CN107024535A - A kind of multiple index depth detection method of the vertical defect based on surface wave - Google Patents

Abstract

A surface wave-based multi-coefficient depth detection method for vertical defects belongs to the field of ultrasonic guided wave nondestructive testing and evaluation. During the propagation process of the surface wave, when it interacts with the defect, there will be scattered body waves below the defect. When the body wave propagates and encounters the lower end surface of the workpiece, it will be reflected back, and when it encounters a defect, it will be converted into a surface wave again (that is, the mode conversion echo) and propagate along both ends of the defect, and the time difference between it and the echo of the defect is the total distance traveled by the shear wave in the thickness direction of the workpiece. The combination of the mode conversion echo at both ends of the defect, the defect echo and the transmitted wave can be used as the characterization parameter of the defect depth. The multi-coefficient vertical defect depth detection method solves the inaccurate detection of the defect depth greater than 0.45 times the wavelength at present. sex and inapplicability. In the fields of health monitoring and non-destructive evaluation, it has great application value and potential.

Description

A multi-coefficient depth detection method for vertical flaws based on surface waves

technical field

The invention relates to a multi-coefficient depth detection method for vertical defects based on surface waves, and belongs to the field of ultrasonic guided wave nondestructive detection and evaluation.

Background technique

At present, when surface waves are used to detect workpiece surface defects, especially for deep detection of surface vertical or quasi-vertical defects, according to the size, material and other parameter information of the workpiece, the surface waves of specific frequency or specific wavelength are emitted by the probe for defect detection. According to the obtained signal waveform (self-excited and self-receiving), calculate the time difference Δt between the excited surface wave and the defect echo, and multiply the known surface wave velocity v in this workpiece by the time difference Δt to obtain the total distance traveled by the surface wave , using half of the total distance to determine the location of the defect. For the detection of defect depth, the form of one stimulation and one collection is often used. First, the simulation coefficient curve is established. According to the signal waveform received by the receiving probe, the amplitude of the direct wave, the amplitude of the defect echo and the amplitude of the transmitted wave are recorded and calculated. The amplitude of the wave and the amplitude of the transmitted wave are divided by the amplitude of the direct wave for normalization to obtain the coefficient representing the depth information of the defect. Then determine the characterization parameters of the workpiece defect depth according to the relevant experiments, and compare the coefficient curve to detect and determine the defect depth. However, the defect depth characterization coefficient only shows good monotonicity in the range where the defect depth is 0.45 times the wavelength, and shows the opposite monotonicity in the range after the defect depth is 0.45 times the wavelength, that is to say, the whole line The coefficient curve has an inflection point at approximately 0.45 times the wavelength of the defect depth. This brings inaccuracy and inapplicability to detection of defect depths greater than 0.45 times the wavelength.

Contents of the invention

Aiming at the above-mentioned existing problems, the present invention proposes a multi-coefficient depth detection method for vertical defects based on surface waves, which solves the inaccuracy and inapplicability of the current detection of defect depths greater than 0.45 times the wavelength. During the propagation process of the surface wave, when it interacts with the defect, there will be scattered body waves under the defect. When the body wave propagates, it will reflect back when it encounters the lower end surface of the workpiece, and it will be converted into a surface wave again when it encounters a defect, as shown in Figure 1-2 ( That is, the mode conversion echo) and propagates along both ends of the defect, and the time difference between it and the defect echo is the total distance traveled by the shear wave in the thickness direction of the workpiece. The combination of the mode conversion echo at both ends of the defect, the defect echo and the transmitted wave can be used as a characterization parameter of the defect depth. The multi-coefficient vertical defect depth detection method solves the inaccurate detection of the current defect depth greater than 0.45 times the wavelength. sex and inapplicability.

Step 1: Establish a simulated multi-coefficient curve

For the parameter information such as the size and material of the target workpiece, determine the appropriate simulation software and establish a suitable simulation model. According to the needs, determine the range of defect depth and size, and conduct a series of simulations. According to the simulation model, extract the waveform information at the observation point or surface, and divide the obtained defect echo value, transmitted wave value and mode conversion echo (as shown in Figure 1) at both ends of the defect by the reference direct wave value After the normalization process, it is used as the ordinate, and then the defect depth or the defect depth divided by the wavelength normalization process is used as the abscissa to establish a multi-coefficient defect depth characterization curve (see Figure 2).

Step 2: Perform experimental testing

According to the size and material parameter information of the target workpiece, as well as the excitation frequency and excitation method established by the simulation multi-coefficient curve, an appropriate probe is selected for the experiment. As shown in Figure 3, the experimental system includes a test workpiece, an excitation-reception probe, an oscilloscope, and an excitation device. Oscilloscope connection. The defect callback, transmitted wave and mode conversion echo at both ends of the defect were measured in the experiment (as shown in Figure 1), and the above four waveform amplitudes representing the defect depth were recorded.

Step 3: Determine Defect Depth

According to the four defect information measured by the experiment, the callback of the defect, the transmitted wave and the mode conversion echo at both ends of the defect, they are divided by the reference value of the direct wave to obtain a set of four coefficient values. Compare a set of four-coefficient defect depth characterization curves obtained by simulation to determine the depth of the defect.

The multiple coefficients include defect echo defect depth representation coefficient, transmitted wave defect depth representation coefficient, defect depth representation coefficient of mode conversion echo at the left end of the defect, defect depth representation coefficient of mode conversion echo at the right end of the defect; the defect depth representation coefficient of defect echo is In the process of surface wave propagation, the defect echo reflected from the defect is encountered, and the defect depth characterization coefficient of the defect echo is established according to the different amplitudes of the defect echo reflected back from the defect at different depths; the defect depth characterization coefficient of the transmitted wave is the surface In the process of wave propagation, the transmitted wave that encounters the defect and continues to propagate through the defect, the defect depth characterization coefficient of the transmitted wave established according to the different amplitudes of the transmitted wave transmitted through the defect at different depths; The characterization coefficient is that when the surface wave interacts with the defect in the propagation process, there will be scattered body waves below the defect. When the body wave propagates and encounters the lower end surface of the workpiece, it will be reflected back, and when it encounters a defect, it will be converted into a surface that propagates along the left end of the defect. The defect depth characterization coefficient of the modal conversion echo at the left end of the defect is established according to the different amplitudes of the modal conversion echo at the left end of the defect at different depths; the defect depth characterization coefficient of the modal conversion echo at the right end of the defect is the surface wave in the propagation process In , when interacting with the defect, there will be scattered body waves below the defect. When the body wave propagates and encounters the lower end surface of the workpiece, it will be reflected back, and when it encounters a defect, it will be converted into a surface wave propagating along the right end of the defect. The defect depth characterization coefficient of the modal conversion echo at the right end of the defect is established based on the different amplitudes of the modal conversion echoes at the right end of the defect.

Description of drawings

Fig. 1 The simulation diagram of the mode conversion echo of the surface wave at both ends of the defect;

Figure 2500kHz multi-coefficient defect depth characterization curve;

Fig. 3 experiment schematic diagram;

Figure 4 COMSOL simulation model;

Figure 5500kHz surface wave propagation in the aluminum plate;

Figure 6500kHz defect callback and mode conversion echo information;

Fig. 7 schematic diagram of electromagnetic acoustic sensor;

Figure 8 is the experimentally measured defect echo and the mode conversion echo on the left side of the defect;

Figure 9 is the experimentally measured transmitted wave and the modal conversion echo on the right side of the defect;

detailed description

Below in conjunction with 500kHz example the content of the present invention is described in further detail:

Step 1: Establish a simulated multi-coefficient curve

For parameter information such as the size and material of the target workpiece, the COMSOL simulation software is determined to establish a suitable simulation model (as shown in Figure 4). According to the surface wave velocity in the target workpiece is 3000m/s, the calculated wavelength is 6mm, the defect depth size range is determined to be 0-7.2mm, and a series of simulations are performed (see Figure 5). According to the simulation model, extract the waveform information at the observation point, use MATlab to read the waveform information at the observation point and perform Hilbert transformation (as shown in Figure 6) to ensure more accurate amplitude information. Extract the maximum value at the wave packet: Divide the obtained defect echo value, transmitted wave value, and mode conversion echo value at both ends of the defect by the reference direct wave value and normalize it as the ordinate, and then use the defect depth Divide by the wavelength normalization process as the abscissa to establish a multi-coefficient defect depth characterization curve (as shown in Figure 2).

Step 2: Perform experimental testing

According to the parameter information such as the size and material of the target workpiece and the excitation frequency 500kHz and point excitation mode established by the simulated multi-coefficient curve, the electromagnetic acoustic sensor (as shown in Figure 7) is selected for experimental detection. As shown in Figure 3, the experimental system includes a 400mm*650mm*25mm steel plate, an excitation-reception receiving probe, an oscilloscope, and excitation equipment. The callback of the workpiece defect, the transmitted wave, and the mode conversion echo at both ends of the defect were measured in the experiment (as shown in Figure 8-9), and the above four waveform amplitudes representing the depth of the defect were recorded.

Step 3: Determine Defect Depth

According to the four defect information measured by the experiment, the defect callback, the transmitted wave and the mode conversion echo at both ends of the defect, use MATlab to read and process, and divide the maximum value of the amplitude by the reference value of the direct wave to obtain a set of four coefficient value. Comparing a set of four-coefficient defect depth characterization curves obtained by simulation, the depth of the defect is determined to be 1 mm.

In the above, a method for detecting the depth of a vertical defect of a plate based on a surface wave provided by the present invention is introduced with an example operation of 500 kHz. The description of the above embodiments is mainly used to help understand that the mode conversion echo generated by the surface wave interacting with the defect cannot be ignored, and the mode conversion echo at both ends of the defect can be added to the traditional defect callback and transmitted wave. To better characterize the depth of the defect, to break through the limitation of the inflection point at the defect depth of 0.45 times the wavelength; at the same time, for those of ordinary skill in the art, the method according to the present invention will have changes in the specific implementation and scope. The content of this specification It should not be construed as a limitation of the invention.

Claims (1)

1. A multi-coefficient depth detection method for vertical defects based on surface waves, characterized in that: the method includes a process as follows: Step 1: Establish a simulated multi-coefficient curve For the parameter information such as the size and material of the target workpiece, determine the appropriate simulation software and establish a suitable simulation model; determine the depth and size range of the defect according to the needs, and perform a series of simulations; according to the simulation model, extract the waveform information at the observation point or surface , divide the obtained defect echo value, transmitted wave value and mode conversion echo value at both ends of the defect by the reference direct wave value and normalize it as the ordinate, and then divide the defect depth or defect depth by the wavelength Normalized processing is used as the abscissa to establish a multi-coefficient defect depth characterization curve; Step 2: Perform experimental testing According to the size and material parameter information of the target workpiece, as well as the excitation frequency and excitation mode established by the simulation multi-coefficient curve, select the appropriate probe for the experiment; the experimental system includes the test workpiece, a excitation-reception probe, an oscilloscope, and excitation equipment; The probe is connected to the test workpiece, and the one-excitation-one-reception probe is connected to the excitation device through the connection line, and the excitation device is connected to the oscilloscope; the test object's defect callback, transmitted wave and mode conversion echo at both ends of the defect are measured experimentally. Record and obtain the above four waveform amplitudes representing the depth of defects; Step 3: Determine Defect Depth According to the four defect information measured by the experiment, the defect callback, the transmitted wave and the mode conversion echo at both ends of the defect, divided by the reference value of the direct wave to obtain a set of four coefficient values; compare the simulation with a set of four Coefficient defect depth characterization curve to determine the depth of the defect; The multiple coefficients include defect echo defect depth representation coefficient, transmitted wave defect depth representation coefficient, defect depth representation coefficient of mode conversion echo at the left end of the defect, defect depth representation coefficient of mode conversion echo at the right end of the defect; the defect depth representation coefficient of defect echo is In the process of surface wave propagation, the defect echo reflected from the defect is encountered, and the defect depth characterization coefficient of the defect echo is established according to the different amplitudes of the defect echo reflected back from the defect at different depths; the defect depth characterization coefficient of the transmitted wave is the surface In the process of wave propagation, the transmitted wave that encounters the defect and continues to propagate through the defect, the defect depth characterization coefficient of the transmitted wave established according to the different amplitudes of the transmitted wave transmitted through the defect at different depths; The characterization coefficient is that when the surface wave interacts with the defect in the propagation process, there will be scattered body waves below the defect. When the body wave propagates and encounters the lower end surface of the workpiece, it will be reflected back, and when it encounters a defect, it will be converted into a surface that propagates along the left end of the defect. The defect depth characterization coefficient of the modal conversion echo at the left end of the defect is established according to the different amplitudes of the modal conversion echo at the left end of the defect at different depths; the defect depth characterization coefficient of the modal conversion echo at the right end of the defect is the surface wave in the propagation process In , when interacting with the defect, there will be scattered body waves below the defect. When the body wave propagates and encounters the lower end surface of the workpiece, it will be reflected back, and when it encounters a defect, it will be converted into a surface wave propagating along the right end of the defect. The defect depth characterization coefficient of the modal conversion echo at the right end of the defect is established based on the different amplitudes of the modal conversion echoes at the right end of the defect.