CN111855803A - Laser ultrasonic high signal-to-noise ratio imaging method for manufacturing micro defects by metal additive - Google Patents
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Abstract
The invention discloses a laser ultrasonic high signal-to-noise ratio imaging method for manufacturing a micro defect by a metal additive, which comprises the following steps of: scanning by a laser ultrasonic system to obtain three-dimensional matrix data; carrying out noise reduction processing on the data; taking the signal of the non-defective area as a reference, and carrying out similarity calculation and maximum amplitude time deviation calculation on all the signals; carrying out normalization processing and translation processing on signals which simultaneously meet the similarity condition and the amplitude offset condition; extracting a two-dimensional matrix corresponding to the maximum amplitude moment from the three-dimensional data; and carrying out isolated point elimination processing and image drawing on the matrix so as to obtain a defect high signal-to-noise ratio image. The invention has the beneficial effects that: the method can eliminate the influence of the rough surface of the additive manufacturing piece on the laser ultrasonic signal and the image, separate the micro defect signal and the strong background noise signal, improve the detection probability of the defect under the condition of not polishing the rough surface, and lay a foundation for the online detection of the additive manufacturing laser ultrasonic.
Description
Technical Field
The invention relates to the technical field of laser ultrasonic nondestructive testing, in particular to a laser ultrasonic high signal-to-noise ratio imaging method for metal additive manufacturing micro defects.
Background
Metal additive manufacturing is a revolutionary advanced manufacturing technique. Due to the characteristic of point-to-point accumulation of metal additive manufacturing, defects are difficult to avoid in the manufacturing process, and the defects are more on a micrometer scale. Non-contact detection techniques, represented by laser ultrasound, are considered to be the most feasible means for online detection of additive manufacturing.
The laser ultrasonic wave excites the surface of the additive through laser, and high-frequency ultrasonic wave can be generated, so that the detection of micro defects is realized. The common laser ultrasonic C scanning method can realize the imaging of the surface micro-defects. However, since the receiver of laser ultrasound is sensitive to the material surface roughness, the received ultrasound signal is usually accompanied by a strong noise signal. In actual detection, these noise signals are often stronger than the ultrasonic signals generated by the micro defects, so that the defect signals are submerged in the noise signals, and false judgment and missing detection of the defects are caused.
In order to realize accurate detection of defects, the existing literature mostly polishes the rough surface of an additive product to be smooth, so as to ensure high signal-to-noise ratio of a laser receiver. However, for online inspection of additive manufacturing, adding a grinding device can interfere with the manufacturing process. Therefore, how to realize high signal-to-noise ratio imaging of the micro defects in the additive manufacturing process by a post-signal processing method on the premise of ensuring that the rough surface is not damaged and the printing process is not disturbed is a key point for restricting the practical application of laser ultrasound.
Disclosure of Invention
The invention aims to provide a laser ultrasonic high signal-to-noise ratio imaging method for metal additive manufacturing micro defects, aiming at overcoming the defects in the prior art, so as to realize high signal-to-noise ratio imaging of the micro defects under the condition that the rough surface of an additive product is not removed, avoid defect omission caused by the fact that a defect signal is submerged by a noise signal, and provide guarantee for laser ultrasonic online detection for realizing additive manufacturing.
The technical scheme adopted by the invention is as follows: a laser ultrasonic high signal-to-noise ratio imaging method for metal additive manufacturing micro defects comprises the following steps:
s1, performing two-dimensional grid scanning on the surface of the additive manufactured piece by using a laser ultrasonic system, completing acquisition of ultrasonic data, and obtaining three-dimensional matrix data A (M, N, t), wherein M is 1 … M, N is 1 … N, and t is 1 … tpWhere M is the number of scanning lines, N is the number of scanning columns, tqThe signal acquisition length;
s2, carrying out noise reduction processing on the acquired ultrasonic signals to obtain an ultrasonic data matrix A1(m, n, t);
s3, selecting a group of signals A1 (m) only with surface waves from the ultrasonic data matrix after noise reduction0,n0T) as a reference signal;
s4, carrying out similarity comparison on all the ultrasonic signals A1(m, n, t) subjected to noise reduction processing and reference signals to obtain a waveform similarity coefficient Nc (m, n);
s5, extracting a reference signal A1 (m)0,n0T) the time t0 corresponding to the maximum value of the surface wave amplitude, extracting the time t (m, n) corresponding to the maximum value of the surface wave amplitude of all the ultrasonic signals after noise reduction, and subtracting the two to obtain the time offset delta t (m, n) between all the ultrasonic signals and the reference signal, which is t (m, n) -t 0;
s6, sequentially judging all the ultrasonic signals A1(m, n, t) subjected to noise reduction, and when the waveform similarity coefficient is larger than a threshold Nc0 and the time offset delta t is smaller than a threshold delta t0, performing normalization processing and waveform offset processing on the signals to obtain a normalized amplitude value 1;
s7, extracting corresponding amplitude values of all signals at the time t0 from the processed signals to form a two-dimensional matrix A2(m, n);
s8, carrying out isolated point elimination processing on the two-dimensional matrix A2(m, n), and setting the value of a certain element to be 1 when the values of four adjacent elements in front of, behind, on the left of and on the right of the element in the two-dimensional matrix are equal and are normalized amplitude values of 1;
and S9, performing image drawing on the two-dimensional matrix processed in the S8 to obtain a defect image with a high signal-to-noise ratio.
According to the above scheme, in S2, the denoising processing method may be one of wavelet denoising processing, hilbert yellow denoising processing, and deep learning self-coding denoising processing.
In the above scheme, in S4, the waveform similarity coefficient Nc (m, n) is obtained using the waveform similarity function of the following formula:
in the above formula, s1(t) denotes a reference signal, s2(t) represents the corresponding ultrasonic signal a1(t) at the specific positions m and n after the noise reduction processing.
According to the scheme, in S6, the normalization processing method comprises the following steps: dividing the signal by the maximum amplitude value of the signal to obtain a normalized amplitude value 1; the normalized signal is:
according to the above scheme, in S6, the waveform offset processing is performed by shifting the signal by Δ t; the waveform offset processed signal is:
according to the scheme, in S6, the method for determining the threshold Nc0 is as follows: the reference signal and the ultrasonic signal of the non-defective region are subjected to similarity coefficient calculation one by one, and the minimum similarity coefficient thereof is taken as the threshold Nc 0. The determination method of the threshold value Δ t0 is as follows: the reference signal and the ultrasonic signal of the non-defective region are subjected to time offset calculation one by one, and the maximum time offset is taken as a threshold value delta t 0.
According to the scheme, in S1, the laser ultrasonic system comprises a pulse laser for ultrasonic signal excitation and a laser Doppler vibrometer for ultrasonic signal reception, and the distance between the light spots of the pulse laser and the laser Doppler vibrometer is kept fixed in the scanning process.
According to the above scheme, in S1, the scan grid size is not larger than the target detection accuracy.
The invention has the beneficial effects that: according to the method, the high signal-to-noise ratio imaging of the laser ultrasonic detection of the additive micro defects is obtained through the integration of methods such as signal noise reduction, similarity comparison, normalization processing and waveform translation processing, the problem that the laser ultrasonic system noise caused by the surface roughness of the additive is strong is solved, the separation of the micro defects and the strong background noise is realized, and therefore the detection probability and the quantitative accuracy of the defects are improved. The method realizes high signal-to-noise ratio imaging of the defects under the condition of not removing the surface roughness of the additive manufacturing piece, has important significance for subsequent intelligent automatic identification of the defects, and lays an important foundation for the application of a laser ultrasonic technology and the online detection of additive manufacturing.
Drawings
FIG. 1 is a schematic view of a laser ultrasound system scan according to an embodiment of the present invention.
FIG. 2 is a diagram illustrating an original defect signal in this embodiment.
Fig. 3 is a schematic diagram of the noise reduction effect in this embodiment.
Fig. 4 is a schematic diagram of signal normalization processing and shift processing in this embodiment.
FIG. 5 is a diagram illustrating defect high SNR imaging in this embodiment.
Detailed Description
For a better understanding of the present invention, reference is made to the following detailed description taken in conjunction with the accompanying drawings.
A316L stainless steel additive manufacturing sample is prepared by a powder spreading and printing mode, the average roughness of the surface of the sample is actually measured to be Ra7.5 mu m, and two holes with the depth of 50 mu m and the diameters of 100 mu m and 50 mu m are processed on the surface of the sample by a laser drilling mode. Based on the sample, the invention provides a method for accurately measuring the defect size of a rough part based on laser ultrasonic imaging, which specifically comprises the following steps:
s1, performing two-dimensional grid scanning on the surface of the additive material part (namely, the sample in the embodiment) by using a laser ultrasonic system to complete acquisition of ultrasonic data and obtain a three-dimensional matrixData a (M, N, t), M1 … M, N1 … N, t 1 … tqWhere M and N denote the positions of the acquired signals, M is the number of scanning lines, N is the number of scanning columns, tqThe length is collected for the signal.
In the invention, the laser ultrasonic system comprises a pulse laser for ultrasonic signal excitation and a laser Doppler vibrometer for ultrasonic signal reception, and the distance between light spots of the pulse laser (namely an excitation device) and the laser Doppler vibrometer (namely a receiving device) is kept fixed in the scanning process.
The size of the scanning grid is not larger than the target detection precision. In the present embodiment, as shown in fig. 1, the mesh size is set to 50 μm; scanning line number M in the three-dimensional matrix is 400, and scanning column number N in the three-dimensional matrix is 130; signal acquisition length tqAt 500, data is collected to render a defect image as shown in fig. 2.
S2, carrying out noise reduction processing on the acquired ultrasonic signals to obtain an ultrasonic data matrix A1(m, n, t).
In the invention, the denoising method can be one of wavelet denoising processing, Hilbert yellow denoising processing and deep learning self-coding denoising processing. In this embodiment, the deep learning self-coding is adopted to perform noise reduction processing on the acquired ultrasonic signals, and an ultrasonic data matrix after the noise reduction processing is shown in fig. 3.
S3, selecting a group of signals A1 (m) only with surface waves from the ultrasonic data matrix after noise reduction0,n0And t) as a reference signal.
S4, all the ultrasonic signals A1(m, n, t) after noise reduction processing and a reference signal A1 (m)0,n0And t) carrying out similarity comparison to obtain a waveform similarity coefficient Nc (m, n).
In the invention, the waveform similarity coefficient Nc (m, n) is obtained by calculating the waveform similarity function shown in the following formula,
in the above formula, s1(t) denotes a reference signal, s2(t) represents the correspondence of the specific positions m and n after the noise reduction processingThe ultrasonic signal a1 (t).
S5, extracting a reference signal A1 (m)0,n0T) the time t0 corresponding to the maximum value of the surface wave amplitude, extracting the time t (m, n) corresponding to the maximum value of the surface wave amplitude of all the ultrasonic signals after noise reduction, and subtracting the two to obtain the time offset delta t (m, n) between all the ultrasonic signals and the reference signal, which is t (m, n) -t 0.
In the present embodiment, the reference signal A1 (m)0,n0T) the time t0 corresponding to the maximum amplitude of the surface wave is 208.
S6, sequentially judging all the ultrasonic signals A1(m, n, t) subjected to noise reduction, and when the waveform similarity coefficient Nc (m, n) is larger than a threshold Nc0 and the time deviation delta t is smaller than a threshold delta t0, performing normalization processing and waveform deviation processing on the signals to obtain a normalized amplitude value 1.
In the invention, the normalization processing method comprises the following steps: dividing the signal meeting the requirement by the maximum amplitude value of the signal to obtain a normalized amplitude value 1; the normalized signal is:
in the invention, the method for waveform offset processing comprises the following steps: shifting the normalized signal by delta t; the signals after the offset are:
in the present invention, the method for determining the threshold Nc0 is: the reference signal and the ultrasonic signal of the non-defective region are subjected to similarity coefficient calculation one by one, and the minimum similarity coefficient thereof is taken as the threshold Nc 0. The determination method of the threshold value Δ t0 is as follows: the reference signal and the ultrasonic signal of the non-defective region are subjected to time offset calculation one by one, and the maximum time offset is taken as a threshold value delta t 0.
In the present embodiment, the threshold Nc0 is 0.6, and the threshold Δ t0 is 5.
And S7, extracting corresponding amplitude values of all signals at the time t0 from the processed signals to form a two-dimensional matrix A2(m, n).
And S8, carrying out isolated point elimination processing on the two-dimensional matrix A2(m, n), and setting the value of a certain element to be 1 when the values of four adjacent elements in front of, behind, on the left and on the right of the element in the two-dimensional matrix are equal and are normalized amplitude values of 1.
And S9, performing image drawing on the two-dimensional matrix processed in the step S8 to obtain a defect image with high signal-to-noise ratio, as shown in FIG. 5.
Compared with the original defect signal schematic diagram shown in fig. 2, the image processed by the technical scheme of the invention is shown in fig. 5, the background noise of the image is completely eliminated, and a pure detection image is obtained: for a 100 mu m defect, after the influence of noise around the defect is eliminated, a defect boundary is displayed, so that the subsequent defect size measurement is facilitated; for a 50 μm defect, the defect boundary in the original defect signal diagram is originally buried by the noise signal and cannot be shown, which is fully shown in FIG. 5.
The invention firstly eliminates the noise of a single signal caused by the rough surface of the additive material through signal noise reduction processing, secondly eliminates the problems of defect amplitude and fluctuation at corresponding moment possibly caused by the uneven surface roughness and the noise reduction algorithm through waveform normalization and translation processing, and thirdly eliminates abnormal signals caused by the detection system in the scanning process through isolated point elimination processing, thereby finally obtaining a defect image without noise. The method can be applied to high signal-to-noise ratio imaging of additive micro-defect laser ultrasonic detection, solves the problem of strong laser ultrasonic system noise caused by additive surface roughness, and realizes separation of micro-defects and strong background noise, thereby improving the detection probability and quantitative accuracy of the defects. The method can realize high signal-to-noise ratio imaging of the defects under the condition of not removing the surface roughness of the additive manufacturing piece, has important significance for subsequent intelligent automatic identification of the defects, and lays an important foundation for the application of a laser ultrasonic technology and the online detection of additive manufacturing.
The above embodiments are merely illustrative of the technical ideas and features of the present invention, and the purpose thereof is to enable those skilled in the art to understand the contents of the present invention and implement the present invention, and not to limit the protection scope of the present invention. All equivalent changes or modifications made according to the spirit of the present invention should be covered within the protection scope of the present invention.
Claims (8)
1. A laser ultrasonic high signal-to-noise ratio imaging method for manufacturing micro defects through metal additive manufacturing is characterized by comprising the following steps:
s1, performing two-dimensional grid scanning on the surface of the additive manufactured piece by using a laser ultrasonic system, completing acquisition of ultrasonic data, and obtaining three-dimensional matrix data A (M, N, t), wherein M is 1 … M, N is 1 … N, and t is 1 … tpWhere M is the number of scanning lines, N is the number of scanning columns, tqThe signal acquisition length;
s2, carrying out noise reduction processing on the acquired ultrasonic signals to obtain an ultrasonic data matrix A1(m, n, t);
s3, selecting a group of signals A1 (m) only with surface waves from the ultrasonic data matrix after noise reduction0,n0T) as a reference signal;
s4, carrying out similarity comparison on all the ultrasonic signals A1(m, n, t) subjected to noise reduction processing and reference signals to obtain a waveform similarity coefficient Nc (m, n);
s5, extracting a reference signal A1 (m)0,n0T) the time t0 corresponding to the maximum value of the surface wave amplitude, extracting the time t (m, n) corresponding to the maximum value of the surface wave amplitude of all the ultrasonic signals after noise reduction, and subtracting the two to obtain the time offset delta t (m, n) between all the ultrasonic signals and the reference signal, which is t (m, n) -t 0;
s6, sequentially judging all the ultrasonic signals A1(m, n, t) subjected to noise reduction, and when the waveform similarity coefficient is larger than a threshold Nc0 and the time offset delta t is smaller than a threshold delta t0, performing normalization processing and waveform offset processing on the signals to obtain a normalized amplitude value 1;
s7, extracting corresponding amplitude values of all signals at the time t0 from the processed signals to form a two-dimensional matrix A2(m, n);
s8, carrying out isolated point elimination processing on the two-dimensional matrix A2(m, n), and setting the value of a certain element to be 1 when the values of four adjacent elements in front of, behind, on the left of and on the right of the element in the two-dimensional matrix are equal and are normalized amplitude values of 1;
and S9, performing image drawing on the two-dimensional matrix processed in the S8 to obtain a defect image with a high signal-to-noise ratio.
2. The laser ultrasonic high signal-to-noise ratio imaging method of claim 1, wherein in S2, the denoising processing method may be one of wavelet denoising processing, hilbert yellow denoising processing, and deep learning self-coding denoising processing.
3. The laser ultrasonic high signal-to-noise ratio imaging method according to claim 1, wherein in S4, a waveform similarity coefficient Nc (m, n) is obtained using a waveform similarity function of the following formula:
in the above formula, s1(t) denotes a reference signal, s2(t) represents the corresponding ultrasonic signal a1(t) at the specific positions m and n after the noise reduction processing.
4. The laser ultrasonic high signal-to-noise ratio imaging method of claim 3, wherein in S6, the normalization processing method is: dividing the signal by the maximum amplitude value of the signal to obtain a normalized amplitude value 1; the normalized signal is:
5. the laser ultrasonic high signal-to-noise ratio imaging method according to claim 3, wherein in S6, the waveform shifting process is performed by shifting the signal by Δ t; the waveform offset processed signal is:
6. the laser ultrasonic high signal-to-noise ratio imaging method according to claim 3, wherein in S6, the threshold Nc0 is determined by: the reference signal and the ultrasonic signal of the non-defective region are subjected to similarity coefficient calculation one by one, and the minimum similarity coefficient thereof is taken as the threshold Nc 0. The determination method of the threshold value Δ t0 is as follows: the reference signal and the ultrasonic signal of the non-defective region are subjected to time offset calculation one by one, and the maximum time offset is taken as a threshold value delta t 0.
7. The laser ultrasonic high signal-to-noise ratio imaging method of claim 1, wherein in S1, the laser ultrasonic system comprises a pulse laser for ultrasonic signal excitation, a laser doppler vibrometer for ultrasonic signal reception, and the spot separation of the pulse laser and the laser doppler vibrometer is kept fixed during scanning.
8. The laser ultrasonic high signal-to-noise ratio imaging method of claim 1, wherein in S1, the scan grid size is not greater than the target detection accuracy.
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| CN112858183A (en) * | 2021-01-22 | 2021-05-28 | 西安增材制造国家研究院有限公司 | Additive manufacturing laser ultrasonic signal defect imaging method based on waveform separation |
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