CN112098526B - Near-surface defect feature extraction method for additive product based on laser ultrasonic technology - Google Patents

Near-surface defect feature extraction method for additive product based on laser ultrasonic technology Download PDF

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CN112098526B
CN112098526B CN202010717976.6A CN202010717976A CN112098526B CN 112098526 B CN112098526 B CN 112098526B CN 202010717976 A CN202010717976 A CN 202010717976A CN 112098526 B CN112098526 B CN 112098526B
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赵纪元
李明
訾艳阳
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Xian Jiaotong University
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
    • G01N29/22—Details, e.g. general constructional or apparatus details
    • G01N29/32—Arrangements for suppressing undesired influences, e.g. temperature or pressure variations, compensating for signal noise
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
    • G01N29/44—Processing the detected response signal, e.g. electronic circuits specially adapted therefor
    • G01N29/4454—Signal recognition, e.g. specific values or portions, signal events, signatures
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
    • G01N29/44—Processing the detected response signal, e.g. electronic circuits specially adapted therefor
    • G01N29/449—Statistical methods not provided for in G01N29/4409, e.g. averaging, smoothing and interpolation
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
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    • G01N2291/01—Indexing codes associated with the measuring variable
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2291/00—Indexing codes associated with group G01N29/00
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    • G01N2291/0289—Internal structure, e.g. defects, grain size, texture

Abstract

The invention provides a method for extracting near-surface defect characteristics of an additive workpiece based on a laser ultrasonic technology, which comprises the steps of exciting ultrasonic waves by utilizing the surface of a pulse laser workpiece and then receiving ultrasonic signals; carrying out time domain average processing on the ultrasonic signals; performing band-pass filtering on the ultrasonic signal subjected to time domain averaging according to the approximate frequency range of the surface wave to obtain a filtered signal; and decomposing the filtering signal by adopting an improved empirical mode decomposition method to obtain M IMF components, and taking the IMF component with the highest frequency as the surface defect characteristic. The invention can solve the environmental vibration interference through the band-pass filtering; the surface wave information is effectively extracted through decomposition by an improved empirical mode decomposition method, so that the problem that the defect echo is not obvious or the defect signal is submerged by noise caused by the interference of surface roughness on the focusing of the received laser is solved, and the effective identification precision of the laser ultrasonic system is improved.

Description

Near-surface defect feature extraction method for additive product based on laser ultrasonic technology
Technical Field
The invention relates to a near-surface defect feature extraction method for additive manufacturing based on a laser ultrasonic technology.
Background
Additive manufacturing has developed certain application in the fields of aviation, aerospace, medical treatment, engineering machinery and the like at present, and has wide application prospect and commercial value. However, the additive manufactured parts inevitably have surface and internal defects such as air holes, slag inclusion, incomplete fusion, cracks and the like, and the extensive engineering application of the additive manufactured parts is greatly restricted, so that the development of an effective additive manufactured part nondestructive testing method is urgently needed.
The additive part can realize the integrated forming of a complex structure, can manufacture a large-size and high-density part with a complex surface shape and an internal structure, and brings serious challenges to the traditional nondestructive testing technology (ultrasound, ray, vortex, magnetic powder and penetration). In recent years, the online detection technology of the material increase manufacturing process is developed to a certain extent, the whole defect detection of a finished piece is completed by detecting a newly formed layer by layer, and the detection problem caused by a complex structure after the finished piece can be effectively avoided to a certain extent.
Laser ultrasonic inspection technology has been introduced into online inspection of additive manufacturing processes as a non-contact inspection means, and has a high inspection resolution. However, under the laser excitation action, the generated laser ultrasonic signal has the characteristics of complex multi-mode, wide frequency band and low signal-to-noise ratio, the problems of ultrasonic mode overlapping display (defect echo and longitudinal wave) and the like easily occur, and meanwhile, noise interference of the external environment also seriously influences the identification of the defects of the workpiece and the accuracy of the detection result. Therefore, it is necessary to improve the signal-to-noise ratio of the laser ultrasonic signal and extract effective defect feature information, thereby improving the accuracy of defect identification.
The laser super-detection technology becomes one of advanced non-destructive detection key technologies, is a method for solving the quality assurance problem in the additive manufacturing process, and along with the continuous expansion of the application field, higher requirements are also put forward on the quantitative evaluation of the defects of the additive parts, and correspondingly, higher requirements are put forward on ultrasonic signals.
Patent No. CN 106018288A-laser ultrasonic on-line nondestructive testing method for additive manufacturing parts disclose a method for detecting metallurgical defects generated in the additive manufacturing process by using the amplitude change of laser excitation ultrasonic surface waves. Through installing detection device on the high energy beam generating device of additive manufacturing equipment, realized the synchronous detection to part defect among the additive manufacturing process, improved the reliability of additive manufacturing part.
Patent No. 201810224085. X- "a vibration material disk component detection method based on manipulator scanning laser ultrasonic signal correlation analysis" discloses a method for meshing a component surface detection area, which uses a manipulator type laser ultrasonic automatic detection system to sequentially scan ultrasonic detection signals in the X and Y directions and calculate different coefficients to realize online rapid positioning of vibration material disk component defects.
The two methods can detect the defects in the additive manufacturing process in time, avoid the detection blind area caused by the complex structure after the parts are manufactured, and have strong practicability and popularization value. However, the signals used by the two methods are ultrasonic original signals, and are directly positioned and processed, and the influence of interference factors such as surface roughness and environmental vibration on the laser ultrasonic detection signals in the additive manufacturing process is not considered, for example, when echo information is submerged in noise. Especially, the ultrasonic signal excited by the laser is a broadband signal, and the received ultrasonic signal has complex multi-mode (multiple mode signals appear or even are mixed), broadband and low signal-to-noise ratio signals, so that the accuracy of defect positioning and quantification is influenced.
Disclosure of Invention
The invention aims to provide a method for extracting near-surface defect characteristics of an additive workpiece based on a laser ultrasonic system, which can solve the problems of complex and multi-mode laser ultrasonic signals, wide frequency band and low signal-to-noise ratio and effectively extract the near-surface defect ultrasonic signals.
In order to realize the purpose, the invention is realized by the following technical scheme:
the method for extracting the near-surface defect characteristics of the additive part based on the laser ultrasonic technology comprises the following steps:
step 1, exciting ultrasonic waves by using the surface of a pulse laser workpiece, and receiving ultrasonic signals;
step 2, performing time domain average processing on the ultrasonic signals;
step 3, performing band-pass filtering on the ultrasonic signal subjected to time domain averaging according to the frequency of the surface wave to obtain a filtered signal;
and 4, decomposing the filtering signal by adopting an improved empirical mode decomposition method to obtain M IMF components, and taking the IMF component with the highest frequency as the surface defect characteristic.
A further improvement of the present invention is that in step 2, 32 time domain averaging processes are applied to the ultrasonic signal.
A further development of the invention is that, in step 3, the frequency f of the surface wavemaxCalculated by the following formula:
Figure GDA0003189353230000031
wherein a is0Is the laser pulse spot radius, cRIs the wave velocity of the surface wave.
The invention further improves that the process of the step 4 is as follows:
adding the single filtered signal to auxiliary white noise; determining a local maximum and a local minimum of the filtered signal after the auxiliary noise is added; obtaining a mean line according to the local maximum value and the local minimum value; judging whether the difference between the filtering signal and the mean line meets two properties of the IMF, if so, obtaining a jth IMF component, judging whether the j +1 to-be-decomposed quantity meets monotony or is constant, and if so, obtaining M IMF components added with white noise each time; decomposing the i +1 th white noise-added filter signal, wherein i is 1,2,3.. N to obtain N × M IMF components; obtaining M IMF components of a single filtering signal according to the NxM IMF components, sequencing the M IMF components from high to low according to frequency, and taking the IMF component with the highest frequency as a defect characteristic, wherein j is 1,2,3 …, and M, i is 1,2,3.
The invention is further improved in that the specific process of the step 4 is as follows:
1) adding auxiliary white noise to the single filtering signal x (t), setting the white noise adding times N and the white noise level to obtain N groups of filtering signals x added with white noisei(t)，i＝1,2,3....N；
2) Determining a filtered signal x after adding auxiliary noisei(t) local maxima and local minima;
3) constructing an upper envelope E by utilizing cubic B splines according to local maxima1Constructing a lower envelope E by utilizing cubic B splines according to local minimum values2According to the upper envelope E1And the lower envelope E2Structural mean line
Figure GDA0003189353230000032
4) Using filtered signal x after addition of auxiliary noisei(t) subtracting the mean value line m to obtain an intermediate variable h (t), judging whether the intermediate variable h (t) meets two properties of the IMF, and if so, obtaining a jth IMF component cjAnd j is 1,2,3 …, M, if two properties of IMF are not satisfied, x isi(t) -h (t), repeating steps 2) to 4) until both properties of IMF are met;
5) obtaining a j +1 th to-be-decomposed quantity r according to the j-th IMF componentj+1(t)＝rj(t)-cjWherein r is1(t)＝x1(t)，rj(t) is the jth to-be-decomposed quantity, and the jth +1 to-be-decomposed quantity r is judgedj+1(t) whether it is monotonous or constant, if rj+1(t) satisfies the condition of monotonous or constant value, each addition being obtainedM IMF components of white noise, completing the filtering of the ith signal xi(t) decomposition; if not, j +1 to-be-decomposed quantities r are addedj+1(t) repeating steps 2) to 5) until j +1 quantities r to be decomposedj+1(t) satisfies monotonic or constant value;
6) adding white noise to the i +1 th filtered signal xi+1(t) decomposing to obtain N × M IMF components;
7) adding the N multiplied by M IMF components to obtain the sum of M IMF components of N groups of white noise adding, dividing the sum by the white noise adding times N to obtain M IMF components of a single filtering signal, sequencing the M IMF components from high to low according to the frequency, and taking the IMF component with the highest frequency as a defect characteristic.
A further improvement of the invention is that in step 4), the two properties of IMF are:
(1) number of extreme points N of intermediate variable h (t)eAnd the number of zero crossings NzEqual or differ by at most one;
(2) intermediate variable h (t) at arbitrary time tiUpper envelope f of upper, local maximum valuesmax(ti) And a lower envelope f consisting of local minimamin(ti) Has an average value of zero.
Compared with the prior art, the invention has the following beneficial effects:
1) the method aims at the characteristics that laser excitation ultrasonic waves have complex multi-mode (multiple mode signals appear or even are mixed), broadband and low signal-to-noise ratio signals, determines the frequency band range of the surface wave through the relation between excitation light spots, the wave speed of the surface wave and the frequency of the surface wave, realizes effective separation of different modes (surface wave, longitudinal wave, grazing longitudinal wave and the like) in the signals through a mode of combining band-pass filtering and an improved empirical mode decomposition method, and extracts surface wave information sensitive to near-surface defects.
2) Compared with the method that the surface wave information is directly extracted from the original signal, the surface wave information is easily interfered by environmental vibration and surface roughness of a measured piece, and defect echo information is submerged. The method can solve the interference of environmental vibration (mainly low-frequency vibration) through band-pass filtering; the surface wave information is effectively extracted through decomposition by an improved empirical mode decomposition method, so that the problem that the defect echo is not obvious or is submerged by noise due to the interference of surface roughness on the focusing of the received laser is solved, and the effective identification precision of the laser ultrasonic system is improved.
3) The ultrasonic signal processing method directly processes the ultrasonic signal in the time domain, does not need signal reconstruction from the time domain to the frequency domain and then to the time domain, can reduce the calculation amount of a computer and improve the efficiency.
Furthermore, the method improves the signal-to-noise ratio of the signal by adopting the reasonable time domain average frequency of 32 times, avoids the unstable effect of echo information under the condition of single signal acquisition, better discovers defect information and reduces the defect omission ratio; and meanwhile, the service life of the laser is prolonged.
Drawings
Fig. 1 is a flow of an implementation of an additive manufacturing near-surface defect feature extraction method based on a laser ultrasonic technology.
Fig. 2 is an experimental schematic diagram of different time domain averaging.
Fig. 3 is a graph showing the effect of different time domain averaging times (the range between two black lines is the defect echo range). Wherein, (a) is 1 time domain average, (b) is 8 time domain averages, (c) is 16 time domain averages, (d) is 32 time domain averages, (e) is 64 time domain averages, and (f) is 128 time domain averages.
Fig. 4 is a flow chart of the EMD algorithm.
FIG. 5 is a flow chart of the EEMD algorithm.
Fig. 6 is a diagram showing an ultrasonic propagation path.
Fig. 7 shows a comparison effect of a-scan signals, where (a) is the original signal and (b) is the surface wave feature extraction.
Fig. 8 is a diagram of a laser ultrasound system.
FIG. 9 is a schematic diagram of the experiment.
FIG. 10 is a real image of the test piece (the mark after excitation in a black frame).
FIG. 11 is a graph comparing the sweeping effect of example A. Wherein, (a) is a raw signal, and (b) is surface wave feature extraction.
FIG. 12 is a comparison graph of the effect of example B sweeps. Wherein, (a) is a raw signal, and (b) is surface wave feature extraction.
Fig. 13 is a comparison graph of the imaging effect of B-scan signals (the black line ranges are surface direct wave and defect echo), where (a) is original signal imaging and (B) is surface wave feature extraction imaging.
Detailed Description
The present invention will be described in detail with reference to the accompanying drawings.
Referring to fig. 1, the main steps of the present invention include: raw signal acquisition, time domain averaging, sensitive band filtering, and EEMD feature extraction.
The method comprises the steps that a laser ultrasonic technology is utilized, excitation laser excites ultrasonic waves on the surface of a workpiece in the additive manufacturing process, ultrasonic signals are received through an interferometer, and the signal-to-noise ratio is improved by adopting the mode of multiple times of the same excitation point and data averaging in a time domain averaging mode aiming at the characteristic that an optical instrument is easily interfered by the outside world to cause the signal-to-noise ratio difference;
according to the receiving and transmitting invariance of the same position and the space invariance of the crack defect, firstly, the signals are subjected to time domain-like average processing, and the time domain average processing method comprises the following steps:
let the ultrasonic signal x (T) be composed of a periodic signal s (T) and white noise N (T), i.e. x (T) ═ s (T) + N (T), the ultrasonic signal x (T) is intercepted with the period T of the periodic signal s (T), i.e. x (T) is divided into N segments, each segment has M points, and the corresponding points of each segment are added. Experiments are carried out on a test block with prefabricated defects, the time domain average frequency is optimized, an experimental schematic diagram is shown in fig. 2, after 32 times, the signal-to-noise ratio is improved without great improvement, the time domain average frequency is determined to be set to be 32 times, a better signal-to-noise ratio can be obtained, and the effects are shown in (a), (b), (c), (d), (e) and (f) in fig. 3.
Aiming at the problem of wide frequency band, the approximate frequency range of the surface wave is determined through the relation between the wave speed of the surface wave and the diameter of the excitation light spot, narrow-band filtering is carried out through a phase-locked band-pass filter, and meanwhile, the phase change of signals in the filtering process is overcome;
the frequency of the surface wave is calculated as follows:
Figure GDA0003189353230000061
wherein a is0Is the laser pulse spot radius, cRIs the wave velocity of the surface wave.
And aiming at the defect echo information feature extraction part, an improved empirical mode decomposition (EEMD) algorithm is adopted to complete feature extraction of the defect echo signals, and B-scan imaging is improved.
Since EEMD is based on EMD, the following description will first describe EMD. The EMD algorithm is aimed at decomposing a signal with poor performance into a set of eigen-Mode functions (IMFs) with better performance, and the IMFs must satisfy the following two properties:
(1) number of extreme points N of signaleAnd the number of zero crossings NzEqual or differ by at most one;
(Nz-1)≤Ne≤(Nz+1) (2)
(2) at an arbitrary point in time tiUpper envelope f of upper, local maximum valuesmax(ti) And a lower envelope f consisting of local minimamin(ti) Has an average value of zero.
[fmax(ti)+fmin(ti)]/2＝0 (3)
Referring to fig. 4, the calculation steps of the EMD algorithm are as follows:
(1) finding out all maximum value points and minimum value points of the original data sequence X (t), and respectively fitting the maximum value points and the minimum value points into an upper envelope line and a lower envelope line of the original sequence by using a cubic spline function; the mean value of the upper envelope line and the lower envelope line is m; subtracting m from the original data sequence to obtain a new sequence h with a low frequency subtracted, namely h ═ x (t) -m; in general h is not necessarily a stationary data sequence for which the above process has to be repeated. Such as h1Has an envelope mean value of m11Then the data sequence with the low frequency component represented by the envelope average removed is h11。
(2) Subtracting c from X (t)1To obtain a new data sequence r with high frequency components removed1(ii) a To r1Then, the above decomposition is performed to obtain a second eigenmode function component c2(ii) a This is repeated until the last data sequence rnCannot be decomposed, at this time, rnRepresenting the trend or mean, r, of the data sequence X (t)nThe mathematical properties of (a): monotonous or constant to a constant value.
Referring to fig. 5, an improved Empirical Mode Decomposition (EEMD) algorithm is proposed to solve the shortcomings of the EMD algorithm, and a noise-aided data analysis method is proposed. EEMD advantages: when the additional white noise is uniformly distributed in the whole time-frequency space, the solving precision of the local mean value of the signal is improved, and the time-frequency space is composed of components with different scales, which are divided by a filter bank.
EEMD principle: adding white Gaussian noise to each eigenmode decomposition process, adding white noise-containing signal x (t) ═ y (t) + n (t), wherein y (t) is the original signal, n (t) is the added white Gaussian noise, and the purpose of adding noise is to search an approximation of the signal y (t)
Figure GDA0003189353230000082
The differences between them are made as small as possible while avoiding pattern breakage and pattern confusion problems during decomposition.
The method for extracting the near-surface defect characteristics of the additive part based on the laser ultrasonic technology comprises the following steps of:
step 1, exciting ultrasonic waves by using the surface of a pulse laser workpiece, and receiving ultrasonic signals by using a probe of a laser interferometer;
and 2, performing time domain average processing on the ultrasonic signals for 32 times, wherein the step effect improves the signal-to-noise ratio of the signals, but cannot delete the modal information.
And 3, according to a calculation formula, the method comprises the following steps:
Figure GDA0003189353230000081
wherein a is0Is the laser pulse spot radius, cRIs a surface waveThe wave velocity of (2). Determining the approximate frequency range f of a surface wavemaxAccording to the approximate frequency range f of the surface wavemaxPerforming band-pass filtering on the ultrasonic signal subjected to time domain averaging to obtain a filtered signal; on the basis of time domain averaging, the approximate frequency band of surface wave information sensitive to near-surface defects is locked, and meanwhile, detrending item processing is carried out on ultrasonic signals.
And 4, decomposing the filtering signal by adopting an improved empirical mode decomposition (EEMD), adding the EMD decomposition results for multiple times by utilizing the idea of ensemble averaging on the basis of EMD, summing and averaging to obtain M IMF components, and taking the 1 st IMF component with the highest frequency as the surface defect characteristic.
1) Adding auxiliary white noise to the single filtered signal x (t), setting the white noise adding times N and the white noise level to obtain N groups of filtered signals x added with white noisei(t)(i＝1,2,3....N)；
2) Ith white noise added xi(t), intermediate variables r (t) initialization r1(t)＝xi(t) determining the filtered signal x after adding the auxiliary noisei(t) local maxima and local minima.
3) Constructing an upper envelope E by utilizing cubic B splines according to local maxima1Constructing a lower envelope E by utilizing cubic B splines according to local minimum values2According to the upper envelope E1And the lower envelope E2Structural mean line
Figure GDA0003189353230000091
4) Using xi(t) -m obtaining an intermediate variable h (t), judging whether the intermediate variable h (t) meets two properties of the IMF, and if so, obtaining j IMF components cj(j ═ 1,2,3 …, M), if the two properties of IMF are not met, x will be changedi(t) -h (t), repeating steps 2), 3) and 4) until two properties of IMF are met;
5) when the jth IMF component is obtained, the jth +1 to-be-decomposed quantity r is carried outj+1(t)＝rj(t)-cjWherein r is1(t)＝x1(t)，rj(t) is the jth to-be-decomposed quantity, and r is judgedj+1(t) whether monotonic behavior is satisfied or the mean line m is constant, if rj+1(t) satisfying monotony or being a constant value, obtaining M IMF components of each time adding white noise, and completing the ith xi(t) decomposition. If not, the j +1 th to-be-decomposed quantity r is determinedj+1(t) repeating steps 2), 3)4) and 5) until r is satisfiedj+1(t) satisfies monotone or a constant value.
6) X for i +1 th white noise additioni+1(t) decomposing, specifically repeating 2), 3), 4) and step 5), until i ═ N, resulting in nxm IMF components.
7) Adding the obtained N multiplied by M IMF components to obtain the sum of M IMF components of N groups of white noise adding, and then dividing the sum by the auxiliary white noise adding times N to obtain more accurate M IMF components of a single filtering signal, sequencing the M IMF components from high to low according to the frequency, and taking the 1 st IMF component with the highest frequency as a defect characteristic. Effective extraction of surface wave information is realized, and the arrival time t of the direct surface wave can be respectively obtained1And defect echo arrival time t2Arrival time t of direct surface wave1Are respectively V1Time of arrival t of defect echo2Has an amplitude of V2。
8) According to the direct wave and the defect echo information, the defect positioning is realized by utilizing the wave velocity V of the surface wave, and the ultrasonic wave propagation path is shown in figure 6;
the ultrasonic wave excited by the exciting laser as the surface direct wave is directly received by the receiving laser, the propagation path is d, and the arrival time is t1Calculating formula (4):
Figure GDA0003189353230000092
the defect echo is that ultrasonic waves excited by excitation laser propagate towards the defect direction, are reflected by the defect edge and are received by the received laser, the propagation path is d +2L, and the arrival time t2Calculating formula (5):
Figure GDA0003189353230000101
calculating the distance L between the defect and the excitation laser point by utilizing the ratio of the arrival time of the defect echo to the arrival time of the direct wave, and calculating the formula (6):
Figure GDA0003189353230000102
and 5, acquiring a large amount of A-scanning data through B-scanning, and realizing data expansion and B-scanning imaging in the step. Scanning the A and performing the steps 1-4 to improve the signal-to-noise ratio of each signal, wherein the comparison effect of the A scanning signals is shown in (a) and (b) in fig. 7, unknown defect echoes are caused by non-prefabricated defects, and are difficult to find in original signals through industrial CT verification and can be found after feature extraction; meanwhile, the B-scan imaging quality is improved, and the defect positioning precision and visualization are improved.
The following is a specific example.
The invention provides a method for extracting near-surface defect characteristics of additive manufacturing based on a laser ultrasonic technology, which comprises the following steps: in a laser ultrasonic system, an excitation laser probe is used for generating ultrasonic waves on the surface of a workpiece in an additive manufacturing process, a controller is used for controlling deflection of a galvanometer, range scanning is achieved, an interferometer is used for receiving ultrasonic signals, an analog signal is converted into a digital signal through an acquisition card, and the laser ultrasonic system is shown in fig. 8. The average time of the acquired ultrasonic signals in the time domain is 32 times, so that the signal-to-noise ratio of the signals is improved; determining the upper and lower cut-off bandwidths of the phase-locked band-pass filter through the acoustic characteristics of the material and the diameter of the excitation light spot, and realizing the retention of effective information of defect echo information; and the extraction of the ultrasonic surface wave information is realized by combining an EEMD algorithm, and the information interference of other modes is eliminated.
The invention utilizes an additive manufacturing near-surface defect feature extraction method based on a laser ultrasonic technology to identify the processing state of a workpiece in the additive manufacturing process, and the method is implemented according to the following specific steps:
(1) time domain averaging
Firstly, under the condition of completely identical experimental conditions, changing the excitation times and carrying out time domain average processing on signals;
then, an optimal excitation number (i.e. the average number of time in the time domain is 32) is selected, the signal-to-noise ratio of the signal is improved, and the service life of the laser is prolonged.
(2) Sensitive band filtering
Firstly, measuring the wave speed of the ultrasonic surface wave corresponding to a processing material;
then, measuring the diameter of an excitation laser spot at the focus, and calculating the approximate range of the frequency of the surface wave by using a formula (7);
the frequency of the surface wave is calculated as follows:
Figure GDA0003189353230000111
wherein a is0Is the laser pulse spot radius, cRIs the wave velocity of the surface wave.
And finally, setting the upper and lower cut-off frequencies of the phase-locked filter, solving the problem of laser ultrasonic broadband information redundancy, and extracting narrow-band information.
(3) EEMD feature extraction
The ultrasonic signals after time domain averaging and sensitive frequency band filtering are determined to have standard deviation of 0.2 times of the ultrasonic signals and the adding times of 50 times by adopting an improved empirical mode decomposition method, so that the further division of the frequency bands is realized, the effective extraction of the surface wave information is realized, and the direct surface waves and the defect echoes can be obtained.
And finally, the defect information is fed back, so that corresponding compensation measures can be taken conveniently in the additive manufacturing process.
The following gives a specific application example process, and at the same time, the effectiveness of the invention in engineering application is verified.
This experiment is carried out at laser ultrasonic system laboratory bench, and at first, the accepting point is 8mm apart from the groove edge distance, and the degree of depth in groove is: 1mm, excitation and reception were kept fixed, ensuring that 5mm of ultrasound signal was acquired by a robot in a B-scan fashion (parallel to the slot edges), as shown in fig. 9:
the test block after the experiment is shown in fig. 10.
The subjects and parameters are shown in Table 1.
TABLE 1 test objects and test parameters
Figure GDA0003189353230000121
The effect comparison is performed from three aspects of the a signal, the B scan signal, and the B scan imaging, see (a) and (B) in fig. 11, (a) and (B) in fig. 12, and (a) and (B) in fig. 13. After feature extraction, only surface wave information is reserved, and irrelevant modes (such as longitudinal waves and grazing longitudinal waves) are removed. A large amount of experimental data verification is carried out, and the results show that the method for extracting the characteristics of the laser ultrasonic detection signals of the near-surface defects in the additive manufacturing process improves the signal-to-noise ratio of the signals, avoids the problem of interference of different ultrasonic modes, realizes the extraction of surface wave signals sensitive to the near-surface defects, improves the ultrasonic B-scan imaging effect, and improves the defect identification rate.

Claims (4)

1. The method for extracting the near-surface defect characteristics of the additive part based on the laser ultrasonic technology is characterized by comprising the following steps of:
step 1, exciting ultrasonic waves by using the surface of a pulse laser workpiece, and receiving ultrasonic signals;
step 2, performing time domain average processing on the ultrasonic signals;
step 3, performing band-pass filtering on the ultrasonic signal subjected to time domain averaging according to the frequency of the surface wave to obtain a filtered signal;
step 4, decomposing the filtering signal by adopting an improved empirical mode decomposition method to obtain M IMF components, and taking the IMF component with the highest frequency as the surface defect characteristic;
the specific process of step 4 is as follows:
1) adding auxiliary white noise to the single filtered signal x (t), setting the white noise adding times N and the white noise level to obtain N groups of added white noiseFiltered signal x after white noise additioni(t)，i＝1，2，3....N；
2) Determining a filtered signal x after adding auxiliary noisei(t) local maxima and local minima;
3) constructing an upper envelope E by utilizing cubic B splines according to local maxima1Constructing a lower envelope E by utilizing cubic B splines according to local minimum values2According to the upper envelope E1And the lower envelope E2Structural mean line
Figure FDA0003189353220000011
4) Using filtered signal x after addition of auxiliary noisei(t) subtracting the mean value line m to obtain an intermediate variable h (t), judging whether the intermediate variable h (t) meets two properties of the IMF, and if so, obtaining a jth IMF component cj1,2,3, M, if both properties of IMF are not satisfied, then x is addedi(t) -h (t), repeating steps 2) to 4) until both properties of IMF are met;
5) obtaining a j +1 th to-be-decomposed quantity r according to the j-th IMF componentj+1(t)＝rj(t)-cjWherein r is1(t)＝xi(t)，rj(t) is the jth to-be-decomposed quantity, and the jth +1 to-be-decomposed quantity r is judgedj+1(t) whether it is monotonous or constant, if rj+1(t) satisfying monotony or constant value, obtaining M IMF components of each time adding white noise, and completing the filtering of the ith filtering signal xi(t) decomposition; if not, the j +1 th component r is addedj+1(t) repeating steps 2) to 5) until the j +1 th component rj+1(t) satisfies monotonic or constant value;
6) adding white noise to the i +1 th filtered signal xi+1(t) decomposing to obtain N × M IMF components;
7) adding the N multiplied by M IMF components to obtain the sum of M IMF components of N groups of white noise adding, dividing the sum by the white noise adding times N to obtain M IMF components of a single filtering signal, sequencing the M IMF components from high to low according to the frequency, and taking the IMF component with the highest frequency as a defect characteristic.
2. The method for extracting the near-surface defect characteristics of the additive manufactured part based on the laser ultrasonic technology as claimed in claim 1, wherein in the step 2, 32 time domain averaging processes are applied to the ultrasonic signals.
3. The method for extracting near-surface defect features of additive parts based on laser ultrasonic technology as claimed in claim 1, wherein in step 3, the frequency f of the surface wavemaxCalculated by the following formula:
Figure FDA0003189353220000021
wherein a is0Is the laser pulse spot radius, cRIs the wave velocity of the surface wave.
4. The method for extracting the near-surface defect features of the additive workpiece based on the laser ultrasonic technology according to claim 1, wherein in the step 4), two properties of the IMF are as follows:
(1) number of extreme points N of intermediate variable h (t)eAnd the number of zero crossings NzEqual or differ by at most one;
(2) intermediate variable h (t) at arbitrary time tiUpper envelope f of upper, local maximum valuesmax(ti) And a lower envelope f consisting of local minimamin(ti) Has an average value of zero.
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