Acoustic emission collision identification method and device for ultrasonic vibration assisted electric spark forming

Abstract

The invention provides an acoustic emission collision recognition method and device for ultrasonic vibration assisted electric spark forming, and the method comprises the steps: obtaining acoustic emission signal data when an electrode is in ultrasonic vibration for electric spark machining, and carrying out the discrete wavelet transformation of the acoustic emission signal data, and obtaining a filtered acoustic emission signal; determining the maximum amplitude in the filtered acoustic emission signal, the maximum coefficient value in the filtered acoustic emission signal, the frequency with the highest amplitude in the filtered acoustic emission signal and the corresponding amplitude value; according to the maximum amplitude, the maximum coefficient value, the frequency with the highest amplitude and the corresponding amplitude value, whether the electrode collides or not is judged, and if the electrode collides, machining parameters of the electrode are adjusted. The problem that whether the electrode collides with the cutter or not cannot be correctly recognized in an ultrasonic vibration state is solved through the difference of acoustic emission signals under the cutter collision condition and the cutter non-collision condition; the accuracy of collision recognition is improved by jointly judging three characteristics of the maximum amplitude, the maximum coefficient value, the frequency with the highest amplitude and the corresponding amplitude value.

Description

Technical Field

[0001] The present application relates to the field of micro-electro-spark forming processing, and in particular to an acoustic emission collision recognition method and device for ultrasonic vibration-assisted electro-spark forming. Background Art

[0002] The principle of sinker EDM is to create a spark discharge channel between the machining electrode and the workpiece, where the resulting discharge removes material through electro-erosion. EDM is a non-contact process suitable for machining high-strength, high-hardness, and high-brittleness materials.

[0003] Ultrasonic-assisted sinker EDM involves applying an up-and-down vibration to the tool electrode along the machining direction, subjecting it to ultrasonic vibrations before performing EDM. This process improves the inter-electrode discharge state during EDM, increasing material removal rates, reducing electrode loss, and improving machining accuracy, effectively boosting machining efficiency.

[0004] During ultrasonic-assisted EDM machining, there's a chance the tool electrode will collide with the workpiece. This collision can affect surface roughness and machining accuracy, making it a preventable condition. Traditional EDM methods detect tool collisions by measuring whether the circuit is short-circuited. If the electrode and workpiece collide, a short circuit occurs, and the servo should control the electrode to retreat. However, if the tool collision occurs during ultrasonic vibration, a short circuit cannot be used as a tool collision indicator, and the servo will continue to control the electrode to advance, affecting machining accuracy. Summary of the Invention

[0005] In view of the above problems, the present application is proposed to provide an acoustic emission collision identification method and device for ultrasonic vibration-assisted electrospark forming that overcomes the above problems or at least partially solves the above problems, including:

[0006] A method for acoustic emission collision identification in ultrasonic vibration-assisted electrospark forming, the method involving a workpiece to be machined, an electrode, and an acoustic emission sensor, wherein the workpiece to be machined and the electrode are arranged in a non-contact manner, the acoustic emission sensor is in close contact with the workpiece to be machined, and an acoustic emission coupling agent is coated between the acoustic emission sensor and the workpiece to be machined;

[0007] When the electrode is in ultrasonic vibration for electric spark machining, acquiring acoustic emission signal data during the machining process, and performing discrete wavelet transform processing on the acoustic emission signal data to obtain a filtered acoustic emission signal;

[0008] Determining the maximum amplitude in the filtered acoustic emission signal, the maximum coefficient value in the filtered acoustic emission signal, and the frequency with the highest amplitude in the filtered acoustic emission signal and the corresponding amplitude value;

[0009] Whether the electrodes collide is determined based on the maximum amplitude, the maximum coefficient value, the frequency with the highest amplitude, and the corresponding amplitude value; if the electrodes collide, the processing parameters of the electrodes are adjusted.

[0010] Furthermore, the step of performing discrete wavelet transform on the acoustic emission signal data to obtain a filtered acoustic emission signal includes:

[0011] Determining a valid acoustic emission signal segment in the acoustic emission signal data that is higher than a preset threshold;

[0012] The effective acoustic emission signal segment is decomposed step by step based on discrete wavelet transform to obtain the filtered acoustic emission signal below a preset frequency.

[0013] Furthermore, the step of decomposing the effective acoustic emission signal segment step by step based on discrete wavelet transform to obtain the filtered acoustic emission signal below the preset frequency includes:

[0014] Performing multi-level decomposition on the effective acoustic emission signal segment based on discrete wavelet transform, and obtaining corresponding high-pass coefficients and corresponding approximate coefficients at each level of decomposition;

[0015] Repeating the decomposition of the approximate coefficients of the previous level to the next level until the frequency component corresponding to the approximate coefficients of the final level is lower than the preset frequency;

[0016] The filtered acoustic emission signal is determined based on the approximation coefficients of the final stage.

[0017] Furthermore, the step of determining the maximum amplitude in the filtered acoustic emission signal, the maximum coefficient value in the filtered acoustic emission signal, the frequency with the highest amplitude in the filtered acoustic emission signal and the corresponding amplitude value includes:

[0018] determining a maximum amplitude in the filtered acoustic emission signal based on a maximum function; and

[0019] Determining the maximum coefficient value in the filtered acoustic emission signal based on continuous wavelet transform; and

[0020] The frequency with the highest amplitude and the corresponding amplitude value in the filtered acoustic emission signal are determined based on fast Fourier transform.

[0021] Furthermore, the step of determining the maximum coefficient value in the filtered acoustic emission signal based on continuous wavelet transform includes:

[0022] Analyzing the filtered acoustic emission signal based on continuous wavelet transform to obtain energy intensity distribution data of the filtered acoustic emission signal at different frequencies and different time points;

[0023] The maximum coefficient value is determined from the energy intensity distribution data.

[0024] Furthermore, the step of determining the frequency with the highest amplitude and the corresponding amplitude value in the filtered acoustic emission signal based on fast Fourier transform includes:

[0025] Performing a fast Fourier transform on the filtered acoustic emission signal to obtain a frequency domain amplitude spectrum;

[0026] The frequency with the highest amplitude and the corresponding amplitude value are determined according to the frequency domain amplitude spectrum.

[0027] Furthermore, the step of determining whether the electrodes collide based on the maximum amplitude, the maximum coefficient value, the frequency with the highest amplitude, and the corresponding amplitude value includes:

[0028] When the maximum amplitude is between 12-13.5, the maximum coefficient value is between 9.5-10.5, the frequency with the highest amplitude is between 203kHz-205kHz, and the frequency with the highest amplitude and the corresponding amplitude are between 1-1.2, it is determined that the electrodes have collided.

[0029] An acoustic emission collision identification device for ultrasonic vibration-assisted electrospark forming, the device involving a workpiece to be processed, an electrode, and an acoustic emission sensor, wherein the workpiece to be processed and the electrode are arranged in a non-contact manner, the acoustic emission sensor is in close contact with the workpiece to be processed, and an acoustic emission coupling agent is coated between the acoustic emission sensor and the workpiece to be processed, characterized in that the device comprises:

[0030] a signal acquisition and processing module, configured to obtain acoustic emission signal data during the electrospark machining process when the electrode is in ultrasonic vibration, and to perform discrete wavelet transform processing on the acoustic emission signal data to obtain a filtered acoustic emission signal;

[0031] a feature extraction module, configured to determine the maximum amplitude in the filtered acoustic emission signal, the maximum coefficient value in the filtered acoustic emission signal, and the frequency with the highest amplitude in the filtered acoustic emission signal and the corresponding amplitude value;

[0032] The judgment and adjustment module is used to judge whether the electrodes collide based on the maximum amplitude, the maximum coefficient value, the frequency with the highest amplitude and the corresponding amplitude value, and if the electrodes collide, adjust the processing parameters of the electrodes.

[0033] This application has the following advantages:

[0034] In an embodiment of the present application, in contrast to the technical problem in the prior art that "when the tool is colliding in the state of ultrasonic vibration, a short circuit cannot be used as a tool collision judgment", the present application provides a solution for using acoustic emission signals to judge whether the tool has collided. Specifically, when the electrode is in ultrasonic vibration for electric spark machining, the acoustic emission signal data during the machining process is obtained, and the acoustic emission signal data is processed by discrete wavelet transform to obtain a filtered acoustic emission signal; the maximum amplitude in the filtered acoustic emission signal, the maximum coefficient value in the filtered acoustic emission signal, and the frequency with the highest amplitude and the corresponding amplitude value in the filtered acoustic emission signal are determined; based on the maximum amplitude, the maximum coefficient value, the frequency with the highest amplitude and the corresponding amplitude value, it is judged whether the electrode has collided. If the electrode has collided, the machining parameters of the electrode are adjusted. The difference in the acoustic emission signal between the tool collision and non-tool collision solves the problem of not being able to correctly identify whether the electrode has collided in the ultrasonic vibration state; by jointly judging the three features of the maximum amplitude, the maximum coefficient value, the frequency with the highest amplitude and the corresponding amplitude value, the accuracy of collision recognition is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solution of the present application, the following is a brief introduction to the drawings required for the description of the present application. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0036] Figure 1 This is a schematic structural diagram of an acoustic emission collision recognition device provided in one embodiment of the present application;

[0037] Figure 2 is a side view of an acoustic emission collision identification device provided in one embodiment of the present application;

[0038] Figure 3 This is a flowchart of the steps of an acoustic emission collision identification method for ultrasonic vibration-assisted electrospark forming provided by one embodiment of the present application;

[0039] Figure 4 This is a flow chart of an acoustic emission collision identification method for ultrasonic vibration-assisted electrospark forming provided by one embodiment of the present application;

[0040] Figure 5 This is a structural block diagram of an acoustic emission collision recognition method and device for ultrasonic vibration-assisted electrospark forming provided in one embodiment of the present application.

[0041] The reference numerals in the drawings of the specification are as follows:

[0042] 1. Electrode; 2. Workpiece to be machined; 3. Vise; 4. Acoustic emission sensor; 5. Acoustic emission sensor fixture; 6. Electric spark pulse power supply. DETAILED DESCRIPTION

[0043] To make the objectives, features, and advantages of this application more readily apparent, the present application is further described below in conjunction with the accompanying drawings and specific embodiments. It is apparent that the embodiments described are only a portion of the embodiments of this application, not all of them. All other embodiments derived by persons of ordinary skill in the art based on the embodiments in this application without inventive effort are also within the scope of protection of this application.

[0044] By analyzing existing technologies, the inventors discovered that, under ultrasonic vibration, the frequency of the vibration is too high, so the circuit system's response is not a short circuit, making it impossible to use a short circuit as a criterion for determining a knife collision. To address this issue, the inventors introduced acoustic emission detection, using the difference in the acoustic emission signal between knife collision and non-knife collision conditions as a criterion for determining whether a knife collision has occurred.

[0045] It should be noted that, referring to Figure 1 and Figure 2 The present invention relates to a workpiece 2 to be machined, an electrode 1, and an acoustic emission sensor 4. The workpiece 2 is clamped in a vise 3. The acoustic emission sensor 4 is secured by an acoustic emission sensor 4 fixture and pressed against the workpiece 2. The acoustic emission sensor 4 and the workpiece 2 are in close contact, and an acoustic emission coupling agent is applied between them. The electrode 1 is mounted on a chuck, and an electric spark pulse power supply 6 and an ultrasonic power supply are turned on. The electrode 1 is controlled to slowly approach the workpiece 2 for machining.

[0046] Reference Figure 3 and Figure 4 , showing an acoustic emission collision identification method for ultrasonic vibration-assisted electrospark forming provided by an embodiment of the present application;

[0047] The method comprises:

[0048] S1. When the electrode is in ultrasonic vibration for electric spark machining, acquiring acoustic emission signal data during the machining process, and performing discrete wavelet transform processing on the acoustic emission signal data to obtain a filtered acoustic emission signal;

[0049] S2. Determine the maximum amplitude in the filtered acoustic emission signal, the maximum coefficient value in the filtered acoustic emission signal, and the frequency with the highest amplitude in the filtered acoustic emission signal and the corresponding amplitude value;

[0050] S3. Determine whether the electrodes collide based on the maximum amplitude, the maximum coefficient value, the frequency with the highest amplitude, and the corresponding amplitude value; if the electrodes collide, adjust the processing parameters of the electrodes.

[0051] In the embodiments of the present application, compared to the technical problem in the prior art that "when the blade is struck during ultrasonic vibration, a short circuit cannot be used as a tool collision judgment," the present application provides a solution for using acoustic emission signals to determine whether the blade has been struck. By distinguishing the acoustic emission signals between the blade and the blade, the problem of being unable to correctly identify whether the electrode has been struck during ultrasonic vibration is solved. By jointly judging the three characteristics of the maximum amplitude, the maximum coefficient value, and the frequency with the highest amplitude and the corresponding amplitude value, the accuracy of collision recognition is improved.

[0052] Next, an acoustic emission collision identification method for ultrasonic vibration-assisted electrospark forming in this exemplary embodiment will be further described.

[0053] As described in step S1, when the electrode is in ultrasonic vibration for electric spark machining, acoustic emission signal data during the machining process is acquired, and discrete wavelet transform processing is performed on the acoustic emission signal data to obtain a filtered acoustic emission signal.

[0054] It's important to note that in ultrasonic-assisted machining, when electrode discharge melts the workpiece, it changes the workpiece's material structure, generating an acoustic emission signal. This signal typically propagates through the workpiece as an elastic wave. This signal is transmitted via an acoustic couplant to an acoustic emission sensor mounted on the workpiece surface, allowing the sensor to collect the signal data.

[0055] In one embodiment of the present invention, the specific process of "performing discrete wavelet transform on the acoustic emission signal data to obtain a filtered acoustic emission signal" in step S1 can be further explained in combination with the following description.

[0056] S11 , determining a valid acoustic emission signal segment in the acoustic emission signal data that is higher than a preset threshold.

[0057] It should be noted that a preset threshold is set. When the signal voltage exceeds the preset threshold, the system starts recording until the signal falls back and is lower than the preset threshold, at which time the recording stops. The signal recorded during this period is a valid signal segment.

[0058] In a specific implementation, during the processing, acoustic emission signal data is collected and first divided into effective acoustic emission signal segments according to the acoustic emission signal threshold method. The threshold is about 0.1V, and the time period from the first time it exceeds the threshold to the subsequent time it falls below the threshold is an acoustic emission signal segment.

[0059] S12. Decompose the effective acoustic emission signal segment step by step based on discrete wavelet transform to obtain the filtered acoustic emission signal below a preset frequency.

[0060] It should be noted that the Discrete Wavelet Transform (DWT) is a mathematical tool widely used in signal processing and image processing. It is a discretized form of the wavelet transform. The DWT decomposes a signal into components of different scales and positions using a set of discrete wavelet basis functions, which helps analyze the characteristics of the signal at different frequencies and time resolutions.

[0061] In one embodiment of the present invention, the specific process of step S12 "decomposing the effective acoustic emission signal segment step by step based on discrete wavelet transform to obtain the filtered acoustic emission signal below the preset frequency" can be further explained in combination with the following description.

[0062] As described in the following steps, the effective acoustic emission signal segment is decomposed at multiple levels based on discrete wavelet transform, and each level of decomposition obtains a corresponding high-pass coefficient and a corresponding approximate coefficient; the approximate coefficient of the previous level is repeatedly decomposed at the next level until the frequency component corresponding to the approximate coefficient of the final level is lower than the preset frequency; and the filtered acoustic emission signal is determined based on the approximate coefficient of the final level.

[0063] As an example, discrete wavelet transform is used to extract the signal of the component below 1MHz frequency. Discrete wavelet transform decomposes the acoustic emission signal step by step, and each level decomposes a high-pass coefficient and an approximate coefficient (the approximate coefficient represents the low-frequency part of the signal, and the high-pass coefficient represents the high-frequency part of the signal). For example, the signal X is decomposed into a relative high-pass coefficient cD1 and an approximate coefficient cA1 after discrete wavelet transform, and then the approximate coefficient cA1 is decomposed into a relative high-pass coefficient cD2 and an approximate coefficient cA2, and further decomposed into cD3 and cA3.

[0064] X=cA3+cD3+cD2+cD1

[0065] After decomposition, the coefficients of cA3 are extracted and recombined into a new filtered acoustic emission signal.

[0066] As described in step S2, the maximum amplitude in the filtered acoustic emission signal, the maximum coefficient value in the filtered acoustic emission signal, and the frequency with the highest amplitude in the filtered acoustic emission signal and the corresponding amplitude value are determined.

[0067] In one embodiment of the present invention, the specific process of "determining the maximum amplitude in the filtered acoustic emission signal, the maximum coefficient value in the filtered acoustic emission signal, and the frequency with the highest amplitude in the filtered acoustic emission signal and the corresponding amplitude value" in step S2 can be further explained in combination with the following description.

[0068] As described in the following steps, the maximum amplitude in the filtered acoustic emission signal is determined based on the maximum function; the maximum coefficient value in the filtered acoustic emission signal is determined based on the continuous wavelet transform; and the frequency with the highest amplitude and the corresponding amplitude value in the filtered acoustic emission signal are determined based on the fast Fourier transform.

[0069] In one embodiment of the present invention, the specific process of “determining the maximum amplitude in the filtered acoustic emission signal based on the maximum function” may be further explained in combination with the following description.

[0070] It should be noted that the point with the loudest sound in the filtered acoustic emission signal is the maximum amplitude of the signal.

[0071] As an example, the amplitude of the filtered acoustic signal is obtained using a maximum function.

[0072] y=max(AE)

[0073] Where y is the amplitude and AE is the acoustic emission signal.

[0074] In one embodiment of the present invention, the specific process of “determining the maximum coefficient value in the filtered acoustic emission signal based on continuous wavelet transform” may be further explained in combination with the following description.

[0075] As described in the following steps, the filtered acoustic emission signal is analyzed based on continuous wavelet transform to obtain energy intensity distribution data of the filtered acoustic emission signal at different frequencies and different time points; and the maximum coefficient value is determined from the energy intensity distribution data.

[0076] It should be noted that the continuous wavelet transform (CWT) decomposes the signal into energy intensity distributions at different time points and frequencies, and finds the coefficient with the largest intensity, that is, the maximum coefficient value, in the energy intensity distribution.

[0077] As an example, the continuous wavelet transform is used to calculate the maximum coefficient value in the effective signal. The formula of the continuous wavelet transform is as follows:

[0078]

[0079] Where f(t) is the filtered acoustic emission signal; a is the scale parameter, a>0; b is the translation parameter.

[0080] After calculation using continuous wavelet transform, different components located in different frequency bands and time periods can be obtained, and then the maximum coefficient value can be obtained.

[0081] In one embodiment of the present invention, the specific process of “determining the frequency with the highest amplitude and the corresponding amplitude value in the filtered acoustic emission signal based on fast Fourier transform” can be further explained in combination with the following description.

[0082] As described in the following steps, the filtered acoustic emission signal is subjected to a fast Fourier transform to obtain a frequency domain amplitude spectrum; and the frequency with the highest amplitude and the corresponding amplitude value are determined based on the frequency domain amplitude spectrum.

[0083] Use FFT (Fast Fourier Transform) to process the filtered acoustic emission signal and extract the frequency with the highest amplitude and the corresponding amplitude value. Specifically, perform Fast Fourier Transform on the filtered signal to convert the time domain signal into frequency domain representation to obtain the complex spectrum. The formula is as follows:

[0084] X(f)=FFT(x(t))

[0085] Where x(t) is the time domain signal and X(f) is the frequency domain complex sequence.

[0086] Taking the absolute value (modulus) of the complex spectrum gives the magnitude (amplitude) of each frequency component:

[0087] A(f)=|X(f)|

[0088] The amplitude spectrum reflects the energy distribution of the signal at different frequencies.

[0089] Scan the amplitude spectrum A(f) and find the frequency point with the maximum amplitude fpeak and its corresponding amplitude Apeak:

[0090] Apeak=max(A(f)), fpeak=argmax(A(f))

[0091] Frequency resolution Δf = sampling rate / FFT points. Ensure that the resolution is sufficient to identify the target frequency band.

[0092] As described in step S3, whether the electrodes collide is determined based on the maximum amplitude, the maximum coefficient value, the frequency with the highest amplitude, and the corresponding amplitude value. If the electrodes collide, the processing parameters of the electrodes are adjusted.

[0093] In one embodiment of the present invention, the specific process of "determining whether the electrodes collide based on the maximum amplitude, the maximum coefficient value, the frequency with the highest amplitude and the corresponding amplitude value" in step S3 can be further explained in combination with the following description.

[0094] As described in the following steps, when the maximum amplitude is between 12-13.5, the maximum coefficient value is between 9.5-10.5, the frequency with the highest amplitude is between 203kHz-205kHz, and the frequency with the highest amplitude and the corresponding amplitude are between 1-1.2, it is determined that the electrodes have collided.

[0095] As an example, by plotting the above values into a statistical graph and analyzing and comparing them, we can find the characteristic combination that has the greatest difference between a collision event and a normal discharge. The specific characteristics of a collision are:

[0096] The frequency with the highest amplitude extracted by FFT is between 203kHz and 205kHz, and the corresponding amplitude value is between 1 and 1.2;

[0097] The amplitude of the filtered acoustic signal is between 12 and 13.5;

[0098] The maximum coefficient value of the continuous wavelet transform is between 9.5 and 10.5.

[0099] If the above characteristic indicators fall into the collision value range at the same time, it is determined to be a collision event.

[0100] In one embodiment of the present invention, the specific process of “adjusting the processing parameters of the electrodes if the electrodes collide” may be further explained in combination with the following description.

[0101] When the system detects a collision, it stops and retracts immediately to prevent further collisions. It also adjusts the feed rate to prevent another collision immediately after feeding. If it determines there is no tool collision, processing continues normally.

[0102] As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0103] Reference Figure 5 , shows an acoustic emission collision identification device for ultrasonic vibration-assisted electrospark forming provided by one embodiment of the present application; the device comprises a workpiece to be processed, an electrode, and an acoustic emission sensor, wherein the workpiece to be processed and the electrode are arranged in a non-contact manner, the acoustic emission sensor is in close contact with the workpiece to be processed, and an acoustic emission coupling agent is coated between the acoustic emission sensor and the workpiece to be processed;

[0104] Specifically include:

[0105] The signal acquisition and processing module 510 is used to obtain acoustic emission signal data during the electrospark machining process when the electrode is in ultrasonic vibration, and perform discrete wavelet transform processing on the acoustic emission signal data to obtain a filtered acoustic emission signal;

[0106] a feature extraction module 520 for determining the maximum amplitude in the filtered acoustic emission signal, the maximum coefficient value in the filtered acoustic emission signal, and the frequency with the highest amplitude in the filtered acoustic emission signal and the corresponding amplitude value;

[0107] The judgment and adjustment module 530 is used to judge whether the electrodes collide based on the maximum amplitude, the maximum coefficient value, the frequency with the highest amplitude and the corresponding amplitude value, and adjust the processing parameters of the electrodes if the electrodes collide.

[0108] In one embodiment of the present invention, the signal acquisition and processing module 510 includes:

[0109] A division submodule, configured to determine a valid acoustic emission signal segment in the acoustic emission signal data that is higher than a preset threshold;

[0110] The decomposition submodule is used to decompose the effective acoustic emission signal segment step by step based on discrete wavelet transform to obtain the filtered acoustic emission signal below a preset frequency.

[0111] In one embodiment of the present invention, the decomposition submodule includes:

[0112] A first decomposition unit is configured to perform multi-level decomposition on the effective acoustic emission signal segment based on discrete wavelet transform, and obtain corresponding high-pass coefficients and corresponding approximate coefficients at each level of decomposition;

[0113] a step-by-step decomposition unit, configured to repeatedly perform next-level decomposition on the approximate coefficients of the previous level until the frequency component corresponding to the approximate coefficients of the final level is lower than the preset frequency;

[0114] A combining unit is configured to determine the filtered acoustic emission signal according to the approximation coefficient of the final stage.

[0115] In one embodiment of the present invention, the feature extraction module 520 includes:

[0116] a maximum amplitude calculation submodule, configured to determine the maximum amplitude of the filtered acoustic emission signal based on a maximum value function; and

[0117] a maximum coefficient value calculation submodule, configured to determine the maximum coefficient value in the filtered acoustic emission signal based on continuous wavelet transform; and

[0118] The FFT analysis submodule is used to determine the frequency with the highest amplitude and the corresponding amplitude value in the filtered acoustic emission signal based on fast Fourier transform.

[0119] In one embodiment of the present invention, the maximum coefficient value calculation submodule includes:

[0120] a continuous wavelet transform unit, configured to analyze the filtered acoustic emission signal based on continuous wavelet transform to obtain energy intensity distribution data of the filtered acoustic emission signal at different frequencies and time points;

[0121] A coefficient determination unit is used to determine the maximum coefficient value from the energy intensity distribution data.

[0122] In one embodiment of the present invention, the FFT analysis submodule includes:

[0123] an amplitude spectrum generating unit, configured to perform a fast Fourier transform on the filtered acoustic emission signal to obtain a frequency domain amplitude spectrum;

[0124] The frequency and amplitude value generating unit is used to determine the frequency with the highest amplitude and the corresponding amplitude value according to the frequency domain amplitude spectrum.

[0125] In one embodiment of the present invention, the determination and adjustment module 530 includes:

[0126] The judgment and analysis submodule is used to determine that the electrodes have collided when the maximum amplitude is between 12-13.5, the maximum coefficient value is between 9.5-10.5, the frequency with the highest amplitude is between 203kHz-205kHz, and the frequency with the highest amplitude and the corresponding amplitude are between 1-1.2.

[0127] Although preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they become aware of the basic inventive concepts. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0128] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or terminal device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or terminal device that includes the element.

[0129] The above is a detailed introduction to the acoustic emission collision identification method and device for ultrasonic vibration-assisted electrospark forming provided by this application. This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core idea of this application; at the same time, for general technical personnel in this field, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for acoustic emission collision recognition in ultrasonic vibration-assisted electrospark forming, the method involving a workpiece to be machined, an electrode, and an acoustic emission sensor, wherein the workpiece to be machined and the electrode are arranged in a non-contact manner, the acoustic emission sensor is in close contact with the workpiece to be machined, and an acoustic emission coupling agent is coated between the acoustic emission sensor and the workpiece to be machined, characterized in that: The method comprises: When the electrode is in ultrasonic vibration for electric spark machining, acquiring acoustic emission signal data during the machining process, and performing discrete wavelet transform processing on the acoustic emission signal data to obtain a filtered acoustic emission signal; Determining the maximum amplitude in the filtered acoustic emission signal, the maximum coefficient value in the filtered acoustic emission signal, and the frequency with the highest amplitude in the filtered acoustic emission signal and the corresponding amplitude value; Whether the electrodes collide is determined based on the maximum amplitude, the maximum coefficient value, the frequency with the highest amplitude, and the corresponding amplitude value; if the electrodes collide, the processing parameters of the electrodes are adjusted.

2. The method according to claim 1, characterized in that The step of performing discrete wavelet transform processing on the acoustic emission signal data to obtain a filtered acoustic emission signal comprises: Determining a valid acoustic emission signal segment in the acoustic emission signal data that is higher than a preset threshold; The effective acoustic emission signal segment is decomposed step by step based on discrete wavelet transform to obtain the filtered acoustic emission signal below a preset frequency.

3. The method according to claim 2, characterized in that The step of decomposing the effective acoustic emission signal segment step by step based on discrete wavelet transform to obtain the filtered acoustic emission signal below a preset frequency includes: Performing multi-level decomposition on the effective acoustic emission signal segment based on discrete wavelet transform, and obtaining corresponding high-pass coefficients and corresponding approximate coefficients at each level of decomposition; Repeating the decomposition of the approximate coefficients of the previous level to the next level until the frequency component corresponding to the approximate coefficients of the final level is lower than the preset frequency; The filtered acoustic emission signal is determined based on the approximation coefficients of the final stage.

4. The method according to claim 1, wherein The step of determining the maximum amplitude in the filtered acoustic emission signal, the maximum coefficient value in the filtered acoustic emission signal, the frequency with the highest amplitude in the filtered acoustic emission signal and the corresponding amplitude value comprises: determining a maximum amplitude in the filtered acoustic emission signal based on a maximum function; and Determining the maximum coefficient value in the filtered acoustic emission signal based on continuous wavelet transform; and The frequency with the highest amplitude and the corresponding amplitude value in the filtered acoustic emission signal are determined based on fast Fourier transform.

5. The method according to claim 4, characterized in that The step of determining the maximum coefficient value in the filtered acoustic emission signal based on continuous wavelet transform comprises: Analyzing the filtered acoustic emission signal based on continuous wavelet transform to obtain energy intensity distribution data of the filtered acoustic emission signal at different frequencies and different time points; The maximum coefficient value is determined from the energy intensity distribution data.

6. The method according to claim 4, characterized in that The step of determining the frequency with the highest amplitude and the corresponding amplitude value in the filtered acoustic emission signal based on fast Fourier transform includes: Performing a fast Fourier transform on the filtered acoustic emission signal to obtain a frequency domain amplitude spectrum; The frequency with the highest amplitude and the corresponding amplitude value are determined according to the frequency domain amplitude spectrum.

7. The method according to claim 1, characterized in that The step of determining whether the electrodes collide based on the maximum amplitude, the maximum coefficient value, the frequency with the highest amplitude, and the corresponding amplitude value comprises: When the maximum amplitude is between 12-13.5, the maximum coefficient value is between 9.5-10.5, the frequency with the highest amplitude is between 203kHz-205kHz, and the frequency with the highest amplitude and the corresponding amplitude are between 1-1.2, it is determined that the electrodes have collided.

8. An acoustic emission collision recognition device for ultrasonic vibration-assisted electrospark forming, the device comprising a workpiece to be processed, an electrode, and an acoustic emission sensor, wherein the workpiece to be processed and the electrode are arranged in a non-contact manner, the acoustic emission sensor is in close contact with the workpiece to be processed, and an acoustic emission coupling agent is coated between the acoustic emission sensor and the workpiece to be processed, characterized in that: The device comprises: a signal acquisition and processing module, configured to obtain acoustic emission signal data during the electrospark machining process when the electrode is in ultrasonic vibration, and to perform discrete wavelet transform processing on the acoustic emission signal data to obtain a filtered acoustic emission signal; a feature extraction module, configured to determine the maximum amplitude in the filtered acoustic emission signal, the maximum coefficient value in the filtered acoustic emission signal, and the frequency with the highest amplitude in the filtered acoustic emission signal and the corresponding amplitude value; The judgment and adjustment module is used to judge whether the electrodes collide based on the maximum amplitude, the maximum coefficient value, the frequency with the highest amplitude and the corresponding amplitude value, and if the electrodes collide, adjust the processing parameters of the electrodes.

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