiating excitation line light sources and detection point light sources on two sides of a sample and scanning excitation light is adopted to obtain flight time of excitation ultrasonic longitudinal wave signals at different positions, and the thickness of the sample is obtained by combining spatial position information of each light source. As shown in fig. 3, the center of the model is taken as the origin of coordinates, the excitation distance is xmm, the thickness of the model is dmm, the propagation path length of ultrasonic longitudinal waves received by the detection point is smm, the wave velocity of the bulk longitudinal waves in the material is VLm/s, the time spent by the longitudinal waves reaching the bottom surface of the model is ts, and the following relationship exists among the physical quantities:
s 2 ＝d 2 +x 2
s＝V L ×t
thus x2 is proportional to t2, and the laser excitation position x is known and the arrival time of the longitudinal wave can be obtained from the received ultrasonic signal. The wave velocity of the longitudinal wave and the thickness of the material can thus be obtained by fitting x2 to t 2.
The initial value of the temperature during the simulation was set to 300K. The laser has a spot radius of 0.5mm, a laser pulse rise time of 8ns, a laser power density of 1×1012W/m2, and a material light absorption coefficient of 0.37. The selected calculation time step is 1X 10-9s, the grid maximum cell size is 2X 10-5m, and is less than 1/10 of the ultrasonic wavelength. The dimensions of the two-dimensional model were 4×20mm. Taking the center position of the lower side of the model as a receiving point, and scanning the model at a distance of 6mm from the center position of the upper side, wherein the scanning interval is 0.2mm. A total of 31 scans were performed to obtain 31 sets of data.
Collecting laser ultrasonic signals of ductile cast iron pipes with different thicknesses; signals at different excitation points of the ductile cast iron pipes with different thicknesses are collected by using laser ultrasonic equipment, and the arrangement is shown in figure 4. YAG solid laser with 1064nm wavelength, 1-20Hz repetition frequency, 8ns pulse width and 200mJ single pulse maximum output energy; (2) A QUARTET-FH interferometer with a wavelength of 532nm and a detection signal bandwidth of 0-10 MHz; (3) VA-NT1510Y scanning digital galvanometer (X, Y two-direction scanning can be achieved); (4) NIPCI5114 oscilloscope card and laser ultrasound scanning software; (5) A singlechip control circuit (ensuring the output of the laser, the movement of the galvanometer and the acquisition of an oscilloscope card);
during the experiment, the interferometer was fixed on the roughened surface of the ductile iron pipe sample as a receiver. The laser beam is focused on the inner side through a vibrating mirror, and the focal points of the interferometer and the laser are on the same horizontal line. And then, the laser spots are scanned along the axial direction of the ductile cast iron pipe by deflection of the galvanometer, the scanning interval is 0.2mm, and 51 times of scanning are performed. The sampling frequency of the acquisition card is set to 125MHz, and the sampling length of each group of data is 5000.
Noise reduction processing is carried out on the acquired signals; and carrying out frequency domain analysis on the signals obtained by the experiment, carrying out 0-10MHz filtering on the acquired signals by utilizing a Fir1 low-pass filter, carrying out VMD decomposition on the filtered signals, constructing an adaptive function suitable for the method in the VMD decomposition process, and optimizing parameters [ k, alpha ] in the VMD decomposition process by a particle swarm algorithm. And finally, recombining the decomposed signals, thereby realizing noise reduction of the original signals. Fig. 5 shows the laser ultrasonic original signal and the recombined signal after the decomposition of the optimal parameter VMD, and the noise reduction can be obviously seen. Fig. 6 is a signal obtained by decomposing the noise parameter-optimized VMD proposed by the present application.
Fitting the squares of the time and position of arrival of the longitudinal wave; b scanning imaging is carried out on the signals after noise reduction, the arrival time of the longitudinal wave at different excitation positions is calculated, and fitting is carried out on the arrival time of the longitudinal wave and the square of the position.
Calculating the thickness of the nodular cast iron pipe by using the fitted curve; and obtaining the intercept and the slope of the fitting curve, wherein the intercept and the slope correspond to the wave speed of the longitudinal wave and the thickness of the nodular cast iron pipe respectively.
Notably, calculating the signal correlation for each IMF component belongs to the prior art, see in particular:
[1]HuangQ,XieL,YinG,etal.Acousticsignalanalysisfordetectingdefectsinsidean arcmagnetusingacombinationofvariationalmodedecompositionandbeetleantennae search[J].ISAtransactions,2020,102:347-364.
[2] dynasty pavilion, li Hongkun, yang Rui, etc. planetary gearbox fault diagnosis study based on adaptive noise parameters optimizing ELMD [ J ] vibration and shock, 2020,39 (18): 60-69.
The foregoing is merely a preferred embodiment of the present application and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present application, which are intended to be comprehended within the scope of the present application.
Claims (3)
1. The nodular cast iron pipe wall thickness measuring method based on laser ultrasonic and parameter optimization VMD is characterized by comprising the following steps:
s1: taking a plurality of ductile cast iron pipes with different thicknesses as samples for test, and sequentially collecting original laser ultrasonic signals at different positions of each ductile cast iron pipe through a laser ultrasonic system;
s2: noise reduction treatment is carried out on the original laser ultrasonic signals, and longitudinal waves are extracted;
s3: performing curve fitting on the squares of the arrival time and the arrival position of the longitudinal wave to obtain a fitted curve;
s4: establishing a ductile cast iron pipe simulation model through COMSOL software, obtaining a longitudinal wave of the ductile cast iron pipe simulation model, fitting the square of the arrival time of the longitudinal wave and the square of the arrival position, obtaining a fitted curve, and verifying the fitted curve obtained in the step S3;
s5: calculating the thickness of the nodular cast iron pipe by utilizing the fitting curve obtained in the step S3;
in step S1: the laser ultrasound system includes: the system comprises a solid laser, an interferometer, a scanning digital galvanometer, an oscilloscope card, laser ultrasonic scanning software and a singlechip control circuit; the realization process for obtaining the original laser ultrasonic signal comprises the following steps: firstly, fixing a light beam of an interferometer at an outer side center point of a spheroidal graphite cast iron pipe, focusing a solid laser generated laser beam at an inner side center point of the spheroidal graphite cast iron pipe through a scanning digital vibrating mirror, keeping the light beam of the interferometer and the laser beam of the solid laser on the same horizontal line, taking the inner side center point and the outer side center point of the spheroidal graphite cast iron pipe as starting points, enabling the laser beam to scan at intervals along the y axis of the spheroidal graphite cast iron pipe through the deflection of the scanning digital vibrating mirror, and collecting signals of a plurality of excitation points as original laser ultrasonic signals of the spheroidal graphite cast iron pipe;
the implementation process of the step S2 is as follows: firstly, respectively carrying out spectrum analysis on each original laser ultrasonic signal, then carrying out 0-10MHz low-pass filtering on each original laser ultrasonic signal through a Fir1 filter, and then judging the frequency band in which the energy of the filtered original laser ultrasonic signals is concentrated through a Wigner-Ville time-frequency distribution map;
the optimal position and the optimal fitness value of the particles are obtained through a particle swarm algorithm:
(1) Initializing the population particle number n=20, the iteration number m=50, K= [2-10], alpha= [500-5000], and taking [ K, alpha ] combination as the position of the initial population particles, wherein alpha is an acquisition penalty factor, and K is a modal decomposition component;
(2) Carrying out VMD signal analysis on different particle positions, and calculating the fitness function f (K, alpha) value;
(3) Updating the individual optimal value and the global optimal value according to the size of the fitness function f (K, alpha);
(4) Updating the speed and position of the particles according to the following formula;
wherein: v (V) i (t+1) is the updated particle velocity, V i (t) is the current particle velocity, ω is the inertial weight factor, c 1 And c 2 Is the acceleration factor, r 1 And r 2 Is [0,1]Random number, x between i (t) is the current particle position, x i (t+1) is the updated particle position, P best (t) is an individual extremum, pg (t) is a population global extremum;
(5) Until the set iteration times m=50 are reached, calculating and stopping, and outputting the optimal position [ K, alpha ] of the particles and the optimal fitness value;
inputting K and alpha of the optimal position of the particle into a variational modal decomposition algorithm VMD, decomposing each original laser ultrasonic signal to obtain a plurality of IMF components, calculating the signal correlation of each IMF component, sequencing the signal correlation from large to small, selecting the IMF components with the front 60-80% and adding, and taking the obtained signal as a laser ultrasonic signal after noise reduction;
the implementation process of the step S4 is as follows: setting excitation points by taking the center of the established ductile cast iron pipe simulation model as a coordinate origin, collecting signals of a plurality of excitation points as original laser ultrasonic signals according to the scanning interval of the step S1, and extracting longitudinal waves, wherein the distance between the excitation points and the center is assumed to be x mm, the thickness of the ductile cast iron pipe simulation model is dmm, the propagation path length of the longitudinal waves received by an interferometer beam is S mm, and the wave speed of the longitudinal waves in the material is V L m/s, the time taken for the longitudinal wave to reach the interferometer beam is t s, and the physical quantities exist betweenIn the following relationship:
s 2 ＝d 2 +x 2
s＝V L ×t
by fitting x 2 And t 2 Fitting a curve, and verifying the fitted curve obtained in the step 3);
and calculating the thickness of the nodular cast iron pipe by using the verified fitting curve: and obtaining the intercept and the slope of the fitting curve, wherein the intercept and the slope correspond to the wave speed of the longitudinal wave and the thickness of the nodular cast iron pipe respectively.
2. The ductile iron pipe wall thickness measuring method based on laser ultrasound and parameter optimization VMD according to claim 1, wherein the constructing method of the fitness function f (K, α) is:
the correlation coefficient c (K, a),
wherein: l is the sequence length of the original laser ultrasonic signal, j is a variable, the value is 1,2,3 …, L, x (j) is the laser ultrasonic signal after noise reduction, y (j) is the original laser ultrasonic signal, u x Sum sigma x Respectively representing the mean and variance of the original laser ultrasonic signal, u y Sum sigma y Mean and variance of laser ultrasonic signals after noise reduction are respectively represented, mu K，α Representing IMF components, d is a sequence of L lengths,
constructing a combination factor b (K, alpha),
wherein a is 1 And a 2 As normalization factor, a 1 ＝1/max(β i )；a 2 ＝1/max(δ i )；ω＝{ω 1 ,ω 2 …,ω i -the center frequency of the different IMF components, fs being the sampling frequency of the signal; beta i Is the difference of central frequencies delta i Is the maximum and minimum modal center frequency difference;
the fitness function is constructed as:
f(K,α)＝c(K,α)+b(K,α)。
3. the ductile iron pipe wall thickness measuring method based on laser ultrasonic and parameter optimization VMD according to claim 2, characterized in that in step 3), B-scan imaging is performed on the laser ultrasonic signal after noise reduction, the arrival time of the longitudinal wave at different excitation points is calculated, and fitting is performed on the square of the time of arrival and the square of the position of the longitudinal wave to obtain a fitting curve.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211222655.4A CN115979138B (en) | 2022-10-08 | 2022-10-08 | Nodular cast iron pipe wall thickness measuring method based on laser ultrasonic and parameter optimization VMD |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211222655.4A CN115979138B (en) | 2022-10-08 | 2022-10-08 | Nodular cast iron pipe wall thickness measuring method based on laser ultrasonic and parameter optimization VMD |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115979138A CN115979138A (en) | 2023-04-18 |
| CN115979138B true CN115979138B (en) | 2023-11-07 |
Family
ID=85971035
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211222655.4A Active CN115979138B (en) | 2022-10-08 | 2022-10-08 | Nodular cast iron pipe wall thickness measuring method based on laser ultrasonic and parameter optimization VMD |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115979138B (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2326269A1 (en) * | 1999-11-17 | 2001-05-17 | Sms Demag Ag | Method and device for contactless online measuring of the wall thickness of hot-rolled pipes |
| CN1357100A (en) * | 1999-04-20 | 2002-07-03 | 加拿大国家研究局 | Laser-ultrasonic measurement of wall thickness |
| CN110865357A (en) * | 2019-10-25 | 2020-03-06 | 南京理工大学 | Laser radar echo signal noise reduction method based on parameter optimization VMD |
| CN112098526A (en) * | 2020-07-23 | 2020-12-18 | 西安交通大学 | Near-surface defect feature extraction method for additive product based on laser ultrasonic technology |
| CN113537112A (en) * | 2021-07-26 | 2021-10-22 | 山东省科学院激光研究所 | Laser ultrasonic surface defect imaging noise reduction method based on improved wavelet threshold and VMD |
-
2022
- 2022-10-08 CN CN202211222655.4A patent/CN115979138B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1357100A (en) * | 1999-04-20 | 2002-07-03 | 加拿大国家研究局 | Laser-ultrasonic measurement of wall thickness |
| CA2326269A1 (en) * | 1999-11-17 | 2001-05-17 | Sms Demag Ag | Method and device for contactless online measuring of the wall thickness of hot-rolled pipes |
| JP2001194136A (en) * | 1999-11-17 | 2001-07-19 | Sms Demag Ag | Non-contact on-line type high temperature wall thickness measuring method and apparatus for pipe |
| CN110865357A (en) * | 2019-10-25 | 2020-03-06 | 南京理工大学 | Laser radar echo signal noise reduction method based on parameter optimization VMD |
| CN112098526A (en) * | 2020-07-23 | 2020-12-18 | 西安交通大学 | Near-surface defect feature extraction method for additive product based on laser ultrasonic technology |
| CN113537112A (en) * | 2021-07-26 | 2021-10-22 | 山东省科学院激光研究所 | Laser ultrasonic surface defect imaging noise reduction method based on improved wavelet threshold and VMD |
Non-Patent Citations (1)
| Title |
|---|
| 基于优化变分模态分解算法的回波信号去噪;王祺等;《激光与光电子学进展研》;第58卷(第20期);第1-14页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115979138A (en) | 2023-04-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Lee et al. | Laser ultrasonic anomalous wave propagation imaging method with adjacent wave subtraction: algorithm | |
| Leonard et al. | Lamb wave tomography of pipe-like structures | |
| CN101620203B (en) | Deficiency ultrasonic detecting equipment of mechanical equipment based on wavelet theory | |
| CN112098526A (en) | Near-surface defect feature extraction method for additive product based on laser ultrasonic technology | |
| CN105424804A (en) | Ultrasonic detecting method for defect of remanufactured composite part | |
| Van Pamel et al. | A methodology for evaluating detection performance of ultrasonic array imaging algorithms for coarse-grained materials | |
| Jun et al. | Method for evaluation of surface crack size of wind turbine main shaft by using ultrasonic diffracted waves | |
| CN115979138B (en) | Nodular cast iron pipe wall thickness measuring method based on laser ultrasonic and parameter optimization VMD | |
| Fendt et al. | Ultrasonic defect characterization in heavy rotor forgings by means of the synthetic aperture focusing technique and optimization methods | |
| CN109839442B (en) | Grain size nondestructive evaluation method and system based on laser ultrasonic center frequency shift | |
| Shi et al. | Resolution enhancement of ultrasonic imaging at oblique incidence by using WTFM based on FMC-AR | |
| Chassignole et al. | Study of ultrasonic characterization and propagation in austenitic welds: The MOSAICS project | |
| Tian et al. | Phased array ultrasonic S-scan testing of near-detection-surface defects based on a background subtraction algorithm | |
| CN115901940B (en) | Method for detecting round hole defects of laser ultrasonic material-increasing workpiece based on multi-mode frequency domain synthetic aperture | |
| Xue et al. | Nondestructive testing of internal defects by ring-laser-excited ultrasonic | |
| Li et al. | Intelligent Evaluation of Crack detection with Laser Ultrasonic technique | |
| Li et al. | Optimal matched filter design for ultrasonic NDE of coarse grain materials | |
| Zhang et al. | Grain size characterization of Ti-6Al-4V titanium alloy based on laser ultrasonic random forest regression | |
| Jiang et al. | Finite element simulation of laser-synthetic aperture focusing technique (SAFT) detection of additive manufactured parts | |
| Chen et al. | Research of SNR enhancement for coarse-grained CASS based on phase coherence imaging | |
| Eberhard | Ultrasonic tomography for nondestructive evaluation | |
| Jiang et al. | Visual Detection of AM Surface Defects Based on Laser Ultrasound Technology | |
| Calmon et al. | Simulation-based performances assessment of FMC-TFM array imaging techniques | |
| Zhang et al. | The detection and classification method of sub-macroscopic defects inside steel with an ultrasonic testing and CatBoost-based stacking model | |
| Zhang et al. | Ultrasonic flaw recognition by multi-angle phased array data integration |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |