Sheet material defect size quantification and movement condition detection method in rapid movement

Abstract

The invention provides a sheet material defect size quantification and motion condition detection method in rapid motion, which comprises the following steps: acquiring two-axis motion-generated eddy current signals of defects of experimental sheet materials with different motion speeds, lift-off distances and defect sizes, the two-axis motion-generated eddy current signals comprising parallel and vertical motion-generated eddy current signals; multiple characteristic parameters are obtained, and then a sample data set is constructed; a quantitative detection model is constructed based on a neural network, a parallel feature parameter input module in the quantitative detection model is connected with a fusion module through a first quantitative detection module, and a vertical feature parameter input module is connected with the fusion module through a second quantitative detection module. The output of each of the two quantitative detection modules comprises a movement speed, a lift-off distance and a defect size; training the model to obtain a target quantitative detection model; and obtaining a target two-axis motion-generated eddy current signal of the defect of the target thin plate material to be quantified, and outputting the defect size, the motion speed and the lift-off distance of the target thin plate material by using the target quantification detection model.

Description

Technical Field

[0001] The present invention relates to the technical field of thin plate material defect detection in rapid motion, and in particular to a method for quantifying defect sizes of thin plate materials in rapid motion and detecting motion conditions. Background Art

[0002] Thin plate materials are often processed into various thin structural components and are widely used in many fields. In the process of rapid production of such structural components, such thin plates are prone to defects such as bubbles, cracks and various non-metallic inclusions. Even if the defects are very small, they will seriously affect the quality and performance of the product.

[0003] At present, the defect detection of thin plate materials mostly adopts magnetic flux leakage detection. However, magnetic flux leakage detection is greatly affected by interference signals in the environment and is limited by the metal properties of thin plate materials, so the false alarm rate is high. There is also eddy current detection technology in the prior art, but the detection signal is easily distorted for moving objects, so the detection accuracy needs to be improved. In addition, the precise positioning of the detected defects also needs to consider the movement conditions of the thin plate material, but there is a lack of relevant technology in the prior art.

[0004] In summary, accurate detection of thin plate material defects in a high-speed production process and quantification of defect size and motion conditions are of great significance for product quality control and accurate defect positioning. Summary of the invention

[0005] The present invention aims to solve one of the technical problems in the related art at least to a certain extent.

[0006] To this end, the first purpose of the present invention is to propose a method for quantifying the defect size of thin plate materials in rapid motion and detecting the motion condition, so as to accurately quantify the defect size and accurately detect the motion condition during the rapid motion process of thin plate material production.

[0007] The second object of the present invention is to provide a system for quantifying defect sizes of thin plate materials in rapid motion and detecting motion conditions.

[0008] A third objective of the present invention is to provide an electronic device.

[0009] A fourth object of the present invention is to provide a computer-readable storage medium.

[0010] To achieve the above-mentioned purpose, the first aspect of the present invention provides a method for quantifying defect size of a thin plate material in rapid motion and detecting motion conditions, comprising:

[0011] Obtaining two-axis motion-induced eddy current signals of defects in the test thin plate material at different movement speeds, different lift-off distances, and different defect sizes, wherein the two-axis motion-induced eddy current signals include parallel motion-induced eddy current signals and vertical motion-induced eddy current signals;

[0012] Performing feature extraction on the parallel motion-induced eddy current signal and the vertical motion-induced eddy current signal to obtain a plurality of parallel feature parameters and a plurality of vertical feature parameters, and constructing a sample data set based on the plurality of parallel feature parameters and the plurality of vertical feature parameters, as well as corresponding motion speeds, lift-off distances, and defect sizes;

[0013] A quantitative detection model is constructed based on a neural network, wherein the quantitative detection model includes a parallel feature parameter input module, a first quantitative detection module, a vertical feature parameter input module, a second quantitative detection module, a fusion module and an output module, wherein the parallel feature parameter input module is connected to the fusion module via the first quantitative detection module, the vertical feature parameter input module is connected to the fusion module via the second quantitative detection module, the fusion module is also connected to the output module, and the outputs of the first quantitative detection module and the second quantitative detection module both include movement speed, lift-off distance and defect size;

[0014] Using the sample data set to train the first quantization detection module and the second quantization detection module to obtain a target quantization detection model;

[0015] Obtaining target two-axis motional eddy current signals of defects in target thin plate materials to be quantified, and then obtaining corresponding multiple target parallel characteristic parameters and multiple target vertical characteristic parameters;

[0016] The plurality of target parallel characteristic parameters and the plurality of target vertical characteristic parameters are input into a target quantitative detection model to output the defect size, movement speed and lifting distance of the target thin plate material.

[0017] In the method of the first aspect of the present invention, the first quantification detection module includes a first defect quantification module and a first motion condition detection module, and the parallel characteristic parameter input module is connected to the fusion module via the first defect quantification module and the first motion condition detection module respectively; the second quantification detection module includes a second defect quantification module and a second motion condition detection module, and the vertical characteristic parameter input module is connected to the fusion module via the second defect quantification module and the second motion condition detection module respectively; the outputs of the first defect quantification module and the second defect quantification module both include defect size; the outputs of the first motion condition detection module and the second motion condition detection module both include motion speed and lift-off distance.

[0018] In the method of the first aspect of the present invention, both the first defect quantization module and the second defect quantization module adopt a convolutional network and a bidirectional long short-term memory neural network.

[0019] In the method of the first aspect of the present invention, both the first motion condition detection module and the second motion condition detection module adopt convolutional networks.

[0020] In the method of the first aspect of the present invention, the fusion module adopts a fully connected layer.

[0021] In the method of the first aspect of the present invention, the multiple parallel characteristic parameters include the amplitude, peak-to-peak value, pulse width, signal area, signal energy, pulse deflection obtained by fast Fourier transform, the ratio of signal area to signal peak-to-peak value, and the ratio of signal area to pulse width extracted from the parallel motion-induced eddy current signal; the multiple vertical characteristic parameters include the amplitude, peak-to-peak value, pulse width, signal area, signal energy, pulse deflection obtained by fast Fourier transform, the ratio of signal area to signal peak-to-peak value, and the ratio of signal area to pulse width extracted from the vertical motion-induced eddy current signal.

[0022] In the method of the first aspect of the present invention, two-axis motion-induced eddy current signals of defects in the experimental thin plate material at different movement speeds, different lift-off distances, and different defect sizes are obtained through finite element software modeling simulation or actual measurement.

[0023] To achieve the above-mentioned purpose, the second aspect of the present invention provides a system for quantifying defect size of thin plate materials in rapid motion and detecting motion conditions, comprising:

[0024] A sample data set construction unit is used to obtain two-axis motion-induced eddy current signals of defects in experimental thin plate materials under different movement speeds, different lift-off distances, and different defect sizes, wherein the two-axis motion-induced eddy current signals include parallel motion-induced eddy current signals and vertical motion-induced eddy current signals; feature extraction is performed on the parallel motion-induced eddy current signals and the vertical motion-induced eddy current signals to obtain a variety of parallel feature parameters and a variety of vertical feature parameters, and a sample data set is constructed based on the multiple parallel feature parameters and the multiple vertical feature parameters, as well as the corresponding movement speeds, lift-off distances, and defect sizes;

[0025] A modeling unit, for constructing a quantitative detection model based on a neural network, wherein the quantitative detection model includes a parallel feature parameter input module, a first quantitative detection module, a vertical feature parameter input module, a second quantitative detection module, a fusion module and an output module, wherein the parallel feature parameter input module is connected to the fusion module via the first quantitative detection module, the vertical feature parameter input module is connected to the fusion module via the second quantitative detection module, the fusion module is also connected to the output module, and the outputs of the first quantitative detection module and the second quantitative detection module both include movement speed, lift-off distance and defect size; and the first quantitative detection module and the second quantitative detection module are trained using the sample data set to obtain a target quantitative detection model;

[0026] An acquisition unit is used to acquire target two-axis motional eddy current signals of defects of a target thin plate material to be quantified, and then obtain corresponding multiple target parallel characteristic parameters and multiple target vertical characteristic parameters;

[0027] The quantitative detection unit is used to input the multiple target parallel characteristic parameters and the multiple target vertical characteristic parameters into the target quantitative detection model to output the defect size, movement speed and lifting distance of the target thin plate material.

[0028] To achieve the above-mentioned purpose, the third aspect of the present invention proposes an electronic device, comprising: a processor, and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method proposed in the first aspect of the present invention.

[0029] To achieve the above-mentioned purpose, the fourth aspect of the present invention proposes a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the method proposed in the first aspect of the present invention.

[0030] The present invention provides a method, system, electronic device and storage medium for quantifying defect size and detecting motion conditions of thin plate materials in rapid motion. The method obtains two-axis motion-induced eddy current signals of defects of experimental thin plate materials under different motion speeds, different lift-off distances and different defect sizes. The two-axis motion-induced eddy current signals include parallel motion-induced eddy current signals and vertical motion-induced eddy current signals. Feature extraction is performed on the parallel motion-induced eddy current signals and the vertical motion-induced eddy current signals to obtain a variety of parallel feature parameters and a variety of vertical feature parameters, and a sample data set is constructed based on the multiple parallel feature parameters and the multiple vertical feature parameters, as well as the corresponding motion speeds, lift-off distances and defect sizes. A quantitative detection model is constructed based on a neural network, and the quantitative detection model includes a parallel feature parameter input module, a first quantitative detection module, a vertical feature parameter input module, and a second quantitative detection module. The invention relates to a detection module, a fusion module and an output module. The parallel characteristic parameter input module is connected to the fusion module via the first quantitative detection module, and the vertical characteristic parameter input module is connected to the fusion module via the second quantitative detection module. The fusion module is also connected to the output module. The outputs of the first quantitative detection module and the second quantitative detection module include movement speed, lift-off distance and defect size. The first quantitative detection module and the second quantitative detection module are trained with a sample data set to obtain a target quantitative detection model. The target two-axis motion-induced eddy current signal of the defect of the target thin plate material to be quantified is obtained, and then a corresponding plurality of target parallel characteristic parameters and a plurality of target vertical characteristic parameters are obtained. The plurality of target parallel characteristic parameters and a plurality of target vertical characteristic parameters are input into the target quantitative detection model to output the defect size, movement speed and lift-off distance of the target thin plate material.In this case, the motion-induced eddy current signals in two directions, the parallel motion-induced eddy current signals and the vertical motion-induced eddy current signals, are comprehensively considered, and features are extracted from the motion-induced eddy current signals in the two directions to obtain corresponding multiple parallel feature parameters and multiple vertical feature parameters, and then a sample data set is constructed. A quantitative detection model is also constructed based on a neural network. The quantitative detection model includes a parallel feature parameter input module, a first quantitative detection module, a vertical feature parameter input module, a second quantitative detection module, a fusion module and an output module, wherein the parallel feature parameter input module is connected to the first quantitative detection module, the first quantitative detection module processes and analyzes the parallel feature parameters to obtain corresponding movement speed, lift-off distance and defect size, and the vertical feature parameter input module It is connected to the second quantification detection module, which processes and analyzes the vertical characteristic parameters to obtain the corresponding movement speed, lifting distance and defect size. The fusion module fuses the outputs of the first quantification detection module and the second quantification detection module to obtain the final movement speed, lifting distance and defect size. The quantification detection model is trained using the sample data set to obtain a target quantification detection model. The target quantification detection model can accurately quantify the defect size and accurately detect the movement condition. In actual scenarios, the target quantification detection model is used for the defect size quantification and movement condition detection of the target thin plate material to be quantified, so that the defect size can be accurately quantified and the movement condition can be accurately detected during the rapid movement of the thin plate material production.

[0031] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0033] Figure 1 A schematic flow chart of a method for quantifying defect size of a thin plate material in rapid motion and detecting motion conditions provided by an embodiment of the present invention;

[0034] Figure 2 A schematic diagram of a parallel motion induced eddy current signal provided in an embodiment of the present invention;

[0035] Figure 3 A schematic diagram of a quantitative detection model provided by an embodiment of the present invention;

[0036] Figure 4 A schematic diagram of training a quantization detection model provided by an embodiment of the present invention;

[0037] Figure 5A block diagram of a system for quantifying defect sizes of thin plate materials in rapid motion and detecting motion conditions provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0038] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0039] The following describes the method and system for quantifying defect sizes of thin plate materials and detecting motion conditions during rapid motion according to an embodiment of the present invention with reference to the accompanying drawings.

[0040] The embodiment of the present invention provides a method for quantifying defect sizes of thin plate materials in rapid motion and detecting motion conditions, so as to accurately quantify defect sizes and accurately detect motion conditions during rapid motion of thin plate material production.

[0041] Figure 1 A schematic flow chart of a method for quantifying defect size of a thin plate material in rapid motion and detecting motion conditions provided by an embodiment of the present invention.

[0042] like Figure 1 As shown, the method for quantifying defect size of thin plate material and detecting motion conditions during rapid motion includes the following steps:

[0043] Step S101, obtaining two-axis motion-induced eddy current signals of defects in the test thin plate material at different movement speeds, different lift-off distances, and different defect sizes, wherein the two-axis motion-induced eddy current signals include parallel motion-induced eddy current signals and vertical motion-induced eddy current signals.

[0044] In step S101, by means of finite element software modeling simulation or actual measurement, experimental thin plate materials with different defect sizes can be subjected to different movement speeds and different lifting distances to obtain two-axis motion-induced eddy current signals of defects in the experimental thin plate materials with different defect sizes at different movement speeds and different lifting distances.

[0045] For example, the parallel motion-induced eddy current signals and vertical motion-induced eddy current signals of 100μm, 200μm, 300μm, 400μm, 500μm, 700μm and 800μm through-hole defect sizes on 0.1mm thick copper foil at a motion speed of 15m / s and a lift-off distance of 0.5mm were obtained through actual measurement. The motion speed and lift-off distance were reset to obtain a set of two-axis motion-induced eddy current signals of 100μm, 200μm, 300μm, 400μm, 500μm, 700μm and 800μm through-hole defect sizes on 0.1mm thick copper foil at different motion speeds and lift-off distances.

[0046] It is easy to understand that the movement speed in step S101 refers to the movement speed of the thin plate material. The lifting distance refers to the distance from the thin plate material to the probe. The defect size includes the defect length, defect width, and defect depth of the thin plate material.

[0047] In step S101, the two-axis motion-induced eddy current signal includes a parallel motion-induced eddy current signal and a vertical motion-induced eddy current signal. The parallel motion-induced eddy current signal is the parallel component, which refers to the direction of movement of the sheet material. The vertical motion-induced eddy current signal is the vertical component, which refers to the vertical direction of the sheet material.

[0048] In step S102, feature extraction is performed on the parallel motion-induced eddy current signal and the vertical motion-induced eddy current signal to obtain a plurality of parallel feature parameters and a plurality of vertical feature parameters, and a sample data set is constructed based on the plurality of parallel feature parameters and the plurality of vertical feature parameters, as well as the corresponding movement speed, lift-off distance and defect size.

[0049] In step S102, a variety of parallel characteristic parameters include but are not limited to the amplitude Up, peak-to-peak value Up-p, pulse width Dv-v, signal area Sa, signal energy E, pulse deflection I obtained by fast Fourier transform, and ratio of signal area to signal peak-to-peak value extracted from the parallel motion eddy current signal. Ratio of signal area to pulse width

[0050] Figure 2 This is a schematic diagram of a parallel motion induced eddy current signal provided in an embodiment of the present invention. Figure 2 The figure shows the parallel motion eddy current signal generated by the defect of the experimental thin plate material under one condition, where Figure 2 Only some types of characteristic parameters are shown, for example, Up is the amplitude, Up-p is the peak-to-peak value, Dv-v is the pulse width, and Sa is the signal area.

[0051] In step S102, a variety of vertical characteristic parameters include but are not limited to the amplitude Up, peak-to-peak value Up-p, pulse width Dv-v, signal area Sa, signal energy E, pulse deflection I obtained by fast Fourier transform, and ratio of signal area to signal peak-to-peak value extracted from the vertical motional eddy current signal. Ratio of signal area to pulse width

[0052] The amplitude is the difference between the peak value of the defect signal (i.e., the parallel motion eddy current signal or the vertical motion eddy current signal) and the baseline. The coordinate when the voltage is zero is taken as the baseline. The calculation formula is:

[0053] U p =min{p i}, i=0,1,2,…,N

[0054] Where N is the number of peaks. i is the ith peak.

[0055] The peak-to-peak value is the absolute value of the difference between the larger peak value and the valley value of the defect signal, and the calculation formula is:

[0056] U p-p =|max{p i}-min{p i}|

[0057] The pulse width is the time interval between the larger peak and the smaller peak in the defect signal in the time domain (it can also be the number of sampling points). The data point numbers at the larger peak and the smaller peak are m and n respectively, and the calculation formula is:

[0058] D v-v =|mn|

[0059] The signal area is calculated as:

[0060]

[0061] The signal energy is calculated as:

[0062]

[0063] In the formula, Mean{p i} is the peak mean.

[0064] The calculation formula of pulse skewness is:

[0065]

[0066] Among them, μ tran is the average pulse amplitude obtained by fast Fourier transform. pTi represents the i-th frequency component after fast Fourier transform; M represents the total number of frequency components.

[0067] The calculation formula for the ratio of signal area to signal peak-to-peak value is:

[0068]

[0069] The ratio of signal area to pulse width is calculated as:

[0070]

[0071] Where, T s is the sampling period.

[0072] In step S102, a sample data set is constructed based on a plurality of parallel characteristic parameters and a plurality of vertical characteristic parameters, as well as corresponding movement speeds, lift-off distances and defect sizes.

[0073] Specifically, the sample data set constructed in step S102 of this embodiment includes a first sample data set and a second sample data set. The first sample data set includes a plurality of parallel feature parameters, and corresponding movement speeds, lift-off distances, and defect sizes. Among them, the plurality of parallel feature parameters in the first sample data set are used as input data for model training. The second sample data set includes a plurality of vertical feature parameters, and corresponding movement speeds, lift-off distances, and defect sizes. Among them, the plurality of vertical feature parameters in the second sample data set are used as input data for model training.

[0074] In some embodiments, the first sample data set includes a first quantization sample data set and a first detection sample data set. The first quantization sample data set includes a plurality of parallel characteristic parameters and corresponding defect sizes. The first detection sample data set includes a plurality of parallel characteristic parameters, and corresponding movement speeds and lift-off distances. The second sample data set includes a second quantization sample data set and a second detection sample data set. The second quantization sample data set includes a plurality of vertical characteristic parameters and corresponding defect sizes. The second detection sample data set includes a plurality of vertical characteristic parameters, and corresponding movement speeds and lift-off distances.

[0075] Step S103, constructing a quantitative detection model based on a neural network, the quantitative detection model includes a parallel feature parameter input module, a first quantitative detection module, a vertical feature parameter input module, a second quantitative detection module, a fusion module and an output module, the parallel feature parameter input module is connected to the fusion module via the first quantitative detection module, the vertical feature parameter input module is connected to the fusion module via the second quantitative detection module, the fusion module is also connected to the output module, and the outputs of the first quantitative detection module and the second quantitative detection module both include movement speed, lift-off distance and defect size.

[0076] In step S103, the parallel feature parameter input module is used to input a variety of parallel feature parameters. The first quantitative detection module is used to process and analyze the various parallel feature parameters to obtain corresponding movement speeds, lift-off distances and defect sizes. The vertical feature parameter input module is used to input a variety of vertical feature parameters. The second quantitative detection module is used to process and analyze the various vertical feature parameters to obtain corresponding movement speeds, lift-off distances and defect sizes. The fusion module is used to fuse the outputs of the first quantitative detection module and the second quantitative detection module to obtain the final movement speed, lift-off distance and defect size. The output module is used to output the final movement speed, lift-off distance and defect size.

[0077] In some embodiments, the first quantification detection module in step S103 includes a first defect quantification module and a first motion condition detection module, and the parallel characteristic parameter input module is connected to the fusion module via the first defect quantification module and the first motion condition detection module respectively; the second quantification detection module includes a second defect quantification module and a second motion condition detection module, and the vertical characteristic parameter input module is connected to the fusion module via the second defect quantification module and the second motion condition detection module respectively; the outputs of the first defect quantification module and the second defect quantification module both include defect size; the outputs of the first motion condition detection module and the second motion condition detection module both include motion speed and lift-off distance.

[0078] In some embodiments, the first defect quantization module and the second defect quantization module both use a convolutional network and a bidirectional long short-term memory neural network.

[0079] In some embodiments, the first motion condition detection module and the second motion condition detection module both use convolutional networks.

[0080] In some embodiments, the fusion module employs a fully connected layer.

[0081] Figure 3 A schematic diagram of a quantitative detection model provided by an embodiment of the present invention. Figure 3 As shown, the parallel feature parameter input module is the first input layer, which is used to receive n types of parallel feature parameters, namely x 1 、x 2 、x 3 , …, x n-2 、x n-1 、x n For example, when n is 8, the eight parallel characteristic parameters can be, for example, the amplitude Up, peak-to-peak value Up-p, pulse width Dv-v, signal area Sa, signal energy E, pulse deflection I obtained by fast Fourier transform, and the ratio of signal area to signal peak-to-peak value extracted from the parallel motion eddy current signal. Ratio of signal area to pulse width

[0082]

[0083] The first motion condition detection module is a first convolutional network, and the first convolutional network can adopt a convolutional neural network (CNN). The input end of the first convolutional network is connected to the first input layer. The output of the first convolutional network includes motion speed and lift-off distance.

[0084] The first quantitative detection module includes a first convolutional layer and a first BILSTM (bidirectional long short-term memory neural network) layer. The first convolutional layer can use a convolutional neural network (CNN). Using CNN and BILSTM can fully explore the relationship between the temporal features and spatial features in the input data and the defect size. The output of the first BILSTM layer is the defect size.

[0085] The vertical feature parameter input module is the second input layer. The second input layer is used to receive n types of vertical feature parameters, namely z 1 、z 2 、z 3 ,…,z n-2 、z n-1 、z n For example, when n is 8, the eight vertical characteristic parameters may be, for example, the amplitude Up, peak-to-peak value Up-p, pulse width Dv-v, signal area Sa, signal energy E, pulse deflection I obtained by fast Fourier transform, and ratio of signal area to signal peak-to-peak value extracted from the vertical motional eddy current signal. Ratio of signal area to pulse width

[0086] The second defect quantization module includes a second convolutional layer and a second BILSTM (bidirectional long short-term memory neural network) layer. The second convolutional layer can use a convolutional neural network (CNN). The output of the second BILSTM layer is the defect size.

[0087] The second motion condition detection module is a second convolutional network, and the second convolutional network can adopt a convolutional neural network (CNN). The input end of the second convolutional network is connected to the second input layer. The output of the second convolutional network includes motion speed and lift-off distance.

[0088] The fusion module uses a fully connected layer. The fully connected layer is connected to the output of the first convolutional network, the output of the first BILSTM layer, the output of the second BILSTM layer, and the output of the second convolutional network. The fully connected layer fuses the output of the first convolutional network with the output of the second convolutional network, and also fuses the output of the first BILSTM layer with the output of the second BILSTM layer, thereby obtaining the final motion speed, lift-off distance, and defect size.

[0089] The output layer is used to output the final motion speed, lift-off distance and defect size.

[0090] Step S104: using the sample data set to train the first quantization detection module and the second quantization detection module to obtain a target quantization detection model.

[0091] In step S104, the first sample data set and the second sample data set are used to train the first quantization detection module and the second quantization detection module respectively to obtain the trained first quantization detection module and the second quantization detection module. The parallel feature parameter input module, the trained first quantization detection module, the vertical feature parameter input module, the trained second quantization detection module, the fusion module and the output module constitute the target quantization detection model.

[0092] In step S104, the first sample data set can also be divided into a first sample training set and a first sample test set, the first sample training set is used to train the first quantitative detection module, and the first sample test set is used to test the first quantitative detection module, so that the result of the model is more accurate. The training of the second quantitative detection module can refer to the training of the first quantitative detection module.

[0093] In some embodiments, a first sample verification set may be further divided from the first sample data set, and the tested first quantitative detection module may be further verified using the first sample verification set to further improve the accuracy of the model result.

[0094] Figure 4 Schematic diagram of the training of the quantization detection model provided by the embodiment of the present invention. Taking the training of the first quantization detection module as an example, Figure 4 As shown, the first sample data set is divided into a first sample training set and a first sample test set, the initial parameters of the first quantization detection module are set, the first sample training set is used to train the first quantization detection module to update the network parameters in the first quantization detection module, and then the first sample test set is used to test the trained first quantization detection module, thereby obtaining a trained first quantization detection module.

[0095] In step S104, when the first quantization detection module includes a first defect quantization module and a first motion condition detection module, the first defect quantization module is trained using the first quantization sample data set, and the first motion condition detection module is trained using the first detection sample data set. When the second quantization detection module includes a second defect quantization module and a second motion condition detection module, the second defect quantization module is trained using the second quantization sample data set, and the second motion condition detection module is trained using the second detection sample data set.

[0096] Step S105, obtaining target two-axis motional eddy current signals of defects of the target thin plate material to be quantified, and then obtaining corresponding multiple target parallel characteristic parameters and multiple target vertical characteristic parameters.

[0097] In step S105, in actual application, the target two-axis motion-induced eddy current signal of the defect of the target thin plate material to be quantified is obtained, and the target two-axis motion-induced eddy current signal includes a target parallel motion-induced eddy current signal and a target vertical motion-induced eddy current signal. Feature extraction is performed on the target parallel motion-induced eddy current signal and the target vertical motion-induced eddy current signal to obtain a plurality of target parallel feature parameters and a plurality of target vertical feature parameters. The types and quantity of the target parallel feature parameters are consistent with the types and quantity of the parallel feature parameters selected during training. The types and quantity of the target vertical feature parameters are consistent with the types and quantity of the vertical feature parameters selected during training.

[0098] Step S106, inputting a plurality of target parallel characteristic parameters and a plurality of target vertical characteristic parameters into a target quantitative detection model to output defect size, movement speed and lifting distance of the target thin plate material.

[0099] In step S106, multiple target parallel characteristic parameters and multiple target vertical characteristic parameters are input into the target quantitative detection model, and the defect size of the target thin plate material and the movement speed and lifting distance of the target thin plate material are output. The defect size of the target thin plate material includes the length, width and depth of the defect of the target thin plate material.

[0100] In other embodiments of the present invention, the quantitative detection model can be optimized to improve efficiency while ensuring the accuracy of the quantitative detection model. The optimized quantitative detection model includes a first input module, a first defect quantization module, a second input module, a first motion condition detection module, a third input module, a second defect quantization module, a fourth input module, a second motion condition detection module, a fusion module and an output module. The first input module is connected to the fusion module via the first defect quantization module, the second input module is connected to the fusion module via the first motion condition detection module, the third input module is connected to the fusion module via the second defect quantification module, and the fourth input module is connected to the fusion module via the second motion condition detection module. The input parameters of the first input module and the second input module are parallel characteristic parameters.

[0101] The parallel characteristic parameters of the first input module may be the eight parallel characteristic parameters in step S102, and the parallel characteristic parameters of the second input module may be the signal reference value U extracted from the parallel motion induced eddy current signal. f , signal slope and energy dispersion The input parameters of the third input module and the fourth input module are vertical characteristic parameters, wherein the vertical characteristic parameters of the third input module can be the 8 vertical characteristic parameters in step S102, and the vertical characteristic parameters of the fourth input module can be the signal reference value U extracted from the vertical motion-induced eddy current signal. f , signal slope and energy dispersion

[0102] A corresponding sample data set is constructed for the optimized quantitative detection model to train the optimized quantitative detection model, and then applied to the actual defect size quantification and motion condition detection of the target thin plate material to be quantified. It should be noted that in actual application, the characteristic parameters obtained are consistent with the type and quantity of the input data in the corresponding sample data set.

[0103] Compared with the quantization detection model before optimization, the optimized quantization detection model of the present invention has less input data when performing motion condition detection, which saves calculation time and improves efficiency. f , signal slope and energy dispersion It has a high correlation with the motion conditions, so although the number of input parameters is reduced, the accuracy of the quantitative detection model is still guaranteed.

[0104] In order to implement the above-mentioned embodiment, the present invention further proposes a system for quantifying defect sizes of thin plate materials in rapid motion and detecting motion conditions.

[0105] Figure 5 A block diagram of a system for quantifying defect sizes of thin plate materials in rapid motion and detecting motion conditions provided by an embodiment of the present invention.

[0106] like Figure 5 As shown, the system for quantifying defect size of thin plate materials in rapid motion and detecting motion conditions includes a sample data set construction unit 11, a modeling unit 12, an acquisition unit 13 and a quantification detection unit 14, wherein:

[0107] The sample data set construction unit 11 is used to obtain two-axis motion-induced eddy current signals of defects of the experimental thin plate material under different movement speeds, different lift-off distances, and different defect sizes, wherein the two-axis motion-induced eddy current signals include parallel motion-induced eddy current signals and vertical motion-induced eddy current signals; feature extraction is performed on the parallel motion-induced eddy current signals and the vertical motion-induced eddy current signals to obtain a variety of parallel feature parameters and a variety of vertical feature parameters, and a sample data set is constructed based on the multiple parallel feature parameters and the multiple vertical feature parameters, as well as the corresponding movement speeds, lift-off distances, and defect sizes;

[0108] A modeling unit 12 is used to construct a quantitative detection model based on a neural network, wherein the quantitative detection model includes a parallel feature parameter input module, a first quantitative detection module, a vertical feature parameter input module, a second quantitative detection module, a fusion module and an output module, wherein the parallel feature parameter input module is connected to the fusion module via the first quantitative detection module, and the vertical feature parameter input module is connected to the fusion module via the second quantitative detection module, and the fusion module is also connected to the output module, wherein the outputs of the first quantitative detection module and the second quantitative detection module both include movement speed, lift-off distance and defect size; and the first quantitative detection module and the second quantitative detection module are trained using a sample data set to obtain a target quantitative detection model;

[0109] An acquisition unit 13 is used to acquire target two-axis motional eddy current signals of defects of a target sheet material to be quantified, and then obtain corresponding multiple target parallel characteristic parameters and multiple target vertical characteristic parameters;

[0110] The quantitative detection unit 14 is used to input a plurality of target parallel characteristic parameters and a plurality of target vertical characteristic parameters into a target quantitative detection model to output defect size, movement speed and lifting distance of the target thin plate material.

[0111] Furthermore, in a possible implementation of an embodiment of the present invention, the sample data set construction unit 11 obtains two-axis motion-induced eddy current signals of defects in the experimental thin plate material under different movement speeds, different lifting distances, and different defect sizes through finite element software modeling simulation or actual measurement.

[0112] Furthermore, in a possible implementation of an embodiment of the present invention, the multiple parallel characteristic parameters in the sample data set construction unit 11 include the amplitude, peak-to-peak value, pulse width, signal area, signal energy, pulse deflection obtained by fast Fourier transform, the ratio of signal area to signal peak-to-peak value, and the ratio of signal area to pulse width extracted from the parallel motion-induced eddy current signal; the multiple vertical characteristic parameters include the amplitude, peak-to-peak value, pulse width, signal area, signal energy, pulse deflection obtained by fast Fourier transform, the ratio of signal area to signal peak-to-peak value, and the ratio of signal area to pulse width extracted from the vertical motion-induced eddy current signal.

[0113] Furthermore, in a possible implementation of an embodiment of the present invention, the first quantification detection module in the modeling unit 12 includes a first defect quantification module and a first motion condition detection module, and the parallel characteristic parameter input module is connected to the fusion module via the first defect quantification module and the first motion condition detection module respectively; the second quantification detection module includes a second defect quantification module and a second motion condition detection module, and the vertical characteristic parameter input module is connected to the fusion module via the second defect quantification module and the second motion condition detection module respectively; the outputs of the first defect quantification module and the second defect quantification module both include defect size; the outputs of the first motion condition detection module and the second motion condition detection module both include motion speed and lift-off distance.

[0114] Furthermore, in a possible implementation of the embodiment of the present invention, the first defect quantization module and the second defect quantization module in the modeling unit 12 both adopt a convolutional network and a bidirectional long short-term memory neural network.

[0115] Furthermore, in a possible implementation of the embodiment of the present invention, the first motion condition detection module and the second motion condition detection module in the modeling unit 12 both adopt convolutional networks.

[0116] Furthermore, in a possible implementation of the embodiment of the present invention, the fusion module in the modeling unit 12 adopts a fully connected layer.

[0117] It should be noted that the above explanation of the embodiment of the method for quantifying the defect size of thin plate materials in rapid motion and detecting the motion condition is also applicable to the system for quantifying the defect size of thin plate materials in rapid motion and detecting the motion condition of this embodiment, and will not be repeated here.

[0118] In an embodiment of the present invention, two-axis motion-induced eddy current signals of defects in an experimental thin plate material under different movement speeds, different lift-off distances, and different defect sizes are obtained, and the two-axis motion-induced eddy current signals include parallel motion-induced eddy current signals and vertical motion-induced eddy current signals; feature extraction is performed on the parallel motion-induced eddy current signals and the vertical motion-induced eddy current signals, respectively, to obtain a variety of parallel feature parameters and a variety of vertical feature parameters, and a sample data set is constructed based on the multiple parallel feature parameters and the multiple vertical feature parameters, as well as the corresponding movement speeds, lift-off distances, and defect sizes; a quantitative detection model is constructed based on a neural network, and the quantitative detection model includes a parallel feature parameter input module, a first quantitative detection module, a vertical feature parameter input module, a second quantitative detection module, a fusion module, and an output module. The characteristic parameter input module is connected to the fusion module via the first quantification detection module, and the vertical characteristic parameter input module is connected to the fusion module via the second quantification detection module. The fusion module is also connected to the output module. The outputs of the first quantification detection module and the second quantification detection module both include movement speed, lift-off distance and defect size. The first quantification detection module and the second quantification detection module are trained using the sample data set to obtain a target quantification detection model. The target two-axis motion-induced eddy current signal of the defect of the target thin plate material to be quantified is obtained, and then the corresponding multiple target parallel characteristic parameters and multiple target vertical characteristic parameters are obtained. The multiple target parallel characteristic parameters and multiple target vertical characteristic parameters are input into the target quantification detection model to output the defect size, movement speed and lift-off distance of the target thin plate material.In this case, the motion-induced eddy current signals in two directions, the parallel motion-induced eddy current signals and the vertical motion-induced eddy current signals, are comprehensively considered, and features are extracted from the motion-induced eddy current signals in the two directions to obtain corresponding multiple parallel feature parameters and multiple vertical feature parameters, and then a sample data set is constructed. A quantitative detection model is also constructed based on a neural network. The quantitative detection model includes a parallel feature parameter input module, a first quantitative detection module, a vertical feature parameter input module, a second quantitative detection module, a fusion module and an output module, wherein the parallel feature parameter input module is connected to the first quantitative detection module, the first quantitative detection module processes and analyzes the parallel feature parameters to obtain corresponding movement speed, lift-off distance and defect size, and the vertical feature parameter input module It is connected to the second quantification detection module, which processes and analyzes the vertical characteristic parameters to obtain the corresponding movement speed, lifting distance and defect size. The fusion module fuses the outputs of the first quantification detection module and the second quantification detection module to obtain the final movement speed, lifting distance and defect size. The quantification detection model is trained using the sample data set to obtain a target quantification detection model. The target quantification detection model can accurately quantify the defect size and accurately detect the movement condition. In actual scenarios, the target quantification detection model is used for the defect size quantification and movement condition detection of the target thin plate material to be quantified, so that the defect size can be accurately quantified and the movement condition can be accurately detected during the rapid movement of the thin plate material production.

[0119] The method and system of the present invention extract the characteristics of the two-axis kinematic eddy current signal, and establish a neural network model in combination with the motion speed, lift-off distance, length, width and depth of the defect, so as to realize the accurate quantification of the defect size of different thin plate materials, as well as the detection of the motion speed and lift-off distance. Considering that the defect size alone cannot realize the accurate positioning of the defect, and the positioning of the defect is also related to the motion conditions, the present invention takes into account the accurate quantification of the defect size, as well as the detection of the motion speed and lift-off distance, so that the subsequent comprehensive defect size, motion speed and lift-off distance can realize the accurate positioning of the defect. The method and system of the present invention are suitable for the rapid production process of thin plate materials, and can be widely used in the field of non-destructive detection of defects in thin plate materials. It has great engineering significance and good application prospects.

[0120] In order to implement the above embodiments, the present invention also proposes an electronic device, comprising: a processor, and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method provided by the above embodiments.

[0121] In order to implement the above embodiments, the present invention further proposes a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the methods provided by the above embodiments.

[0122] In order to implement the above embodiments, the present invention further provides a computer program product, including a computer program, which implements the methods provided by the above embodiments when executed by a processor.

[0123] In the description of the aforementioned embodiments, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

[0124] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0125] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present invention belong.

[0126] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute the instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0127] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0128] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0129] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0130] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present invention. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A method for quantifying defect size of thin plate materials in rapid motion and detecting motion conditions, characterized in that: include: Obtaining two-axis motion-induced eddy current signals of defects in the test thin plate material at different movement speeds, different lift-off distances, and different defect sizes, wherein the two-axis motion-induced eddy current signals include parallel motion-induced eddy current signals and vertical motion-induced eddy current signals; Performing feature extraction on the parallel motion-induced eddy current signal and the vertical motion-induced eddy current signal to obtain a plurality of parallel feature parameters and a plurality of vertical feature parameters, and constructing a sample data set based on the plurality of parallel feature parameters and the plurality of vertical feature parameters, as well as corresponding motion speeds, lift-off distances, and defect sizes; A quantitative detection model is constructed based on a neural network, wherein the quantitative detection model includes a parallel feature parameter input module, a first quantitative detection module, a vertical feature parameter input module, a second quantitative detection module, a fusion module and an output module, wherein the parallel feature parameter input module is connected to the fusion module via the first quantitative detection module, the vertical feature parameter input module is connected to the fusion module via the second quantitative detection module, the fusion module is also connected to the output module, and the outputs of the first quantitative detection module and the second quantitative detection module both include movement speed, lift-off distance and defect size; Using the sample data set to train the first quantization detection module and the second quantization detection module to obtain a target quantization detection model; Obtaining target two-axis motional eddy current signals of defects in target thin plate materials to be quantified, and then obtaining corresponding multiple target parallel characteristic parameters and multiple target vertical characteristic parameters; The plurality of target parallel characteristic parameters and the plurality of target vertical characteristic parameters are input into a target quantitative detection model to output the defect size, movement speed and lifting distance of the target thin plate material.

2. The method for quantifying defect size of thin plate material and detecting motion conditions in rapid motion according to claim 1, characterized in that: The first quantification detection module includes a first defect quantification module and a first motion condition detection module, and the parallel characteristic parameter input module is connected to the fusion module via the first defect quantification module and the first motion condition detection module respectively; the second quantification detection module includes a second defect quantification module and a second motion condition detection module, and the vertical characteristic parameter input module is connected to the fusion module via the second defect quantification module and the second motion condition detection module respectively; the outputs of the first defect quantification module and the second defect quantification module both include defect size; the outputs of the first motion condition detection module and the second motion condition detection module both include motion speed and lift-off distance.

3. The method for quantifying defect size of thin plate material and detecting motion conditions in rapid motion according to claim 2, characterized in that: The first defect quantization module and the second defect quantization module both adopt convolutional networks and bidirectional long short-term memory neural networks.

4. The method for quantifying defect size of thin plate material and detecting motion conditions in rapid motion according to claim 2, characterized in that: The first motion condition detection module and the second motion condition detection module both adopt convolutional networks.

5. The method for quantifying defect size of thin plate material and detecting motion conditions in rapid motion according to claim 1, characterized in that: The fusion module adopts a fully connected layer.

6. The method for quantifying defect size of thin plate material and detecting motion conditions in rapid motion according to claim 1, characterized in that: The multiple parallel characteristic parameters include the amplitude, peak-to-peak value, pulse width, signal area, signal energy, pulse deflection obtained by fast Fourier transform, the ratio of signal area to signal peak-to-peak value, and the ratio of signal area to pulse width extracted from the parallel motion-induced eddy current signal; the multiple vertical characteristic parameters include the amplitude, peak-to-peak value, pulse width, signal area, signal energy, pulse deflection obtained by fast Fourier transform, the ratio of signal area to signal peak-to-peak value, and the ratio of signal area to pulse width extracted from the vertical motion-induced eddy current signal.

7. The method for quantifying defect size of thin plate material and detecting motion conditions in rapid motion according to claim 1, characterized in that: Through finite element software modeling simulation or actual measurement, the two-axis motion-induced eddy current signals of defects in the experimental thin plate material under different movement speeds, different lifting distances, and different defect sizes are obtained.

8. A system for quantifying defect sizes of thin plate materials in rapid motion and detecting motion conditions, characterized in that: include: A sample data set construction unit is used to obtain two-axis motion-induced eddy current signals of defects in experimental thin plate materials under different movement speeds, different lift-off distances, and different defect sizes, wherein the two-axis motion-induced eddy current signals include parallel motion-induced eddy current signals and vertical motion-induced eddy current signals; feature extraction is performed on the parallel motion-induced eddy current signals and the vertical motion-induced eddy current signals to obtain a variety of parallel feature parameters and a variety of vertical feature parameters, and a sample data set is constructed based on the multiple parallel feature parameters and the multiple vertical feature parameters, as well as the corresponding movement speeds, lift-off distances, and defect sizes; A modeling unit, for constructing a quantitative detection model based on a neural network, wherein the quantitative detection model includes a parallel feature parameter input module, a first quantitative detection module, a vertical feature parameter input module, a second quantitative detection module, a fusion module and an output module, wherein the parallel feature parameter input module is connected to the fusion module via the first quantitative detection module, the vertical feature parameter input module is connected to the fusion module via the second quantitative detection module, the fusion module is also connected to the output module, and the outputs of the first quantitative detection module and the second quantitative detection module both include movement speed, lift-off distance and defect size; and the first quantitative detection module and the second quantitative detection module are trained using the sample data set to obtain a target quantitative detection model; An acquisition unit is used to acquire target two-axis motional eddy current signals of defects of a target thin plate material to be quantified, and then obtain corresponding multiple target parallel characteristic parameters and multiple target vertical characteristic parameters; The quantitative detection unit is used to input the multiple target parallel characteristic parameters and the multiple target vertical characteristic parameters into the target quantitative detection model to output the defect size, movement speed and lifting distance of the target thin plate material.

9. An electronic device, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 7 when executed by a processor.

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