Multi-defect ultrasonic guided wave nondestructive testing, positioning and identifying method based on mechanical arm

Abstract

The invention relates to a multi-defect ultrasonic guided wave nondestructive testing, positioning and recognition method based on a mechanical arm, relates to the technical field of nondestructive testing and automatic testing, and solves the technical problems that in the prior art, time and labor are consumed in detection of large components, and positioning recognition and detection precision is difficult to guarantee in a manual detection mode. The method comprises the following steps of: post-processing signals acquired for N times, and setting a signal amplitude threshold value; performing global scanning on the tested piece; and whether the mechanical arm completes global scanning or not is judged. A mechanical arm is used for carrying an electromagnetic acoustic sensor to scan a large plate and a large curved surface structure, and detection and positioning recognition of various different defects are achieved; using the collected data as an original data set to construct a deep learning model and an integrated CNN model, and realizing adaptive scanning detection of internal defects of a large component and a large curved surface structure; the use of a coupling agent is reduced, the detection time efficiency and the detection precision are improved, and the detection accuracy and the defect positioning accuracy are realized.

Description

Technical Field

[0001] The present invention relates to the field of nondestructive testing and automated testing technology, and in particular to a multi-defect ultrasonic guided wave nondestructive testing, positioning and identification method based on a robotic arm. Background Art

[0002] Large plate structures and large curved structures are widely used in the automotive, high-speed rail, aerospace and other fields. Delamination and cracking generated during the processing of these large structures, as well as microcracks and corrosion generated during service, can seriously affect their performance and, in severe cases, can cause the entire device or equipment to fail. Currently, the detection of defects in these components mostly uses traditional ultrasonic non-destructive testing methods. During the detection process, the application of coupling agents and the contact between the sensor and the test piece can seriously affect the detection accuracy. At the same time, most traditional ultrasonic non-destructive testing methods use manual testing, which makes the detection of large components time-consuming and labor-intensive, and it is difficult to ensure positioning accuracy and detection accuracy. Summary of the Invention

[0003] The present invention aims to solve the technical problems in the existing technology that the detection of large plate structures and large curved structures is time-consuming and labor-intensive, and it is difficult to ensure the accuracy of defect positioning and identification as well as the detection accuracy. It provides a multi-defect ultrasonic guided wave nondestructive detection, positioning and identification method based on a robotic arm.

[0004] In order to solve the above technical problems, the technical solutions of the present invention are as follows:

[0005] A method for nondestructive detection, positioning, and identification of multiple defects using ultrasonic guided waves based on a robotic arm, comprising the following steps: First, based on the reference defect signal and noise signal, the signals collected N times are post-processed and analyzed to distinguish the noise signal from the defect signal. At the same time, the signal amplitude thresholds for different defects on the test piece are set to calibrate the signal amplitude for different types of defects. N is the number of signal acquisitions, which can be any positive integer. Secondly, the computer controls the robotic arm carrying the electromagnetic acoustic sensor to perform a global scan of the test piece according to the preset scanning path, and the collected signals are recorded; Compare and analyze the recorded signal with the noise signal and defect signal distinguished in the previous step to determine whether the recorded signal is a defect signal. If so, record the detection position where the defect is detected, and perform multi-position detection based on the detection position distribution with the detection position as the center and a certain distance between the center and other detection positions as the radius to determine the location and type of the defect. If not, continue scanning the test piece according to the preset scanning path and repeat the above steps. Determine whether the robot arm has completed the global scan. If so, record all defect locations and defect types and end the detection process. If not, continue scanning the test piece according to the preset scanning path of the robot arm and repeat the above steps until the global scan is completed.

[0006] In the above technical solution, the method further comprises the following steps:

[0007] The recorded noise and defect signals are sorted and used as the original data set. The original data set is divided into three parts: training set, validation set, and test set, and the ratio is set according to the requirements;

[0008] Build a deep learning model and output a classification prediction result based on the deep learning model. The prediction result is the probability of a defect in a specific area and the specific location of the defect;

[0009] Build an integrated CNN model to extract defect data features and establish the relationship between data features and defect locations;

[0010] The test set data is used to verify the trained deep learning model, and the obtained results are compared with the results of the original data set to determine whether the defect location and type are accurate. If so, the proposed deep learning model is applied to other test pieces for scanning and inspection. Otherwise, the proposed deep learning model and the integrated CNN model should be optimized to improve the output accuracy.

[0011] In the above technical solution, the constructed deep learning model specifically includes: input layer, convolution layer, pooling layer, activation layer and output layer; among them, the input layer serves as the data entry of the model, is responsible for receiving the original data, and converting it into a tensor format that the model can process, providing initial input for subsequent layers; the convolution layer extracts local features of the input data through filters, captures the local correlation of the data, and reduces model parameters through parameter sharing to improve computational efficiency; the pooling layer samples the feature map output by the convolution layer, and reduces the feature dimension and the amount of computation by retaining the maximum value or average value of the local area, while enhancing the robustness of the model to slight deformations of the input data; the activation layer breaks the limitations of the linear model by introducing nonlinear transformations, enabling the model to learn complex nonlinear relationships and improve its ability to express complex patterns; the output layer serves as the result exit of the model, outputs the final result according to the task type, and outputs the classification prediction result according to the model, that is, the probability of whether there is a defect in a specific area, the specific location of the defect, and the specific type of the defect.

[0012] In the above technical solution, the constructed integrated CNN model includes: a CNN model and a regression model; wherein the CNN model is used to extract the data features of the defects, and the regression model is used to establish the relationship between the data features and the defect locations.

[0013] In the above technical solution, the detection position distribution is specifically as follows: the defect position is , detect position to Located with The distance is On the circumference, they are arranged at equal intervals in a clockwise or counterclockwise direction.

[0014] In the above technical solution, the preset scanning path of the robot arm is specifically as follows: the scanning direction is first in the positive horizontal direction; when the preset scanning distance is reached, the robot arm scans in the positive vertical direction. distance; then, the scanning direction is the reverse direction of the horizontal direction; when the preset scanning distance is reached, the forward scanning direction is vertically Then, the scanning direction is adjusted to the horizontal positive direction again, and the above steps are repeated, wherein the forward and reverse preset scanning distances should be less than or equal to the size of the test piece along the scanning direction.

[0015] In the above technical solution, the tested piece is a plate structure or a large curved surface structure.

[0016] In the above technical solution, the computer, the robotic arm, the electromagnetic acoustic sensor, the signal generator / receiver, the power amplifier and the signal amplifier are respectively connected through a BNC port or a USB port.

[0017] In the above technical solution, the types of defects include: regular type and irregular type; regular type includes: rectangle, circle and polygon.

[0018] In the above technical solution, the test system applicable to the method of the present invention includes: a control and test system, and a test piece; wherein: The control and test system includes: a computer, a robotic arm, an electromagnetic acoustic sensor, a signal generator / receiver, a power amplifier, and a signal amplifier; wherein the electromagnetic acoustic sensor includes: a transmitting sensor and a receiving sensor; the transmitting sensor is used to emit a first ultrasonic guided wave; the receiving sensor is used to receive a second ultrasonic guided wave;

[0019] The computer is connected to the signal generator / receiver and the robotic arm respectively; the signal generator / receiver is connected to the power amplifier and the signal amplifier respectively; the electromagnetic acoustic sensor is connected to the power amplifier, the signal amplifier and the robotic arm respectively;

[0020] The computer is used to control the signal generator / receiver and the operation of the robotic arm;

[0021] The robotic arm is used to control the movement position of the electromagnetic acoustic sensor;

[0022] Electromagnetic acoustic sensors are used to detect the test piece;

[0023] A signal generator / receiver is used to generate and / or receive signals;

[0024] The power amplifier is used to amplify the power of the signal generated by the signal generator / receiver;

[0025] The signal amplifier is used to amplify the signal detected and returned by the electromagnetic acoustic sensor.

[0026] The present invention has the following beneficial effects:

[0027] The multi-defect ultrasonic guided wave nondestructive testing, positioning and identification method based on a robotic arm of the present invention mainly uses a robotic arm carrying an electromagnetic acoustic sensor to scan large plates and large curved structures, thereby realizing the detection, positioning and identification of a variety of different defects; it can reduce the use of coupling agents, greatly improve the detection time efficiency, improve the detection accuracy and achieve the accuracy of detection as well as the accuracy of positioning and identification of defects. At the same time, the present invention can perform ultrasonic guided wave multi-defect adaptive detection based on deep learning and a robotic arm system, and uses a robotic arm carrying an electromagnetic acoustic sensor to scan large plates and large curved structures, thereby realizing the detection and positioning of a variety of different defects; it can reduce the use of coupling agents, greatly improve the detection time efficiency, improve the detection accuracy, and achieve the accuracy of detection as well as the accuracy of positioning of defects; the collected data is used as the original data set to construct a deep learning and CNN model to realize adaptive scanning detection of internal defects of large components and large curved structures. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0029] Figure 1 The figure is a schematic structural diagram of a test system applicable to the multi-defect ultrasonic guided wave nondestructive detection, positioning and identification method based on a robotic arm of the present invention.

[0030] Figure 2 Schematic diagram of the robotic arm scanning path.

[0031] Figure 3 Schematic diagram of noise signal.

[0032] Figure 4 Schematic diagram of defect signal.

[0033] Figure 5 Schematic diagram of detection location distribution.

[0034] Figure 6 The figure is a schematic diagram of the detection process of the multi-defect ultrasonic guided wave nondestructive detection, positioning and identification method based on a robotic arm of the present invention.

[0035] Figure 7This is a workflow diagram of the deep learning model.

[0036] The reference numerals in the figures indicate:

[0037] 1: Control and test system;

[0038] 11: Computer; 12: Robotic arm; 13: Electromagnetic acoustic sensor; 14: Signal generator / receiver; 15: Power amplifier; 16: Signal amplifier;

[0039] 131: transmitting sensor; 132: receiving sensor; 133: first ultrasonic guided wave; 134: second ultrasonic guided wave;

[0040] 2: Large-size metal sheet specimen; 21: Defect. DETAILED DESCRIPTION

[0041] The following is combined with Figures 1 to 6 The present invention is described in detail.

[0042] Specific implementation scheme 1: The test system applicable to the multi-defect ultrasonic guided wave nondestructive detection, positioning and identification method based on the robotic arm of the present invention, such as Figure 1 As shown, it includes: a control and test system 1 and a test piece, wherein the test piece is a large-sized metal plate specimen 2 (such as a size of 2 m×2 m×3 mm).

[0043] in:

[0044] The control and test system 1 includes a computer 11, a robotic arm 12, an electromagnetic acoustic sensor 13, a signal generator / receiver 14, a power amplifier 15, and a signal amplifier 16. The electromagnetic acoustic sensor 13 is primarily composed of two parts: a transmitting sensor 131 and a receiving sensor 132. The transmitting sensor 131 is used to emit a first ultrasonic guided wave 133; the receiving sensor 132 is used to receive a second ultrasonic guided wave 134.

[0045] The computer 11 is connected to the signal generator / receiver 14 and the robotic arm 12 respectively; the signal generator / receiver 14 is connected to the power amplifier 15 and the signal amplifier 16 respectively; the electromagnetic acoustic sensor 13 is connected to the power amplifier 15, the signal amplifier 16, and the robotic arm 12 respectively;

[0046] The computer 11 is used to control the operation of the signal generator / receiver 14 and the robotic arm 12;

[0047] The robotic arm 12 is used to control the movement of the electromagnetic acoustic sensor 13; the electromagnetic acoustic sensor 13 is used to detect the large-sized metal plate specimen 2;

[0048] The signal generator / receiver 14 is used to generate and / or receive signals;

[0049] The power amplifier 15 is used to amplify the power of the signal generated by the signal generator / receiver 14;

[0050] The signal amplifier 16 is used to amplify the signal detected and returned by the electromagnetic acoustic sensor 13;

[0051] Specifically, the signal generated by the signal generator / receiver 14 is amplified and processed by the power amplifier 15, and then emitted through the transmitting sensor 131 to form a first ultrasonic guided wave 133; the second ultrasonic guided wave 134 is received by the receiving sensor 132, amplified and processed by the signal amplifier 16, and then received by the signal generator / receiver 14.

[0052] The above devices are connected through BNC, USB and other ports.

[0053] There is a defect 21 on the large-sized metal plate specimen 2. There are many types of defects 21, such as Figure 1 Regular types drawn in the image include rectangles, circles, and polygons, and irregular types such as corrosion and those generated during processing.

[0054] The present invention is a method for nondestructive detection, positioning and identification of multiple defects using ultrasonic guided waves based on a robotic arm. Figure 6 As shown (only the outline of the steps of the method is shown in the figure), the following steps are included:

[0055] First, based on the reference defect signal and noise signal, the signals collected N times are post-processed and analyzed to distinguish the noise signal (such as Figure 3 as shown) and defect signals (as Figure 4 As shown), signal amplitude thresholds for different defects 21 are set at the same time, and signal amplitudes are calibrated for different types of defects 21, such as corrosion, cracks, and delamination defects 21; N is the number of times the signal is collected, and its value can be any positive integer. In this embodiment, N = 1000 is taken as an example;

[0056] Secondly, the computer 11 controls the robot arm 12 carrying the electromagnetic acoustic sensor 13 to scan the path diagram (such as Figure 2 As shown in Figure 2, a global scan is performed on the large-sized metal sheet specimen 2; wherein the scanning step is , The value of can be any value, here is = 2 mm as an example, and record the collected signals;

[0057] The recorded signal is compared with the noise signal (such as Figure 3 as shown) and defect signals (as Figure 4) to compare and analyze the signal to determine whether it is a defect signal. If it is, the detection position where the defect 21 is detected is recorded, and the detection position is taken as the center, and the distance between the center and other detection positions is recorded. The distance is the radius, according to the detection position distribution (such as Figure 5 as shown) for multiple positions ( The specific distribution of the detection positions is as follows: the defect position 21 is , detect position to Located with The distance is On the circumference of the circle, they are arranged in a clockwise or counterclockwise direction with equal spacing, The value of can be any positive integer, here is = 9 as an example, and The recommended value range should be ≥ 6, thereby accurately determining the location of the defect 21; The value of can be any positive integer; otherwise the robot 12 follows the preset scanning path (such as Figure 2 As shown in FIG, the large-sized metal plate specimen 2 is scanned continuously and the above steps are repeated; at the same time, the recorded signal is compared with the calibrated signal of the different types of defects 21 to determine the type of the defect 21; as shown in FIG. Figure 2 As shown, the preset scanning path of the robot arm 12 is specifically as follows: the scanning direction is first in the positive horizontal direction; when the preset scanning distance is reached, the scanning direction is in the positive vertical direction. distance; then, the scanning direction is the reverse direction of the horizontal direction; when the preset scanning distance is reached, the forward scanning direction is vertically Then, the scanning direction is adjusted to the horizontal positive direction again, and the above steps are repeated, and the scanning is continued by analogy, wherein the forward and reverse preset scanning distances should be less than or equal to the size of the test piece along the scanning direction.

[0058] Determine whether the robot arm 12 has completed the global scan. If so, record the detection positions and types of all defects 21 and end the detection process. If not, follow the preset scanning path diagram of the robot arm 12 (such as Figure 2 Continue scanning the test piece and repeat the above steps until the global scan is completed.

[0059] like Figure 2 As shown, in the above specific embodiment, the scanning path of the robot arm 12 in the multi-defect ultrasonic guided wave nondestructive testing, positioning and identification method based on the robot arm of the present invention is a serpentine structure, wherein the scanning step is , The value of can be any value. In other specific implementations, it can also be other customized path trajectory shapes.

[0060] In order to accurately determine the location of the defect 21, in other embodiments, The value of should satisfy: is a positive integer, and ≥6.

[0061] In the above specific embodiments, the test piece is a large-sized metal plate test piece 2; in other specific embodiments, the test piece may also be a plate structure of other materials or a large curved surface structure.

[0062] In the above specific embodiment, the sensor involved is the electromagnetic acoustic sensor 13; in other specific embodiments, the sensor may also be: an eddy current sensor, a traditional piezoelectric ceramic sensor, etc.

[0063] The present invention's multi-defect ultrasonic guided wave nondestructive testing, positioning, and identification method based on a robotic arm primarily uses a robotic arm carrying an electromagnetic acoustic sensor to scan large plates and large curved structures, thereby enabling the detection, positioning, and identification of a variety of different defects. This reduces the use of coupling agents, greatly improves detection timeliness, enhances detection accuracy, and achieves detection accuracy as well as precision in defect positioning and identification.

[0064] Specific implementation plan 2:

[0065] The following is combined with Figure 7 This embodiment will be described.

[0066] This embodiment provides an alternative implementation of a robotic arm-based multi-defect ultrasonic guided wave nondestructive testing, location, and identification method. The testing system and connection method are identical to those in the first embodiment. The difference lies in the ability to construct a deep learning-based adaptive multi-defect detection method using ultrasonic guided waves, based on the aforementioned testing system and test data structure.

[0067] The following is the workflow of the deep learning model, such as Figure 7 As shown (only the outline of the workflow is shown in the figure), it specifically includes the following steps:

[0068] First, the recorded noise signals and defect signals are sorted and used as the original data set. The original data set is divided into three parts: training set, validation set, and test set, and the ratio is set according to the requirements;

[0069] Construct a deep learning model, which specifically includes: input layer, convolution layer, pooling layer, activation layer and output layer; among them, the input layer serves as the data entry of the model, is responsible for receiving the original data and converting it into a tensor format that the model can process, providing initial input for subsequent layers; the convolution layer mainly extracts local features of the input data through filters, captures the local correlation of the data, and reduces model parameters through parameter sharing to improve computational efficiency; the pooling layer samples the feature map output by the convolution layer, and reduces the feature dimension and the amount of computation by retaining the maximum value or average value of the local area, while enhancing the model's robustness to slight deformations of the input data; the activation layer mainly introduces nonlinear transformations to break the limitations of the linear model, enabling the model to learn complex nonlinear relationships and improve its ability to express complex patterns; the output layer serves as the result exit of the model, outputs the final result according to the task type, and can output classification prediction results based on the model, i.e., the probability of the presence of defect 21 in a specific area, the specific location of defect 21, and the specific type of defect 21;

[0070] Construct an integrated CNN model, which mainly includes two parts: a CNN model and a regression model. The CNN model is used to extract the data features of defect 21, and the regression model is used to establish the relationship between the data features and the location of defect 21.

[0071] The test set data is used to verify the trained model, and it is compared with the results of the original data set to determine whether the defect location and type are accurate. If so, the proposed deep learning model can be applied to other test samples for scanning and detection. Otherwise, the proposed deep learning model and the integrated CNN model should be optimized to improve the output accuracy.

[0072] like Figure 2 As shown, in the above specific embodiment, the scanning path of the robot arm 12 in the multi-defect ultrasonic guided wave nondestructive testing, positioning and identification method based on the robot arm of the present invention is a serpentine structure, wherein the scanning step is , The value of can be any value. In other specific implementations, it can also be other customized path trajectory shapes.

[0073] In order to accurately determine the location of the defect 21, in other embodiments, The value of should satisfy: is a positive integer, and ≥6.

[0074] In the above specific embodiments, the test piece is a large-sized metal plate test piece 2; in other specific embodiments, the test piece may also be a plate structure of other materials or a large curved surface structure.

[0075] In the above specific embodiment, the sensor involved is the electromagnetic acoustic sensor 13; in other specific embodiments, the sensor may also be: an eddy current sensor, a traditional piezoelectric ceramic sensor, etc.

[0076] The robotic arm-based multi-defect ultrasonic guided wave nondestructive testing, positioning, and identification method of this embodiment is a deep learning-based ultrasonic guided wave multi-defect adaptive detection method. A robotic arm carrying an electromagnetic acoustic sensor is used to scan large plates and large curved structures, thereby realizing the detection, positioning, and identification of a variety of different defects. The method can reduce the use of coupling agents, greatly improve detection timeliness, and enhance detection accuracy, thereby achieving detection accuracy as well as defect positioning and identification accuracy. The collected data is used as the original data set to construct a deep learning and CNN model to realize adaptive scanning detection of internal defects of large components and large curved structures.

[0077] Obviously, the above embodiments are merely examples for clarity of explanation and are not intended to limit the implementation methods. Those skilled in the art will readily appreciate that other variations or modifications based on the above descriptions are possible. It is not necessary and impossible to enumerate all implementation methods here. Obvious variations or modifications arising therefrom remain within the scope of protection of the present invention.

Claims

1. A method for nondestructive detection, positioning and identification of multiple defects using ultrasonic guided waves based on a robotic arm, characterized in that: The following steps are involved: First, based on the reference defect signal and the noise signal, the signals collected N times are post-processed and analyzed to distinguish the noise signal from the defect signal. At the same time, the signal amplitude thresholds for different defects (21) existing on the test piece are set, and the signal amplitudes of different types of defects (21) are calibrated. N is the number of times the signal is collected, and its value is any positive integer. Secondly, the computer (11) controls the robotic arm (12) carrying the electromagnetic acoustic sensor (13) to perform a global scan of the test piece according to a preset scanning path, and records the collected signals; The recorded signal is compared and analyzed with the noise signal and the defect signal distinguished in the previous step to determine whether the recorded signal is a defect signal. If so, the detection position where the defect (21) is detected is recorded, and with the detection position as the center and a certain distance between the center and other detection positions as the radius, multi-position detection is performed according to the detection position distribution to determine the position of the defect (21) and the type of the defect (21); otherwise, the test piece is scanned continuously according to the preset scanning path and the above steps are repeated; Determine whether the robot arm (12) has completed the global scan. If so, record the positions of all defects (21) and the types of defects (21) and end the detection process. If not, continue to scan the test piece according to the preset scanning path of the robot arm (12) and repeat the above steps until the global scan is completed.

2. The method for nondestructive testing, positioning and identification of multiple defects using ultrasonic guided waves based on a robotic arm according to claim 1, characterized in that: The method further comprises the following steps: The recorded noise signals and defect signals are sorted and used as the original data set, which is then divided into three parts: training set, validation set, and test set, with the ratio set according to requirements. Constructing a deep learning model and outputting a classification prediction result based on the deep learning model, wherein the prediction result is the probability of the presence or absence of a defect (21) in a specific area and the specific location of the defect (21); Construct an integrated CNN model to extract the data features of the defect (21) and establish the relationship between the data features and the location of the defect (21); The test set data is used to verify the trained deep learning model, and the obtained results are compared with the results of the original data set to determine whether the location and type of the defect (21) are accurate. If so, the proposed deep learning model is applied to other test pieces for scanning and detection. If not, the proposed deep learning model and the integrated CNN model should be optimized to improve the output accuracy.

3. The method for nondestructive detection, positioning and identification of multiple defects using ultrasonic guided waves based on a robotic arm according to claim 2, characterized in that: The constructed deep learning model specifically includes: input layer, convolution layer, pooling layer, activation layer and output layer; among them, the input layer serves as the data entry of the model, is responsible for receiving the original data, and converting it into a tensor format that the model can process, providing initial input for subsequent layers; the convolution layer extracts local features of the input data through filters, captures the local correlation of the data, and reduces model parameters through parameter sharing to improve computational efficiency; the pooling layer samples the feature map output by the convolution layer, and reduces the feature dimension and the amount of computation by retaining the maximum value or average value of the local area, while enhancing the robustness of the model to slight deformations of the input data; the activation layer breaks the limitations of the linear model by introducing nonlinear transformations, enabling the model to learn complex nonlinear relationships and improve its ability to express complex patterns; the output layer serves as the result exit of the model, outputs the final result according to the task type, and outputs the classification prediction result according to the model, that is, the probability of whether there is a defect (21) in a specific area, the specific location of the defect (21), and the specific type of the defect (21).

4. The method for nondestructive testing, positioning and identification of multiple defects using ultrasonic guided waves based on a robotic arm according to claim 2, characterized in that: The constructed integrated CNN model includes: a CNN model and a regression model; wherein, the CNN model is used to extract the data features of the defect (21), and the regression model is used to establish the relationship between the data features and the position of the defect (21).

5. The method for nondestructive testing, positioning and identification of multiple defects using ultrasonic guided waves based on a robotic arm according to claim 1 or 2, characterized in that: The specific distribution of detection positions is as follows: the position of defect (21) is , detect position to Located with The distance is On the circumference, they are arranged at equal intervals in a clockwise or counterclockwise direction.

6. The method for nondestructive testing, positioning and identification of multiple defects using ultrasonic guided waves based on a robotic arm according to claim 1 or 2, characterized in that: The preset scanning path of the robot arm (12) is specifically as follows: the scanning direction is first in the positive horizontal direction; when the preset scanning distance is reached, the robot arm (12) scans in the positive vertical direction. distance; then, the scanning direction is the reverse direction of the horizontal direction; when the preset scanning distance is reached, the forward scanning direction is vertically Then, the scanning direction is adjusted to the horizontal positive direction again, and the above steps are repeated, wherein the forward and reverse preset scanning distances should be less than or equal to the size of the test piece along the scanning direction.

7. The method for nondestructive testing, positioning and identification of multiple defects using ultrasonic guided waves based on a robotic arm according to claim 1 or 2, characterized in that: The tested piece is a plate structure or a large curved surface structure.

8. The method for nondestructive testing, positioning and identification of multiple defects using ultrasonic guided waves based on a robotic arm according to claim 1 or 2, characterized in that: The computer (11), the robotic arm (12), the electromagnetic acoustic sensor (13), the signal generator / receiver (14), the power amplifier (15) and the signal amplifier (16) are connected via a BNC port or a USB port, respectively.

9. The method for nondestructive testing, positioning and identification of multiple defects using ultrasonic guided waves based on a robotic arm according to claim 1, characterized in that: The types of defects (21) include regular types and irregular types; regular types include rectangles, circles, and polygons.

10. The method for nondestructive testing, positioning and identification of multiple defects using ultrasonic guided waves based on a robotic arm according to claim 1, characterized in that: The test system applicable to the method includes: a control and test system (1), a test piece; wherein: The control and test system (1) includes: a computer (11), a robotic arm (12), an electromagnetic acoustic sensor (13), a signal generator / receiver (14), a power amplifier (15), and a signal amplifier (16); wherein the electromagnetic acoustic sensor (13) includes: a transmitting sensor (131) and a receiving sensor (132); The transmitting sensor (131) is used to transmit a first ultrasonic guided wave (133); the receiving sensor (132) is used to receive a second ultrasonic guided wave (134); The computer (11) is connected to the signal generator / receiver (14) and the robotic arm (12) respectively; the signal generator / receiver (14) is connected to the power amplifier (15) and the signal amplifier (16) respectively; the electromagnetic acoustic sensor (13) is connected to the power amplifier (15), the signal amplifier (16), and the robotic arm (12) respectively; The computer (11) is used to control the operation of the signal generator / receiver (14) and the robotic arm (12); The robotic arm (12) is used to control the movement position of the electromagnetic acoustic sensor (13); The electromagnetic acoustic sensor (13) is used to detect the test piece; A signal generator / receiver (14) is used to generate and / or receive signals; The power amplifier (15) is used to amplify the power of the signal generated by the signal generator / receiver (14); The signal amplifier (16) is used to amplify the signal detected and returned by the electromagnetic acoustic sensor (13).

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