CN117074514A - Defect detection method and device based on three-dimensional magnetic current imaging - Google Patents
Defect detection method and device based on three-dimensional magnetic current imaging Download PDFInfo
- Publication number
- CN117074514A CN117074514A CN202311058565.0A CN202311058565A CN117074514A CN 117074514 A CN117074514 A CN 117074514A CN 202311058565 A CN202311058565 A CN 202311058565A CN 117074514 A CN117074514 A CN 117074514A
- Authority
- CN
- China
- Prior art keywords
- test piece
- magnetic
- magnetic field
- defect
- magnetic current
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 230000005291 magnetic effect Effects 0.000 title claims abstract description 308
- 230000007547 defect Effects 0.000 title claims abstract description 156
- 238000001514 detection method Methods 0.000 title claims abstract description 125
- 238000003384 imaging method Methods 0.000 title claims abstract description 25
- 238000012360 testing method Methods 0.000 claims abstract description 217
- 238000010586 diagram Methods 0.000 claims abstract description 66
- 230000005284 excitation Effects 0.000 claims abstract description 43
- 239000003302 ferromagnetic material Substances 0.000 claims abstract description 34
- 230000004044 response Effects 0.000 claims abstract description 33
- 230000003068 static effect Effects 0.000 claims abstract description 24
- 239000000523 sample Substances 0.000 claims description 46
- 239000000463 material Substances 0.000 claims description 36
- 230000005355 Hall effect Effects 0.000 claims description 16
- 238000012549 training Methods 0.000 claims description 15
- 230000008859 change Effects 0.000 claims description 10
- 238000005516 engineering process Methods 0.000 claims description 8
- 238000010801 machine learning Methods 0.000 claims description 8
- 238000011156 evaluation Methods 0.000 claims description 7
- 230000002950 deficient Effects 0.000 claims description 6
- 230000005415 magnetization Effects 0.000 claims description 6
- 230000008439 repair process Effects 0.000 claims description 4
- 238000000034 method Methods 0.000 abstract description 8
- 238000011158 quantitative evaluation Methods 0.000 abstract description 3
- 239000010410 layer Substances 0.000 description 7
- 238000003745 diagnosis Methods 0.000 description 4
- 238000012545 processing Methods 0.000 description 4
- 238000007689 inspection Methods 0.000 description 2
- 230000005389 magnetism Effects 0.000 description 2
- 239000007769 metal material Substances 0.000 description 2
- 206010063385 Intellectualisation Diseases 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 230000006698 induction Effects 0.000 description 1
- 230000001788 irregular Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000013139 quantization Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 239000002344 surface layer Substances 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
- 239000011800 void material Substances 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N27/00—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means
- G01N27/72—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating magnetic variables
- G01N27/82—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating magnetic variables for investigating the presence of flaws
- G01N27/83—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating magnetic variables for investigating the presence of flaws by investigating stray magnetic fields
Landscapes
- Chemical & Material Sciences (AREA)
- Chemical Kinetics & Catalysis (AREA)
- Electrochemistry (AREA)
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Investigating Or Analyzing Materials By The Use Of Magnetic Means (AREA)
Abstract
The invention relates to the technical field of detection, and discloses a defect detection method and device based on three-dimensional magnetic current imaging. The method comprises the steps that an electromagnetic excitation assembly is utilized to generate a multi-frequency alternating magnetic field, magnetic currents in a ferromagnetic material test piece are excited, the magnetic current distribution in the test piece is detected by adopting the same detection device and the same detection parameters before and after the magnetic currents in the test piece are excited, static reference data and excitation response data of the test piece can be obtained, three-dimensional magnetic current reconstruction is carried out on the static reference data and the excitation response data respectively by adopting a three-dimensional reconstruction algorithm, a first magnetic field distribution diagram and a second magnetic field distribution diagram can be obtained, the first magnetic field distribution diagram and the second magnetic field distribution diagram are input into a preset first identification model to automatically detect defects in the test piece, quantitative evaluation is carried out on the defects, and defect specific information of the test piece is obtained; the invention can improve the detection precision and the detection efficiency, and improve the detection automation degree and the intelligent degree.
Description
Technical Field
The invention relates to the technical field of detection, in particular to a defect detection method and device based on three-dimensional magnetic current imaging.
Background
At present, a method for detecting defects of a metal material mainly adopts a magnetic current flaw detection technology under the excitation of a single-frequency magnetic field to detect the metal material, detect the defects, cracks and the like in the material. However, the method is difficult to detect complex three-dimensional magnetic current distribution in the test piece, and also difficult to realize accurate diagnosis and quantitative evaluation of detection results, has weak three-dimensional space display capability, is difficult to realize high-precision three-dimensional detection, and has the problem that the quantitative detection precision is to be improved.
Disclosure of Invention
The invention provides a defect detection method and device based on three-dimensional magnetic current imaging, which can quantitatively evaluate defects, improve detection precision and detection efficiency and improve detection automation degree.
In order to solve the technical problems, the invention provides a defect detection method based on three-dimensional magnetic current imaging, which comprises the following steps:
detecting magnetic current distribution in the test piece by utilizing a multichannel probe in a state without an external magnetic field, and acquiring static reference data; wherein the test piece is made of ferromagnetic material;
generating a multi-frequency alternating magnetic field by using an electromagnetic excitation assembly, magnetizing the test piece, and exciting the magnetic current response inside the test piece;
after a preset time interval, detecting magnetic current distribution in the test piece by using the multichannel probe to acquire excitation response data;
adopting a three-dimensional reconstruction algorithm to reconstruct three-dimensional magnetic flow of the test piece according to the static reference data and the excitation response data respectively, and obtaining a first magnetic field distribution diagram and a second magnetic field distribution diagram;
utilizing a preset first identification model to carry out differential comparison analysis on the first magnetic field distribution diagram and the second magnetic field distribution diagram, and identifying specific defect information of the test piece; wherein the defect specific information includes the spatial position, shape, size and depth of the defect.
The invention can generate multi-frequency alternating magnetic field by utilizing the electromagnetic excitation assembly, excite the magnetic current in the ferromagnetic material test piece, respectively detect the magnetic current distribution in the test piece by adopting the same detection device and the same detection parameters before and after the magnetic current in the test piece is excited, acquire the static reference data and the excitation response data of the test piece, respectively reconstruct the static reference data and the excitation response data by adopting a three-dimensional reconstruction algorithm, acquire a first magnetic field distribution diagram and a second magnetic field distribution diagram, input the first magnetic field distribution diagram and the second magnetic field distribution diagram into a preset first identification model, automatically detect the defects in the test piece, realize the accurate diagnosis and evaluation of the detection result, and greatly improve the defect detection efficiency and reliability of the material.
Further, the detecting the magnetic current distribution in the test piece by using the multi-channel probe specifically comprises the following steps:
arranging a multichannel probe on a platform with multi-degree-of-freedom motion;
the magnetic current distribution in the test piece is detected by controlling the movement of the platform and changing the position and the direction of the multichannel probe on the surface or in the test piece.
Further, by controlling the movement of the platform, the position and the direction of the multichannel probe on the surface or in the test piece are changed, and the magnetic current distribution in the test piece is detected, specifically:
the multichannel probe is formed by a plurality of detection coils or Hall effect sensors; the magnetic current distribution in the test piece is obtained, and the magnetic current distribution is specifically one or more of the following combinations:
according to the structural characteristics of the detection coils, detecting the magnetic current changes of different depths in the test piece by using a plurality of detection coils, and detecting the magnetic current distribution in the test piece;
or, arranging a plurality of Hall effect sensors in the same direction according to preset intervals, detecting the magnetic current changes of different depths in the same direction in the test piece, and detecting the magnetic current distribution in the test piece;
or, arranging a plurality of Hall effect sensors in different directions according to preset intervals, detecting the magnetic current changes in the same depth and different directions in the test piece, and detecting the magnetic current distribution in the test piece.
The multi-channel probe can be composed of a plurality of layers of detection coils, a plurality of Hall effect sensors or a combination thereof, acquires detection information of different depths in the same direction or different directions of the same depth in the test piece, and changes the position and the direction of the probe in the test piece by matching with a motion platform, so that three-dimensional magnetic current distribution information in the test piece can be obtained, rich magnetic current information is provided for subsequent analysis, and the detection precision is improved.
Further, the preset first recognition model specifically includes:
detecting a plurality of ferromagnetic materials by adopting a three-dimensional magnetic current detection technology to obtain a plurality of corresponding magnetic field distribution diagrams; wherein the plurality of ferromagnetic materials are free of defect materials and a plurality of defective materials;
extracting material magnetic characteristics from the plurality of magnetic field distribution patterns, and analyzing differences of the plurality of magnetic field distribution patterns;
determining a first material magnetic characteristic for representing defect information of the ferromagnetic material;
selecting a machine learning algorithm to construct an identification model, and training and learning the identification model by utilizing the magnetic field distribution graphs and the magnetic characteristics of the first material to adjust parameters of the identification model;
and after the recognition model training is completed, determining parameters of the recognition model to form a first recognition model based on magnetic characteristics.
The first recognition model is obtained by training a large amount of detection data, a sufficient amount of ferromagnetic material detection data is collected, material magnetism characteristics are extracted from the collected data, a machine learning algorithm is selected to construct the recognition model, and the ferromagnetic material detection data is utilized to train and optimize the recognition model, so that the first recognition model which can realize automatic recognition and is based on the magnetism characteristics is obtained, and the detection accuracy and efficiency are improved.
Further, the differential comparison analysis is performed on the first magnetic field distribution diagram and the second magnetic field distribution diagram by using a preset first identification model, so that defect specific information of the test piece is identified, and the defect specific information is specifically:
using a first recognition model to carry out differential comparison analysis on the first magnetic field distribution diagram and the second magnetic field distribution diagram to obtain the magnetic current distribution variation in the test piece;
determining defects of the test piece according to the magnetic current distribution change in the test piece;
quantitatively evaluating the defects of the test piece, and determining the spatial position, shape, size and depth of the defects to form the specific defect information of the test piece.
According to the invention, the first magnetic field distribution diagram and the second magnetic field distribution diagram are input into the first recognition model, differential comparison analysis is carried out in the model, recognition results of corresponding defects are automatically output, automatic diagnosis and evaluation of the three-dimensional magnetic current detection image are realized, and the degree of automation and the degree of intellectualization of defect detection are improved.
Further, after the defect specific information of the test piece is identified, the method further comprises:
judging the defect type and the defect severity of the test piece according to the defect specific information of the test piece;
and determining the repair measures of the test piece according to the defect type and the defect severity of the test piece.
According to the invention, after the first recognition model outputs the specific defect information of the test piece, the type and the severity of the defect of the test piece can be judged according to the information, and the processing decision of the defect of the test piece is determined according to the information, so that the accuracy of the processing decision of the defect of the test piece is improved.
The invention provides a defect detection method based on three-dimensional magnetic current imaging, which utilizes an electromagnetic excitation assembly to generate a multi-frequency alternating magnetic field to excite magnetic current in a ferromagnetic material test piece, adopts the same detection device and the same detection parameters to respectively detect magnetic current distribution in the test piece before and after the magnetic current in the test piece is excited, can acquire static reference data and excitation response data of the test piece, respectively carries out three-dimensional magnetic current reconstruction on the static reference data and the excitation response data by adopting a three-dimensional reconstruction algorithm, can acquire a first magnetic field distribution map and a second magnetic field distribution map, inputs the first magnetic field distribution map and the second magnetic field distribution map into a preset first identification model to realize automatic detection of defects in the test piece, and carries out quantitative evaluation on the defects to obtain defect specific information of the test piece; the invention can improve the detection precision and the detection efficiency, and improve the detection automation degree and the intelligent degree.
Correspondingly, the invention provides a defect detection device based on three-dimensional magnetic current imaging, which comprises the following components: the device comprises a first detection module, a magnetization module, a second detection module, a magnetic current reconstruction module and an identification module;
the first detection module is used for detecting magnetic current distribution in the test piece by utilizing the multichannel probe under the state of no external magnetic field to obtain static reference data; wherein the test piece is made of ferromagnetic material;
the magnetization module is used for generating a multi-frequency alternating magnetic field by utilizing the electromagnetic excitation assembly, magnetizing the test piece and exciting the magnetic current response in the test piece;
the second detection module is used for detecting magnetic current distribution in the test piece by using the multichannel probe after a preset time interval to acquire excitation response data;
the magnetic current reconstruction module is used for carrying out three-dimensional magnetic current reconstruction on the test piece according to the static reference data and the excitation response data by adopting a three-dimensional reconstruction algorithm to obtain a first magnetic field distribution diagram and a second magnetic field distribution diagram;
the identification module is used for carrying out differential comparison analysis on the first magnetic field distribution diagram and the second magnetic field distribution diagram by utilizing a preset first identification model, and identifying defect specific information of the test piece; wherein the defect specific information includes the spatial position, shape, size and depth of the defect.
Further, the detecting the magnetic current distribution in the test piece by using the multi-channel probe specifically comprises the following steps:
arranging a multichannel probe on a platform with multi-degree-of-freedom motion;
the magnetic current distribution in the test piece is detected by controlling the movement of the platform and changing the position and the direction of the multichannel probe on the surface or in the test piece.
Further, the preset first recognition model specifically includes:
detecting a plurality of ferromagnetic materials by adopting a three-dimensional magnetic current detection technology to obtain a plurality of corresponding magnetic field distribution diagrams; wherein the plurality of ferromagnetic materials are free of defect materials and a plurality of defective materials;
extracting material magnetic characteristics from the plurality of magnetic field distribution patterns, and analyzing differences of the plurality of magnetic field distribution patterns;
determining a first material magnetic characteristic for representing defect information of the ferromagnetic material;
selecting a machine learning algorithm to construct an identification model, and training and learning the identification model by utilizing the magnetic field distribution graphs and the magnetic characteristics of the first material to adjust parameters of the identification model;
and after the recognition model training is completed, determining parameters of the recognition model to form a first recognition model based on magnetic characteristics.
Further, the identification module includes: an analysis unit, a determination unit and an evaluation unit;
the analysis unit is used for carrying out differential comparison analysis on the first magnetic field distribution diagram and the second magnetic field distribution diagram by using a first identification model to obtain the magnetic current distribution variation in the test piece;
the determining unit is used for determining defects of the test piece according to the magnetic current distribution change in the test piece;
the evaluation unit is used for quantitatively evaluating the defects of the test piece, determining the space position, the shape, the size and the depth of the defects, and forming the defect specific information of the test piece.
The invention provides a defect detection device based on three-dimensional magnetic current imaging, which is based on organic combination among modules, can quantitatively evaluate the defects of a ferromagnetic material test piece, improves detection precision and detection efficiency, and improves detection automation degree and intelligent degree.
Drawings
FIG. 1 is a schematic flow chart of an embodiment of a defect detection method based on three-dimensional magnetic current imaging according to the present invention;
FIG. 2 is a schematic diagram of an embodiment of an electromagnetic excitation assembly provided by the present invention;
fig. 3 is a schematic structural diagram of an embodiment of a defect detecting device based on three-dimensional magnetic current imaging according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
Referring to fig. 1, a flow chart of an embodiment of a defect detection method based on three-dimensional magnetic current imaging provided by the present invention is shown, and the method includes steps 101 to 105, where the steps are specifically as follows:
step 101: detecting magnetic current distribution in the test piece by utilizing a multichannel probe in a state without an external magnetic field, and acquiring static reference data; wherein the test piece is made of ferromagnetic material.
Further, in the first embodiment of the present invention, the magnetic current distribution inside the test piece is detected by using the multi-channel probe, specifically:
arranging a multichannel probe on a platform with multi-degree-of-freedom motion;
the magnetic current distribution in the test piece is detected by controlling the movement of the platform and changing the position and the direction of the multichannel probe on the surface or in the test piece.
Further, in the first embodiment of the present invention, by controlling the movement of the platform, the position and direction of the multichannel probe on the surface or inside of the test piece are changed, and the magnetic current distribution inside the test piece is detected, specifically:
the multichannel probe is formed by a plurality of detection coils or Hall effect sensors; the magnetic current distribution in the test piece is obtained, and the magnetic current distribution is specifically one or more of the following combinations:
according to the structural characteristics of the detection coils, detecting the magnetic current changes of different depths in the test piece by using a plurality of detection coils, and detecting the magnetic current distribution in the test piece;
or, arranging a plurality of Hall effect sensors in the same direction according to preset intervals, detecting the magnetic current changes of different depths in the same direction in the test piece, and detecting the magnetic current distribution in the test piece;
or, arranging a plurality of Hall effect sensors in different directions according to preset intervals, detecting the magnetic current changes in the same depth and different directions in the test piece, and detecting the magnetic current distribution in the test piece.
In a first embodiment of the invention, the multi-channel probe may be comprised of a multi-layer detection coil, a plurality of hall effect sensors, or a combination thereof. The detection coil can adopt a multi-layer detection coil or a concentric-circle structure coil. Each coil in the multi-layer detection coils is used for detecting magnetic current changes of different depths in the test piece, the outer layer coils detect shallow surface layers, the inner layer coils detect deep layers, and so on. The coils with concentric circle structures can detect the inside of the test piece from inside to outside, and obtain detection information with different depths. The Hall effect sensors are placed in the same direction according to a certain interval, and each sensor can detect the magnetic current change of different depths of a test piece; the Hall effect sensors are arranged in different directions, so that the magnetic current changes of the test piece in the same depth and different directions can be detected, and three-dimensional detection information can be obtained. The multichannel probe is arranged on a platform with multiple degrees of freedom motion, and detection information of different directions and depths can be obtained by changing the position and the direction of the probe on the surface or in the test piece, so that three-dimensional detection is finally realized.
Step 102: and generating a multi-frequency alternating magnetic field by using an electromagnetic excitation assembly, magnetizing the test piece, and exciting the magnetic current response inside the test piece.
In the first embodiment of the invention, the electromagnetic excitation components with different configurations can generate induction magnetic fields with different shapes and frequencies so as to meet detection requirements. Referring to fig. 2, a schematic structural diagram of an embodiment of an electromagnetic excitation assembly according to the present invention is shown. The electromagnetic excitation assembly consists of a power supply, a control system, a signal generator, a power amplifier, a coil and a magnetic core, wherein the power supply is used for providing driving current required by the operation of the assembly and is connected to the control system, the signal generator and the power amplifier; the signal generator is used for generating alternating current signals with required frequency and intensity and is connected to the control system and the power amplifier; the power amplifier is used for amplifying the output signal of the signal generator to generate a magnetic field with sufficient strength and is connected to the control system and the coil; the coil is used for generating a magnetic field to excite the test piece and is connected to the power amplifier and the control system; the control system is used for synchronously controlling the equipment, adjusting the parameters of the excitation magnetic field and is connected to the signal generator, the power amplifier and the coil.
Step 103: and detecting the magnetic current distribution in the test piece by using the multichannel probe after a preset time interval, and obtaining excitation response data.
In the first embodiment of the present invention, the change of the internal magnetic current of the test piece is detected, and the change between the detection results of the two times is analyzed after the internal magnetic current is detected once before and after the internal magnetic current is excited. Therefore, the magnetic current distribution in the test piece is detected by using the multi-channel probe, static reference data is obtained, an alternating magnetic field is generated by the electromagnetic excitation assembly, after the magnetic current response in the test piece is excited, the magnetic current distribution in the test piece is detected by using the same multi-channel probe after a certain time, response data after excitation is obtained, and rich magnetic current information is provided for subsequent analysis.
Step 104: and carrying out three-dimensional magnetic current reconstruction on the test piece according to the static reference data and the excitation response data by adopting a three-dimensional reconstruction algorithm to obtain a first magnetic field distribution diagram and a second magnetic field distribution diagram.
In the first embodiment of the invention, the static reference data and the excitation response data are multichannel detection data, namely raw data obtained by adopting a multichannel probe for detection, and the raw data comprise magneto-rheological response information of different directions and depths inside a test piece. The three-dimensional reconstruction algorithm is adopted, multichannel detection data are taken as input, the data can be calculated and processed by constructing a relation model of a magnetic field and materials, and finally three-dimensional magnetic current distribution information in a test piece is output, so that a magnetic field distribution map is obtained. The magnetic field distribution diagram can more directly reflect the internal structure and performance condition of the test piece, and the magnetic field distribution diagram is utilized for subsequent identification and analysis, so that the accuracy of defect detection can be improved.
Step 105: utilizing a preset first identification model to carry out differential comparison analysis on the first magnetic field distribution diagram and the second magnetic field distribution diagram, and identifying specific defect information of the test piece; wherein the defect specific information includes the spatial position, shape, size and depth of the defect.
Further, in the first embodiment of the present invention, the preset first recognition model is specifically:
detecting a plurality of ferromagnetic materials by adopting a three-dimensional magnetic current detection technology to obtain a plurality of corresponding magnetic field distribution diagrams; wherein the plurality of ferromagnetic materials are free of defect materials and a plurality of defective materials;
extracting material magnetic characteristics from the plurality of magnetic field distribution patterns, and analyzing differences of the plurality of magnetic field distribution patterns;
determining a first material magnetic characteristic for representing defect information of the ferromagnetic material;
selecting a machine learning algorithm to construct an identification model, and training and learning the identification model by utilizing the magnetic field distribution graphs and the magnetic characteristics of the first material to adjust parameters of the identification model;
and after the recognition model training is completed, determining parameters of the recognition model to form a first recognition model based on magnetic characteristics.
In a first embodiment of the present invention, a preset first recognition model is trained based on magnetic features, and the training process is as follows:
1. a sufficient number of test piece inspection data is collected. And detecting the test pieces of various ferromagnetic materials by using a three-dimensional magnetic current detection technology to obtain a large amount of three-dimensional magnetic field images and detection data. Wherein the data contains the detection results under normal conditions and under different defect conditions.
2. Material magnetic features are extracted from the test data. And analyzing the difference between different detection results to determine the characteristic quantity which can effectively represent the magnetic response and defect information of the material. These feature amounts may be quantization indices of three-dimensional magnetic field distribution, image features, and the like.
3. And constructing an identification model. A machine learning algorithm, such as a deep learning neural network, is selected to train the recognition model using a large amount of detection data. The model needs to learn to map the feature quantities to different defect categories or severity levels. The trained model can automatically identify defect information contained in the new inspection data.
4. And (5) verifying and optimizing the model. And verifying the performance of the recognition model obtained by training by using new detection data, evaluating indexes such as recognition accuracy, false alarm rate and the like, optimizing and adjusting parameters of the recognition model according to an evaluation result, and improving the generalization capability of the model.
After verification and optimization, the identification model can be used for forming an identification model suitable for a specific ferromagnetic material test piece, converting brand-new detection data into a defect identification result, and realizing high-precision automatic diagnosis of the detection result.
Further, in the first embodiment of the present invention, differential comparison analysis is performed on the first magnetic field distribution diagram and the second magnetic field distribution diagram by using a preset first identification model, so as to identify defect specific information of the test piece, which is specifically:
using a first recognition model to carry out differential comparison analysis on the first magnetic field distribution diagram and the second magnetic field distribution diagram to obtain the magnetic current distribution variation in the test piece;
determining defects of the test piece according to the magnetic current distribution change in the test piece;
quantitatively evaluating the defects of the test piece, and determining the spatial position, shape, size and depth of the defects to form the specific defect information of the test piece.
In the first embodiment of the invention, differential comparison analysis is performed on the first magnetic field distribution diagram and the second magnetic field distribution diagram, and according to the differential detection result, the magnetic current distribution variation of the inside of the test piece before and after excitation can be obtained by combining the parameters and the positions of the detection coil or the sensor, so that the variation condition of the internal structure or the performance of the test piece is reflected. According to the magnetic current distribution change in the test piece, the defect of the test piece can be determined, and quantitatively evaluated to obtain the following information:
1. defect location: the specific spatial location of the defect within the test piece includes information such as depth, direction, etc.
2. Defect shape: the shape characteristics of the defect inside the test piece, such as circles, ellipses, irregular shapes, etc., and the definition of the defect boundaries.
3. Defect size: the characteristic dimensions of the defect inside the test piece, such as parameters of diameter, major axis, minor axis, area, etc.
4. Defect depth: the depth position of the defect in the test piece, the nearest distance between the defect and the surface and other parameters.
5. Quantitative parameters such as defect density, area occupation ratio, volume size and the like.
Further, in the first embodiment of the present invention, after the defect specific information of the test piece is identified, the method further includes:
judging the defect type and the defect severity of the test piece according to the defect specific information of the test piece;
and determining the repair measures of the test piece according to the defect type and the defect severity of the test piece.
In the first embodiment of the present invention, after the first recognition model outputs the specific information of the defect of the test piece, the type of the defect, such as a crack, a void, an inclusion, etc., can be determined according to the spatial position, shape, size, etc., of the defect; meanwhile, the severity of the defect of the test piece can be determined, a basis is provided for the processing decision of the defect of the test piece, and the accuracy of the processing decision of the defect of the test piece is improved.
In summary, the first embodiment of the invention provides a defect detection method based on three-dimensional magnetic current imaging, which can generate a multi-frequency alternating magnetic field by using an electromagnetic excitation assembly, excite magnetic current in a ferromagnetic material test piece, respectively detect magnetic current distribution in the test piece once by adopting the same detection device and the same detection parameters before and after the magnetic current in the test piece is excited, acquire static reference data and excitation response data of the test piece, respectively perform three-dimensional magnetic current reconstruction on the static reference data and the excitation response data by adopting a three-dimensional reconstruction algorithm, acquire a first magnetic field distribution map and a second magnetic field distribution map, input the first magnetic field distribution map and the second magnetic field distribution map into a preset first identification model to realize automatic detection of defects in the test piece, quantitatively evaluate the defects, and acquire defect specific information of the test piece; the invention can improve the detection precision and the detection efficiency, and improve the detection automation degree and the intelligent degree.
Example 2
Referring to fig. 3, a schematic structural diagram of an embodiment of a defect detection device based on three-dimensional magnetic current imaging according to the present invention includes a first detection module 201, a magnetization module 202, a second detection module 203, a magnetic current reconstruction module 204, and an identification module 205;
the first detection module 201 is configured to detect magnetic current distribution inside a test piece by using a multi-channel probe in a state without an external magnetic field, and obtain static reference data; wherein the test piece is made of ferromagnetic material;
the magnetization module 202 is used for generating a multi-frequency alternating magnetic field by utilizing the electromagnetic excitation assembly, magnetizing the test piece and exciting the magnetic current response inside the test piece;
the second detection module 203 is configured to detect, after a preset time interval, a magnetic current distribution inside the test piece using the multi-channel probe, and obtain excitation response data;
the magnetic current reconstruction module 204 is configured to perform three-dimensional magnetic current reconstruction on the test piece according to the static reference data and the excitation response data by using a three-dimensional reconstruction algorithm, so as to obtain a first magnetic field distribution diagram and a second magnetic field distribution diagram;
the identifying module 205 is configured to perform differential comparison analysis on the first magnetic field distribution diagram and the second magnetic field distribution diagram by using a preset first identifying model, so as to identify defect specific information of the test piece; wherein the defect specific information includes the spatial position, shape, size and depth of the defect.
Further, in the second embodiment of the present invention, the magnetic current distribution inside the test piece is detected by using the multi-channel probe, specifically:
arranging a multichannel probe on a platform with multi-degree-of-freedom motion;
the magnetic current distribution in the test piece is detected by controlling the movement of the platform and changing the position and the direction of the multichannel probe on the surface or in the test piece.
Further, in the second embodiment of the present invention, by controlling the movement of the platform, the position and direction of the multichannel probe on the surface or inside of the test piece are changed, and the magnetic current distribution inside the test piece is detected, specifically:
the multichannel probe is formed by a plurality of detection coils or Hall effect sensors; the magnetic current distribution in the test piece is obtained, and the magnetic current distribution is specifically one or more of the following combinations:
according to the structural characteristics of the detection coils, detecting the magnetic current changes of different depths in the test piece by using a plurality of detection coils, and detecting the magnetic current distribution in the test piece;
or, arranging a plurality of Hall effect sensors in the same direction according to preset intervals, detecting the magnetic current changes of different depths in the same direction in the test piece, and detecting the magnetic current distribution in the test piece;
or, arranging a plurality of Hall effect sensors in different directions according to preset intervals, detecting the magnetic current changes in the same depth and different directions in the test piece, and detecting the magnetic current distribution in the test piece.
Further, in the second embodiment of the present invention, the preset first recognition model is specifically:
detecting a plurality of ferromagnetic materials by adopting a three-dimensional magnetic current detection technology to obtain a plurality of corresponding magnetic field distribution diagrams; wherein the plurality of ferromagnetic materials are free of defect materials and a plurality of defective materials;
extracting material magnetic characteristics from the plurality of magnetic field distribution patterns, and analyzing differences of the plurality of magnetic field distribution patterns;
determining a first material magnetic characteristic for representing defect information of the ferromagnetic material;
selecting a machine learning algorithm to construct an identification model, and training and learning the identification model by utilizing the magnetic field distribution graphs and the magnetic characteristics of the first material to adjust parameters of the identification model;
and after the recognition model training is completed, determining parameters of the recognition model to form a first recognition model based on magnetic characteristics.
Further, in the second embodiment of the present invention, differential comparison analysis is performed on the first magnetic field distribution diagram and the second magnetic field distribution diagram by using a preset first identification model, so as to identify defect specific information of the test piece, which is specifically:
using a first recognition model to carry out differential comparison analysis on the first magnetic field distribution diagram and the second magnetic field distribution diagram to obtain the magnetic current distribution variation in the test piece;
determining defects of the test piece according to the magnetic current distribution change in the test piece;
quantitatively evaluating the defects of the test piece, and determining the spatial position, shape, size and depth of the defects to form the specific defect information of the test piece.
Further, in a second embodiment of the present invention, after the defect specific information of the test piece is identified, the method further includes:
judging the defect type and the defect severity of the test piece according to the defect specific information of the test piece;
and determining the repair measures of the test piece according to the defect type and the defect severity of the test piece.
In summary, the second embodiment of the present invention provides a defect detection device based on three-dimensional magnetic current imaging, based on the organic combination between modules, an electromagnetic excitation assembly is used to generate a multi-frequency alternating magnetic field to excite magnetic current in a ferromagnetic material test piece, the same detection device and the same detection parameters are adopted to detect the magnetic current distribution in the test piece at one time before and after the magnetic current is excited in the test piece, static reference data and excitation response data of the test piece can be obtained, a three-dimensional reconstruction algorithm is adopted to perform three-dimensional magnetic current reconstruction on the static reference data and the excitation response data respectively, a first magnetic field distribution map and a second magnetic field distribution map can be obtained, and the first magnetic field distribution map and the second magnetic field distribution map are input into a preset first identification model to automatically detect defects in the test piece, and quantitatively evaluate the defects to obtain defect specific information of the test piece; the invention can improve the detection precision and the detection efficiency, and improve the detection automation degree and the intelligent degree.
The foregoing embodiments have been provided for the purpose of illustrating the general principles of the present invention, and are not to be construed as limiting the scope of the invention. It should be noted that any modifications, equivalent substitutions, improvements, etc. made by those skilled in the art without departing from the spirit and principles of the present invention are intended to be included in the scope of the present invention.
Claims (10)
1. The defect detection method based on three-dimensional magnetic current imaging is characterized by comprising the following steps of:
detecting magnetic current distribution in the test piece by utilizing a multichannel probe in a state without an external magnetic field, and acquiring static reference data; wherein the test piece is made of ferromagnetic material;
generating a multi-frequency alternating magnetic field by using an electromagnetic excitation assembly, magnetizing the test piece, and exciting the magnetic current response inside the test piece;
after a preset time interval, detecting magnetic current distribution in the test piece by using the multichannel probe to acquire excitation response data;
adopting a three-dimensional reconstruction algorithm to reconstruct three-dimensional magnetic flow of the test piece according to the static reference data and the excitation response data respectively, and obtaining a first magnetic field distribution diagram and a second magnetic field distribution diagram;
utilizing a preset first identification model to carry out differential comparison analysis on the first magnetic field distribution diagram and the second magnetic field distribution diagram, and identifying specific defect information of the test piece; wherein the defect specific information includes the spatial position, shape, size and depth of the defect.
2. The defect detection method based on three-dimensional magnetic current imaging according to claim 1, wherein the detecting of the magnetic current distribution inside the test piece by using the multi-channel probe specifically comprises:
arranging a multichannel probe on a platform with multi-degree-of-freedom motion;
the magnetic current distribution in the test piece is detected by controlling the movement of the platform and changing the position and the direction of the multichannel probe on the surface or in the test piece.
3. The defect detection method based on three-dimensional magnetic current imaging according to claim 2, wherein the detecting the magnetic current distribution in the test piece by controlling the movement of the platform changes the position and direction of the multichannel probe on the surface or in the test piece comprises the following steps:
the multichannel probe is formed by a plurality of detection coils or Hall effect sensors; the magnetic current distribution in the test piece is obtained, and the magnetic current distribution is specifically one or more of the following combinations:
according to the structural characteristics of the detection coils, detecting the magnetic current changes of different depths in the test piece by using a plurality of detection coils, and detecting the magnetic current distribution in the test piece;
or, arranging a plurality of Hall effect sensors in the same direction according to preset intervals, detecting the magnetic current changes of different depths in the same direction in the test piece, and detecting the magnetic current distribution in the test piece;
or, arranging a plurality of Hall effect sensors in different directions according to preset intervals, detecting the magnetic current changes in the same depth and different directions in the test piece, and detecting the magnetic current distribution in the test piece.
4. The defect detection method based on three-dimensional magnetic current imaging according to claim 1, wherein the preset first recognition model specifically comprises:
detecting a plurality of ferromagnetic materials by adopting a three-dimensional magnetic current detection technology to obtain a plurality of corresponding magnetic field distribution diagrams; wherein the plurality of ferromagnetic materials are free of defect materials and a plurality of defective materials;
extracting material magnetic characteristics from the plurality of magnetic field distribution patterns, and analyzing differences of the plurality of magnetic field distribution patterns;
determining a first material magnetic characteristic for representing defect information of the ferromagnetic material;
selecting a machine learning algorithm to construct an identification model, and training and learning the identification model by utilizing the magnetic field distribution graphs and the magnetic characteristics of the first material to adjust parameters of the identification model;
and after the recognition model training is completed, determining parameters of the recognition model to form a first recognition model based on magnetic characteristics.
5. The defect detection method based on three-dimensional magnetic current imaging according to claim 4, wherein the differential comparison analysis is performed on the first magnetic field distribution map and the second magnetic field distribution map by using a preset first identification model, so as to identify the defect specific information of the test piece, which specifically is:
using a first recognition model to carry out differential comparison analysis on the first magnetic field distribution diagram and the second magnetic field distribution diagram to obtain the magnetic current distribution variation in the test piece;
determining defects of the test piece according to the magnetic current distribution change in the test piece;
quantitatively evaluating the defects of the test piece, and determining the spatial position, shape, size and depth of the defects to form the specific defect information of the test piece.
6. The defect detection method based on three-dimensional magnetic current imaging according to claim 1, further comprising, after the identifying the defect specific information of the test piece:
judging the defect type and the defect severity of the test piece according to the defect specific information of the test piece;
and determining the repair measures of the test piece according to the defect type and the defect severity of the test piece.
7. A defect detection device based on three-dimensional magnetic current imaging, comprising: the device comprises a first detection module, a magnetization module, a second detection module, a magnetic current reconstruction module and an identification module;
the first detection module is used for detecting magnetic current distribution in the test piece by utilizing the multichannel probe under the state of no external magnetic field to obtain static reference data; wherein the test piece is made of ferromagnetic material;
the magnetization module is used for generating a multi-frequency alternating magnetic field by utilizing the electromagnetic excitation assembly, magnetizing the test piece and exciting the magnetic current response in the test piece;
the second detection module is used for detecting magnetic current distribution in the test piece by using the multichannel probe after a preset time interval to acquire excitation response data;
the magnetic current reconstruction module is used for carrying out three-dimensional magnetic current reconstruction on the test piece according to the static reference data and the excitation response data by adopting a three-dimensional reconstruction algorithm to obtain a first magnetic field distribution diagram and a second magnetic field distribution diagram;
the identification module is used for carrying out differential comparison analysis on the first magnetic field distribution diagram and the second magnetic field distribution diagram by utilizing a preset first identification model, and identifying defect specific information of the test piece; wherein the defect specific information includes the spatial position, shape, size and depth of the defect.
8. The defect detection device based on three-dimensional magnetic current imaging according to claim 7, wherein the magnetic current distribution inside the test piece is detected by using a multi-channel probe, specifically:
arranging a multichannel probe on a platform with multi-degree-of-freedom motion;
the magnetic current distribution in the test piece is detected by controlling the movement of the platform and changing the position and the direction of the multichannel probe on the surface or in the test piece.
9. The defect detection device based on three-dimensional magnetic current imaging according to claim 7, wherein the preset first recognition model is specifically:
detecting a plurality of ferromagnetic materials by adopting a three-dimensional magnetic current detection technology to obtain a plurality of corresponding magnetic field distribution diagrams; wherein the plurality of ferromagnetic materials are free of defect materials and a plurality of defective materials;
extracting material magnetic characteristics from the plurality of magnetic field distribution patterns, and analyzing differences of the plurality of magnetic field distribution patterns;
determining a first material magnetic characteristic for representing defect information of the ferromagnetic material;
selecting a machine learning algorithm to construct an identification model, and training and learning the identification model by utilizing the magnetic field distribution graphs and the magnetic characteristics of the first material to adjust parameters of the identification model;
and after the recognition model training is completed, determining parameters of the recognition model to form a first recognition model based on magnetic characteristics.
10. The defect detection device based on three-dimensional magnetic current imaging of claim 8, wherein the identification module comprises: an analysis unit, a determination unit and an evaluation unit;
the analysis unit is used for carrying out differential comparison analysis on the first magnetic field distribution diagram and the second magnetic field distribution diagram by using a first identification model to obtain the magnetic current distribution variation in the test piece;
the determining unit is used for determining defects of the test piece according to the magnetic current distribution change in the test piece;
the evaluation unit is used for quantitatively evaluating the defects of the test piece, determining the space position, the shape, the size and the depth of the defects, and forming the defect specific information of the test piece.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311058565.0A CN117074514A (en) | 2023-08-21 | 2023-08-21 | Defect detection method and device based on three-dimensional magnetic current imaging |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311058565.0A CN117074514A (en) | 2023-08-21 | 2023-08-21 | Defect detection method and device based on three-dimensional magnetic current imaging |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117074514A true CN117074514A (en) | 2023-11-17 |
Family
ID=88701874
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311058565.0A Pending CN117074514A (en) | 2023-08-21 | 2023-08-21 | Defect detection method and device based on three-dimensional magnetic current imaging |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117074514A (en) |
-
2023
- 2023-08-21 CN CN202311058565.0A patent/CN117074514A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Mook et al. | Electromagnetic imaging using probe arrays | |
| CN205861255U (en) | Ferromagnetic material stress based on barkhausen detection device | |
| CN103760230B (en) | Based on the weld defects giant magnetoresistance eddy current detection method of BP neural network | |
| JP2002022708A (en) | Eddy current flaw detection signal evaluating method and its device | |
| CN104655656B (en) | Detection imaging method and device based on wideband magnetic wave transmission model parameter identification | |
| US20230228713A1 (en) | Mechanical property measuring apparatus, mechanical property measuring method, substance manufacturing equipment, substance management method, and substance manufacturing method | |
| Zhou et al. | Recognizing coiled tubing defect characteristics based on weak magnetic detection technology | |
| Ren et al. | Shaking noise elimination for detecting local flaw in steel wire ropes based on magnetic flux leakage detection | |
| CN103760229A (en) | Welding defect giant magnetoresistance vortexing detection method based on supporting vector machine | |
| CN104132949B (en) | A kind of device and method of radial ply tyre moulds surface defect Non-Destructive Testing | |
| CN117074514A (en) | Defect detection method and device based on three-dimensional magnetic current imaging | |
| Qiu et al. | Quantitative identification of microcracks through magnetic flux leakage testing based on improved back-propagation neural network | |
| Cacciola et al. | FEA design and misfit minimization for in-depth flaw characterization in metallic plates with eddy current nondestructive testing | |
| CN108956756A (en) | A kind of highly sensitive ferromagnetic material lossless detection method and system | |
| CN114942378A (en) | Nondestructive detection system and method for detecting micro-nano magnetic characteristic information in chip | |
| RU2724582C1 (en) | Method of non-contact detection of availability, location and degree of danger of concentrators of mechanical stresses in metal of ferromagnetic structures | |
| CN110514734B (en) | Composite magnetic field magneto-optical imaging nondestructive detection system and method | |
| Zhang et al. | Defect reconstruction from magnetic flux leakage measurements employing modified cuckoo search algorithm | |
| Jiang et al. | Automatic detection of microcracks on the surface of special steel wire based on remanence effect | |
| CN112098306A (en) | Steel bar corrosion detection device based on spontaneous magnetic flux leakage | |
| Lei et al. | A new defect classification approach based on the fusion matrix of multi-eigenvalue | |
| CN110378370A (en) | A kind of oil/gas well casing imperfection classification method based on impulse eddy current signal | |
| Ma et al. | Imaging and quantitative detection of complex defects with a flexible electromagnetic array sensor | |
| Chady et al. | Fusion of electromagnetic inspection methods for evaluation of stress-loaded steel samples | |
| US20230251226A1 (en) | Mechanical property measuring apparatus, mechanical property measuring method, substance manufacturing equipment, substance management method, and substance manufacturing method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |