CN112816556B - Defect detection method, device, equipment and storage medium - Google Patents

Defect detection method, device, equipment and storage medium Download PDF

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Publication number
CN112816556B
CN112816556B CN201911130012.5A CN201911130012A CN112816556B CN 112816556 B CN112816556 B CN 112816556B CN 201911130012 A CN201911130012 A CN 201911130012A CN 112816556 B CN112816556 B CN 112816556B
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defect
area
determining
defect area
scanning image
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CN112816556A (en
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肖鹏
刘奎
陈智超
陈健
南方
孟嘉
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Commercial Aircraft Corp of China Ltd
Shanghai Aircraft Manufacturing Co Ltd
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Commercial Aircraft Corp of China Ltd
Shanghai Aircraft Manufacturing Co Ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
    • G01N29/04—Analysing solids
    • G01N29/11—Analysing solids by measuring attenuation of acoustic waves
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
    • G01N29/04—Analysing solids
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
    • G01N29/44—Processing the detected response signal, e.g. electronic circuits specially adapted therefor
    • G01N29/4409—Processing the detected response signal, e.g. electronic circuits specially adapted therefor by comparison
    • G01N29/4418—Processing the detected response signal, e.g. electronic circuits specially adapted therefor by comparison with a model, e.g. best-fit, regression analysis

Abstract

The embodiment of the invention discloses a defect detection method, a device, equipment and a storage medium, wherein the defect detection method comprises the following steps: determining a defect area of the object to be detected according to the ultrasonic penetration C scanning image of the object to be detected; determining a target defect area evaluation algorithm according to the defect property of the defect region; and determining the area of the defect region according to the target defect area evaluation algorithm. The method and the device for detecting the defect area of the object to be detected obtain a C scanning image based on ultrasonic penetration of the object to be detected, determine the defect area of the object to be detected through the C scanning image, and determine the area information of the defect area by adopting a corresponding area evaluation algorithm according to the defect property of the defect area. The defect area is determined by combining the defect property, so that the accuracy of defect detection is improved, and the probability of manual false detection is reduced by detecting the defect area through a corresponding area evaluation algorithm.

Description

Defect detection method, device, equipment and storage medium
Technical Field
The embodiment of the invention relates to the technical field of ultrasonic nondestructive testing, in particular to a defect detection method, a defect detection device, defect detection equipment and a storage medium.
Background
With the development of material technology, more and more advanced materials are put into use. Advanced composite laminates used on large passenger aircraft have become an important development in the aerospace field. Because the composite laminated piece has complex process and a plurality of influencing factors, internal defects such as layering, inclusion, pores, pore density, glue enrichment and the like can occur in the production process. It is therefore necessary to perform defect detection on the composite laminate before it is put into use.
At present, ultrasonic nondestructive testing is one of the important methods for testing the quality of mechanical engineering materials. The principle of the method is that when ultrasonic waves are transmitted in a detected material, acoustic characteristics of the material and changes of internal tissues have certain influence on the transmission of the ultrasonic waves. The commonly used ultrasonic nondestructive testing method is to acquire a C-scan image by an ultrasonic penetration method and judge the defects of the C-scan image by an experienced professional.
However, due to the complexity of the composite material structure, the ultrasonic signal is complex, so that certain difficulty is brought to an experienced professional for judging the defects of the C scanning image, and the manual detection causes long detection time and low efficiency, and also brings certain human errors.
Disclosure of Invention
The embodiment of the invention provides a defect detection method, a defect detection device, defect detection equipment and a storage medium, which are used for improving the defect detection efficiency and reducing manual false detection.
In a first aspect, an embodiment of the present invention provides a defect detection method, including:
determining a defect area of the object to be detected according to the ultrasonic penetration C scanning image of the object to be detected;
determining a target defect area evaluation algorithm according to the defect property of the defect region;
and determining the area of the defect region according to the target defect area evaluation algorithm.
In a second aspect, an embodiment of the present invention further provides a defect detection apparatus, including:
the defect area determining module is used for determining the defect area of the object to be detected according to the ultrasonic penetration C scanning image of the object to be detected;
the area evaluation algorithm determining module is used for determining a target defect area evaluation algorithm according to the defect property of the defect area;
and the defect area determining module is used for determining the area of the defect area according to the target defect area evaluation algorithm.
In a third aspect, an embodiment of the present invention further provides a computer device, including:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement a method of defect detection as in any of the embodiments of the invention.
In a fourth aspect, the embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the defect detection method according to any embodiment of the present invention.
The method and the device for detecting the defect area of the object to be detected obtain a C scanning image based on ultrasonic penetration of the object to be detected, determine the defect area of the object to be detected through the C scanning image, and determine the area information of the defect area by adopting a corresponding area evaluation algorithm according to the defect property of the defect area. The defect area is determined by combining the defect property, so that the accuracy of defect detection is improved, and the probability of manual false detection is reduced by detecting the defect area through a corresponding area evaluation algorithm.
Drawings
FIG. 1 is a flowchart illustrating a defect detection method according to a first embodiment of the present invention;
FIG. 2 is a flowchart of a defect detection method according to a second embodiment of the present invention;
FIG. 3 is a schematic structural diagram of a defect detection apparatus according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of a computer device in the fourth embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Fig. 1 is a flowchart of a defect detection method according to a first embodiment of the present invention, which is applicable to a case where a defect area of an object to be detected is determined according to an ultrasound penetration C-scan image. The method may be performed by a defect detection apparatus, which may be implemented in software and/or hardware, and may be configured in a computer device, for example, the computer device may be a device with communication and computing capabilities, such as a background server. As shown in fig. 1, the method specifically includes:
step 101, determining a defect region of an object to be detected according to an ultrasonic penetration C scanning image of the object to be detected.
The object to be detected is a material which needs to be subjected to defect detection, for example, the material to be detected can be a composite material part, and ultrasonic defect detection is performed on the composite material part. The ultrasonic penetration C-scan image is formed by transmitting ultrasonic waves to an object to be detected, when the ultrasonic waves enter the object to be detected and encounter defects, the ultrasonic waves can penetrate to different degrees, and the ultrasonic penetration C-scan image is obtained by displaying the different penetration degrees of the ultrasonic waves through a receiver. The C scanning image is displayed as a cross-section image of the internal defect of the object to be detected at different depths, and the shape and the control position of the internal defect of the object to be detected can be visually known through the C scanning image. The C scanning image is displayed by converting the ultrasonic penetrating wave energy collected in the depth direction of each position into a voltage signal through the principle of digital ultrasonic scanning imaging, carrying out 256-level digital processing on the amplitude value of the voltage signal, and finally obtaining cross section images representing the interior of the object to be detected with different energies by using different gray values, wherein the C scanning image visually displays the interior defect condition of the object to be detected in a two-dimensional image mode.
The defect area refers to a position range where an internal defect caused in a manufacturing process appears inside an object to be detected, for example, defect types include delamination, inclusion, pores, pore density, glue enrichment and the like in a composite laminated member. The defect area can be determined by analyzing the gray values in the C-scan image.
Specifically, a composite material part needing to detect defects is determined, ultrasonic waves are transmitted to the part, sound waves penetrating through the part are received, a C-scan image is obtained according to the penetration conditions of the sound waves at different degrees, and a defect area on the current cross section is determined according to the gray level change condition of the cross section image of the part in the C-scan image.
Optionally, the ultrasound penetration C-scan image of the object to be detected is used as an input of a defect prediction model, and a defect region of the object to be detected is determined according to the model output;
wherein training the defect prediction model by:
acquiring an ultrasonic penetration C scanning image sample of a test object;
marking a defect area in the C scanning image sample;
and training to obtain the defect prediction model according to the marked C scanning image sample.
The defect prediction model is obtained by training a test set of an object to be detected based on a machine learning algorithm, the defect prediction model is input into an ultrasonic penetration C scanning image of the object to be detected and output into a defect area of the object to be detected, and the output form comprises coordinate point information of the defect area or the boundary of the defect area displayed on the C scanning image.
The method comprises the steps that a defect prediction model needs to be trained before a defect region of an object to be detected is determined, wherein a test object in the training process refers to a detection object with a known defect position prepared for training the model or a detection object with the defect position determined manually, optional test objects comprise a comparison test block, and the comparison test block refers to a composite material part with the known defect position and defect area information and is used for providing accurate reference for the object to be detected with unknown defect information. Marking means marking the defect area of the test object on the ultrasonic penetration C-scan image according to the ultrasonic penetration C-scan image of the test object, and a mode of confirming the boundary coordinate point of the defect area can be adopted.
Specifically, a large number of ultrasonic penetration C scanning images of composite material parts with defects inside are collected and used as a model training sample set, boundary coordinate points of defect areas of the C scanning images in the training sample set are labeled, and characteristic values of the defect areas are extracted. Optionally, the more coordinate point information of the defect region boundary, the more accurately the defect region information can be represented, and the extracted feature value may be a gray value in the C-scan image, that is, a gray value representing the defect region. And inputting the marked C scanning image with the information of the boundary coordinate points of the defect region and the characteristic value information into a machine learning algorithm model, and training the algorithm model to obtain a final defect prediction model by continuously learning the gray information of the marked defect region and the gray information of the non-defect region.
Inputting an ultrasonic penetration C-scan image of an object to be detected in an unknown defect region into a defect prediction model, outputting obtained defect region information by the model through judging gray information in the C-scan image, optionally outputting coordinate point information of a defect region boundary, and forming a defect region on the C-scan image by utilizing the coordinate point information. The defect prediction model is obtained by learning a large amount of marked defect information, so that the defect prediction model learns the characteristic information of the defect region, and the defect region is determined by using the defect prediction model, which is favorable for improving the accuracy of defect region detection.
And step 102, determining a target defect area evaluation algorithm according to the defect property of the defect area.
The defect property comprises a macroscopic defect and a microscopic defect, wherein the microscopic defect refers to a defect with a very small defect area caused in the manufacturing process of the composite material, and for example, the microscopic defect comprises a pore defect, such as dense pores and the like; the macro defects refer to defects with the defect area larger than the pore diameter in the manufacturing process, for example, the macro defects comprise layering, foreign matter inclusion, pores and the like, and are generally distributed at a certain depth of the composite material and cannot penetrate through the thickness of the whole composite material. The classification criteria of the micro defects and the macro defects may be obtained by empirical values.
The defect area evaluation algorithm refers to an algorithm that can calculate the defect area, for example, the area is calculated according to the change range of the gray value of the defect area. The target defect area evaluation algorithm is an adaptive area evaluation algorithm which is selected for the defect according to different defect properties, and is beneficial to improving the accuracy of defect area detection.
Specifically, the defect properties are obtained by analyzing an ultrasonic pulse reflection a-scan image of the defect region. Illustratively, according to the energy fluctuation of ultrasonic wave reflection in the A-scan image, a region with abnormal energy fluctuation is determined, and the defect property of the object to be detected is determined according to the region range and the amplitude, for example, if the positions of the defect waves at different positions in the A-scan image are basically consistent, the defect is a macroscopic defect, and conversely, the defect is a microscopic defect. Different defect area assessment algorithms are selected for the determined defect properties as target defect area assessment algorithms.
The ultrasonic pulse reflection A scanning image is an image obtained according to waveform energy information of ultrasonic waves reflected back, the A scanning image is actually an ultrasonic pulse echo image, the abscissa of the A scanning image represents ultrasonic propagation time, the ordinate represents echo height, namely amplitude of the ultrasonic waves, in the same uniform medium, the propagation time is in direct proportion to depth, therefore, the echo position of the ordinate can determine defect depth information in an object to be detected, and defect properties can be judged from the defect depth information.
Determining a target defect area assessment algorithm according to the defect properties of the defect region, comprising:
if the defect property of the defect area is a macro defect, determining a macro defect area evaluation algorithm as a target defect area evaluation algorithm;
and if the defect property of the defect area is a micro defect, determining a micro defect area evaluation algorithm as a target defect area evaluation algorithm.
The macro defect area evaluation algorithm is an area calculation algorithm set for the area characteristics of macro defects, and can be determined according to the defect area. Optionally, the macroscopic defect area assessment algorithm includes determining the defect area of the object to be detected by using the defect area characteristics in the C-scan image of the reference block. The reference block refers to a part with the same thickness, structure form, forming process and material type number as the object to be detected, and the reference block has the defect of known thickness information and area information.
The microscopic defect area evaluation algorithm is an area calculation algorithm set for the area characteristics of the microscopic defects, and can be determined according to the defect pores. Optionally, the microscopic defect area evaluation algorithm includes determining a defect area of the object to be measured by using a preset porosity evaluation curve. The preset porosity evaluation curve is a relation curve of composite material pores of different types, ultrasonic attenuation and material thickness obtained by analyzing A scanning image information obtained by performing ultrasonic pulse reflection A scanning on a reference block. The relationship between the area information of the defect and the ultrasonic attenuation can be obtained from the preset porosity evaluation curve.
Specifically, if the defect property of the defect region is determined to be a macro defect according to the ultrasonic pulse reflection A scanning image of the defect region, selecting a macro defect area evaluation algorithm as a target defect area evaluation algorithm for calculating the defect area; and if the defect property of the defect area is determined to be the micro defect, selecting a micro defect area evaluation algorithm as a target defect area evaluation algorithm for calculating the defect area. The macro defects and the micro defects have different area characteristics, and if the same area evaluation algorithm is adopted to calculate the defect area, the area calculation result is inaccurate. Therefore, different defect area evaluation algorithms are adopted for the pertinence of the macro defects and the micro defects, and the accuracy of defect area detection is improved.
Optionally, if the defect property of the defect area is a macro defect, the method further includes:
and acquiring an ultrasonic pulse reflection A scanning image of the defect area, and determining the depth of the defect area.
The depth of the defect region refers to the information about the buried depth of the detected defect region in the object to be detected, and optionally, the depth unit of the defect region may be millimeter or micrometer. The defect depth information is judged to further understand the defects in the object to be detected, so that the subsequent defect processing is facilitated.
Specifically, the depth information of the defect A scanning image of the object to be detected is determined according to the pulse reflection A scanning image of the contrast block defect with the known embedded depth. Optionally, a corresponding relationship is established according to the relationship between the occurrence position of the defect wave and the depth information, and then the defect depth information can be obtained according to the occurrence position of the defect wave with unknown defect depth. After the macro defect property is determined, the defect area information and the depth information of the object to be detected are determined according to the relevant information of the corresponding reference block, so that the difference of the identification results caused by the difference of internal materials of different models is avoided, and the detection accuracy of the defect information of the object to be detected is improved.
And 103, determining the area of the defect region according to the target defect area evaluation algorithm.
The area of the defect region refers to the actual area of the defect region on the object to be detected, and optionally, the unit of the area of the defect region may be square millimeters.
Specifically, a contrast block with known defects and various information is preset for each type of object to be detected, after the defect property of the object to be detected is determined, the contrast block corresponding to the object to be detected is determined, an ultrasonic penetration C scanning image of the contrast block is collected, and the defect area of the object to be detected is determined according to the gray level or gray level attenuation threshold of the C scanning image with the known embedded defects on the contrast block. Optionally, the gray attenuation rule of the defect region is determined according to the C-scan image of the reference block, the threshold value for identifying the defect region and the non-defect region is obtained, and the correlation between the gray attenuation information and the threshold value and the area information is established. So as to determine the area of the C scanning image of the object to be detected according to the correlation relationship.
And acquiring ultrasonic pulse reflection A scanning images of different types of reference blocks, and determining preset porosity evaluation curves of different types according to waveform amplitude change signals on the A scanning images. And when the defect property is determined to be the micro defect according to the ultrasonic pulse reflection A scanning image of the defect region, obtaining corresponding defect wave position and amplitude information from the A scanning image, finding a corresponding point on a preset porosity evaluation curve of the same model, and determining the area information of the micro defect.
The defect prediction method and the device have the advantages that the C scanning image is obtained by carrying out ultrasonic penetration on the object to be detected, the defect prediction model is trained, and the defect area in the C scanning image is automatically identified according to the defect prediction model; and then corresponding area rating algorithms are set according to different defect properties, targeted defect area calculation is carried out, and the accuracy of defect area calculation is improved. The defect area is determined by combining the defect property, so that the accuracy of defect detection is improved, and the defect area is detected by a corresponding area evaluation algorithm, so that the probability of manual false detection is reduced.
Example two
Fig. 2 is a flowchart of a defect detection method in example two of the present invention, and example two is another embodiment of the present invention. As shown in fig. 2, the method includes:
step 201, determining a defect region of an object to be detected according to an ultrasonic penetration C scanning image of the object to be detected.
Optionally, determining a defect region of the object to be detected according to the ultrasound penetration C-scan image of the object to be detected includes:
taking an ultrasonic penetration C scanning image of the object to be detected as an input of a defect prediction model, and determining a defect area of the object to be detected according to the output of the model;
wherein training the defect prediction model by:
acquiring an ultrasonic penetration C scanning image sample of a test object;
marking a defect area in the C scanning image sample;
and training to obtain the defect prediction model according to the marked C scanning image sample.
Step 202, evaluating the area of the defect area of the object to be detected, and determining at least two candidate area evaluation results.
Wherein, the area evaluation refers to the calculation of the area of the defect area of the object to be detected. The candidate area evaluation result refers to different area evaluation results aiming at different properties of the defect region, and the candidate area evaluation result can provide a reference basis for determining the properties of the subsequent defects.
Specifically, a defect type area assessment algorithm corresponding to the type of the defect property in the object to be detected is preset, after the position of the defect region of the object to be detected is determined, all preset defect type area assessment algorithms are applied to the defect region, and a candidate area assessment algorithm corresponding to the defect property type is determined. For example, when the defect properties of the object to be detected are three, namely, a defect, B defect and C defect, and the defect characteristics are different due to the difference in defect properties, the defect of each property has a corresponding area evaluation algorithm, namely, an a defect area evaluation algorithm, a B defect area evaluation algorithm and a C defect area evaluation algorithm, and the three algorithms are applied to the defect region respectively to obtain three candidate area evaluation results, namely: candidate a defect area evaluation results, candidate B defect area evaluation results, and candidate C defect area evaluation results. When the position of the defect region is determined but the defect property cannot be determined, an area evaluation algorithm for presetting all the defect properties is applied to the defect region, so that a reference basis can be provided for further determination of the subsequent defect property, and an accurate area evaluation result can be determined immediately after the defect property is determined. Because the different area assessment algorithms are performed in a multi-thread synchronous manner, the defect detection efficiency can be improved.
For example, when the defect properties of the object to be detected are divided into two types: macro defects and micro defects, different defect area evaluation algorithms are preset for each defect property, such as a macro defect area evaluation algorithm and a micro defect area evaluation algorithm, and the specific calculation mode of the algorithms refers to the first embodiment. And respectively carrying out two area evaluation algorithm calculations on the determined defect area to obtain two candidate area evaluation results: candidate macroscopic defect area evaluation results and candidate microscopic defect area evaluation results.
And step 203, determining a final area evaluation result according to the defect property of the defect region.
The defect property refers to a discrimination standard defined for the defect in advance according to the characteristics of the defect region, and optionally, the defect property includes macro defects and micro defects. The determination of the nature of the defect can be made from the reflected a-scan image of the ultrasonic pulse at the defect region. The final area assessment result refers to an area assessment result corresponding to the property of the defect region determined from the candidate area assessment results.
Specifically, after the defect region is determined, ultrasonic pulse emission is performed on the defect region, an ultrasonic pulse reflection a-scan image of the defect region is determined, and the defect property is determined according to the a-scan image. And after the defect property is determined, determining an accurate area evaluation result corresponding to the defect property from the candidate area evaluation results.
For example, when the defect properties of the object to be detected are divided into two types: the evaluation results of the candidate areas are the evaluation results of the candidate macro defect areas and the candidate micro defect areas respectively. If the defect property determined according to the ultrasonic pulse reflection A scanning image of the defect region is a macro defect, taking the candidate macro defect area evaluation result as a final area evaluation result; and if the defect property is determined to be the micro defect, taking the candidate micro defect area evaluation result as a final area evaluation result.
Optionally, if the defect property of the defect area is a macro defect, the method further includes:
and acquiring an ultrasonic pulse reflection A scanning image of the defect area, and determining the depth of the defect area.
Specifically, the first embodiment can be referred to as a method for determining the depth of the defective region.
Optionally, after determining the defect property, area and depth information of the object to be detected, comparing the defect property, area and depth information with the acceptance standard of the object to be detected, and judging whether the defect area and depth information meet the requirements, if so, the defect of the object to be detected is qualified, otherwise, the defect is not qualified.
The method and the device for detecting the defect property of the object to be detected provide a candidate area evaluation result corresponding to the type of the defect property for the defect region based on the determined defect region of the object to be detected, and determine the defect property by combining an ultrasonic pulse reflection A scanning image; and selecting a corresponding final area evaluation result from the candidate area evaluation results according to the defect property. The targeted determination area evaluation result improves the overall accuracy and detection efficiency of defect detection. And the problem that the traditional ultrasonic penetration method needs professionals who depend on experience in evaluating the defect area and depth is solved, the automatic evaluation of the defect position and area is realized, and the personnel cost is reduced.
EXAMPLE III
Fig. 3 is a schematic structural diagram of a defect detection apparatus in a third embodiment of the present invention, which is applicable to a case where a defect area of an object to be detected is determined according to an ultrasound penetration C-scan image. As shown in fig. 3, the apparatus includes:
a defect region determining module 310, configured to determine a defect region of the object to be detected according to the ultrasound penetration C scan image of the object to be detected;
an area assessment algorithm determination module 320, configured to determine a target defect area assessment algorithm according to the defect property of the defect region;
a defect area determining module 330, configured to determine an area of the defect region according to the target defect area evaluation algorithm.
The method and the device for detecting the defect area of the object to be detected obtain a C scanning image based on ultrasonic penetration of the object to be detected, determine the defect area of the object to be detected through the C scanning image, and determine the area information of the defect area by adopting a corresponding area evaluation algorithm according to the defect property of the defect area. The defect area is determined by combining the defect property, so that the accuracy of defect detection is improved, and the probability of manual false detection is reduced by detecting the defect area through a corresponding area evaluation algorithm.
Optionally, the defective area determining module 310 is specifically configured to:
taking an ultrasonic penetration C scanning image of the object to be detected as an input of a defect prediction model, and determining a defect area of the object to be detected according to the output of the model;
wherein training the defect prediction model by:
acquiring an ultrasonic penetration C scanning image sample of a test object;
marking a defect area in the C scanning image sample;
and training to obtain the defect prediction model according to the marked C scanning image sample.
Optionally, the defect area determining module 330 is specifically configured to:
if the defect property of the defect area is a macro defect, determining a macro defect area evaluation algorithm as a target defect area evaluation algorithm;
and if the defect property of the defect area is a micro defect, determining a micro defect area evaluation algorithm as a target defect area evaluation algorithm.
Optionally, the apparatus further comprises:
and acquiring an ultrasonic pulse reflection A scanning image of the defect area, and determining the depth of the defect area.
The defect detection device provided by the embodiment of the invention can execute the defect detection method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of executing the defect detection method.
Example four
Fig. 4 is a schematic structural diagram of a computer device according to a fourth embodiment of the present invention. FIG. 4 illustrates a block diagram of an exemplary computer device 12 suitable for use in implementing embodiments of the present invention. The computer device 12 shown in FIG. 4 is only one example and should not bring any limitations to the functionality or scope of use of embodiments of the present invention.
As shown in FIG. 4, computer device 12 is in the form of a general purpose computing device. The components of computer device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory device 28, and a bus 18 that couples various system components including the system memory device 28 and the processing unit 16.
Bus 18 represents one or more of any of several types of bus structures, including a memory device bus or memory device controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, such architectures include, but are not limited to, Industry Standard Architecture (ISA) bus, micro-channel architecture (MAC) bus, enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.
Computer device 12 typically includes a variety of computer system readable media. Such media may be any available media that is accessible by computer device 12 and includes both volatile and nonvolatile media, removable and non-removable media.
The system storage 28 may include computer system readable media in the form of volatile storage, such as Random Access Memory (RAM)30 and/or cache storage 32. Computer device 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 4, and commonly referred to as a "hard drive"). Although not shown in FIG. 4, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 by one or more data media interfaces. Storage 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.
A program/utility 40 having a set (at least one) of program modules 42 may be stored, for example, in storage 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. Program modules 42 generally carry out the functions and/or methodologies of the described embodiments of the invention.
Computer device 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), with one or more devices that enable a user to interact with computer device 12, and/or with any devices (e.g., network card, modem, etc.) that enable computer device 12 to communicate with one or more other computing devices. Such communication may be through an input/output (I/O) interface 22. Also, computer device 12 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the Internet) via network adapter 20. As shown, network adapter 20 communicates with the other modules of computer device 12 via bus 18. It should be understood that although not shown in the figures, other hardware and/or software modules may be used in conjunction with computer device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
The processing unit 16 executes various functional applications and data processing by running programs stored in the system storage device 28, for example, to implement the defect detection method provided by the embodiment of the present invention, including:
determining a defect area of the object to be detected according to the ultrasonic penetration C scanning image of the object to be detected;
determining a target defect area evaluation algorithm according to the defect property of the defect region;
and determining the area of the defect region according to the target defect area evaluation algorithm.
EXAMPLE five
The fifth embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the defect detection method provided in the fifth embodiment of the present invention, and the method includes:
determining a defect area of the object to be detected according to the ultrasonic penetration C scanning image of the object to be detected;
determining a target defect area evaluation algorithm according to the defect property of the defect region;
and determining the area of the defect region according to the target defect area evaluation algorithm.
Computer storage media for embodiments of the invention may employ any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims (10)

1. A method of defect detection, comprising:
determining a defect area of the object to be detected according to the ultrasonic penetration C scanning image of the object to be detected;
determining a target defect area evaluation algorithm according to the defect property of the defect region;
determining the area of the defect region according to the target defect area evaluation algorithm;
after the defect area is determined, ultrasonic pulse emission is carried out on the defect area, an ultrasonic pulse reflection A scanning image of the defect area is determined, and the defect property is determined according to the A scanning image;
and when the defect property is determined to be the micro defect according to the ultrasonic pulse reflection A scanning image of the defect region, obtaining corresponding defect wave position and amplitude information from the A scanning image, finding a corresponding point on a preset porosity evaluation curve of the same model, and determining the area information of the micro defect.
2. The method of claim 1, wherein determining the defect region of the object to be detected from the ultrasound-transmitted C-scan image of the object to be detected comprises:
taking an ultrasonic penetration C scanning image of the object to be detected as an input of a defect prediction model, and determining a defect area of the object to be detected according to the output of the model;
wherein training the defect prediction model by:
acquiring an ultrasonic penetration C scanning image sample of a test object;
marking a defect area in the C scanning image sample;
and training to obtain the defect prediction model according to the marked C scanning image sample.
3. The method of claim 1, wherein determining a target defect area assessment algorithm based on the defect properties of the defect region comprises:
if the defect property of the defect area is a macro defect, determining a macro defect area evaluation algorithm as a target defect area evaluation algorithm;
and if the defect property of the defect area is a micro defect, determining a micro defect area evaluation algorithm as a target defect area evaluation algorithm.
4. The method of claim 3, wherein if the defect property of the defect region is a macro defect, further comprising:
and acquiring an ultrasonic pulse reflection A scanning image of the defect area, and determining the depth of the defect area.
5. A defect detection apparatus, comprising:
the defect area determining module is used for determining the defect area of the object to be detected according to the ultrasonic penetration C scanning image of the object to be detected;
the area evaluation algorithm determining module is used for determining a target defect area evaluation algorithm according to the defect property of the defect area;
the defect area determining module is used for determining the area of the defect area according to the target defect area evaluation algorithm;
after the defect area is determined, ultrasonic pulse emission is carried out on the defect area, an ultrasonic pulse reflection A scanning image of the defect area is determined, and the defect property is determined according to the A scanning image;
and when the defect property is determined to be the micro defect according to the ultrasonic pulse reflection A scanning image of the defect region, obtaining corresponding defect wave position and amplitude information from the A scanning image, finding a corresponding point on a preset porosity evaluation curve of the same model, and determining the area information of the micro defect.
6. The apparatus of claim 5, wherein the defective region determining module is specifically configured to:
taking an ultrasonic penetration C scanning image of the object to be detected as an input of a defect prediction model, and determining a defect area of the object to be detected according to the output of the model;
wherein training the defect prediction model by:
acquiring an ultrasonic penetration C scanning image sample of a test object;
marking a defect area in the C scanning image sample;
and training to obtain the defect prediction model according to the marked C scanning image sample.
7. The apparatus of claim 5, wherein the defect area determination module is specifically configured to:
if the defect property of the defect area is a macro defect, determining a macro defect area evaluation algorithm as a target defect area evaluation algorithm;
and if the defect property of the defect area is a micro defect, determining a micro defect area evaluation algorithm as a target defect area evaluation algorithm.
8. The apparatus of claim 7, further comprising:
and acquiring an ultrasonic pulse reflection A scanning image of the defect area, and determining the depth of the defect area.
9. A computer device, comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the defect detection method of any of claims 1-4.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method for defect detection according to any one of claims 1 to 4.
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