Component porosity lossless prediction method and device, storage medium and electronic equipment

Abstract

The invention discloses a component porosity lossless prediction method and device, a storage medium and electronic equipment. The method comprises the steps that a to-be-detected target component is placed in a detection platform to be subjected to frequency detection, a frequency detection result is obtained, and a laser excitation probe and a three-dimensional laser vibration meter are placed on the two sides of the detection platform; determining a highest frequency signal in the frequency detection result; determining an ultrasonic signal corresponding to the highest frequency signal through a laser excitation probe, and receiving the ultrasonic signal through a three-dimensional laser vibration meter; and processing the ultrasonic signal to obtain a non-linear zero-frequency wave signal, determining a target deep learning network model based on the non-linear zero-frequency wave signal, and predicting the porosity of the target component through the target deep learning network model. The problem that in the prior art, the detection accuracy of the porosity of the additive manufacturing component is low is solved.

Description

Background Art

[0002] At present, there are several commonly used methods for porosity detection of complex and special-shaped components manufactured by additive manufacturing: metallographic method is the most commonly used method for porosity detection of additively manufactured components. This method evaluates the porosity by metallographic phase of the material being tested. However, the process of preparing metallographic phase is destructive, and this method cannot achieve non-destructive testing. In addition, this method has a certain degree of randomness, so a large number of metallographic phases need to be collected, and the detection cost is high; CT method is a radiographic non-destructive testing method. CT detection can obtain a three-dimensional image of the internal structure of the tested sample, and the porosity of the tested sample can be obtained through post-processing. However, the CT method has low detection efficiency, high cost, long detection time, and cannot achieve real-time detection. In addition, radiation is harmful to the human body and requires strict protection; linear ultrasonic method is an ultrasonic non-destructive testing method. Linear ultrasonic can construct a mapping relationship between the internal porosity of the component based on acoustic parameters such as the sound velocity and attenuation of the tested sample, thereby realizing the detection of the component porosity. However, linear ultrasound is not sensitive to micro-pores inside additively manufactured components, and has low detection accuracy. In addition, detection based on acoustic parameters such as sound velocity and attenuation has certain requirements on the geometric shape of the sample being tested, and cannot be carried out on samples with complex geometric structures. Therefore, the existing porosity detection methods for additively manufactured components cannot meet the needs of efficient and high-precision non-destructive testing of additively manufactured samples with complex geometric structures.

[0003] Currently, no effective solution has been proposed to the problem of low accuracy in detecting the porosity of additively manufactured components in related technologies. Summary of the invention

[0004] The main purpose of the present application is to provide a method, device, storage medium and electronic device for non-destructive prediction of component porosity, so as to solve the problem of low accuracy in porosity detection of additively manufactured components in related technologies.

[0005] In order to achieve the above-mentioned purpose, according to the first aspect of the present application, a non-destructive prediction method for component porosity is provided. The method comprises: placing the target component to be detected in a detection platform for frequency detection to obtain a frequency detection result, wherein the detection platform is placed on both sides with: a laser excitation probe and a three-dimensional laser vibrometer; determining the highest frequency signal in the frequency detection result; determining the ultrasonic signal corresponding to the highest frequency signal by the laser excitation probe, and receiving the ultrasonic signal by the three-dimensional laser vibrometer; processing the ultrasonic signal to obtain a nonlinear zero-frequency wave signal, determining a target deep learning network model based on the nonlinear zero-frequency wave signal, and predicting the porosity of the target component by the target deep learning network model, wherein the target deep learning network model is used to map the corresponding relationship between the nonlinear zero-frequency wave signal and the porosity of the target component.

[0006] Further, the ultrasonic signal is processed to obtain a nonlinear zero-frequency wave signal, including: performing wavelet noise reduction processing on the ultrasonic signal to obtain a noise-reduced ultrasonic signal; and performing low-pass filtering on the noise-reduced ultrasonic signal to obtain a filtered nonlinear zero-frequency wave signal.

[0007] Furthermore, a target deep learning network model is determined based on the ultrasonic signal, including: determining a data set between the nonlinear zero-frequency wave signal and the porosity sample; dividing the data set into a training set and a test set; and training the initial deep learning network model based on the training set and the test set to obtain a target deep learning network model.

[0008] Furthermore, the target deep learning network model includes: a sequentially connected input layer, a convolutional neural network module, a gated recurrent unit module, a fully connected layer and an output layer, wherein the convolutional neural network module includes a first convolutional layer, a first pooling layer, a second convolutional layer and a second pooling layer connected sequentially, and the gated recurrent unit module includes a first gated recurrent layer, a first activation layer, a second gated recurrent layer and a second activation layer.

[0009] Furthermore, the porosity of the target component is predicted through a target deep learning network model, including: extracting local features of the nonlinear zero-frequency wave signal based on a convolutional neural network module; training the dependency relationship between local features based on a gated recurrent unit module; mapping the dependency relationship through a fully connected layer to obtain the correspondence between the nonlinear zero-frequency wave signal and the porosity of the target component; and predicting the porosity of the target component based on the correspondence relationship.

[0010] Furthermore, the ultrasonic signal is subjected to wavelet denoising processing to obtain a denoised ultrasonic signal, including: determining a wavelet function, and performing a multi-layer wavelet transform on the ultrasonic signal based on the wavelet function to obtain high-frequency coefficients and low-frequency coefficients of the ultrasonic signal; and processing the high-frequency coefficients and the low-frequency coefficients respectively to obtain a denoised ultrasonic signal.

[0011] Furthermore, the target component is placed on a detection platform and kept in a fixed position during the detection process, wherein a laser excitation probe and a three-dimensional laser vibrometer are placed on both sides of the detection platform and kept at a fixed interval.

[0012] In order to achieve the above-mentioned purpose, according to the second aspect of the present application, a non-destructive prediction device for component porosity is provided. The device includes: a detection unit, which is used to place the target component to be detected in the detection platform for frequency detection to obtain the frequency detection result, wherein the two sides of the detection platform are placed with: a laser excitation probe and a three-dimensional laser vibrometer; a first determination unit, which is used to determine the highest frequency signal in the frequency detection result; a receiving unit, which is used to determine the ultrasonic signal corresponding to the highest frequency signal through the laser excitation probe, and receive the ultrasonic signal through the three-dimensional laser vibrometer; a second determination unit, which is used to process the ultrasonic signal to obtain a nonlinear zero-frequency wave signal, determine the target deep learning network model based on the nonlinear zero-frequency wave signal, and predict the porosity of the target component through the target deep learning network model, wherein the target deep learning network model is used to map the corresponding relationship between the nonlinear zero-frequency wave signal and the porosity of the target component.

[0013] Furthermore, the second determination unit includes: a processing module for performing wavelet noise reduction processing on the ultrasonic signal to obtain a noise-reduced ultrasonic signal; and a filtering module for performing low-pass filtering on the noise-reduced ultrasonic signal to obtain a filtered nonlinear zero-frequency wave signal.

[0014] Furthermore, the second determination unit includes: a determination module, used to determine the data set between the nonlinear zero-frequency wave signal and the porosity sample; a division module, used to divide the data set into a training set and a test set; a first training module, used to train the initial deep learning network model based on the training set and the test set to obtain a target deep learning network model.

[0015] Furthermore, the target deep learning network model includes: a sequentially connected input layer, a convolutional neural network module, a gated recurrent unit module, a fully connected layer and an output layer, wherein the convolutional neural network module includes a first convolutional layer, a first pooling layer, a second convolutional layer and a second pooling layer connected sequentially, and the gated recurrent unit module includes a first gated recurrent layer, a first activation layer, a second gated recurrent layer and a second activation layer.

[0016] Furthermore, the second determination unit includes: an extraction module for extracting local features of the nonlinear zero-frequency wave signal based on a convolutional neural network module; a second training module for training the dependency relationship between local features based on a gated recurrent unit module; a mapping module for mapping the dependency relationship through a fully connected layer to obtain a correspondence between the nonlinear zero-frequency wave signal and the porosity of the target component; and a prediction module for predicting the porosity of the target component based on the correspondence relationship.

[0017] Furthermore, the processing module includes: a determination submodule, used to determine the wavelet function, and perform multi-layer wavelet transform on the ultrasonic signal based on the wavelet function to obtain the high-frequency coefficients and low-frequency coefficients of the ultrasonic signal; a processing submodule, used to process the high-frequency coefficients and low-frequency coefficients respectively to obtain the denoised ultrasonic signal.

[0018] Furthermore, the target component is placed on a detection platform and kept in a fixed position during the detection process, wherein a laser excitation probe and a three-dimensional laser vibrometer are placed on both sides of the detection platform and kept at a fixed interval.

[0019] According to a third aspect of an embodiment of the present application, there is provided an electronic device, comprising: a processor; and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the non-destructive prediction method of component porosity of any one of the above items is implemented.

[0020] According to a fourth aspect of an embodiment of the present application, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the method for non-destructive prediction of the porosity of a component according to any one of the above items is implemented.

[0021] Through this application, the following steps are adopted: placing the target component to be detected in the detection platform for frequency detection to obtain the frequency detection result, wherein the detection platform is placed on both sides: laser excitation probe, three-dimensional laser vibrometer; determining the highest frequency signal in the frequency detection result; determining the ultrasonic signal corresponding to the highest frequency signal through the laser excitation probe, and receiving the ultrasonic signal through the three-dimensional laser vibrometer; processing the ultrasonic signal to obtain a nonlinear zero-frequency wave signal, determining the target deep learning network model based on the nonlinear zero-frequency wave signal, and predicting the porosity of the target component through the target deep learning network model, wherein the target deep learning network model is used to map the corresponding relationship between the nonlinear zero-frequency wave signal and the porosity of the target component, solving the problem of low detection accuracy of the porosity of the additive manufacturing component in the related art. Through the constructed target deep learning network model, the corresponding relationship between the nonlinear zero-frequency wave signal and the porosity of the target component is mapped, thereby achieving the effect of improving the detection accuracy of the porosity of the additive manufacturing component. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0023] Figure 1 is a flow chart of a nondestructive prediction method for component porosity provided in accordance with an embodiment of the present application;

[0024] Figure 2 is a schematic diagram of a nonlinear zero-frequency wave ultrasonic detection signal provided according to an embodiment of the present application;

[0025] Figure 3 is a schematic diagram of the structure of a target deep learning network model provided according to an embodiment of the present application;

[0026] Figure 4 This is a schematic diagram of a non-contact non-destructive evaluation framework for the porosity of a complex special-shaped component manufactured by additive manufacturing provided in this application;

[0027] Figure 5 is a schematic diagram of a nondestructive prediction device for component porosity provided in accordance with an embodiment of the present application;

[0028] Figure 6 It is a schematic diagram of the network architecture of an electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0029] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0030] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0031] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0032] According to an embodiment of the present application, a non-destructive prediction method for component porosity is provided.

[0033] Figure 1 : is a flow chart of a non-destructive prediction method of component porosity according to an embodiment of the present application. Figure 1As shown, the method comprises the following steps:

[0034] Step S101, placing the target component to be detected on a detection platform for frequency detection to obtain a frequency detection result, wherein a laser excitation probe and a three-dimensional laser vibrometer are placed on both sides of the detection platform.

[0035] Among them, the target component can be a complex and special-shaped component manufactured by additive manufacturing. In this case, the complex and special-shaped component manufactured by additive manufacturing to be inspected is placed on the inspection platform, and the positions of the laser excitation probe and the three-dimensional laser vibrometer are fixed. The complex and special-shaped component manufactured by additive manufacturing to be inspected is located between the laser excitation probe and the three-dimensional laser vibrometer. This not only improves the accuracy and reliability of detection, reduces production costs, but also improves production efficiency.

[0036] Step S102, determining the highest frequency signal in the frequency detection result.

[0037] Exemplarily, ultrasonic sweep frequency detection is performed on the complex and special-shaped additively manufactured components being tested, with the sweep frequency range being 1-10 MHz. Based on the ultrasonic sweep frequency detection results, the frequency signal f1 with the strongest response in the detection signal spectrum is selected as the ultrasonic excitation frequency for subsequent porosity detection. In this case, by detecting the highest frequency signal, that is, the frequency signal f1 with the strongest response, the detection sensitivity can be significantly improved, the detection efficiency can be optimized, and the reliability of the detection results can be improved.

[0038] It should be noted that due to the manufacturing principle of additively manufactured components, the surface roughness of the components is higher than that of machined specimens. The conventional contact ultrasonic nondestructive testing method cannot guarantee the consistency of the coupling conditions between the probe and the test specimen during each test, that is, the robustness of the test results cannot be guaranteed. In addition, for complex and special-shaped components, it is even more difficult to ensure the coupling conditions between the contact probe and the component surface. Therefore, the use of laser ultrasound in this case can effectively reduce the impact of the surface roughness and complex geometric structure of the tested specimen on the test results.

[0039] Step S103: determining the ultrasonic signal corresponding to the highest frequency signal by means of a laser excitation probe, and receiving the ultrasonic signal by means of a three-dimensional laser vibrometer.

[0040] Among them, the laser excitation probe excites a long-period ultrasonic detection signal with a frequency of f1 and a period of 200, and the corresponding ultrasonic signal is received by the three-dimensional laser vibrometer. The duration of receiving the ultrasonic signal is 5 times that of the exciting ultrasonic signal, ensuring that the ultrasonic wave fully interacts with the micropores in the inspected component in the inspected sample. That is, in this case, the laser excitation probe is used to excite ultrasonic waves at a specific frequency in the inspected sample, and the ultrasonic signal is received by the three-dimensional laser vibrometer, which not only realizes non-contact, high-precision and high-efficiency detection, but also has rich waveform and wide-band detection capabilities.

[0041] Step S104, processing the ultrasonic signal to obtain a nonlinear zero-frequency wave signal, determining a target deep learning network model based on the nonlinear zero-frequency wave signal, and predicting the porosity of the target component through the target deep learning network model, wherein the target deep learning network model is used to map the correspondence between the nonlinear zero-frequency wave signal and the porosity of the target component.

[0042] Specifically, the ultrasonic signal is processed to obtain a nonlinear zero-frequency wave signal, which can be obtained by the following steps: performing wavelet noise reduction processing on the ultrasonic signal to obtain a noise-reduced ultrasonic signal; and performing low-pass filtering on the noise-reduced ultrasonic signal to obtain a filtered nonlinear zero-frequency wave signal.

[0043] Exemplarily, the received ultrasonic signal is processed by wavelet transformation, and the signal after wavelet threshold denoising is further low-pass filtered, and the filtering range is f<1 / 20f1, and its cut-off frequency is 1 / 20 of the selected ultrasonic excitation frequency f1. Thereafter, a nonlinear zero-frequency wave signal is obtained after filtering, and the nonlinear zero-frequency wave signal in the detection signal obtained after filtering is recorded as S1. In this case, by processing the ultrasonic signal, a nonlinear zero-frequency wave signal is obtained, which can significantly improve the signal quality, maintain the important characteristics of the signal, remove high-frequency noise and protect the low-frequency effective signal.

[0044] It should be noted that when ultrasonic waves propagate inside the inspected component, in addition to the excitation frequency, nonlinear responses at other frequencies will also be generated, such as high-order harmonics, subharmonics and nonlinear zero-frequency waves. This type of acoustic nonlinear response is very sensitive to the microstructural changes and microdamage inside the inspected material. The nonlinear response of sound can effectively characterize the nonlinearity inside the material. The frequency of the nonlinear zero-frequency wave is very low, so its acoustic attenuation is very small, which can greatly increase the detection range and improve the detection efficiency. In the preparation process of additive manufacturing components, it is inevitable that certain micropores will be generated inside. The size of these micropores is very small, far less than half the length of the commonly used linear ultrasonic wave. Therefore, these micropores cannot be detected by linear ultrasound. The intensity of the acoustic nonlinear response is related to the nonlinearity of the material. When the number of micropores inside the inspected component increases, the intensity of the nonlinear zero-frequency wave generated by the fundamental frequency ultrasound during the propagation process will also increase. Therefore, the nonlinear zero-frequency wave can be used to characterize the micropores inside the inspected sample. Due to its unique processing technology, additive manufacturing technology has strong processing flexibility and can prepare components with complex geometric structures that cannot be processed by conventional subtractive manufacturing. Conventional ultrasonic testing has certain requirements on the geometric structure of the component being tested, and changes in the geometric structure will change the propagation path of the sound. When a long-period resonant signal is excited (i.e., the frequency with the strongest response in the frequency domain of the swept frequency detection signal), the energy of the detection signal is improved, and the intensity of the corresponding nonlinear zero-frequency wave is also greatly increased. The longer detection time (the duration of receiving the ultrasonic signal is 5 times that of the exciting ultrasonic signal) ensures that the nonlinear zero-frequency wave generated by the detection signal is fully propagated in the complex and special-shaped additively manufactured component being tested, and fully interacts with its internal micropores, so that the nonlinear zero-frequency wave signal in the detection signal can contain a large amount of information related to the micropores. The nonlinear zero-frequency wave ultrasonic detection signal, such as Figure 2 shown.

[0045] In some embodiments, a target deep learning network model is determined based on an ultrasonic signal, which can be obtained by the following steps: determining a data set between a nonlinear zero-frequency wave signal and a porosity sample; dividing the data set into a training set and a test set; and training an initial deep learning network model based on the training set and the test set to obtain a target deep learning network model.

[0046] Exemplarily, by changing the additive manufacturing preparation process, samples with different porosities xi are obtained, and the nonlinear zero-frequency wave signals Si corresponding to the samples with different porosities are obtained to form a porosity-nonlinear zero-frequency wave signal data set. The data set is divided into a training set and a test set to obtain the target deep learning network model of this application, and the trained deep learning network model is used to predict the porosity of complex special-shaped components manufactured by additive manufacturing.

[0047] In some embodiments, the target deep learning network includes: a sequentially connected input layer, a convolutional neural network module, a gated recurrent unit module, a fully connected layer and an output layer, wherein the convolutional neural network module includes a first convolutional layer, a first pooling layer, a second convolutional layer and a second pooling layer connected sequentially, and the gated recurrent unit module includes a first gated recurrent layer, a first activation layer, a second gated recurrent layer and a second activation layer.

[0048] Specifically, the convolutional neural network (CNN)-gated recurrent unit (GRU) network model in this case includes a sequentially connected input layer, CNN module (convolutional neural network module), GRU module (gated recurrent unit module), fully connected layer and output layer. Among them, the input layer inputs the porosity-nonlinear zero-frequency wave signal information set; the CNN module is used to extract local features in the detection signal; the GRU module is used to extract and learn the long-term dependencies in the local features; the fully connected layer is used to connect neurons between layers, construct a mapping relationship between porosity and detection signals, and realize the prediction of porosity; the output layer is used to output the prediction results of the deep learning network model.

[0049] In some embodiments, the porosity of the target component is predicted by a target deep learning network model, which can be obtained by the following steps: extracting local features of the nonlinear zero-frequency wave signal based on a convolutional neural network module; training the dependencies between local features based on a gated recurrent unit module; mapping the dependencies through a fully connected layer to obtain a correspondence between the nonlinear zero-frequency wave signal and the porosity of the target component; and predicting the porosity of the target component based on the correspondence.

[0050] For example, Figure 3As shown, the CNN module includes the first convolution layer, the first pooling layer, the second convolution layer and the second pooling layer connected in sequence. The first convolution layer is used to process the porosity-detection signal information set and extract the local features of the data; the first pooling layer is used to reduce the matrix size of the local features of the extracted data while retaining important information; the second convolution layer is used to further extract important information from the local features; the second pooling layer is used to convert the important information into a one-dimensional matrix. The GRU module includes the first GRU layer (first gated recurrent layer), the first ReLU layer (first activation layer), the second GRU layer (second gated recurrent layer) and the second ReLU layer (second activation layer) connected in sequence. The first GRU layer is used to process the one-dimensional matrix of important information extracted by the CNN module, and to capture and learn the long-term dependencies in the above information; the first ReLU layer is used to introduce nonlinearity into the network and increase the nonlinear prediction function of the network; the second GRU layer is used to further capture and learn the long-term dependencies of the extracted features, and map the long-term dependencies through the fully connected layer to obtain the correspondence between the nonlinear zero-frequency wave signal and the porosity of the target component, thereby realizing the prediction of the porosity, avoiding the gradient vanishing phenomenon of the overall network. At the same time, the second ReLU layer can also reduce the interdependence between features and alleviate the occurrence of overfitting.

[0051] In some embodiments, wavelet denoising is performed on an ultrasonic signal to obtain a denoised ultrasonic signal, which can be obtained by the following steps: determining a wavelet function, and performing a multi-layer wavelet transform on the ultrasonic signal based on the wavelet function to obtain high-frequency coefficients and low-frequency coefficients of the ultrasonic signal; and processing the high-frequency coefficients and the low-frequency coefficients respectively to obtain a denoised ultrasonic signal.

[0052] Exemplarily, the specific process of wavelet transform denoising is as follows: the selected wavelet function is "db4" wavelet, the decomposition layer number is 4, and the high-frequency coefficients and low-frequency coefficients of the signal are obtained; further wavelet threshold processing is performed on the high-frequency coefficients of each layer, and the wavelet hard threshold denoising method is adopted; finally, the low-frequency coefficients and the high-frequency coefficients after threshold processing are reconstructed to obtain the denoised signal. Wavelet denoising processing of ultrasonic signals can significantly improve signal quality, improve signal feature recognition, and improve detection accuracy and reliability.

[0053] In some embodiments, the target component is placed on a detection platform and kept in a fixed position during the detection process, wherein a laser excitation probe and a three-dimensional laser vibrometer are placed on both sides of the detection platform and kept at a fixed interval.

[0054] That is, in order to ensure that the coupling state between the laser excitation probe and the three-dimensional laser vibrometer and the test piece is constant, the laser excitation probe and the three-dimensional laser vibrometer are placed at fixed positions at both ends of the test area of ​​the test sample during the detection process. Specifically, in the process of detecting the porosity of different samples, the distance between the laser excitation probe and the three-dimensional laser vibrometer remains unchanged, and the distance between the laser excitation probe and the three-dimensional laser vibrometer at both ends of the test sample remains fixed.

[0055] Optionally, Figure 4 This is a schematic diagram of a non-contact non-destructive evaluation framework for the porosity of a complex special-shaped component manufactured by additive manufacturing, such as Figure 4 As shown, 1—computer, 2—pulse solid laser, 3—laser excitation probe, 4—additive manufacturing complex special-shaped component, 5—detection platform, 6—three-dimensional laser vibrometer, 7—low-pass filter, 8—power amplifier, 9—oscilloscope; the additive manufacturing complex special-shaped component 4 is placed on the detection platform 5 and keeps the position fixed during the detection process. The laser excitation probe 3 and the three-dimensional laser vibrometer 6 are placed on both sides of the detection platform 5 and keep the interval fixed. The computer 1 is used to control the pulse solid laser 2 to excite the ultrasonic signal of the appropriate frequency, and the ultrasonic signal is introduced into the additive manufacturing complex special-shaped component 4 to be detected through the laser excitation probe 3. The three-dimensional laser vibrometer 6 is placed on the other side of the additive manufacturing complex special-shaped component 4 to be detected. The detection signal is received by the three-dimensional laser vibrometer 6, and further passed through the low-pass filter 7 and the power amplifier 8 to remove the high-frequency noise and the baseband signal. The processed signal is sent to the three-dimensional laser vibrometer 6 again and further sent to the computer 1 and the oscilloscope 9 for subsequent signal analysis. In addition, the cut-off frequency of the low-pass filter used is 1 / 20 of the selected ultrasonic excitation frequency f1.

[0056] The nondestructive prediction method of component porosity provided in the embodiment of the present application is to place the target component to be detected on the detection platform for frequency detection to obtain the frequency detection result, wherein the detection platform is placed on both sides with: laser excitation probe and three-dimensional laser vibrometer; determine the highest frequency signal in the frequency detection result; determine the ultrasonic signal corresponding to the highest frequency signal by the laser excitation probe, and receive the ultrasonic signal by the three-dimensional laser vibrometer; process the ultrasonic signal to obtain a nonlinear zero-frequency wave signal, determine the target deep learning network model based on the nonlinear zero-frequency wave signal, and predict the porosity of the target component by the target deep learning network model, wherein the target deep learning network model is used to map the corresponding relationship between the nonlinear zero-frequency wave signal and the porosity of the target component, thereby solving the problem of low detection accuracy of the porosity of additive manufacturing components in related technologies. By constructing the target deep learning network model, the corresponding relationship between the nonlinear zero-frequency wave signal and the porosity of the target component is mapped, thereby achieving the effect of improving the detection accuracy of the porosity of the additive manufacturing component.

[0057] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0058] The embodiment of the present application also provides a nondestructive prediction device for component porosity. It should be noted that the nondestructive prediction device for component porosity in the embodiment of the present application can be used to execute the nondestructive prediction method for component porosity provided in the embodiment of the present application. The nondestructive prediction device for component porosity provided in the embodiment of the present application is introduced below.

[0059] Figure 5 Schematic diagram of a nondestructive prediction device for component porosity according to an embodiment of the present application. Figure 5 As shown, the device 500 includes: a detection unit 501, a first determination unit 502, a receiving unit 503, and a second determination unit 504.

[0060] Specifically, the detection unit 501 is used to place the target component to be detected on the detection platform for frequency detection to obtain the frequency detection result, wherein the two sides of the detection platform are placed with: a laser excitation probe and a three-dimensional laser vibrometer;

[0061] A first determining unit 502, configured to determine the highest frequency signal in the frequency detection result;

[0062] A receiving unit 503, configured to determine the ultrasonic signal corresponding to the highest frequency signal through a laser excitation probe, and receive the ultrasonic signal through a three-dimensional laser vibrometer;

[0063] The second determination unit 504 is used to process the ultrasonic signal to obtain a nonlinear zero-frequency wave signal, determine the target deep learning network model based on the nonlinear zero-frequency wave signal, and predict the porosity of the target component through the target deep learning network model, wherein the target deep learning network model is used to map the correspondence between the nonlinear zero-frequency wave signal and the porosity of the target component.

[0064] The nondestructive prediction device for component porosity provided in the embodiment of the present application is used to place the target component to be detected on the detection platform for frequency detection through the detection unit 501 to obtain the frequency detection result, wherein the detection platform is placed on both sides with: a laser excitation probe and a three-dimensional laser vibrometer; a first determination unit 502 is used to determine the highest frequency signal in the frequency detection result; a receiving unit 503 is used to determine the ultrasonic signal corresponding to the highest frequency signal through the laser excitation probe, and receive the ultrasonic signal through the three-dimensional laser vibrometer; a second determination unit 504 is used to process the ultrasonic signal to obtain a nonlinear zero-frequency wave signal, determine the target deep learning network model based on the nonlinear zero-frequency wave signal, and predict the porosity of the target component through the target deep learning network model, wherein the target deep learning network model is used to map the corresponding relationship between the nonlinear zero-frequency wave signal and the porosity of the target component, thereby solving the problem of low detection accuracy of the porosity of the additive manufacturing component in the related art. Through the constructed target deep learning network model, the corresponding relationship between the nonlinear zero-frequency wave signal and the porosity of the target component is mapped, thereby achieving the effect of improving the detection accuracy of the porosity of the additive manufacturing component.

[0065] Optionally, in the non-destructive prediction device for component porosity provided in an embodiment of the present application, the second determination unit includes: a processing module, used to perform wavelet denoising on the ultrasonic signal to obtain a denoised ultrasonic signal; and a filtering module, used to perform low-pass filtering on the denoised ultrasonic signal to obtain a filtered nonlinear zero-frequency wave signal.

[0066] Optionally, in the nondestructive prediction device for component porosity provided in an embodiment of the present application, the second determination unit includes: a determination module for determining a data set between a nonlinear zero-frequency wave signal and a porosity sample; a division module for dividing the data set into a training set and a test set; and a first training module for training an initial deep learning network model based on the training set and the test set to obtain a target deep learning network model.

[0067] Optionally, in the non-destructive prediction device for component porosity provided in an embodiment of the present application, the target deep learning network model includes: an input layer, a convolutional neural network module, a gated recurrent unit module, a fully connected layer and an output layer connected in sequence, wherein the convolutional neural network module includes a first convolutional layer, a first pooling layer, a second convolutional layer and a second pooling layer connected in sequence, and the gated recurrent unit module includes a first gated recurrent layer, a first activation layer, a second gated recurrent layer and a second activation layer.

[0068] Optionally, in the nondestructive prediction device for component porosity provided in an embodiment of the present application, the second determination unit includes: an extraction module for extracting local features of the nonlinear zero-frequency wave signal based on a convolutional neural network module; a second training module for training the dependency relationship between local features based on a gated recurrent unit module; a mapping module for mapping the dependency relationship through a fully connected layer to obtain a correspondence between the nonlinear zero-frequency wave signal and the porosity of the target component; and a prediction module for determining a prediction of the porosity of the target component based on the correspondence relationship.

[0069] Optionally, in the non-destructive prediction device for component porosity provided in an embodiment of the present application, the processing module includes: a determination submodule, used to determine a wavelet function, and perform a multi-layer wavelet transform on the ultrasonic signal based on the wavelet function to obtain high-frequency coefficients and low-frequency coefficients of the ultrasonic signal; and a processing submodule, used to process the high-frequency coefficients and the low-frequency coefficients respectively to obtain a denoised ultrasonic signal.

[0070] Optionally, in the nondestructive prediction device for component porosity provided in an embodiment of the present application, the target component is placed on a detection platform and kept in a fixed position during the detection process, wherein a laser excitation probe and a three-dimensional laser vibrometer are placed on both sides of the detection platform and kept at a fixed interval.

[0071] The nondestructive prediction device for component porosity includes a processor and a memory, and the detection unit 501, the first determination unit 502, the receiving unit 503, the second determination unit 504, etc. are all stored in the memory as program units, and the processor executes the program units stored in the memory to realize corresponding functions.

[0072] The processor includes a kernel, which calls the corresponding program unit from the memory. One or more kernels can be set, and the porosity of the component can be non-destructively predicted by adjusting the kernel parameters.

[0073] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0074] In the exemplary embodiment of the present application, a computer storage medium capable of implementing the above method is also provided. A program product capable of implementing the above method of the present specification is stored thereon. In some possible embodiments, various aspects of the present application can also be implemented in the form of a program product, which includes a program code. When the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps described in the "Exemplary Method" section of the present specification according to various exemplary embodiments of the present application, such as the following steps: placing the target member to be detected on a detection platform for frequency detection to obtain a frequency detection result, wherein the detection platform is placed on both sides with: a laser excitation probe and a three-dimensional laser vibrometer; determining the highest frequency signal in the frequency detection result; determining the ultrasonic signal corresponding to the highest frequency signal by the laser excitation probe, and receiving the ultrasonic signal by the three-dimensional laser vibrometer; processing the ultrasonic signal to obtain a nonlinear zero-frequency wave signal, determining a target deep learning network model based on the nonlinear zero-frequency wave signal, and predicting the porosity of the target member by the target deep learning network model, wherein the target deep learning network model is used to map the corresponding relationship between the nonlinear zero-frequency wave signal and the porosity of the target member.

[0075] In an optional implementation: performing wavelet noise reduction processing on the ultrasonic signal to obtain a noise-reduced ultrasonic signal; performing low-pass filtering on the noise-reduced ultrasonic signal to obtain a filtered nonlinear zero-frequency wave signal.

[0076] In an optional implementation: determine a data set between a nonlinear zero-frequency wave signal and a porosity sample; divide the data set into a training set and a test set; train an initial deep learning network model based on the training set and the test set to obtain a target deep learning network model.

[0077] In an optional embodiment: it includes a sequentially connected input layer, a convolutional neural network module, a gated recurrent unit module, a fully connected layer and an output layer, wherein the convolutional neural network module includes a first convolutional layer, a first pooling layer, a second convolutional layer and a second pooling layer connected sequentially, and the gated recurrent unit module includes a first gated recurrent layer, a first activation layer, a second gated recurrent layer and a second activation layer.

[0078] In an optional embodiment: local features of the nonlinear zero-frequency wave signal are extracted based on a convolutional neural network module; dependencies between local features are trained based on a gated recurrent unit module; the dependencies are mapped through a fully connected layer to obtain a correspondence between the nonlinear zero-frequency wave signal and the porosity of the target component; and the porosity of the target component is predicted based on the correspondence.

[0079] In an optional implementation: a wavelet function is determined, and a multi-layer wavelet transform is performed on the ultrasonic signal based on the wavelet function to obtain high-frequency coefficients and low-frequency coefficients of the ultrasonic signal; the high-frequency coefficients and low-frequency coefficients are processed separately to obtain a denoised ultrasonic signal.

[0080] In an optional implementation, the target component is placed on a detection platform and kept in a fixed position during the detection process, wherein a laser excitation probe and a three-dimensional laser vibrometer are placed on both sides of the detection platform and kept at a fixed interval.

[0081] In an optional embodiment, the embodiment of the present application may also include a program product for implementing the above method, which may adopt a portable compact disk read-only memory (CD-ROM) and include program code, and can be run on a terminal device, such as a personal computer. However, the program product of the present application is not limited thereto, and in this document, a readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, apparatus, or device.

[0082] The program product may use any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0083] Computer readable signal media may include data signals propagated in baseband or as part of a carrier wave, in which readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Readable signal media may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0084] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the foregoing.

[0085] Program code for performing the operations of the present application may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0086] In addition, in an exemplary embodiment of the present application, an electronic device capable of implementing the above method is also provided.

[0087] Those skilled in the art will appreciate that various aspects of the present application may be implemented as a system, method or program product. Therefore, various aspects of the present application may be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software, which may be collectively referred to as "circuit", "module" or "system" herein.

[0088] Refer to the following Figure 6 The electronic device 600 according to this embodiment of the present application is described. Figure 6 The electronic device 600 shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0089] like Figure 6 As shown, the electronic device 600 is in the form of a general computing device. The components of the electronic device 600 may include, but are not limited to: the at least one processing unit 610, the at least one storage unit 620, a bus 630 connecting different system components (including the storage unit 620 and the processing unit 610), and a display unit 640.

[0090] The storage unit stores a program code, and the program code can be executed by the processing unit 610, so that the processing unit 610 executes the steps described in the above "Exemplary Method" section of this specification according to various exemplary embodiments of the present application. For example, the processing unit 610 can execute the following steps: placing the target component to be detected in the detection platform for frequency detection to obtain a frequency detection result, wherein the two sides of the detection platform are placed with: a laser excitation probe and a three-dimensional laser vibrometer; determining the highest frequency signal in the frequency detection result; determining the ultrasonic signal corresponding to the highest frequency signal through the laser excitation probe, and receiving the ultrasonic signal through the three-dimensional laser vibrometer; processing the ultrasonic signal to obtain a nonlinear zero-frequency wave signal, determining a target deep learning network model based on the nonlinear zero-frequency wave signal, and predicting the porosity of the target component through the target deep learning network model, wherein the target deep learning network model is used to map the correspondence between the nonlinear zero-frequency wave signal and the porosity of the target component.

[0091] In an optional implementation: performing wavelet noise reduction processing on the ultrasonic signal to obtain a noise-reduced ultrasonic signal; performing low-pass filtering on the noise-reduced ultrasonic signal to obtain a filtered nonlinear zero-frequency wave signal.

[0092] In an optional implementation: determine a data set between a nonlinear zero-frequency wave signal and a porosity sample; divide the data set into a training set and a test set; train an initial deep learning network model based on the training set and the test set to obtain a target deep learning network model.

[0093] In an optional embodiment: it includes a sequentially connected input layer, a convolutional neural network module, a gated recurrent unit module, a fully connected layer and an output layer, wherein the convolutional neural network module includes a first convolutional layer, a first pooling layer, a second convolutional layer and a second pooling layer connected sequentially, and the gated recurrent unit module includes a first gated recurrent layer, a first activation layer, a second gated recurrent layer and a second activation layer.

[0094] In an optional embodiment: local features of the nonlinear zero-frequency wave signal are extracted based on a convolutional neural network module; dependencies between local features are trained based on a gated recurrent unit module; the dependencies are mapped through a fully connected layer to obtain a correspondence between the nonlinear zero-frequency wave signal and the porosity of the target component; and the porosity of the target component is predicted based on the correspondence.

[0095] In an optional implementation: a wavelet function is determined, and a multi-layer wavelet transform is performed on the ultrasonic signal based on the wavelet function to obtain high-frequency coefficients and low-frequency coefficients of the ultrasonic signal; the high-frequency coefficients and low-frequency coefficients are processed separately to obtain a denoised ultrasonic signal.

[0096] In an optional implementation, the target component is placed on a detection platform and kept in a fixed position during the detection process, wherein a laser excitation probe and a three-dimensional laser vibrometer are placed on both sides of the detection platform and kept at a fixed interval.

[0097] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 6201 and / or a cache storage unit 6202 , and may further include a read-only storage unit (ROM) 6203 .

[0098] The storage unit 620 may also include a program / utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0099] Bus 630 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0100] The electronic device 600 may also communicate with one or more external devices 700 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 600, and / or communicate with any device that enables the electronic device 600 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 650. Furthermore, the electronic device 600 may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter 660. As shown, the network adapter 660 communicates with other modules of the electronic device 600 via a bus 630. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0101] Through the description of the above implementation methods, it is easy for those skilled in the art to understand that the example implementation methods described here can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the implementation methods of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the implementation methods of the present application.

[0102] In addition, the above-mentioned figures are only schematic illustrations of the processes included in the method according to the exemplary embodiments of the present application, and are not intended to be limiting. It is easy to understand that the processes shown in the above-mentioned figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.

[0103] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. The present application is intended to cover any variations, uses or adaptations of the present application, which follow the general principles of the present application and include common knowledge or customary technical means in the art that are not disclosed in the present application. The specification and embodiments are to be regarded as exemplary only, and the true scope and spirit of the present application are indicated by the claims.

Claims

1. A nondestructive prediction method for component porosity, characterized in that: include: The target component to be detected is placed on a detection platform for frequency detection to obtain a frequency detection result, wherein a laser excitation probe and a three-dimensional laser vibrometer are placed on both sides of the detection platform; Determine the highest frequency signal in the frequency detection result; Determine the ultrasonic signal corresponding to the highest frequency signal by the laser excitation probe, and receive the ultrasonic signal by the three-dimensional laser vibrometer; The ultrasonic signal is processed to obtain a nonlinear zero-frequency wave signal, a target deep learning network model is determined based on the nonlinear zero-frequency wave signal, and the porosity of the target component is predicted by the target deep learning network model, wherein the target deep learning network model is used to map the correspondence between the nonlinear zero-frequency wave signal and the porosity of the target component.

2. The method according to claim 1, characterized in that Processing the ultrasonic signal to obtain a nonlinear zero-frequency wave signal includes: Performing wavelet noise reduction processing on the ultrasonic signal to obtain a noise-reduced ultrasonic signal; The denoised ultrasonic signal is low-pass filtered to obtain the filtered nonlinear zero-frequency wave signal.

3. The method according to claim 1, characterized in that Determining a target deep learning network model based on the ultrasonic signal includes: determining a data set between the nonlinear zero-frequency wave signal and a porosity sample; Dividing the data set into a training set and a test set; The initial deep learning network model is trained based on the training set and the test set to obtain the target deep learning network model.

4. The method according to claim 3, characterized in that The target deep learning network model includes: a sequentially connected input layer, a convolutional neural network module, a gated recurrent unit module, a fully connected layer and an output layer, wherein the convolutional neural network module includes a first convolutional layer, a first pooling layer, a second convolutional layer and a second pooling layer connected sequentially, and the gated recurrent unit module includes a first gated recurrent layer, a first activation layer, a second gated recurrent layer and a second activation layer.

5. The method according to claim 4, characterized in that Predicting the porosity of the target component by using the target deep learning network model includes: Extracting local features of the nonlinear zero-frequency wave signal based on the convolutional neural network module; Training the dependency relationship between the local features based on the gated recurrent unit module; Mapping the dependency relationship through the fully connected layer to obtain a corresponding relationship between the nonlinear zero-frequency wave signal and the porosity of the target component; The porosity of the target component is predicted according to the corresponding relationship.

6. The method according to claim 2, characterized in that Performing wavelet noise reduction processing on the ultrasonic signal to obtain a noise-reduced ultrasonic signal includes: Determine a wavelet function, and perform a multi-layer wavelet transform on the ultrasonic signal based on the wavelet function to obtain a high-frequency coefficient and a low-frequency coefficient of the ultrasonic signal; The high-frequency coefficient and the low-frequency coefficient are processed respectively to obtain the denoised ultrasonic signal.

7. The method according to claim 1, characterized in that The target component is placed on the detection platform and kept in a fixed position during the detection process, wherein the laser excitation probe and the three-dimensional laser vibrometer are placed on both sides of the detection platform and kept at a fixed interval.

8. A nondestructive prediction device for component porosity, characterized in that: include: A detection unit is used to place the target component to be detected on a detection platform for frequency detection to obtain a frequency detection result, wherein: a laser excitation probe and a three-dimensional laser vibrometer are placed on both sides of the detection platform; A first determining unit, configured to determine the highest frequency signal in the frequency detection result; A receiving unit, configured to determine the ultrasonic signal corresponding to the highest frequency signal through the laser excitation probe, and receive the ultrasonic signal through the three-dimensional laser vibrometer; The second determination unit is used to process the ultrasonic signal to obtain a nonlinear zero-frequency wave signal, determine a target deep learning network model based on the nonlinear zero-frequency wave signal, and predict the porosity of the target component through the target deep learning network model, wherein the target deep learning network model is used to map the correspondence between the nonlinear zero-frequency wave signal and the porosity of the target component.

9. A computer-readable storage medium, characterized in that: The storage medium includes a stored program, wherein the program executes the nondestructive prediction method for component porosity according to any one of claims 1 to 7.

10. An electronic device, characterized in that: include: One or more processors, a memory, a display device, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include a method for performing the non-destructive prediction of the porosity of a component as described in any one of claims 1 to 7.

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