A metal parts detection system and method based on directional pulsed eddy current

Abstract

The present invention discloses a metal component detection system and method based on directional pulsed eddy current. The system includes a directional pulsed eddy current probe, which is used to scan the test piece and obtain defect information of the test piece through the eddy current effect. The defect information includes the depth, length and width of the defect. The invention fully detects the induced magnetic field through three mutually orthogonal annular detection coils to improve the accuracy of eddy current detection. The method calibrates the defect information of the defect position of the test piece and integrates the temperature information to establish a three-dimensional surface analytical expression, so that the defect information of the specific defect position of the test piece can be obtained by importing real-time temperature information and sensed voltage values.

Description

Technical Field

[0001] The present invention belongs to the technical field of eddy current detection, and in particular relates to a metal component detection system and method based on directional pulsed eddy current. Background Art

[0002] Pulsed eddy current testing offers advantages such as numerous characteristic quantities, a wide spectrum, fast testing speed, deep testing depth, high sensitivity, and easy quantification. Existing eddy current probes primarily include impedance-type and field-type eddy current probes. Impedance-type eddy current probes typically utilize a cylindrical coil, which serves as both an excitation and detection unit. The characteristics of the object being tested are determined by measuring changes in the coil's equivalent impedance. This coil's equivalent impedance is typically measured using a measurement bridge within the eddy current testing system, which is difficult to balance under pulsed and multi-frequency excitation conditions.

[0003] The equivalent impedance of the coil is generally detected through the measurement bridge in the eddy current testing system. For example, based on the circuit-eddy current loop model of the "equivalence principle", the induced eddy current in the test piece is equivalent to a ring current of finite size, thereby equating the coupling relationship between the excitation coil and the test piece to the coupling relationship between the excitation coil and the current loop, and simplifying the complex electromagnetic field model into a circuit model-coil coupling model. The measurement bridge of this model is not easy to balance under pulse excitation and multi-frequency excitation conditions.

[0004] Chinese patent publication number CN103196996A proposes an orthogonal combination probe, each of which includes an excitation coil and two induction coils.

[0005] The probe's induction coil senses a plane perpendicular to the excitation coil. Assuming the excitation coil is placed horizontally on a plane, these orthogonal induction coils enable flaw detection with equal accuracy in both the vertical and horizontal directions of a two-dimensional plane, ultimately improving sensitivity to tiny defects on the test piece. However, the induced magnetic field is three-dimensional, and the probe in this solution cannot determine the vector characteristics of the induced magnetic field, meaning it lacks anti-interference properties.

[0006] In addition, during the eddy current detection process, the temperature of the excitation coil will increase after long-term use. The design of eddy current sensors in the existing technology has ignored the impact of temperature increase on eddy current detection and has not performed quantitative analysis, which may cause the impedance matching of the detection device to lose its original function and fail to obtain accurate defect information. Summary of the Invention

[0007] In response to the shortcomings of the pulsed eddy current testing technology in the prior art, a metal component inspection system based on directional pulsed eddy current is provided. The system includes a directional pulsed eddy current probe, which is used to scan the test piece and obtain defect information of the test piece through the eddy current effect. The defect information includes the depth, length and width of the defect;

[0008] The directional pulsed eddy current probe comprises:

[0009] The excitation coil includes a plurality of circular rectangular strip metal frames and corresponding wires, wherein the wires are wound around the circular rectangular strip metal frames; the plurality of circular rectangular strip metal frames are coaxially stacked along the central axis direction to strengthen the induced magnetic field in the z-axis direction;

[0010] Take the plane where the circular rectangular metal frame is located as the xoz plane, take the central axis of the circular rectangular metal frame as the y axis, and establish a three-dimensional rectangular coordinate system o-xyz in space, with o as the origin;

[0011] an induction coil, the induction coil being disposed within the excitation coil and comprising an x-axis induction coil, a y-axis induction coil, and a z-axis induction coil, for receiving induced magnetic fields in the x-axis direction, the y-axis direction, and the z-axis direction, respectively; the induction coil comprising a wire, a metal ball, and three circular strip-shaped metal frames that are orthogonal to each other in space; the wire being wound around the three circular strip-shaped metal frames to form the x-axis induction coil, the y-axis induction coil, and the z-axis induction coil, and the induction coil being fixed in a cavity formed by the overlapping circular strip-shaped metal frames;

[0012] The length of the excitation coil: the width of the excitation coil: the depth of the excitation coil: the maximum radius of the induction coil = 2.7:2:1.4:1~4:3:2:1;

[0013] Magnetic field signal sensed by the induction coil Expressed as:

[0014] ;

[0015] ;

[0016] ;

[0017] in, are the azimuth and elevation angles of the induced magnetic field, Indicates the magnetic field signal in the x-axis direction sensed by the x-axis induction coil, It represents the induced magnetic field in the y-axis direction sensed by the y-axis induction coil. Represents the induced magnetic field in the z-axis direction sensed by the z-axis induction coil;

[0018] The temperature sensor is connected to the excitation coil signal and is used to detect the temperature of the excitation coil.

[0019] Furthermore, the system further comprises:

[0020] a power amplifier electrically connected to the excitation coil of the directional pulsed eddy current probe and performing square wave excitation;

[0021] a signal conditioning circuit electrically connected to the directional pulsed eddy current probe to obtain temperature information of the temperature sensor and a voltage value corresponding to the magnetic field strength of the induction coil;

[0022] An A / D conversion module is connected to the signal conditioning circuit signal;

[0023] A main controller is connected to the A / D conversion module signal;

[0024] A DDS signal generator is connected to the main controller and the power amplifier;

[0025] The power supply module is electrically connected to the temperature sensor, power amplifier, main controller and signal conditioning circuit of the directional pulsed eddy current probe and provides a DC voltage supply.

[0026] Further,

[0027] The main controller is an STM32 single-chip microcomputer;

[0028] The DDS signal generator is an AD9910 chip;

[0029] The power amplifier uses OPA549 as a driver chip;

[0030] The signal conditioning circuit is a dual-channel operational amplifier chip LM358;

[0031] The A / D conversion module is an AD7606 chip;

[0032] The temperature sensor is an NTC thermistor.

[0033] Furthermore, the present invention also provides a metal component detection method based on directional pulsed eddy current, the method comprising:

[0034] Step S1: Setting the initial excitation coil temperature for the test, and in the absence of a test piece, increasing the excitation coil temperature by a set threshold, and obtaining the voltage value obtained by the induction coil at each excitation coil temperature, thereby performing calibration and obtaining the calibration temperature;

[0035] Step S2: Select several standard test pieces, each of which is preset with several defects of different sizes and shapes. Use a directional pulsed eddy current probe to obtain the voltage values ​​corresponding to the magnetic field in different directions in three-dimensional space at the calibration temperature in step S1, thereby completing further calibration and obtaining calibration defect information.

[0036] Step S3: constructing a three-dimensional surface analytical expression according to the calibration temperature in step S1 and the calibration defect information in step S2;

[0037] Step S4: Enter the actual measurement environment, obtain the real-time excitation coil temperature and the voltage values ​​corresponding to the magnetic fields in different directions sensed by the induction coil in three-dimensional space, import them into the three-dimensional surface analytical formula in step S3, and obtain the defect information of the defect of the test piece.

[0038] Furthermore, step S1 is specifically as follows:

[0039] The initial temperature of the excitation coil under test is set to 22°C. The temperature of the excitation coil is gradually increased with the set threshold as the temperature increment. During this process, the voltage values ​​of the induction coil in different directions in three-dimensional space are obtained as reference voltage values. The corresponding excitation coil temperatures and reference voltage values ​​are matched one by one to complete the calibration and obtain the calibration temperature.

[0040] Furthermore, step S2 is specifically as follows:

[0041] A square steel plate is selected as the standard test piece. The material of the square steel plate is steel1008 carbon steel. Several defects with different defect information are processed on the square steel plate.

[0042] According to the calibration temperature in step S1, the defects on the square steel plate are scanned in sequence by a directional pulsed eddy current probe, and the voltage values ​​sensed by the induction coil in different directions in three-dimensional space are obtained as induced voltage values. Further calibration is completed based on the defect information to obtain calibrated defect information.

[0043] Furthermore, step S3 is specifically as follows:

[0044] The induced voltage value obtained by detection is differentiated from the reference voltage value to obtain the voltage difference value of the induction coil in different directions in three-dimensional space; it can be expressed as:

[0045] ;

[0046] ;

[0047] ;

[0048] in, Represents the voltage difference value of the x-axis induction coil in the x-axis direction in three-dimensional space, Represents the voltage difference value of the y-axis induction coil in the y-axis direction in three-dimensional space, As the voltage difference value of the z-axis induction coil in the z-axis direction in three-dimensional space, Indicates the calibration temperature, Indicates the calibration defect depth, Indicates the calibration defect length, Indicates the calibration defect width;

[0049] Obtain the voltage differential values ​​of n sets of induction coils in different directions in three-dimensional space, and use them to establish a three-dimensional surface analytical expression, specifically:

[0050] According to the calibration temperature, calibration defect depth, calibration defect length, calibration defect width, the least square method is used to fit several groups of function curves with defect depth, defect length, defect width as independent variables and corresponding voltage difference as dependent variable. The function curves include the function curve in the x-axis direction. , function curve in the y-axis direction And the function curve in the z-axis direction ;

[0051] The function curve is expressed as:

[0052] ;

[0053] in, 、 and are the least square fitting coefficients of the mth set of polynomials corresponding to any defect depth based on the voltage difference values ​​of the i-th set of induction coils in the x-axis direction, the y-axis direction, and the z-axis direction, respectively. 、 and are the least squares fitting coefficients of the mth set of polynomials corresponding to arbitrary defect lengths based on the voltage difference values ​​of the i-th set of induction coils in the x-axis direction, the y-axis direction, and the z-axis direction, respectively. 、 and are the least squares fitting coefficients of the mth set of polynomials corresponding to the arbitrary defect widths based on the voltage differential values ​​of the i-th set of induction coils in the x-axis direction, the y-axis direction, and the z-axis direction, respectively, where m is a preset accuracy value; Respectively represent arbitrary defect depth, arbitrary defect length and arbitrary defect width; Representing the mth-order high-order terms of arbitrary defect depth, arbitrary defect length and arbitrary defect width, respectively, for nonlinear fitting, ;

[0054] The coefficients of each polynomial are obtained by using the least squares surface fitting algorithm based on the rectangular domain; the above function curve is fitted into a continuous and smooth three-dimensional surface to obtain the three-dimensional surface analytical expression. 、 and ;

[0055] in, For any temperature.

[0056] Furthermore, step S4 is specifically as follows:

[0057] Enter the actual measurement environment, obtain the current excitation coil temperature through the temperature sensor, obtain the voltage values ​​corresponding to the defects of the test piece in different directions in three-dimensional space through the induction coil, import the excitation coil temperature and the voltage values ​​in different directions in three-dimensional space into the three-dimensional surface analytical expression in step S3 to obtain the defect information of the defect being tested.

[0058] Furthermore, the process of obtaining the defect information of the defect under test in step S4 is implemented by a CNN convolutional network; specifically, as follows:

[0059] Step S4A: Obtain the voltage differential value and the corresponding calibration temperature and calibration defect information as training set data, import the real-time induced voltage value and the real-time excitation coil temperature, obtain the voltage differential value through the calibration temperature as the input of the CNN convolutional network, and the CNN convolutional network outputs the predicted defect information. The CNN convolutional network is trained with the training set data. During the training process, the learning rate of the CNN convolutional network is optimized by the particle swarm algorithm to obtain the optimized CNN convolutional network.

[0060] Furthermore, in step S4A, the CNN convolutional network is optimized by the particle swarm algorithm, specifically:

[0061] Step S4A1: Set the induced voltage difference value to a randomly generated cluster, formalized as a matrix with D columns and N rows , expressed as:

[0062] ;

[0063] in, is the number of data points corresponding to the learning rate, is the feature dimension, and denote the lower and upper bounds of the solution space, respectively. A random number between 0 and 1;

[0064] Step S4A2: Divide the training set data into two parts, one for global exploration and the other for local exploration; update the position of the individual through global exploration or local exploration;

[0065] Global exploration is expressed as:

[0066] ;

[0067] in, Indicates the In the iteration individual positions, Indicates the In the iteration The individual position of represents a random number vector based on Gaussian distribution, represents term-by-term multiplication, Represents a number randomly selected from [0,1]; represents the current optimal solution, represents an individual randomly selected from a group of elites in the population; Represents a number randomly selected from [0,1]; Indicates the location of an average individual; represents the training set data used for global exploration, Represents the training set data used for local detection; Indicates in The centroid position of the entire group in the iteration;

[0068] During the local detection process, the iterative process of ordinary individuals is expressed as:

[0069] ;

[0070] ;

[0071] in, represents the individual after local detection iteration, is the maximum number of iterations, Represents a dynamic adjustment function;

[0072] Step S4A3: Obtain the optimal individual position through the preset fitness function and use it as the learning rate of the CNN convolutional network.

[0073] The positive progress effect of the present invention is:

[0074] 1) This invention provides a novel probe for eddy current testing. Its three orthogonal annular detection coils fully detect the vector characteristics of the induced magnetic field, improving the accuracy and anti-interference capabilities of eddy current testing. Furthermore, by adaptively replacing the excitation coils with stacked rectangular coils, the eddy currents generated at the defect maintain a consistent intensity across different directions in three-dimensional space at the location of the induction coils.

[0075] 2) Furthermore, the present invention also provides a method for using the above-mentioned probe, including calibrating the defect information of the defect position, and fusing the temperature information to establish a three-dimensional surface analytical expression, so that the defect information of the defect position of the test piece can be obtained by importing real-time temperature information and perceived voltage values; based on this, the present invention also obtains defect information through a CNN convolutional network based on a particle swarm optimization algorithm, thereby improving the speed of data convergence and the accuracy of classification while making full use of the calibration data. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 This is a schematic diagram of a metal component detection system based on directional pulsed eddy current according to the present invention.

[0077] Figure 2 The figure is a schematic diagram of the probe structure of a metal component detection system based on directional pulsed eddy current of the present invention.

[0078] Figure 3 : is the distribution diagram of induced eddy current on the inspected surface of a conductor in one example.

[0079] Figure 4 FIG. 1 is a diagram showing the eddy current field distribution of the induced eddy current perpendicular to the defect in one example. FIG.

[0080] Figure 5 FIG. 1 is a diagram showing the eddy current field distribution when the induced eddy current is parallel to the defect in one example. FIG.

[0081] Figure 6 A diagram of a simulation verification model in another example.

[0082] Figure 7 For the Figure 6 Schematic diagram of the magnetic field distribution after excitation of the simulation verification model diagram.

[0083] Figure 8 This is a front view of defect distribution of a steel plate used as a test piece in one example of the present invention.

[0084] Figure 9 This is a side view of defect distribution of a steel plate used as a test piece in one example of the present invention. DETAILED DESCRIPTION

[0085] The following describes the embodiments of the present invention through specific examples. Those skilled in the art will readily understand the other advantages and benefits of the present invention from the disclosure herein. The present invention may also be implemented or applied through various other specific embodiments, and the details in this specification may be modified or altered based on different viewpoints and applications without departing from the spirit of the present invention.

[0086] Reference Figure 1~Figure 2, a metal component inspection system based on directional pulsed eddy current, the system comprising a directional pulsed eddy current probe, the directional pulsed eddy current probe being used to scan a test piece and obtain defect information of the test piece through eddy current effect, the defect information including the depth, length and width of the defect;

[0087] The directional pulsed eddy current probe comprises:

[0088] The excitation coil includes a plurality of circular rectangular strip metal frames and corresponding wires, wherein the wires are wound around the circular rectangular strip metal frames; the plurality of circular rectangular strip metal frames are coaxially stacked along the central axis direction to strengthen the induced magnetic field in the z-axis direction;

[0089] Take the plane where the circular rectangular metal frame is located as the xoz plane, take the central axis of the circular rectangular metal frame as the y axis, and establish a three-dimensional rectangular coordinate system o-xyz in space, with o as the origin;

[0090] an induction coil, the induction coil being disposed within the excitation coil and comprising an x-axis induction coil, a y-axis induction coil, and a z-axis induction coil, for receiving induced magnetic fields in the x-axis direction, the y-axis direction, and the z-axis direction, respectively; the induction coil comprising a wire, a metal ball, and three circular strip-shaped metal frames that are orthogonal to each other in space; the wire being wound around the three circular strip-shaped metal frames to form the x-axis induction coil, the y-axis induction coil, and the z-axis induction coil, and the induction coil being fixed in a cavity formed by the overlapping circular strip-shaped metal frames;

[0091] The length of the excitation coil: the width of the excitation coil: the depth of the excitation coil: the maximum radius of the induction coil = 2.7:2:1.4:1~4:3:2:1;

[0092] Magnetic field signal sensed by the induction coil Expressed as:

[0093] ;

[0094] ;

[0095] ;

[0096] in, are the azimuth and elevation angles of the induced magnetic field, Indicates the magnetic field signal in the x-axis direction sensed by the x-axis induction coil, It represents the induced magnetic field in the y-axis direction sensed by the y-axis induction coil. Represents the induced magnetic field in the z-axis direction sensed by the z-axis induction coil;

[0097] The temperature sensor is connected to the excitation coil signal and is used to detect the temperature of the excitation coil.

[0098] Furthermore, the system further comprises:

[0099] a power amplifier electrically connected to the excitation coil of the directional pulsed eddy current probe and performing square wave excitation;

[0100] a signal conditioning circuit electrically connected to the directional pulsed eddy current probe to obtain temperature information of the temperature sensor and a voltage value corresponding to the magnetic field strength of the induction coil;

[0101] An A / D conversion module is connected to the signal conditioning circuit signal;

[0102] A main controller is connected to the A / D conversion module signal;

[0103] A DDS signal generator is connected to the main controller and the power amplifier;

[0104] The power supply module is electrically connected to the temperature sensor, power amplifier, main controller and signal conditioning circuit of the directional pulsed eddy current probe and provides a DC voltage supply.

[0105] Further,

[0106] The main controller is an STM32 single-chip microcomputer;

[0107] The DDS signal generator is an AD9910 chip;

[0108] The power amplifier uses OPA549 as a driver chip;

[0109] The signal conditioning circuit is a dual-channel operational amplifier chip LM358;

[0110] The A / D conversion module is an AD7606 chip;

[0111] The temperature sensor is an NTC thermistor.

[0112] Furthermore, the present invention also provides a metal component detection method based on directional pulsed eddy current, the method comprising:

[0113] Step S1: Setting the initial excitation coil temperature for the test, and in the absence of a test piece, increasing the excitation coil temperature by a set threshold, and obtaining the voltage value obtained by the induction coil at each excitation coil temperature, thereby performing calibration and obtaining the calibration temperature;

[0114] Step S2: Select several standard test pieces, each of which is preset with several defects of different sizes and shapes. Use a directional pulsed eddy current probe to obtain the voltage values ​​corresponding to the magnetic field in different directions in three-dimensional space at the calibration temperature in step S1, thereby completing further calibration and obtaining calibration defect information.

[0115] Step S3: constructing a three-dimensional surface analytical expression according to the calibration temperature in step S1 and the calibration defect information in step S2;

[0116] Step S4: Enter the actual measurement environment, obtain the real-time excitation coil temperature and the voltage values ​​corresponding to the magnetic fields in different directions sensed by the induction coil in three-dimensional space, import them into the three-dimensional surface analytical formula in step S3, and obtain the defect information of the defect of the test piece.

[0117] Furthermore, step S1 is specifically as follows:

[0118] The initial temperature of the excitation coil under test is set to 22°C. The temperature of the excitation coil is gradually increased with the set threshold as the temperature increment. During this process, the voltage values ​​of the induction coil in different directions in three-dimensional space are obtained as reference voltage values. The corresponding excitation coil temperatures and reference voltage values ​​are matched one by one to complete the calibration and obtain the calibration temperature.

[0119] Furthermore, step S2 is specifically as follows:

[0120] A square steel plate is selected as the standard test piece. The material of the square steel plate is steel1008 carbon steel. Several defects with different defect information are processed on the square steel plate.

[0121] According to the calibration temperature in step S1, the defects on the square steel plate are scanned in sequence by a directional pulsed eddy current probe, and the voltage values ​​sensed by the induction coil in different directions in three-dimensional space are obtained as induced voltage values. Further calibration is completed based on the defect information to obtain calibrated defect information.

[0122] Furthermore, step S3 is specifically as follows:

[0123] The induced voltage value obtained by detection is differentiated from the reference voltage value to obtain the voltage difference value of the induction coil in different directions in three-dimensional space; it can be expressed as:

[0124] ;

[0125] ;

[0126] ;

[0127] in, Represents the voltage difference value of the x-axis induction coil in the x-axis direction in three-dimensional space, Represents the voltage difference value of the y-axis induction coil in the y-axis direction in three-dimensional space, As the voltage difference value of the z-axis induction coil in the z-axis direction in three-dimensional space, Indicates the calibration temperature, Indicates the calibration defect depth, Indicates the calibration defect length, Indicates the calibration defect width;

[0128] Obtain the voltage differential values ​​of n sets of induction coils in different directions in three-dimensional space, and use them to establish a three-dimensional surface analytical expression, specifically:

[0129] According to the calibration temperature, calibration defect depth, calibration defect length, calibration defect width, the least square method is used to fit several groups of function curves with defect depth, defect length, defect width as independent variables and corresponding voltage difference as dependent variable. The function curves include the function curve in the x-axis direction. , function curve in the y-axis direction And the function curve in the z-axis direction ;

[0130] The function curve is expressed as:

[0131] ;

[0132] in, 、 and are the least square fitting coefficients of the mth set of polynomials corresponding to any defect depth based on the voltage difference values ​​of the i-th set of induction coils in the x-axis direction, the y-axis direction, and the z-axis direction, respectively. 、 and are the least squares fitting coefficients of the mth set of polynomials corresponding to arbitrary defect lengths based on the voltage difference values ​​of the i-th set of induction coils in the x-axis direction, the y-axis direction, and the z-axis direction, respectively. 、 and are the least squares fitting coefficients of the mth set of polynomials corresponding to the arbitrary defect widths based on the voltage differential values ​​of the i-th set of induction coils in the x-axis direction, the y-axis direction, and the z-axis direction, respectively, where m is a preset accuracy value; Respectively represent arbitrary defect depth, arbitrary defect length and arbitrary defect width; Representing the mth-order high-order terms of arbitrary defect depth, arbitrary defect length and arbitrary defect width, respectively, for nonlinear fitting, ;

[0133] The coefficients of each polynomial are obtained by using the least squares surface fitting algorithm based on the rectangular domain; the above function curve is fitted into a continuous and smooth three-dimensional surface to obtain the three-dimensional surface analytical expression. 、 and ;

[0134] in, For any temperature.

[0135] Furthermore, step S4 is specifically as follows:

[0136] Enter the actual measurement environment, obtain the current excitation coil temperature through the temperature sensor, obtain the voltage values ​​corresponding to the defects of the test piece in different directions in three-dimensional space through the induction coil, import the excitation coil temperature and the voltage values ​​in different directions in three-dimensional space into the three-dimensional surface analytical expression in step S3 to obtain the defect information of the defect being tested.

[0137] Furthermore, the process of obtaining the defect information of the defect under test in step S4 is implemented by a CNN convolutional network; specifically, as follows:

[0138] Step S4A: Obtain the voltage differential value and the corresponding calibration temperature and calibration defect information as training set data, import the real-time induced voltage value and the real-time excitation coil temperature, obtain the voltage differential value through the calibration temperature as the input of the CNN convolutional network, and the CNN convolutional network outputs the predicted defect information. The CNN convolutional network is trained with the training set data. During the training process, the learning rate of the CNN convolutional network is optimized by the particle swarm algorithm to obtain the optimized CNN convolutional network.

[0139] Furthermore, in step S4A, the CNN convolutional network is optimized by the particle swarm algorithm, specifically:

[0140] Step S4A1: Set the induced voltage difference value to a randomly generated cluster, formalized as a matrix with D columns and N rows , expressed as:

[0141] ;

[0142] in, is the number of data points corresponding to the learning rate, is the feature dimension, and denote the lower and upper bounds of the solution space, respectively. A random number between 0 and 1;

[0143] Step S4A2: Divide the training set data into two parts, one for global exploration and the other for local exploration; update the position of the individual through global exploration or local exploration;

[0144] Global exploration is expressed as:

[0145] ;

[0146] in, Indicates the In the iteration individual positions, Indicates the In the iteration The individual position of represents a random number vector based on Gaussian distribution, represents term-by-term multiplication, Represents a number randomly selected from [0,1]; represents the current optimal solution, represents an individual randomly selected from a group of elites in the population; Represents a number randomly selected from [0,1]; Indicates the location of an average individual; represents the training set data used for global exploration, Represents the training set data used for local detection; Indicates in The centroid position of the entire group in the iteration;

[0147] During the local detection process, the iterative process of ordinary individuals is expressed as:

[0148] ;

[0149] ;

[0150] in, represents the individual after local detection iteration, is the maximum number of iterations, Represents a dynamic adjustment function;

[0151] Step S4A3: Obtain the optimal individual position through the preset fitness function and use it as the learning rate of the CNN convolutional network.

[0152] It should be noted that, referring to Figures 3 to 5 When the probe in the prior art is not disturbed by other external conditions, the distribution of the induced eddy current is determined by the coil structure and its scanning method.

[0153] For example, when a rectangular coil is placed tangentially above a conductor and scanned, the pulse excitation signal amplitude is set to 2.52V, the frequency is 100Hz, the duty cycle is 50%, and the lift-off height is 2mm. The induced eddy current of the conductor at a certain moment is analyzed.

[0154] When there are defects in the material being tested, the uniform eddy current field will be blocked by the defects. In actual testing, the scanning detection of the probe has two directions: X and Y. Figure 3 As shown, the X direction is parallel to the excitation current in the excitation coil, and the Y direction is parallel to the magnetic induction line generated by the excitation coil. When the eddy current is perpendicular to the defect, the defect has the greatest disturbance on the eddy current field, as shown in Figure 4 As shown; when the eddy current is parallel to the defect, the disturbance of the defect on the eddy current field is minimal, as shown Figure 5 As shown in the figure, it can be seen that the probe has a sensitive direction during the scanning detection process, that is, when the defect is perpendicular to the eddy current, the detection sensitivity of the defect is greater.

[0155] Further simulation verification of the above example is carried out, refer to Figure 6 The smaller rectangular block can be regarded as a conductor, and the larger rectangular block can be regarded as the space where the conductor is located. Specifically, the excitation signal of the pulse eddy current is set to a periodic square wave pulse current with adjustable amplitude, frequency and duty cycle. The induced eddy current inside the conductor to be measured will only appear near the rising and falling edges of the signal. When establishing the finite element model, an exponentially rising current source is used. The analog excitation signal is expressed as:

[0156] ;

[0157] Where, is the time constant, represents the initial amplitude of the excitation signal of the pulsed eddy current, is a unit step function, >0 o'clock =1.

[0158] For a typical eddy current problem, the solution area can be Divided into eddy zones and non-eddy current areas Two parts, using The potential function method is used to solve the problem, where A represents the vector magnetic potential, represents the scalar potential. Ignoring the displacement current, considering the uniqueness of the solution, and incorporating the Coulomb gauge condition, it is formalized as: ,in, Represents the divergence operator, then the control equations of each part are as follows:

[0159] In the eddy current zone Inside:

[0160] ;

[0161] ;

[0162] In the formula, the solution area ; The eddy current region contains a conductive medium with a conductivity of , the magnetic permeability is .

[0163] In the non-eddy zone Inside:

[0164] Since there is no conductive medium, the conductivity is zero, the equation in the non-eddy region will be simplified to:

[0165] ;

[0166] ;

[0167] in, is the magnetic permeability of air.

[0168] right Figure 6 The simulation model in the simulation is excited by a square wave pulse of 500mA, 976Hz frequency, and 50% duty cycle. The waveform generated on the rising edge is as follows Figure 7 As shown, it can be seen that the directions of the excitation current and the induced current are basically consistent with the theory.

[0169] Furthermore, in one example, referring to Figure 8 and Figure 9 , step S2 is specifically as follows:

[0170] Three square steel plates with a length × width × height of 200 mm × 200 mm × 10 mm were selected as standard test pieces. A total of nine strip defects distributed in a 3 × 3 pattern with an equal interval of 70 mm were processed in the center of the square steel plates. The widths of the first, second, and third steel plates were 3 mm, 6 mm, and 9 mm, respectively. The defect lengths of each steel plate were 10 mm, 15 mm, and 20 mm from the first row to the third row, and the depths were 1 mm, 3 mm, and 5 mm from the first column to the third column, respectively.

[0171] The present invention has been described in detail above with reference to the embodiments of the accompanying drawings. A person skilled in the art can make various modifications to the present invention based on the above description. Therefore, certain details in the embodiments should not be construed as limiting the present invention. The scope of protection of the present invention shall be determined by the scope defined by the appended claims.

Claims

1. A metal parts detection system based on directional pulsed eddy current, characterized in that: The system includes a directional pulsed eddy current probe, which is used to scan the test piece and obtain defect information of the test piece through the eddy current effect. The defect information includes the depth, length and width of the defect; The directional pulsed eddy current probe comprises: The excitation coil includes a plurality of circular rectangular strip metal frames and corresponding wires, wherein the wires are wound around the circular rectangular strip metal frames; the plurality of circular rectangular strip metal frames are coaxially stacked along the central axis direction to strengthen the induced magnetic field in the z-axis direction; Take the plane where the circular rectangular metal frame is located as the xoz plane, take the central axis of the circular rectangular metal frame as the y axis, and establish a three-dimensional rectangular coordinate system o-xyz in space, with o as the origin; an induction coil, the induction coil being disposed within the excitation coil and comprising an x-axis induction coil, a y-axis induction coil, and a z-axis induction coil, for receiving induced magnetic fields in the x-axis direction, the y-axis direction, and the z-axis direction, respectively; the induction coil comprising a wire, a metal ball, and three circular strip-shaped metal frames that are orthogonal to each other in space; the wire being wound around the three circular strip-shaped metal frames to form the x-axis induction coil, the y-axis induction coil, and the z-axis induction coil, and the induction coil being fixed in a cavity formed by the overlapping circular strip-shaped metal frames; The length of the excitation coil: the width of the excitation coil: the depth of the excitation coil: the maximum radius of the induction coil = 2.7:2:1.4:1~4:3:2:1; Magnetic field signal sensed by the induction coil Expressed as: ； ； ； in, are the azimuth and elevation angles of the induced magnetic field, Indicates the magnetic field signal in the x-axis direction sensed by the x-axis induction coil, It represents the induced magnetic field in the y-axis direction sensed by the y-axis induction coil. Represents the induced magnetic field in the z-axis direction sensed by the z-axis induction coil; A temperature sensor, connected to the excitation coil signal, for detecting the temperature of the excitation coil; A method for detecting metal parts based on directional pulsed eddy current comprises the following steps: Step S1: obtaining the calibration temperature; Step S2: Obtain calibration defect information; Step S3: constructing a three-dimensional surface analytical expression according to the calibration temperature in step S1 and the calibration defect information in step S2; Step S4: Enter the actual measurement environment, obtain the real-time excitation coil temperature and the voltage values ​​corresponding to the magnetic fields in different directions sensed by the induction coil in three-dimensional space, import them into the three-dimensional surface analytical formula in step S3, and obtain the defect information of the defect of the test piece.

2. A metal component detection system based on directional pulsed eddy current according to claim 1, characterized in that: The system further comprises: A power amplifier electrically connected to the excitation coil of the directional pulsed eddy current probe and performing square wave excitation; a signal conditioning circuit electrically connected to the directional pulsed eddy current probe to obtain temperature information of the temperature sensor and a voltage value corresponding to the magnetic field strength of the induction coil; An A / D conversion module is connected to the signal conditioning circuit signal; A main controller is connected to the A / D conversion module signal; A DDS signal generator is connected to the main controller and the power amplifier; The power supply module is electrically connected to the temperature sensor, power amplifier, main controller and signal conditioning circuit of the directional pulsed eddy current probe and provides a DC voltage supply.

3. A metal component detection system based on directional pulsed eddy current according to claim 2, characterized in that: The main controller is an STM32 single-chip microcomputer; The DDS signal generator is an AD9910 chip; The power amplifier uses OPA549 as a driver chip; The signal conditioning circuit is a dual-channel operational amplifier chip LM358; The A / D conversion module is an AD7606 chip; The temperature sensor is an NTC thermistor.

4. A metal component detection method based on directional pulsed eddy current, used in a metal component detection system based on directional pulsed eddy current according to any one of claims 1 to 3, characterized in that: The method comprises: Step S1: Setting the initial excitation coil temperature for the test, and in the absence of a test object, increasing the excitation coil temperature by a set threshold, and obtaining the voltage value obtained by the induction coil at each excitation coil temperature, thereby performing calibration and obtaining the calibration temperature; Step S2: Select several standard test pieces, each of which is preset with several defects of different sizes and shapes. Use a directional pulsed eddy current probe to obtain the voltage values ​​corresponding to the magnetic field in different directions in three-dimensional space at the calibration temperature in step S1, thereby completing further calibration and obtaining calibration defect information. Step S3: constructing a three-dimensional surface analytical expression according to the calibration temperature in step S1 and the calibration defect information in step S2; Step S4: Enter the actual measurement environment, obtain the real-time excitation coil temperature and the voltage values ​​corresponding to the magnetic fields in different directions sensed by the induction coil in three-dimensional space, import them into the three-dimensional surface analytical formula in step S3, and obtain the defect information of the defect of the test piece.

5. The metal component detection method based on directional pulsed eddy current according to claim 4, characterized in that: Step S1 is specifically as follows: The initial temperature of the excitation coil under test is set to 22°C. The temperature of the excitation coil is gradually increased with the set threshold as the temperature increment. During this process, the voltage values ​​of the induction coil in different directions in three-dimensional space are obtained as reference voltage values. The corresponding excitation coil temperatures and reference voltage values ​​are matched one by one to complete the calibration and obtain the calibration temperature.

6. A metal component detection method based on directional pulsed eddy current according to claim 5, characterized in that: Step S2 is specifically as follows: A square steel plate is selected as the standard test piece. The material of the square steel plate is steel1008 carbon steel. Several defects with different defect information are processed on the square steel plate. According to the calibration temperature in step S1, the defects on the square steel plate are scanned in sequence by a directional pulsed eddy current probe, and the voltage values ​​sensed by the induction coil in different directions in three-dimensional space are obtained as induced voltage values. Further calibration is completed based on the defect information to obtain calibrated defect information.

7. A metal component detection method based on directional pulsed eddy current according to claim 6, characterized in that: Step S3 is specifically as follows: The induced voltage value obtained by detection is differentiated from the reference voltage value to obtain the voltage difference value of the induction coil in different directions in three-dimensional space; it can be expressed as: ； ； ； in, Represents the voltage difference value of the x-axis induction coil in the x-axis direction in three-dimensional space, Represents the voltage difference value of the y-axis induction coil in the y-axis direction in three-dimensional space, As the voltage difference value of the z-axis induction coil in the z-axis direction in three-dimensional space, Indicates the calibration temperature, Indicates the calibration defect depth, Indicates the calibration defect length, Indicates the calibration defect width; Obtain the voltage differential values ​​of n sets of induction coils in different directions in three-dimensional space, and use them to establish a three-dimensional surface analytical expression, specifically: According to the calibration temperature, calibration defect depth, calibration defect length, calibration defect width, the least square method is used to fit several groups of function curves with defect depth, defect length, defect width as independent variables and corresponding voltage difference as dependent variable. The function curves include the function curve in the x-axis direction. , function curve in the y-axis direction And the function curve in the z-axis direction ; The function curve is expressed as: ； in, 、 and are the least square fitting coefficients of the mth set of polynomials corresponding to any defect depth based on the voltage difference values ​​of the i-th set of induction coils in the x-axis direction, the y-axis direction, and the z-axis direction, respectively. 、 and are the least squares fitting coefficients of the mth set of polynomials corresponding to arbitrary defect lengths based on the voltage difference values ​​of the i-th set of induction coils in the x-axis direction, the y-axis direction, and the z-axis direction, respectively. 、 and are the least squares fitting coefficients of the mth set of polynomials corresponding to the arbitrary defect widths based on the voltage differential values ​​of the i-th set of induction coils in the x-axis direction, the y-axis direction, and the z-axis direction, respectively, where m is a preset accuracy value; Respectively represent arbitrary defect depth, arbitrary defect length and arbitrary defect width; Representing the mth-order high-order terms of arbitrary defect depth, arbitrary defect length and arbitrary defect width, respectively, for nonlinear fitting, ; The coefficients of each polynomial are obtained by using the least squares surface fitting algorithm based on the rectangular domain; the above function curve is fitted into a continuous and smooth three-dimensional surface to obtain the three-dimensional surface analytical expression. 、 and ; in, For any temperature.

8. The metal component detection method based on directional pulsed eddy current according to claim 7, characterized in that: Step S4 is specifically as follows: Enter the actual measurement environment, obtain the current excitation coil temperature through the temperature sensor, obtain the voltage values ​​corresponding to the defects of the test piece in different directions in three-dimensional space through the induction coil, import the excitation coil temperature and the voltage values ​​in different directions in three-dimensional space into the three-dimensional surface analytical expression in step S3 to obtain the defect information of the defect being tested.

9. The metal component detection method based on directional pulsed eddy current according to claim 8, characterized in that: The process of obtaining the defect information of the defect under test in step S4 is implemented by the CNN convolutional network; specifically, Step S4A: Obtain the voltage differential value and the corresponding calibration temperature and calibration defect information as training set data, import the real-time induced voltage value and the real-time excitation coil temperature, obtain the voltage differential value through the calibration temperature as the input of the CNN convolutional network, and the CNN convolutional network outputs the predicted defect information. The CNN convolutional network is trained with the training set data. During the training process, the learning rate of the CNN convolutional network is optimized by the particle swarm algorithm to obtain the optimized CNN convolutional network.

10. The metal component detection method based on directional pulsed eddy current according to claim 9, characterized in that: In step S4A, the CNN convolutional network is optimized by the particle swarm algorithm, specifically: Step S4A1: Set the induced voltage difference value to a randomly generated cluster, formalized as a matrix with D columns and N rows , expressed as: ； in, is the number of data points corresponding to the learning rate, is the feature dimension, and denote the lower and upper bounds of the solution space, respectively. A random number between 0 and 1; Step S4A2: Divide the training set data into two parts, one for global exploration and the other for local exploration; update the position of the individual through global exploration or local exploration; Global exploration is expressed as: ； in, Indicates the In the iteration individual positions, Indicates the In the iteration The individual position of represents a random number vector based on Gaussian distribution, represents term-by-term multiplication, Represents a number randomly selected from [0,1]; represents the current optimal solution, represents an individual randomly selected from a group of elites in the population; Represents a number randomly selected from [0,1]; Indicates the location of an average individual; represents the training set data used for global exploration, Represents the training set data used for local detection; Indicates in The centroid position of the entire group in the iteration; During the local detection process, the iterative process of ordinary individuals is expressed as: ； ； in, represents the individual after local detection iteration, is the maximum number of iterations, Represents a dynamic adjustment function; Step S4A3: Obtain the optimal individual position through the preset fitness function and use it as the learning rate of the CNN convolutional network.

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