Subsurface defect detection method, device, apparatus and storage medium

Abstract

The application belongs to the technical field of ultrasonic detection, and discloses a subsurface defect detection method, device, equipment and storage medium. The method comprises the following steps: acquiring surface roughness and surface material corresponding to a metal sample to be detected; searching for corresponding optimal laser detection power from a database according to the surface roughness and the surface material; and performing laser ultrasonic detection on the metal sample to be detected based on the optimal laser detection power, so as to obtain a subsurface defect detection result corresponding to the metal sample to be detected. In the above manner, the optimal laser detection power is selected according to the roughness and material of the metal sample for laser ultrasonic detection, so that the laser ultrasonic detection precision and efficiency are effectively improved, and the accuracy of metal subsurface defect detection is further improved.

Description

Technical Field

[0001] This invention relates to the field of ultrasonic testing technology, and in particular to a method, apparatus, equipment and storage medium for detecting subsurface defects. Background Technology

[0002] Because metal additive manufacturing involves complex coupling of physical, chemical, and other physical fields, the manufactured parts are prone to defects such as cracks, pores, and impurities. These defects can severely affect the performance of the parts and even lead to their damage. Therefore, the generation of defects is a major obstacle to the development of laser additive manufacturing, and how to reduce or eliminate the defect rate is an urgent problem to be solved. Currently, traditional defect detection methods are divided into destructive testing and non-destructive testing. Destructive testing cannot meet the needs of industrial production because it damages the surface of the parts; non-destructive testing is widely used because it causes little or no damage to the surface of the parts. The main non-destructive testing methods at present include: X-ray tomography, water immersion ultrasonic testing, traditional ultrasonic testing, high-frequency ultrasonic testing, electromagnetic ultrasonic testing, and laser ultrasonic testing. Laser ultrasonic testing is widely used due to its low cost and high practicality.

[0003] Since the roughness of the metal surface greatly affects the absorption of laser light and the scattering of ultrasonic waves, laser ultrasound cannot effectively detect small subsurface defects. Selecting an appropriate detection laser power under a certain roughness can effectively improve the accuracy and efficiency of laser ultrasound detection.

[0004] The above content is only used to help understand the technical solution of the present invention and does not represent an admission that the above content is prior art. Summary of the Invention

[0005] The main objective of this invention is to provide a method, apparatus, device, and storage medium for detecting subsurface defects, aiming to solve the technical problem of how to improve the detection accuracy of laser ultrasonic testing for metal surface defects.

[0006] To achieve the above objectives, the present invention provides a subsurface defect detection method, the method comprising the following steps:

[0007] Obtain the surface roughness and surface material of the metal sample to be tested;

[0008] The optimal laser detection power is retrieved from the database based on the surface roughness and the surface material.

[0009] Based on the optimal laser detection power, the metal sample to be tested is subjected to laser ultrasonic testing to obtain the subsurface defect detection results corresponding to the metal sample to be tested.

[0010] Optionally, before obtaining the surface roughness and surface material of the metal sample to be tested, the method further includes:

[0011] The optimal laser detection power under different roughnesses and materials was analyzed using a pre-constructed two-dimensional rectangular metal part model.

[0012] Different materials, different roughnesses, and their corresponding optimal laser detection power are stored in a database.

[0013] Optionally, before analyzing the optimal laser detection power for different roughnesses and materials using the constructed two-dimensional rectangular metal part model, the method further includes:

[0014] Two-dimensional rectangular metal part models with different roughness and materials were constructed using COMSOL.

[0015] The target defect is pre-set on the two-dimensional rectangular metal part model to obtain the constructed two-dimensional rectangular metal part model.

[0016] Optionally, the step of analyzing the optimal laser detection power under different roughnesses and materials using the constructed two-dimensional rectangular metal part model includes:

[0017] Laser ultrasonic testing was performed on a pre-constructed two-dimensional rectangular metal part model based on multiple different laser detection powers to obtain the laser detection results corresponding to each laser detection power.

[0018] Based on the laser detection results, the optimal laser detection power for different roughnesses and materials is selected from the multiple different laser detection powers.

[0019] Optionally, selecting the optimal laser detection power for different roughnesses and materials from the plurality of different laser detection powers based on the laser detection results includes:

[0020] Based on the laser detection results, determine the laser ultrasonic detection signal intensity and defect quantitative analysis results corresponding to each laser detection power;

[0021] Based on the intensity of the laser ultrasonic detection signal and the results of the quantitative analysis of defects, the optimal laser detection power for different roughnesses and materials is selected from among the multiple different laser detection powers.

[0022] Optionally, before performing laser ultrasonic testing on the constructed two-dimensional rectangular metal part model based on multiple different laser detection powers to obtain the laser detection results corresponding to each laser detection power, the method further includes:

[0023] For the constructed two-dimensional rectangular metal part model, the corresponding physical field interface is set to solid mechanics coupled to solid heat transfer, and the time step is set to a preset step size.

[0024] Optionally, after pre-setting the target defect on the two-dimensional rectangular metal part model to obtain the constructed two-dimensional rectangular metal part model, the method further includes:

[0025] The finite element network at the location of the target defect in the constructed two-dimensional rectangular metal part model is locally densified;

[0026] After performing laser ultrasonic testing on the constructed two-dimensional rectangular metal part model based on multiple different laser detection powers, the method further includes:

[0027] The finite element mesh at the pulsed laser incident point is locally refined.

[0028] Furthermore, to achieve the above objectives, the present invention also proposes a subsurface defect detection device, the subsurface defect detection device comprising:

[0029] The acquisition module is used to acquire the surface roughness and surface material of the metal sample to be tested;

[0030] The search module is used to search the database for the corresponding optimal laser detection power based on the surface roughness and the surface material;

[0031] The detection module is used to perform laser ultrasonic testing on the metal sample to be tested based on the optimal laser detection power, and obtain the subsurface defect detection result corresponding to the metal sample to be tested.

[0032] Furthermore, to achieve the above objectives, the present invention also proposes a subsurface defect detection device, the subsurface defect detection device comprising: a memory, a processor, and a subsurface defect detection program stored in the memory and executable on the processor, the subsurface defect detection program being configured to implement the subsurface defect detection method as described above.

[0033] In addition, to achieve the above objectives, the present invention also proposes a storage medium storing a subsurface defect detection program, which, when executed by a processor, implements the subsurface defect detection method as described above.

[0034] This invention obtains the surface roughness and surface material of the metal sample to be tested; searches for the optimal laser detection power from a database based on the surface roughness and surface material; and performs laser ultrasonic testing on the metal sample based on the optimal laser detection power to obtain the subsurface defect detection results of the metal sample. By selecting the optimal laser detection power for laser ultrasonic testing based on the roughness and material of the metal sample, the accuracy and efficiency of laser ultrasonic testing are effectively improved, further enhancing the accuracy of metal subsurface defect detection. Attached Figure Description

[0035] Figure 1 This is a schematic diagram of the subsurface defect detection device in the hardware operating environment involved in the embodiments of the present invention;

[0036] Figure 2 This is a schematic flowchart of the first embodiment of the subsurface defect detection method of the present invention;

[0037] Figure 3 This is a schematic diagram of the layout of the laser ultrasonic detection system of the present invention;

[0038] Figure 4 This is a flowchart illustrating the second embodiment of the subsurface defect detection method of the present invention;

[0039] Figure 5 This is a schematic diagram of the mesh division of the present invention;

[0040] Figure 6 This is a structural block diagram of the first embodiment of the subsurface defect detection device of the present invention.

[0041] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0042] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.

[0043] Reference Figure 1 , Figure 1 This is a schematic diagram of the subsurface defect detection device structure in the hardware operating environment involved in the embodiments of the present invention.

[0044] like Figure 1As shown, the subsurface defect detection device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen or an input unit such as a keyboard; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wireless-Fidelity (Wi-Fi) interface). The memory 1005 may be high-speed random access memory (RAM) or stable non-volatile memory (NVM), such as a disk storage device. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0045] Those skilled in the art will understand that Figure 1 The structure shown does not constitute a limitation on the subsurface defect detection device and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0046] like Figure 1 As shown, the memory 1005, which serves as a storage medium, may include an operating system, a network communication module, a user interface module, and a subsurface defect detection program.

[0047] exist Figure 1 In the subsurface defect detection device shown, the network interface 1004 is mainly used for data communication with the network server; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and the memory 1005 in the subsurface defect detection device of the present invention can be set in the subsurface defect detection device, and the subsurface defect detection device calls the subsurface defect detection program stored in the memory 1005 through the processor 1001 and executes the subsurface defect detection method provided in the embodiment of the present invention.

[0048] This invention provides a method for detecting subsurface defects, referring to... Figure 2 , Figure 2 This is a flowchart illustrating the first embodiment of the subsurface defect detection method of the present invention.

[0049] In this embodiment, the subsurface defect detection method includes the following steps:

[0050] Step S10: Obtain the surface roughness and surface material of the metal sample to be tested.

[0051] It is understood that the execution subject of this embodiment is a subsurface defect detection device, which can be a computer, server, terminal or other device connected to the laser ultrasonic testing device. This embodiment does not limit this.

[0052] It should be noted that, referring to Figure 3 , Figure 3 This is a schematic diagram of the laser ultrasonic testing system layout of the present invention. The metal sample to be tested is placed in... Figure 3 At position 6, the surface roughness of the metal sample to be tested is obtained by scanning the surface of the metal sample using laser scanning sensor 2. In specific implementation, the surface material can be input into the computer 10 by the user, or the laser absorbance and luminescence intensity of the metal sample to be tested can be determined based on the reflectance measurement device for qualitative analysis, thereby determining the surface material corresponding to the metal sample to be tested.

[0053] Step S20: Find the corresponding optimal laser detection power from the database based on the surface roughness and the surface material.

[0054] It should be understood that the database is pre-set with optimal laser detection power for different roughnesses and materials. When the surface roughness and surface material of the metal sample currently placed at position 6 are detected, the database is searched based on the surface roughness and surface material to determine the corresponding optimal laser detection power.

[0055] Step S30: Perform laser ultrasonic testing on the metal sample to be tested based on the optimal laser detection power to obtain the subsurface defect detection result corresponding to the metal sample to be tested.

[0056] It should be noted that this embodiment combines laser ultrasound technology to analyze the transient temperature field, stress field, and sound field of the laser ultrasound. Based on the transient temperature field, stress field, and sound field of the laser ultrasound, the defect type and defect depth of the metal sample to be tested are determined, and the subsurface defect detection results of the metal sample to be tested are obtained.

[0057] This embodiment obtains the surface roughness and surface material of the metal sample to be tested; based on the surface roughness and surface material, it searches the database for the optimal laser detection power; and performs laser ultrasonic testing on the metal sample based on the optimal laser detection power to obtain the subsurface defect detection results of the metal sample. By selecting the optimal laser detection power for laser ultrasonic testing based on the roughness and material of the metal sample, the accuracy and efficiency of laser ultrasonic testing are effectively improved, further enhancing the accuracy of metal subsurface defect detection.

[0058] refer to Figure 4 , Figure 4 This is a flowchart illustrating the second embodiment of the subsurface defect detection method of the present invention.

[0059] Based on the first embodiment described above, the subsurface defect detection method of this embodiment further includes, before step S10:

[0060] Step S101: Analyze the optimal laser detection power under different roughness and different materials using the constructed two-dimensional rectangular metal part model.

[0061] It is understandable that in this embodiment, COMSOL is used to simulate metal parts with different roughness and materials. Different sizes of defects and different laser detection powers are preset in the constructed two-dimensional rectangular metal part model. Combined with laser ultrasonic technology, the basic parameters of laser ultrasonic are analyzed, and the optimal laser detection power under different materials and roughness is obtained based on the parameter analysis results.

[0062] Furthermore, prior to step S101, the method further includes: constructing two-dimensional rectangular metal part models with different roughnesses and materials based on COMSOL; and presetting target defects on the two-dimensional rectangular metal part models to obtain the constructed two-dimensional rectangular metal part models.

[0063] It should be noted that, preferably, the target defect is a hole defect. In this embodiment, a dynamic data modeling method is used to establish a laser ultrasonic simulation geometric model with different surface roughness. Optionally, a 20mm×6mm two-dimensional rectangle is constructed using the geometry module in the COMSOL simulation software. Hole defects with radii of 200μm, 150μm, 100μm, and 50μm are preset in the two-dimensional rectangle. Different roughness curves are set, different laser detection powers are set, physical field interfaces are set, and different materials are set for the model.

[0064] Specifically, step S101 includes: performing laser ultrasonic testing on the constructed two-dimensional rectangular metal part model based on multiple different laser detection powers to obtain laser detection results corresponding to each laser detection power; and selecting the optimal laser detection power for different roughnesses and different materials from the multiple different laser detection powers based on the laser detection results.

[0065] It should be understood that in this embodiment, holes and defects are pre-set in the two-dimensional rectangular metal part model, different roughness curves are set, a free triangular mesh is used for extremely fine division, different laser detection powers are set, and the transient temperature field, stress field and sound field of laser ultrasound under different laser detection powers are analyzed. The laser ultrasound detection signal intensity under different roughness and different laser detection powers is obtained, and the optimal laser detection power is selected based on the laser ultrasound detection signal intensity corresponding to various laser detection powers.

[0066] In the specific implementation, the physical field interface is selected as solid mechanics coupled to solid heat transfer. Optionally, the material properties are set to the material properties corresponding to the metal sample to be tested. The optimal laser detection power corresponding to the metal sample to be tested is determined by simulation detection, so as to perform laser ultrasonic detection based on the optimal laser detection power and improve the detection accuracy.

[0067] In the solid heat transfer interface, the upper surface of the model and the surface of internal pores and defects are set as thermally insulating boundaries, with an initial value of T = 239.15K, i.e., room temperature. A "deposited laser beam power" is added to the interface and applied to the upper surface of the model. In the solid mechanics module, all edges of the model are set as free boundaries, and low-reflection boundary conditions are added to the left and right boundaries of the model. The damping wave types are defined as "L-wave and S-wave," i.e., ultrasonic longitudinal and transverse waves, ensuring that the left and right boundaries of the model absorb most of the ultrasonic longitudinal and transverse waves during the calculation.

[0068] Furthermore, the heat source Q of the pulsed laser incident on the workpiece surface is:

[0069] Q = AI0exp(-Az)f(x)g(t)

[0070] Where A is the absorption coefficient of the material under test to the pulsed laser energy, I0 is the maximum power density at the center of the pulsed laser spot, f(x) is the spatial distribution of the pulsed laser, and g(t) is the temporal distribution of the pulsed laser.

[0071] Furthermore, the spatial distribution function f(x) is:

[0072]

[0073] Where (x-x0) is the position of the laser point of action, and r0 is the radius of the pulsed laser spot.

[0074] Furthermore, the time distribution function g(t) is:

[0075]

[0076] Where t0 is the pulse width of the pulsed laser.

[0077] Furthermore, the formula for the center frequency of surface acoustic waves is:

[0078]

[0079] Among them, c R The surface wave velocity is represented by r0, and the laser pulse spot radius is r0.

[0080] Furthermore, the wavelength corresponding to surface acoustic waves is:

[0081]

[0082] Furthermore, the time step is:

[0083]

[0084] Among them, f max This indicates the highest desired frequency.

[0085] Specifically, the function of laser action is equivalent to the above-mentioned function of time and space. In this embodiment, the applied heat source is a Gaussian pulse laser heat source, and the laser spot radius, single pulse energy, and pulse rise time (i.e., pulse width) are all set according to the actual detection data.

[0086] Specifically, selecting the optimal laser detection power for different roughnesses and materials from the plurality of different laser detection powers based on the laser detection results includes: determining the laser ultrasonic detection signal intensity and defect quantitative analysis results corresponding to each of the laser detection powers based on the laser detection results; and selecting the optimal laser detection power for different roughnesses and materials from the plurality of different laser detection powers based on the laser ultrasonic detection signal intensity and the defect quantitative analysis results.

[0087] It should be noted that, in this embodiment, in addition to analyzing the transient temperature field, stress field, and sound field of laser ultrasound under different laser detection powers, and obtaining the laser ultrasound detection signal intensity under different roughnesses and laser detection powers, a quantitative defect analysis is also performed to determine the defect type, the depth of each type of defect, and the proportion of each type of defect. Based on the laser ultrasound detection signal intensity corresponding to different laser detection powers and the quantitative defect analysis results, the optimal laser detection power corresponding to each type of roughness and material is selected from multiple different laser detection powers. The optimal laser detection power corresponds to a laser ultrasound detection signal intensity higher than a preset threshold, and the quantitative defect analysis results are consistent with the preset defects.

[0088] Specifically, before performing laser ultrasonic testing on the constructed two-dimensional rectangular metal part model based on multiple different laser detection powers to obtain the laser detection results corresponding to each laser detection power, the method further includes: for the constructed two-dimensional rectangular metal part model, setting the corresponding physical field interface to solid mechanics coupled to solid heat transfer, and setting the time step to a preset step size.

[0089] It should be noted that in this embodiment, the physical field interface is selected as solid-mechanical coupling solid-heat transfer, and the time step is set to the aforementioned Δt. In specific implementation, the time step has a significant impact on the accuracy of the simulation results. If the time step is too large, the simulation may lose high-frequency components due to errors in higher-order modal responses, thus making the simulation results inaccurate. If the time step is too small, although high-frequency components can be obtained, making the simulation results more accurate, the calculation process will consume a lot of memory, affecting efficiency. In this embodiment, an appropriate time step is selected to further improve the simulation accuracy, facilitate the simulation to obtain the optimal laser detection power, and provide data support for subsequent metal subsurface defect detection.

[0090] Furthermore, after presetting the target defect on the two-dimensional rectangular metal part model to obtain the constructed two-dimensional rectangular metal part model, the method further includes: performing local densification processing on the finite element network at the location of the target defect in the constructed two-dimensional rectangular metal part model;

[0091] After performing laser ultrasonic testing on the constructed two-dimensional rectangular metal part model based on multiple different laser detection powers, the method further includes: performing local refinement processing on the finite element mesh of the pulsed laser incident point.

[0092] It should be understood that, with reference Figure 5 , Figure 5 This is a schematic diagram of the mesh generation for the present invention. In this embodiment, different materials, roughnesses, and laser detection efficiencies are set for the simulation geometric model. To obtain more accurate results at the locations of holes and defects, this embodiment performs local mesh refinement at the locations of holes and defects. Since the absorption of pulsed laser energy by the material only occurs on the surface layer of the workpiece, to improve the calculation accuracy, this embodiment performs local mesh refinement at the finite element mesh of the pulsed laser incident point. The mesh generation directly affects the accuracy of the simulation results. If the mesh is too coarse, the simulation results will inevitably be inaccurate; if the mesh is too fine, even if relatively accurate results can be obtained, the efficiency will be low. This embodiment performs appropriate mesh generation, balancing the accuracy of the simulation results and the calculation efficiency.

[0093] Step S102: Store different materials, different roughnesses, and the corresponding optimal laser detection power in the database.

[0094] It should be noted that, in this embodiment, a laser ultrasonic testing database is established based on the results of simulation analysis. The database stores different materials, different roughnesses, and the corresponding optimal laser testing power. When the surface roughness data of a metal part is scanned, the database is searched and the optimal laser testing power is selected for laser ultrasonic testing.

[0095] This embodiment utilizes a pre-constructed two-dimensional rectangular metal part model to analyze the optimal laser detection power under different roughnesses and materials. Different materials, roughnesses, and their corresponding optimal laser detection powers are stored in a database. The surface roughness and surface material of the metal sample to be tested are obtained. Based on the surface roughness and surface material, the corresponding optimal laser detection power is searched from the database. Laser ultrasonic testing is performed on the metal sample based on the optimal laser detection power to obtain the subsurface defect detection results. By storing the simulation analysis results in a database and obtaining the optimal laser detection power for laser ultrasonic testing based on the roughness and material of the metal sample, the accuracy and efficiency of laser ultrasonic testing are effectively improved, further enhancing the accuracy of metal subsurface defect detection.

[0096] Furthermore, this embodiment of the invention also proposes a storage medium storing a subsurface defect detection program, which, when executed by a processor, implements the subsurface defect detection method as described above.

[0097] Since this storage medium adopts all the technical solutions of all the above embodiments, it has at least all the beneficial effects brought about by the technical solutions of the above embodiments, which will not be repeated here.

[0098] Reference Figure 6 , Figure 6 This is a structural block diagram of the first embodiment of the subsurface defect detection device of the present invention.

[0099] like Figure 6 As shown, the subsurface defect detection device proposed in this embodiment of the invention includes:

[0100] The acquisition module 10 is used to acquire the surface roughness and surface material of the metal sample to be tested.

[0101] The lookup module 20 is used to look up the corresponding optimal laser detection power from the database based on the surface roughness and the surface material.

[0102] The detection module 30 is used to perform laser ultrasonic testing on the metal sample to be tested based on the optimal laser detection power, and obtain the subsurface defect detection result corresponding to the metal sample to be tested.

[0103] It should be understood that the above are merely illustrative examples and do not constitute any limitation on the technical solutions of the present invention. In specific applications, those skilled in the art can make settings as needed, and the present invention does not impose any restrictions on this.

[0104] This embodiment obtains the surface roughness and surface material of the metal sample to be tested; based on the surface roughness and surface material, it searches the database for the optimal laser detection power; and performs laser ultrasonic testing on the metal sample based on the optimal laser detection power to obtain the subsurface defect detection results of the metal sample. By selecting the optimal laser detection power for laser ultrasonic testing based on the roughness and material of the metal sample, the accuracy and efficiency of laser ultrasonic testing are effectively improved, further enhancing the accuracy of metal subsurface defect detection.

[0105] It should be noted that the workflow described above is merely illustrative and does not limit the scope of protection of this invention. In practical applications, those skilled in the art can select some or all of the workflow to achieve the purpose of this embodiment according to actual needs, and no restrictions are imposed here.

[0106] In addition, for technical details not described in detail in this embodiment, please refer to the subsurface defect detection method provided in any embodiment of the present invention, which will not be repeated here.

[0107] In one embodiment, the subsurface defect detection device further includes an analysis module;

[0108] The analysis module is used to analyze the optimal laser detection power under different roughnesses and materials using the constructed two-dimensional rectangular metal part model; and to store different materials, different roughnesses and the corresponding optimal laser detection power in the database.

[0109] In one embodiment, the subsurface defect detection device further includes a model building module;

[0110] The model building module is used to build two-dimensional rectangular metal part models with different roughness and different materials based on COMSOL; target defects are preset on the two-dimensional rectangular metal part models to obtain the constructed two-dimensional rectangular metal part models.

[0111] In one embodiment, the analysis module is further configured to perform laser ultrasonic testing on the constructed two-dimensional rectangular metal part model based on multiple different laser detection powers, and obtain laser detection results corresponding to each laser detection power; and select the optimal laser detection power for different roughness and different materials from the multiple different laser detection powers based on the laser detection results.

[0112] In one embodiment, the analysis module is further configured to determine the laser ultrasonic detection signal intensity and defect quantitative analysis result corresponding to each of the laser detection powers based on the laser detection results; and to select the optimal laser detection power for different roughnesses and different materials from the plurality of different laser detection powers according to the laser ultrasonic detection signal intensity and the defect quantitative analysis result.

[0113] In one embodiment, the analysis module is further configured to set the corresponding physical field interface of the constructed two-dimensional rectangular metal part model as solid mechanics coupled to solid heat transfer, and set the time step to a preset step size.

[0114] In one embodiment, the model building module is further configured to perform local densification processing on the finite element mesh at the location of the target defect in the constructed two-dimensional rectangular metal part model; and to perform local densification processing on the finite element mesh at the pulsed laser incident point.

[0115] Furthermore, it should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0116] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0117] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory (ROM) / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0118] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A method for detecting subsurface defects, characterized in that, The subsurface defect detection method includes: The optimal laser detection power under different roughnesses and materials was analyzed using a pre-constructed two-dimensional rectangular metal part model. Store different materials, different roughnesses, and their corresponding optimal laser detection power in a database; Obtain the surface roughness and surface material of the metal sample to be tested; The optimal laser detection power is retrieved from the database based on the surface roughness and the surface material. Based on the optimal laser detection power, the metal sample to be tested is subjected to laser ultrasonic testing to obtain the subsurface defect detection result corresponding to the metal sample to be tested. The analysis of optimal laser detection power under different roughnesses and materials using a pre-constructed two-dimensional rectangular metal part model includes: Laser ultrasonic testing was performed on a pre-constructed two-dimensional rectangular metal part model based on multiple different laser detection powers to obtain the laser detection results corresponding to each laser detection power. Based on the laser detection results, the optimal laser detection power for different roughnesses and different materials is selected from the multiple different laser detection powers; Before performing laser ultrasonic testing on the constructed two-dimensional rectangular metal part model based on multiple different laser detection powers to obtain the laser detection results corresponding to each laser detection power, the method further includes: For the constructed two-dimensional rectangular metal part model, the corresponding physical field interface is set to solid mechanics coupled to solid heat transfer, and the time step is set to a preset step size. Before analyzing the optimal laser detection power under different roughnesses and materials using the constructed two-dimensional rectangular metal part model, the method further includes: Two-dimensional rectangular metal part models with different roughness and materials were constructed using COMSOL. The target defect is pre-set on the two-dimensional rectangular metal part model to obtain the constructed two-dimensional rectangular metal part model.

2. The subsurface defect detection method as described in claim 1, characterized in that, The selection of the optimal laser detection power for different roughnesses and materials based on the laser detection results includes: Based on the laser detection results, determine the laser ultrasonic detection signal intensity and defect quantitative analysis results corresponding to each laser detection power; Based on the intensity of the laser ultrasonic detection signal and the results of the quantitative analysis of defects, the optimal laser detection power for different roughnesses and materials is selected from among the multiple different laser detection powers.

3. The subsurface defect detection method as described in claim 1, characterized in that, After pre-setting the target defect on the two-dimensional rectangular metal part model to obtain the constructed two-dimensional rectangular metal part model, the method further includes: The finite element network at the location of the target defect in the constructed two-dimensional rectangular metal part model is locally densified; After performing laser ultrasonic testing on the constructed two-dimensional rectangular metal part model based on multiple different laser detection powers, the method further includes: The finite element mesh at the pulsed laser incident point is locally refined.

4. A subsurface defect detection device, characterized in that, The subsurface defect detection device includes: The acquisition module is used to acquire the surface roughness and surface material of the metal sample to be tested; The search module is used to search the database for the corresponding optimal laser detection power based on the surface roughness and the surface material; The detection module is used to perform laser ultrasonic testing on the metal sample to be tested based on the optimal laser detection power, and obtain the subsurface defect detection result corresponding to the metal sample to be tested; The analysis module is used to analyze the optimal laser detection power under different roughnesses and materials using a pre-constructed two-dimensional rectangular metal part model; and to store different materials, different roughnesses and their corresponding optimal laser detection powers in the database. The analysis module is also used to perform laser ultrasonic testing on the constructed two-dimensional rectangular metal part model based on multiple different laser detection powers, and obtain the laser detection results corresponding to each laser detection power; based on the laser detection results, the optimal laser detection power for different roughness and different materials is selected from the multiple different laser detection powers; The analysis module is also used to set the corresponding physical field interface as solid mechanics coupled to solid heat transfer for the constructed two-dimensional rectangular metal part model, and to set the time step to a preset step size. The model building module is used to build two-dimensional rectangular metal part models with different roughness and materials based on COMSOL; target defects are preset on the two-dimensional rectangular metal part models to obtain the constructed two-dimensional rectangular metal part models.

5. A subsurface defect detection device, characterized in that, The device includes: a memory, a processor, and a subsurface defect detection program stored in the memory and executable on the processor, the subsurface defect detection program being configured to implement the subsurface defect detection method as described in any one of claims 1 to 3.

6. A storage medium, characterized in that, The storage medium stores a subsurface defect detection program, which, when executed by a processor, implements the subsurface defect detection method as described in any one of claims 1 to 3.

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