An ultrasonic vibration assisted electric spark forming acoustic emission collision identification method and device

Abstract

The application provides an acoustic emission collision identification method and device for ultrasonic vibration assisted electric spark forming. When an electrode is in ultrasonic vibration for electric spark machining, acoustic emission signal data is acquired, and discrete wavelet transform processing is performed on the acoustic emission signal data to obtain filtered acoustic emission signals. The maximum amplitude in the filtered acoustic emission signals, the maximum coefficient value in the filtered acoustic emission signals, and the frequency with the highest amplitude and the corresponding amplitude value in the filtered acoustic emission signals are determined. Whether the electrode collides is judged according to the maximum amplitude, the maximum coefficient value, the frequency with the highest amplitude and the corresponding amplitude value. If the electrode collides, the machining parameters of the electrode are adjusted. By using the difference between the acoustic emission signals in the case of collision and the case of no collision, the problem that the electrode collision cannot be correctly identified in the ultrasonic vibration state is solved. By jointly judging the maximum amplitude, the maximum coefficient value, and the frequency with the highest amplitude and the corresponding amplitude value, the accuracy of collision identification is improved.

Description

Technical Field

[0001] This application relates to the field of micro-electro-discharge forming, and in particular to a method and apparatus for acoustic emission collision identification in ultrasonic vibration-assisted electrical discharge forming. Background Technology

[0002] The principle of electrical discharge machining (EDM) is to create a spark discharge channel between the machining electrode and the workpiece, and the electrolytic erosion generated by the discharge removes the material. EDM is a non-contact machining process, suitable for machining high-strength, high-hardness, and high-brittle materials.

[0003] Ultrasonic-assisted electrical discharge machining (EDM) involves adding a vertical vibration motion along the machining direction to the tool electrode, placing the tool electrode under ultrasonic vibration before performing electrical discharge machining. Ultrasonic-assisted EDM can improve the inter-electrode discharge state during EDM, increase material removal rate, reduce electrode wear, improve machining accuracy, and effectively enhance machining efficiency.

[0004] During ultrasonic-assisted electrical discharge machining (EDM), there is a chance that the tool electrode will collide with the workpiece. This collision can affect the surface roughness and machining accuracy, making it a situation that needs to be prevented. Traditional EDM collision detection methods measure whether the circuit is short-circuited. If the electrode and workpiece come into contact, the circuit is short-circuited, and the servo should control the electrode to retract. However, if the collision occurs under ultrasonic vibration, a short circuit cannot be used as a detection method, and the servo will continue to control the electrode to advance, thus affecting machining accuracy. Summary of the Invention

[0005] In view of the aforementioned problems, this application is proposed to provide an acoustic emission collision identification method and apparatus for ultrasonic vibration-assisted electrical discharge forming that overcomes or at least partially solves the aforementioned problems, comprising:

[0006] An acoustic emission collision identification method for ultrasonic vibration-assisted electrical discharge forming, the method involves a workpiece to be processed, an electrode, and an acoustic emission sensor, wherein the workpiece to be processed is disposed in a non-contact manner with the electrode, 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.

[0007] When the electrode is subjected to ultrasonic vibration for electrical discharge machining, acoustic emission signal data during the machining process is acquired, and the acoustic emission signal data is processed by discrete wavelet transform to obtain a filtered acoustic emission signal.

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

[0009] Based on the maximum amplitude value, the maximum coefficient value, the highest frequency of the amplitude, and the corresponding amplitude value, it is determined whether the electrode has collided. If the electrode has collided, the processing parameters of the electrode are adjusted.

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

[0011] Identify the valid acoustic emission signal segments in the acoustic emission signal data that are 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 the 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 a preset frequency includes:

[0014] The effective acoustic emission signal segment is decomposed into multiple levels based on discrete wavelet transform, and each level of decomposition yields the corresponding high-pass coefficient and the corresponding approximation coefficient.

[0015] Repeat the process of decomposing the approximation coefficients of the previous level into the next level until the frequency component corresponding to the final level approximation coefficient is lower than the preset frequency.

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

[0017] Further, the steps of determining the maximum amplitude value, the maximum coefficient value, the frequency with the highest amplitude, and the corresponding amplitude value in the filtered acoustic emission signal include:

[0018] The maximum amplitude value in the filtered acoustic emission signal is determined based on the maximum value function; and,

[0019] The maximum coefficient value in the filtered acoustic emission signal was determined based on continuous wavelet transform; and,

[0020] The frequency with the highest amplitude and the corresponding amplitude value in the filtered acoustic emission signal were determined based on the 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] The filtered acoustic emission signal is analyzed based on continuous wavelet transform to obtain the 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 the Fast Fourier Transform includes:

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

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

[0027] Furthermore, the step of determining whether the electrode has collided based on the maximum amplitude value, the maximum coefficient value, the highest frequency of the amplitude, and the corresponding amplitude value includes:

[0028] When the maximum amplitude is between 12 and 13.5, the maximum coefficient is between 9.5 and 10.5, the highest frequency of amplitude is between 203 kHz and 205 kHz, and the highest frequency of amplitude and the corresponding amplitude are between 1 and 1.2, it is determined that the electrode has collided.

[0029] An acoustic emission collision identification device for ultrasonic vibration-assisted electrical discharge forming, the device comprising a workpiece to be processed, an electrode, and an acoustic emission sensor, wherein the workpiece to be processed is disposed in non-contact with the electrode, 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] The signal acquisition and processing module is used to acquire acoustic emission signal data during the electrical discharge machining process when the electrode is subjected to ultrasonic vibration, and to perform discrete wavelet transform processing on the acoustic emission signal data to obtain a filtered acoustic emission signal.

[0031] The feature extraction module is used to determine the maximum amplitude value, the maximum coefficient value, and the frequency with the highest amplitude and its corresponding amplitude value in the filtered acoustic emission signal.

[0032] The judgment and adjustment module is used to determine whether the electrode has collided based on the maximum amplitude value, the maximum coefficient value, the highest frequency of the amplitude, and the corresponding amplitude value. If the electrode has collided, the processing parameters of the electrode are adjusted.

[0033] This application has the following advantages:

[0034] In the embodiments of this application, in contrast to the technical problem in the prior art that "when the electrode is in a state of ultrasonic vibration and collision occurs, a short circuit cannot be used as a method for determining whether a collision has occurred," this application provides a solution for determining whether a collision has occurred using acoustic emission signals. Specifically, when the electrode is in a state of ultrasonic vibration during electrical discharge machining, acoustic emission signal data during the machining process is acquired, and the acoustic emission signal data is processed by discrete wavelet transform to obtain a filtered acoustic emission signal. The maximum amplitude value, the maximum coefficient value, and the frequency with the highest amplitude and its corresponding amplitude value in the filtered acoustic emission signal are determined. Based on the maximum amplitude value, the maximum coefficient value, the frequency with the highest amplitude, and its corresponding amplitude value, it is determined whether the electrode has collided. If the electrode has collided, the machining parameters of the electrode are adjusted. By using the difference in acoustic emission signals under collision and non-collision conditions, the problem of not being able to correctly identify whether the electrode has collided under ultrasonic vibration conditions is solved. By jointly judging the three features of the maximum amplitude value, the maximum coefficient value, and the frequency with the highest amplitude and its corresponding amplitude value, the accuracy of collision identification is improved. Attached Figure Description

[0035] To more clearly illustrate the technical solution of this application, the drawings used in the description of this application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

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

[0037] Figure 2 This is a side view of an acoustic emission collision recognition device provided in an embodiment of this application;

[0038] Figure 3 This is a flowchart illustrating the steps of an acoustic emission collision identification method for ultrasonic vibration-assisted electrical discharge forming according to an embodiment of this application.

[0039] Figure 4 This is a flowchart of an acoustic emission collision recognition method for ultrasonic vibration-assisted electrical discharge forming according to an embodiment of this application;

[0040] Figure 5 This is a structural block diagram of an acoustic emission collision recognition method device for ultrasonic vibration-assisted electrical discharge forming according to an embodiment of this application.

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

[0042] 1. Electrode; 2. Workpiece to be processed; 3. Vise; 4. Acoustic emission sensor; 5. Acoustic emission sensor fixture; 6. Electrical discharge pulse power supply. Detailed Implementation

[0043] To make the objectives, features, and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0044] The inventors, through analysis of existing technology, discovered that under ultrasonic vibration conditions, due to the high frequency of ultrasonic vibration, the response of the circuit system is not a short circuit, therefore a short circuit cannot be used as a criterion for determining whether a collision has occurred. To address this, the inventors introduced acoustic emission detection of the acoustic emission signal, using the difference in the acoustic emission signal between collision and non-collision states as a criterion for determining whether a collision has occurred.

[0045] It should be noted that, referring to Figure 1 and Figure 2 This invention relates to a workpiece 2, an electrode 1, and an acoustic emission sensor 4. The workpiece 2 is clamped in a vise 3. The acoustic emission sensor 4 is fixed by an acoustic emission sensor 4 clamp 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 needs to be applied between them. The electrode 1 is mounted on the chuck. The electrical discharge pulse power supply 6 and the ultrasonic power supply are turned on, and the electrode 1 is slowly moved closer to the workpiece 2 for processing.

[0046] Reference Figure 3 and Figure 4 This application illustrates an embodiment of an acoustic emission collision identification method for ultrasonic vibration-assisted electrical discharge forming.

[0047] The method includes:

[0048] S1. When the electrode is subjected to ultrasonic vibration for electrical discharge machining, the acoustic emission signal data during the machining process is acquired, and the acoustic emission signal data is subjected to discrete wavelet transform processing to obtain a filtered acoustic emission signal.

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

[0050] S3. Determine whether the electrode has collided based on the maximum amplitude value, the maximum coefficient value, the frequency of the highest amplitude, and the corresponding amplitude value. If the electrode has collided, adjust the processing parameters of the electrode.

[0051] In the embodiments of this application, in contrast to the technical problem in the prior art that "when a collision occurs under ultrasonic vibration, a short circuit cannot be used as a basis for collision detection," this application provides a solution for determining whether a collision has occurred using acoustic emission signals. By observing the differences in acoustic emission signals under collision and non-collision conditions, the problem of not being able to correctly identify whether an electrode is colliding under ultrasonic vibration conditions is solved; by jointly judging three features—maximum amplitude, maximum coefficient value, and the frequency with the highest amplitude and its corresponding amplitude value—the accuracy of collision detection is improved.

[0052] The acoustic emission collision recognition method for ultrasonic vibration-assisted electrical discharge molding in this exemplary embodiment will be further described below.

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

[0054] It should be noted that in ultrasonic-assisted machining, the melting of the workpiece by electrode discharge alters the workpiece's material structure, thereby generating acoustic emission signals. These acoustic emission signals typically propagate within the workpiece as elastic waves. The signals are transmitted via an acoustic coupling agent to an acoustic emission sensor mounted on the workpiece surface, allowing the sensor to collect acoustic emission 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 described in conjunction with the following description.

[0056] S11. Determine the effective acoustic emission signal segments in the acoustic emission signal data that are 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 drops back below the preset threshold, at which point the recording stops. The period of time recorded is a valid signal segment.

[0058] In one specific implementation, during the processing, acoustic emission signal data is collected. First, the effective acoustic emission signal segments are divided according to the acoustic emission signal threshold method. The threshold is about 0.1V. The time period from the first time it is higher than the threshold to the time period when it is lower than the threshold is a segment of acoustic emission signal.

[0059] S12. The effective acoustic emission signal segment is decomposed step by step based on discrete wavelet transform to obtain the filtered acoustic emission signal with a frequency lower than the 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 locations using a set of discrete wavelet basis functions, which helps in analyzing the characteristics of a 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 conjunction with the following description.

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

[0063] As an example, discrete wavelet transform is used to extract signals with frequencies below 1 MHz. The discrete wavelet transform decomposes the acoustic emission signal step-by-step, with each step producing a high-pass coefficient and an approximation coefficient (the approximation coefficient represents the low-frequency part of the signal, while the high-pass coefficient represents the high-frequency part). For example, signal X is decomposed by discrete wavelet transform to produce a relative high-pass coefficient cD1 and an approximation coefficient cA1. The approximation coefficient cA1 is then decomposed into a relative high-pass coefficient cD2 and an approximation 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 to form a new filtered acoustic emission signal.

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

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

[0068] As described in the following steps, the maximum amplitude value in the filtered acoustic emission signal is determined based on the maximum value 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 value in the filtered acoustic emission signal based on the maximum value function" can be further explained in conjunction with the following description.

[0070] It should be noted that the point where the sound is loudest 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 the maximum value function.

[0072] y = max(AE)

[0073] In the formula, 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" can be further explained in conjunction 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; 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 in the energy intensity distribution, that is, the maximum coefficient value.

[0077] As an example, the maximum coefficient value in the effective signal is calculated using the continuous wavelet transform. The formula for the continuous wavelet transform is as follows:

[0078]

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

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

[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 the Fast Fourier Transform" can be further explained in conjunction with the following description.

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

[0083] The filtered acoustic emission signal is processed using FFT (Fast Fourier Transform) to extract the frequency with the highest amplitude and its corresponding amplitude value. Specifically, a Fast Fourier Transform is performed on the filtered signal to convert the time-domain signal into a frequency-domain representation, obtaining the complex spectrum, as shown in the following formula:

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

[0085] In the formula, 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 yields the amplitude of each frequency component:

[0087] A(f)=∣X(f)∣

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

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

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

[0091] Frequency resolution Δf = sampling rate / number of FFT points. It is necessary to ensure that the resolution is sufficient to identify the target frequency band.

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

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

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

[0095] As an example, by plotting the above values ​​into a statistical graph and analyzing the results, we can identify the combination of features that most significantly differs from normal discharge. The specific characteristics of a collision are:

[0096] The highest frequency of amplitude extracted by FFT is between 203kHz and 205kHz, with corresponding amplitude values ​​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 all of the above characteristic indicators fall within the numerical range of a collision, 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 electrode if the electrode collides" can be further explained in conjunction with the following description.

[0101] When the system detects a collision event, it immediately stops the movement and retracts to prevent further collisions. Simultaneously, the feed rate is adjusted appropriately to prevent another collision immediately after feeding. If no collision is detected, normal machining proceeds.

[0102] As the apparatus embodiment is basically similar to the method embodiment, it is described in a relatively simple manner. For relevant details, please refer to the description of the method embodiment.

[0103] Reference Figure 5 This application illustrates an acoustic emission collision identification device for ultrasonic vibration-assisted electrical discharge forming according to an embodiment of the present application; the device involves a workpiece to be processed, an electrode, and an acoustic emission sensor, wherein the workpiece to be processed is disposed in non-contact with the electrode, 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, it includes:

[0105] The signal acquisition and processing module 510 is used to acquire acoustic emission signal data during the machining process when the electrode is subjected to ultrasonic vibration for electrical discharge machining, and to perform discrete wavelet transform processing on the acoustic emission signal data to obtain a filtered acoustic emission signal.

[0106] The feature extraction module 520 is used to determine the maximum amplitude value, the maximum coefficient value, and the frequency with the highest amplitude and the corresponding amplitude value in the filtered acoustic emission signal.

[0107] The judgment and adjustment module 530 is used to determine whether the electrode has collided based on the maximum amplitude value, the maximum coefficient value, the highest frequency of the amplitude and the corresponding amplitude value. If the electrode has collided, the processing parameters of the electrode are adjusted.

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

[0109] A sub-module is used to determine the effective acoustic emission signal segments in the acoustic emission signal data that are 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] The first decomposition unit is used to perform multi-level decomposition of the effective acoustic emission signal segment based on discrete wavelet transform, and each level of decomposition yields the corresponding high-pass coefficient and the corresponding approximation coefficient.

[0113] The step-by-step decomposition unit is used to repeatedly decompose the approximation coefficients of the previous level into the next level until the frequency component corresponding to the final level approximation coefficient is lower than the preset frequency.

[0114] A combination unit is used to determine the filtered acoustic emission signal based on the approximation coefficients of the final stage.

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

[0116] The maximum amplitude calculation submodule is used to determine the maximum amplitude value in the filtered acoustic emission signal based on the maximum value function; and,

[0117] The maximum coefficient value calculation submodule is used 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 the Fast Fourier Transform.

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

[0120] A continuous wavelet transform unit is used to analyze the filtered acoustic emission signal based on the continuous wavelet transform to obtain the energy intensity distribution data of the filtered acoustic emission signal at different frequencies and different 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 generation unit is used 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 generation unit is used to determine the frequency with the highest amplitude and the corresponding amplitude value based on the frequency domain amplitude spectrum.

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

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

[0127] Although preferred embodiments of the present application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present application.

[0128] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0129] The above provides a detailed description of the acoustic emission collision identification method and apparatus for ultrasonic vibration-assisted electrical discharge forming provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for acoustic emission collision identification in ultrasonic vibration-assisted electrical discharge molding, characterized in that, The method involves a workpiece to be processed, an electrode, and an acoustic emission sensor. The workpiece to be processed is disposed in a non-contact manner with the electrode, 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. The method includes: When the electrode is subjected to ultrasonic vibration for electrical discharge machining, acoustic emission signal data during the machining process is acquired, and the acoustic emission signal data is processed by discrete wavelet transform to obtain a filtered acoustic emission signal. The maximum amplitude value, the maximum coefficient value, and the frequency with the highest amplitude and corresponding amplitude value in the filtered acoustic emission signal are determined. Specifically, 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 time points. The maximum coefficient value is then determined from the energy intensity distribution data. When the maximum amplitude is between 12 and 13.5, the maximum coefficient is between 9.5 and 10.5, the highest frequency of amplitude is between 203 kHz and 205 kHz, and the highest frequency of amplitude and the corresponding amplitude are between 1 and 1.2, it is determined that the electrode has collided, and the processing parameters of the electrode 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 includes: Identify the valid acoustic emission signal segments in the acoustic emission signal data that are 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 the preset frequency.

3. The method according to claim 2, characterized in that, The step of decomposing the effective acoustic emission signal segment stepwise based on discrete wavelet transform to obtain the filtered acoustic emission signal below a preset frequency includes: The effective acoustic emission signal segment is decomposed into multiple levels based on discrete wavelet transform, and each level of decomposition yields the corresponding high-pass coefficient and the corresponding approximation coefficient. Repeat the process of decomposing the approximation coefficients of the previous level into the next level until the frequency component corresponding to the final level approximation coefficient 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, characterized in that, The steps of determining the maximum amplitude value, the maximum coefficient value, the frequency with the highest amplitude, and the corresponding amplitude value in the filtered acoustic emission signal include: The maximum amplitude value in the filtered acoustic emission signal is determined based on the maximum value function; and, The maximum coefficient value in the filtered acoustic emission signal was determined based on continuous wavelet transform; and, The frequency with the highest amplitude and the corresponding amplitude value in the filtered acoustic emission signal were determined based on the Fast Fourier Transform.

5. The method according to claim 4, characterized in that, The steps for determining the highest frequency and corresponding amplitude value in the filtered acoustic emission signal based on the Fast Fourier Transform include: Perform a fast Fourier transform on the filtered acoustic emission signal to obtain the frequency domain amplitude spectrum; The frequency with the highest amplitude and the corresponding amplitude value are determined based on the frequency domain amplitude spectrum.

6. An acoustic emission collision identification device for ultrasonic vibration-assisted electrical discharge forming, the device comprising a workpiece to be processed, an electrode, and an acoustic emission sensor, wherein the workpiece to be processed is disposed in non-contact with the electrode, 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 includes: The signal acquisition and processing module is used to acquire acoustic emission signal data during the electrical discharge machining process when the electrode is subjected to 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 is used to determine the maximum amplitude value, the maximum coefficient value, and the frequency with the highest amplitude and its corresponding amplitude value in the filtered acoustic emission signal. The feature extraction module includes: a continuous wavelet transform unit, used 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 different time points; and a coefficient determination unit, used to determine the maximum coefficient value from the energy intensity distribution data. The judgment and adjustment module is used to determine that the electrode has collided when the maximum amplitude value is between 12 and 13.5, the maximum coefficient value is between 9.5 and 10.5, the frequency of the highest amplitude is between 203 kHz and 205 kHz, and the frequency of the highest amplitude and the corresponding amplitude are between 1 and 1.2, and then adjust the processing parameters of the electrode.

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