A method, device and apparatus for online monitoring of laser shock peening quality acoustic emission

Abstract

The application discloses a kind of laser shock peening quality acoustic emission online monitoring method, device and equipment, real-time acquisition laser shock peening acoustic emission downsampling signal and acoustic emission multimodal spectrum information;According to acoustic emission multimodal spectrum information, acoustic emission downsampling signal is carried out multimodal division, and the frequency domain range of each mode is obtained;According to the frequency domain range of each mode, acoustic emission downsampling signal is decomposed, and several decomposition sub-signals containing a mode information are selected from the decomposition result;Wavelet time-frequency analysis is carried out to each decomposition sub-signal containing a mode information, and the corresponding two-dimensional wavelet time-frequency diagram is obtained;According to the frequency domain range of each mode, instantaneous peak energy curve is extracted on the corresponding two-dimensional wavelet time-frequency diagram;The peak energy and peak time of instantaneous peak energy curve are extracted, and the quality of laser shock peening is characterized by peak energy and peak time.The application can stably and reliably monitor the quality of laser shock peening.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of laser shock peening, and particularly relates to a laser shock peening quality acoustic emission online monitoring method, device and equipment. BACKGROUND

[0002] Laser Shock Peening (LSP) is a new type of surface treatment process, which mainly generates high-pressure plasma shock waves on the surface of a material through high-energy laser, and the shock waves propagate to the interior of the material under the action of a constraint layer and an absorption layer, so that the surface layer of the material is plastically deformed to obtain a residual compressive stress layer with a certain depth and size, thereby improving the macroscopic performance of the material. At present, this technology has been widely used in the fields of aerospace, nuclear power and the like by processing and treating key core components to greatly improve the fatigue life of the components.

[0003] At present, in the existing laser shock peening acoustic emission monitoring method, Chinese Patent No. CN 113390963 A proposes a laser shock peening quality online monitoring method based on a time window energy attenuation coefficient. First, the original signal is windowed, and then the window signal energy is exponentially fitted, and finally the online detection of the laser shock effect is realized based on the fitting parameters. However, this method has obvious shortcomings. The extracted time window energy attenuation coefficient feature is a macroscopic statistical feature obtained based on the original signal. Experimental results show that this statistical feature is easily affected by the environment during processing, and has poor robustness and reliability, so it is urgent to define a new feature to improve the laser shock peening acoustic emission monitoring accuracy. SUMMARY

[0004] In view of the problems in the prior art, the application provides a laser shock peening quality acoustic emission online monitoring method, device and equipment, which can fully utilize the highly non-stationary characteristics of laser shock peening acoustic emission signals, combine a wavelet time-frequency analysis method, define an instantaneous peak value energy curve, extract peak value energy and peak value time features, and stably and reliably monitor the laser shock peening quality.

[0005] To solve the above technical problems, the application is implemented by the following technical scheme:

[0006] A laser shock peening quality acoustic emission online monitoring method, comprising:

[0007] real-time acquisition of a laser shock peening acoustic emission down-sampling signal and acoustic emission multi-modal spectrum information;

[0008] multi-modal division of the acoustic emission down-sampling signal according to the acoustic emission multi-modal spectrum information to obtain a frequency domain range of each mode;

[0009] decompose the acoustic emission down-sampling signal according to the frequency range of each modal, select a plurality of decomposition sub-signals containing one modal information from the decomposition result;

[0010] perform wavelet time-frequency analysis on each of the decomposition sub-signals containing one modal information to obtain a corresponding two-dimensional wavelet time-frequency diagram;

[0011] extract an instantaneous peak energy curve on the corresponding two-dimensional wavelet time-frequency diagram according to the frequency range of each modal;

[0012] extract the peak energy and the peak time of the instantaneous peak energy curve, and represent the laser shock peening quality by the peak energy and the peak time.

[0013] Further, the wavelet time-frequency analysis on each of the decomposition sub-signals containing one modal information to obtain a corresponding two-dimensional wavelet time-frequency diagram comprises:

[0014] perform wavelet time-frequency analysis on the decomposition sub-signals containing one modal information by using continuous wavelet transform to obtain a corresponding two-dimensional wavelet time-frequency diagram, and the continuous wavelet transform expression is:

[0015]

[0016] In the formula, W s is a wavelet coefficient matrix; a is a scale factor; τ is a time translation factor; s(t) is a decomposition sub-signal; Ψ(t) is a mother wavelet function; and Ψ * is a conjugate of the mother wavelet function.

[0017] Further, the extraction of the instantaneous peak energy curve on the corresponding two-dimensional wavelet time-frequency diagram according to the frequency range of each modal comprises:

[0018] determining upper and lower cutoff frequencies according to the frequency range of each modal;

[0019] extracting the instantaneous peak energy curve on the corresponding two-dimensional wavelet time-frequency diagram according to the upper and lower cutoff frequencies, and the extraction formula is:

[0020] IPEC(t)=max[|W s (f1:f2,t)|]

[0021] In the formula, [f1:f2] is the upper and lower cutoff frequencies of each modal; and |·| is a modulus operation.

[0022] Further, the extraction of the peak energy and the peak time of the instantaneous peak energy curve comprises:

[0023] [A,T]=max[IPEC(t)]

[0024] In the formula: A is peak energy; T is peak time; IPEC is instantaneous peak energy curve.

[0025] Further, the acoustic emission down-sampling signal is decomposed according to the frequency range of each mode, and a plurality of decomposition sub-signals containing one mode information are selected from the decomposition result.

[0026] Based on the frequency range of each mode, the acoustic emission down-sampling signal is decomposed by using a harmonic wavelet packet decomposition method, specifically:

[0027] The bandwidth of the harmonic wavelet packet decomposition is determined according to the frequency range of each mode;

[0028] The number of decomposition sub-signals containing at most one mode information is determined according to the bandwidth;

[0029] The acoustic emission down-sampling signal is decomposed by using a harmonic wavelet packet decomposition method according to the number of decomposition sub-signals containing at most one mode information, and a plurality of decomposition sub-signals containing one mode information are selected from the decomposition result.

[0030] Further, the acoustic emission down-sampling signal and the acoustic emission multi-modal frequency spectrum information of laser shock peening are obtained in real time, including:

[0031] The acoustic emission signal of laser shock peening is obtained in real time;

[0032] The acoustic emission signal is subjected to Fourier transform to obtain the acoustic emission multi-modal frequency spectrum information;

[0033] The acoustic emission signal is subjected to down-sampling processing according to the Shannon sampling theorem to obtain the acoustic emission down-sampling signal.

[0034] A laser shock peening quality acoustic emission online monitoring device, comprising:

[0035] An acquisition module is configured to obtain in real time an acoustic emission down-sampling signal and acoustic emission multi-modal frequency spectrum information of laser shock peening;

[0036] A division module is configured to divide the acoustic emission down-sampling signal according to the acoustic emission multi-modal frequency spectrum information to obtain a frequency range of each mode;

[0037] A decomposition module is configured to decompose the acoustic emission down-sampling signal according to the frequency range of each mode, and select a plurality of decomposition sub-signals containing one mode information from the decomposition result.

[0038] an analysis module, configured to perform wavelet time-frequency analysis on each of the decomposition sub-signals containing the modal information to obtain a corresponding two-dimensional wavelet time-frequency diagram;

[0039] a first extraction module, configured to extract an instantaneous peak value energy curve on the corresponding two-dimensional wavelet time-frequency diagram according to a frequency domain range of each of the modes;

[0040] a second extraction module, configured to extract average peak value energy and average peak value time of the instantaneous peak value energy curve, and to represent laser shock peening quality by the average peak value energy and the average peak value time.

[0041] An apparatus includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the laser shock peening quality acoustic emission online monitoring method when executing the computer program.

[0042] A computer readable storage medium stores a computer program, and the computer program implements the steps of the laser shock peening quality acoustic emission online monitoring method when executed by a processor.

[0043] Compared with the prior art, the present application has at least the following beneficial effects:

[0044] The present application provides a reliable and stable laser shock peening acoustic emission monitoring method, effectively solving the problems of instability and low precision of existing laser shock peening monitoring features and indicators.

[0045] On the one hand, the present application extracts the modal signal closely related to the processing quality by the modal decomposition method, and the features based on the single modal signal can more reliably represent the processing quality compared to the original signal; on the other hand, the present application analyzes the highly non-stationary single modal signal by the wavelet time-frequency analysis method, which can more clearly reveal the change rule of the signal, and the two time-frequency features defined and extracted, peak value energy and peak value time, more accurately reflect the quality change in the processing process, and have high reliability and robustness.

[0046] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the following preferred embodiments are described in detail below, and the accompanying drawings are described as follows. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the specific embodiments of the present invention, the drawings used in the description of the specific embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0048] Figure 1 This is a technical flowchart of an online monitoring method for acoustic emission of laser-strengthened materials according to the present invention;

[0049] Figure 2 This is a schematic diagram of the online monitoring system for acoustic emission of laser-strengthened quality in an embodiment of the present invention;

[0050] Figure 3 The figures shown are a diagram of the workpiece dimensions to be impacted, a schematic diagram of the sensor position, and a schematic diagram of the impact path in an embodiment of the present invention, wherein (a) is a front view and (b) is a side view;

[0051] Figure 4 The following are the spectrum diagrams of the acoustic emission signal before and after downsampling in an embodiment of the present invention, where (a) is the spectrum diagram before downsampling and (b) is the spectrum diagram after downsampling;

[0052] Figure 5 The above are the time-domain and frequency-domain waveforms of the first 8 decomposed sub-signals after harmonic wavelet packet decomposition in an embodiment of the present invention.

[0053] Figure 6 This is the wavelet time-frequency diagram of Imf4 in an embodiment of the present invention;

[0054] Figure 7 The peak energy curves of Imf4 at different laser energies in the embodiments of the present invention are shown.

[0055] Figure 8 The figure shows the relationship between peak energy A and peak time T with different laser energies in an embodiment of the present invention, where (a) is the characteristic of peak energy A and (b) is the characteristic of peak time T.

[0056] In the diagram: 1-Industrial control computer, 2-Data acquisition board, 3-Preamplifier, 4-Acoustic emission sensor, 5-Laser main control console, 6-Robotic arm, 7-Laser generator, 8-Workpiece to be impacted, 9-Absorption protective layer, 10-Constraint layer. Detailed Implementation

[0057] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions of the present application will be described clearly and completely below with reference to the drawings. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0058] As a specific embodiment of the present application, as shown in Figure 1 a laser shock peening quality acoustic emission online monitoring method, specifically comprising the following steps:

[0059] S1, real-time acquisition of laser shock peening acoustic emission down-sampling signal and acoustic emission multi-modal spectrum information.

[0060] In the present embodiment, the laser shock peening acoustic emission down-sampling signal is acquired, the data length is reduced, and the data processing speed is improved. The acoustic emission multi-modal spectrum information is acquired, which can intuitively reflect the modal components contained in the laser shock peening acoustic emission signal, and can also determine the maximum frequency component of the laser shock peening acoustic emission signal.

[0061] Preferably, the acquisition method of the laser shock peening acoustic emission down-sampling signal and the acoustic emission multi-modal spectrum information is as follows:

[0062] real-time acquisition of laser shock peening acoustic emission signal;

[0063] Fourier transform is performed on the acoustic emission signal to obtain the acoustic emission multi-modal spectrum information;

[0064] According to the Shannon sampling theorem, the acoustic emission signal is down-sampled to obtain the acoustic emission down-sampling signal.

[0065] The present application selects a relatively mature acoustic emission detection technology to realize laser shock peening quality online monitoring,

[0066] combined with Figure 2As shown, the embodiment adopts an acoustic emission acquisition system to acquire the acoustic emission signal of laser shock peening in real time. In the embodiment, the acoustic emission acquisition system is sequentially connected in order by an acoustic emission sensor 4, a preamplifier 3, a data acquisition card 2 and an industrial computer 1. The preamplifier 3 completes amplification, analog-to-digital conversion and noise reduction of the signal. The data acquisition card 2 completes data acquisition. The industrial computer 1 realizes data storage and analysis. In the embodiment, the selected acoustic emission sensor 4 is an RS-54A sensor, which is a wideband sensor and can accept more information. The RS-54A type does not have a resonance frequency, and the response bandwidth of the sensor is wider, ranging from 100 Hz to 900 kHz, so that more abundant information in the LSP processing process can be acquired. The sensitivity of the RS-54A type is 70 dB ± 5 dB. The gain of the preamplifier 3 is 40 dB, and the sampling rate of the data acquisition board card 2 is 3 MHz. The acoustic emission sensor 4 is used to capture the acoustic emission source signal generated in the impact process in real time. The acoustic emission sensor 4 is 50 mm away from the boundary of the laser impact area. Coupling agent is used to ensure that the acoustic emission sensor 4 is closely attached to the workpiece 8 to be impacted. A C-shaped clamp is used to fix the sensor to ensure that it does not shake during processing. The acoustic emission signal can be collected in real time, and noise interference can be reduced to a certain extent.

[0067] S2, performing multi-modal division on the acoustic emission down-sampling signal according to the acoustic emission multi-modal frequency spectrum information, to obtain a frequency domain range of each mode.

[0068] S3, decomposing the acoustic emission down-sampling signal according to the frequency domain range of each mode, and selecting a plurality of decomposition sub-signals containing one mode information from a decomposition result.

[0069] Preferably, the acoustic emission down-sampling signal is decomposed by a harmonic wavelet packet decomposition method based on the frequency domain range of each mode, and the specific method is as follows:

[0070] The bandwidth of the harmonic wavelet packet decomposition is determined according to the frequency domain range of each mode.

[0071] The number of decomposition sub-signals containing at most one mode information is determined according to the bandwidth, and the calculation formula is as follows:

[0072] j=f s / 2B

[0073] In the formula, j is the number of decomposition sub-signals; f s is the sampling frequency of the acoustic emission down-sampling signal; and B is the bandwidth of the harmonic wavelet packet decomposition.

[0074] According to the number of the decomposition sub-signals containing at most one modal information, the acoustic emission down-sampling signal is subjected to harmonic wavelet packet decomposition, and several decomposition sub-signals containing one modal information are selected from the decomposition result.

[0075] In the embodiment, the acoustic emission down-sampling signal is decomposed by using the harmonic wavelet packet decomposition method, so that the decomposition sub-signals have strict box spectrum, and the method is particularly suitable for decomposing multi-modal signals.

[0076] In the embodiment, the harmonic wavelet packet decomposition method selects a harmonic wavelet as a base function, and the frequency domain expression of the harmonic wavelet is as follows:

[0077]

[0078] The time domain expression of the harmonic wavelet is as follows:

[0079]

[0080] In the formula, W m，n is the frequency domain expression; ω m，n is the time domain expression; m and n are scale parameters; ω is frequency; and x is time.

[0081] S4, wavelet time-frequency analysis is performed on each of the decomposition sub-signals containing one modal information, so as to obtain a corresponding two-dimensional wavelet time-frequency diagram.

[0082] Preferably, in the embodiment, continuous wavelet transform is used to perform wavelet time-frequency analysis on the decomposition sub-signals containing one modal information, so as to obtain a corresponding two-dimensional wavelet time-frequency diagram, and the expression of the continuous wavelet transform is as follows:

[0083]

[0084] In the formula, W s is a wavelet coefficient matrix; a is a scale factor; τ is a time translation factor; s(t) is a time sequence; Ψ(t) is a mother wavelet function; and Ψ * is a conjugate of the mother wavelet function.

[0085] S5, according to the frequency domain range of each modal, an instantaneous peak energy curve is extracted on the corresponding two-dimensional wavelet time-frequency diagram.

[0086] Preferably, in the embodiment, upper and lower cutoff frequencies are determined according to the frequency domain range of each modal, and an instantaneous peak energy curve is extracted on the corresponding two-dimensional wavelet time-frequency diagram according to the upper and lower cutoff frequencies, and the extraction formula is as follows:

[0087] IPEC(t) = max[|W s (f1: f2, t) |]

[0088] wherein: [f1: f2] is the upper and lower cutoff frequency of each mode; |·| is the modulo operation.

[0089] S6, extracting the peak energy and peak time of the instantaneous peak energy curve, and representing the laser shock peening quality through the peak energy and the peak time.

[0090] Preferably, in the embodiment, the average peak energy and the average peak time of the instantaneous peak energy curve are extracted by the following formula:

[0091] [A, T] = max [IPEC(t)]

[0092] wherein: A is the peak energy; T is the peak time; and IPEC is the instantaneous peak energy curve.

[0093] The present application utilizes the multi-mode and highly non-stationary characteristics of the acoustic emission signal of LSP, and realizes accurate separation of a single mode by using mode decomposition and time-frequency analysis methods in succession, further extracts the instantaneous peak energy curve of the mode signal and extracts the features, and has strong robustness, stability and reliability, and high engineering practicability.

[0094] The embodiment provides a laser shock peening quality acoustic emission online monitoring device for realizing the monitoring method, and comprises:

[0095] An acquisition module is configured to acquire an acoustic emission down-sampling signal and acoustic emission multi-mode spectrum information of laser shock peening in real time.

[0096] A division module is configured to divide the acoustic emission down-sampling signal according to the acoustic emission multi-mode spectrum information to obtain a frequency domain range of each mode.

[0097] A decomposition module is configured to decompose the acoustic emission down-sampling signal according to the frequency domain range of each mode, and select a plurality of decomposition sub-signals containing one mode information from a decomposition result.

[0098] An analysis module is configured to perform wavelet time-frequency analysis on each of the decomposition sub-signals containing one mode information to obtain a corresponding two-dimensional wavelet time-frequency diagram.

[0099] A first extraction module is configured to extract an instantaneous peak energy curve from the corresponding two-dimensional wavelet time-frequency diagram according to the frequency domain range of each mode.

[0100] A second extraction module is configured to extract the average peak energy and the average peak time of the instantaneous peak energy curve, and represent the laser shock peening quality through the average peak energy and the average peak time.

[0101] A specific embodiment is provided below to explain the laser shock peening quality acoustic emission online monitoring method more clearly, as follows:

[0102] As shown in Figure 2 , the laser shock peening acoustic emission online monitoring schematic diagram in the embodiment mainly includes an industrial computer 1, a data acquisition board card 2, a preamplifier 3, an acoustic emission sensor 4, a laser main console 5, a mechanical arm 6, a laser generator 7, a workpiece to be impacted 8, an absorbing protective layer 9 and a constraint layer 10. The industrial computer 1, the data acquisition board card 2, the preamplifier 3 and the acoustic emission sensor 4 are sequentially connected to constitute an acoustic emission signal acquisition system, and the laser main console 5, the mechanical arm 6 and the laser generator 7 constitute a laser shock peening system. The acoustic emission acquisition system captures the acoustic emission signals inside the material in real time during the laser impact process.

[0103] The experimental parameters in the example mainly include: laser energy 2J, 3J, 4J, single impact, spot diameter 3mm, repetition frequency 1Hz, absorbing protective layer selects black tape, constraint layer selects transparent water constraint, laser impact path is a “one” type impact path, 25 points are impacted on each path, and the overlap rate is 50%. In data analysis, 25 impact points can be analyzed as independent samples. In the acoustic emission signal acquisition system, the preamplifier gain is 40dB, and the sampling frequency is 3MHz. The workpiece size is 250mm*60mm*4mm, and the sensor distance from the impact area boundary is 50mm, as shown in Figure 3 , which is a workpiece size diagram, a sensor position schematic diagram and an impact path schematic diagram.

[0104] In the example, as shown in Figure 2 and Figure 3 , the laser shock peening quality acoustic emission online monitoring system is correctly built. Based on the experimental equipment and experimental parameters, the acoustic emission signals in the laser shock peening process are acquired in real time, and the acoustic emission signals are subjected to Fourier transform to obtain the acoustic emission multi-modal frequency spectrum information, as shown in Figure 4 (a). From the acoustic emission multi-modal frequency spectrum information, it can be known that the maximum frequency component of the acoustic emission signal is 500kHz, which has obvious multi-modal characteristics, and there are multiple modes inside, which are 31kHz, 70kHz, 115kHz, 164kHz and 217kHz in turn. Further, the acoustic emission signal is subjected to 3 times down-sampling processing according to the Shannon sampling theorem, to obtain the acoustic emission down-sampling signal. At this time, the sampling frequency f s of the acoustic emission down-sampling signal is 1MHz, and the frequency spectrum is shown in Figure 4 (b).

[0105] In the embodiment, the acoustic emission multi-modal frequency spectrum information is used to divide the acoustic emission down-sampling signal into multiple modes, and the number of modes is preliminarily determined to be 5, and the frequency range of each mode is obtained in sequence, and the upper and lower cutoff frequencies [f1:f2] of each mode are determined. According to the frequency range of each mode, the bandwidth B of the harmonic wavelet packet decomposition is determined to be 10 kHz, and according to the sampling rate f s =1MHz, the number j of the decomposition sub-signals containing at most one mode information is determined to be 50, and the acoustic emission down-sampling signal is subjected to harmonic wavelet packet decomposition to obtain 50 decomposition sub-signals. As shown in Table 1, the frequency range of the first 8 decomposition sub-signals after the harmonic wavelet packet decomposition is shown. Figure 5 As shown in Table 1, the frequency range of the first 8 decomposition sub-signals after the harmonic wavelet packet decomposition is shown.

[0106] Table 1 Frequency range of the first 8 decomposition sub-signals after the harmonic wavelet packet decomposition

[0107]

[0108]

[0109] In the embodiment, in order to avoid repetition of discussion, only one decomposition sub-signal containing one mode information is selected from the 50 decomposition sub-signals. In the embodiment, the Imf4 decomposition sub-signal containing the 31 kHz mode is finally selected as an example for subsequent data analysis and processing. The experimental analysis results and conclusions are embodied in the remaining several decomposition sub-signals containing one mode information.

[0110] In the embodiment, the wavelet time-frequency analysis is performed on the highly non-stationary Imf4 decomposition signal, the Morlet wavelet basis function is selected for continuous wavelet transform, and the two-dimensional wavelet time-frequency diagram of the Imf4 decomposition sub-signal is obtained. As shown in Figure 6 The wavelet time-frequency diagram of Imf4 is shown. According to the upper and lower cutoff frequencies of the 31 kHz mode contained in the Imf4 decomposition sub-signal, the instantaneous peak energy curve (IPEC) is extracted on the basis of the two-dimensional wavelet time-frequency diagram, and the Imf4 peak energy curves of different laser energies are shown in Figure 7 It can be seen from Figure 7 that the IPEC curves of different laser energies have certain differences, mainly reflected in the differences between the peak values and the peak times.

[0111] In the embodiment, the peak energy A and the peak time T features are defined and extracted on the basis of the instantaneous peak energy curve. To ensure the universality of the experimental results, 25 impact points on the impact path are extracted for statistical analysis. Table 2 shows the average peak energy A and the average peak time T of the 25 impact points of the Imf4 sub-signal at different laser energies. Figure 8 As shown in FIG. 6, the peak energy A and the peak time T are linearly increased with the increase of the laser energy.

[0112] Table 2 Average peak energy A and average peak time T of Imf4 sub-signal at different laser energies

[0113]

[0114] In the embodiment, the peak energy A and the peak time T are used to represent the quality of the laser shock peening. As shown in FIG. 7, the box plot of the peak energy A and the peak time T at different laser energies is shown. It can be seen from the figure that the extracted peak energy A and the peak time T features are also linearly increased with the increase of the laser energy. Figure 8

[0115] In the above experiments and embodiments, the multi-modal and highly non-stationary acoustic emission signals generated in the laser shock processing are fully analyzed. The modal decomposition method and the wavelet time-frequency analysis method are used in succession, and finally the peak energy A and the peak time T two time-frequency features are extracted to realize the online monitoring of the quality of the laser shock peening. The monitoring method has reliable and stable monitoring function, and the monitoring index features are simple to calculate, have clear physical meaning, strong robustness and other characteristics, which provides an effective technical means for realizing the online monitoring of the laser shock peening effect.

[0116] ​The application provides a computer device in an embodiment, the computer device includes a processor and a memory, the memory is used for storing a computer program, the computer program includes program instructions, and the processor is used for executing the program instructions stored by the computer storage medium. The processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components and the like, which are the computing core and control core of the terminal, and are suitable for implementing one or more instructions, and are specifically suitable for loading and executing one or more instructions to implement a corresponding method flow or corresponding function; the processor in the embodiment of the application can be used to implement the operation of the laser shock peening quality acoustic emission online monitoring method.

[0117] The laser shock peening quality acoustic emission online monitoring method in the embodiment of the application can be stored in a computer readable storage medium if it is implemented in the form of a software function unit and sold or used as an independent product. Based on such understanding, all or part of the flows in the above-mentioned embodiment methods can also be completed by instructing related hardware through a computer program, and the computer program can be stored in a computer readable storage medium. The computer program can implement the steps of each method embodiment when the processor executes the computer program. The computer program includes computer program codes, and the computer program codes can be in the form of source code, object code, executable files or some intermediate forms and the like. The computer readable storage medium includes permanent and non-permanent, removable and non-removable media, and can be implemented by any method or technology to store information. The information can be computer readable instructions, data structures, program modules or other data.

[0118] The computer storage medium can be any available medium or data storage device that can be accessed by a computer, including but not limited to magnetic storage (such as a floppy disk, a hard disk, a magnetic tape, a magneto-optical disk (MO) and the like), an optical storage (such as a CD, a DVD, a BD, a HVD and the like), and a semiconductor storage (such as a ROM, an EPROM, an EEPROM, a non-volatile memory (NAND FLASH), a solid state disk (SSD)) and the like.

[0119] Those skilled in the art will appreciate that embodiments of the application can be readily used as a method, a system or a computer program product. Accordingly, the application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the application can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) embodying computer-readable program code.

[0120] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 means for functionally implementing the steps listed in the flowchart block or blocks.

[0121] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 means for functionally implementing the steps listed in the flowchart block or blocks.

[0122] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 means for functionally implementing the steps listed in the flowchart block or blocks.

[0123] Finally, it should be noted that the above-described embodiments are merely specific embodiments of the present application, which are used to illustrate the technical solutions of the present application, but not to limit the same. The protection scope of the present application is not limited thereto. Although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those skilled in the art that any person skilled in the art can still modify or easily think of changes to the technical solutions recorded in the foregoing embodiments, or make equivalent replacements to some of the technical features, within the technical scope disclosed by the present application. The modifications, changes or replacements do not cause the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for online monitoring of laser shock peening quality acoustic emission, characterized in that, The method comprises the following steps: real-time acquisition of acoustic emission down-sampling signals and acoustic emission multi-modal spectrum information of laser shock peening; multi-modal division of the acoustic emission down-sampling signals according to the acoustic emission multi-modal spectrum information, to obtain a frequency domain range of each mode; decomposition of the acoustic emission down-sampling signals according to the frequency domain range of each mode, and selection of a plurality of decomposition sub-signals containing one mode information from a decomposition result, specifically: based on the frequency domain range of each mode, the acoustic emission down-sampling signals are decomposed by using a harmonic wavelet packet decomposition method, the bandwidth of the harmonic wavelet packet decomposition is determined according to the frequency domain range of each mode, the number of decomposition sub-signals containing at most one mode information is determined according to the bandwidth, the acoustic emission down-sampling signals are decomposed by using the harmonic wavelet packet decomposition method according to the number of decomposition sub-signals containing at most one mode information, and a plurality of decomposition sub-signals containing one mode information are selected from the decomposition result; wavelet time-frequency analysis of each of the decomposition sub-signals containing one mode information, to obtain a corresponding two-dimensional wavelet time-frequency graph, specifically: the continuous wavelet transform is used to perform wavelet time-frequency analysis on the decomposition sub-signals containing one mode information, to obtain a corresponding two-dimensional wavelet time-frequency graph, and the continuous wavelet transform expression is: wherein: is a wavelet coefficient matrix; is a scale factor; is a time translation factor; is a decomposition sub-signal; is a mother wavelet function; is a conjugate of the mother wavelet function; extraction of an instantaneous peak value energy curve on the corresponding two-dimensional wavelet time-frequency graph according to the frequency domain range of each mode, specifically: upper and lower cutoff frequencies are determined according to the frequency domain range of each mode; and the instantaneous peak value energy curve is extracted on the corresponding two-dimensional wavelet time-frequency graph according to the upper and lower cutoff frequencies, and the extraction formula is: In the formulae: is the upper and lower cut-off frequency for each modality; is a modulo operation; extraction of peak value energy and peak value time of the instantaneous peak value energy curve, and representation of laser shock peening quality by the peak value energy and the peak value time.

2. A method for online monitoring of laser shock peening quality acoustic emission according to claim 1, characterized in that, The extraction formula of the peak value energy and the peak value time of the instantaneous peak value energy curve is: wherein: is the peak energy; is the peak time; is the instantaneous peak energy curve.

3. A method for online monitoring of laser shock peening quality acoustic emission according to claim 1, characterized in that, The real-time acquisition of acoustic emission down-sampling signals and acoustic emission multi-modal spectrum information of laser shock peening comprises the following steps: real-time acquisition of acoustic emission signals of laser shock peening; Fourier transform of the acoustic emission signals, to obtain the acoustic emission multi-modal spectrum information; de-sampling processing of the acoustic emission signals according to the Shannon sampling theorem, to obtain the acoustic emission down-sampling signals.

4. A laser shock peening quality acoustic emission online monitoring device, characterized in that, A monitoring device for implementing the laser shock peening quality acoustic emission online monitoring method of any one of claims 1 to 3, comprising: an acquisition module configured to acquire acoustic emission down-sampling signals and acoustic emission multi-modal spectrum information of laser shock peening in real time; a division module configured to perform multi-modal division of the acoustic emission down-sampling signals according to the acoustic emission multi-modal spectrum information, to obtain a frequency domain range of each mode; a decomposition module configured to decompose the acoustic emission down-sampling signals according to the frequency domain range of each mode, and select a plurality of decomposition sub-signals containing one mode information from a decomposition result; an analysis module configured to perform wavelet time-frequency analysis of each of the decomposition sub-signals containing one mode information, to obtain a corresponding two-dimensional wavelet time-frequency graph; a first extraction module configured to extract an instantaneous peak energy curve on the corresponding two-dimensional wavelet time-frequency map according to a frequency domain range of each modality; a second extraction module configured to extract an average peak energy and an average peak time of the instantaneous peak energy curve, and to represent a laser shock peening quality by the average peak energy and the average peak time.

5. An apparatus comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein, The processor executes the computer program to implement the steps of the laser shock peening quality acoustic emission online monitoring method according to any one of claims 1 to 3.

6. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 5. The computer program is executed by the processor to implement the steps of the laser shock peening quality acoustic emission online monitoring method according to any one of claims 1 to 3.

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