Statistical partition-based weld ultrasonic simulation modeling method, system and medium

Abstract

The present application relates to a kind of based on partition statistics weld ultrasonic simulation modeling method, system and medium.Simulation modeling method includes: weld is divided into multiple scanning areas respectively to carry out EBSD scanning to obtain complete grain orientation data;The orientation data of different scanning areas are statistically sampled, and are constructed by pole figure contrast representative orientation library;Weld is subdivided into multiple modeling feature areas;Based on Voronoi algorithm, columnar grain geometric model is generated;Euler angle in representative orientation library is converted into elastic stiffness matrix, and corresponding Voronoi grain entity is given;In finite element software, ultrasonic testing model is established to carry out simulation.The present application combines the strategy of regional EBSD scanning strategy and the strategy of reconstructing geometric structure with Voronoi algorithm, simultaneously proposes "statistical sampling based on pole figure consistency" method, and assigns value reconstructs geometric structure, realizes the balance of simulation calculation efficiency and microstructure restoration degree.

Description

Technical Field

[0001] This invention relates to the field of weld inspection technology, and in particular to a method, system and medium for ultrasonic simulation modeling of welds based on partitioned statistics. Background Technology

[0002] Coarse-grained materials (such as nickel-based alloys and austenitic stainless steel) exhibit large grains and significant anisotropy, resulting in strong scattering and beam deflection of ultrasonic waves during propagation. This leads to low signal-to-noise ratios and difficulties in defect identification. Therefore, establishing a high-fidelity simulation model that accurately reflects the microscopic acoustic characteristics of the weld is crucial for optimizing the inspection process and imaging algorithms. This model can provide better prediction and guidance for subsequent ultrasonic inspection operations.

[0003] In existing technologies, two modeling approaches are commonly used: one is pixel-based modeling directly based on EBSD (electron backscatter diffraction) patterns, such as the invention patent with publication number CN104090033A entitled "Invention Patent on Establishing Simulation Model of FDTD Ultrasonic Testing of Coarse-grained Materials Based on EBSD Patterns," which directly maps each pixel of the EBSD scan to a finite element mesh. Although this method has high accuracy, it has significant limitations: first, for large-sized welds, the required number of finite element meshes is extremely large, resulting in extremely low computational efficiency and extremely high hardware resource requirements; second, limited by the single-scan field of view of the EBSD device (usually only a few millimeters), it is difficult to obtain the complete crystal orientation pattern of the entire thick weld in one go. Second, there is pure geometric modeling based on Voronoi diagrams, such as the invention patent with publication number CN112163356A entitled "A Method for Finite Element Modeling of Microstructure Ultrasonic Detection in Additive Manufacturing". It uses algorithms to generate grain geometry, but the grain orientation is often based on idealized assumptions or random generation, lacking the support of real statistical data, and is difficult to accurately reflect the complex texture evolution law under specific welding processes (such as multi-layer multi-pass welding). Summary of the Invention

[0004] To address the technical problem of the conflict between computational efficiency and microstructure reproduction in existing simulation modeling methods, this invention provides a method, system, and medium for ultrasonic simulation modeling of welds based on partitioned statistics.

[0005] In a first aspect, the present invention proposes a weld ultrasonic simulation modeling method based on partitioned statistics, which includes: The weld seam is divided into I scanning regions, and EBSD scanning is performed on each region to obtain weld grain orientation data. Grain orientation data from the i-th scanning region is randomly sampled to generate the i-th sampled dataset. The pole figure of the i-th sampled dataset is compared with the pole figure of the original dataset. If the texture intensity center positions and distribution patterns of the two match, the i-th sampled dataset is retained as the representative orientation library for the i-th scanning region. i∈[1,I]. The original dataset is the set of all pixel data points in the scanning region.

[0006] The weld is divided into N feature regions based on grain growth characteristics, and the grain geometry parameters of the nth feature region are statistically analyzed. n∈[1,N]. Initial equiaxed crystals are generated using the Voronoi algorithm. The initial equiaxed crystals are stretched and rotated to generate columnar crystal geometry regions that match the geometric parameters of the nth grain. The nth columnar crystal geometry region is cut into the same shape as the nth feature region and then assembled according to the macroscopic shape of the weld to form a complete weld geometry model.

[0007] Grain orientation data from I representative orientation libraries are converted into elastic stiffness matrices and assigned to the weld geometry model according to the positions of I scanning regions. The assigned weld geometry model is then imported into finite element software for ultrasonic testing simulation.

[0008] Secondly, this invention proposes a weld ultrasonic simulation modeling system based on partitioned statistics and model reconstruction, which uses the weld ultrasonic simulation modeling method based on partitioned statistics and model reconstruction from the first aspect. The weld ultrasonic simulation modeling system includes: a data acquisition module, a model reconstruction module, and a simulation module.

[0009] The data acquisition module is used to divide the weld into I scanning regions and perform EBSD scanning on each region to obtain weld grain orientation data. Grain orientation data from the i-th scanning region is randomly sampled to generate the i-th sampled dataset. The pole figure of the i-th sampled dataset is compared with the pole figure of the original dataset. If the texture intensity center position and distribution pattern match, the i-th sampled dataset is retained as the representative orientation library for the i-th scanning region. i∈[1,I]. The model reconstruction module is used to divide the weld into N feature regions based on grain growth characteristics and to statistically analyze the grain geometric parameters of the n-th feature region. n∈[1,N]. Initial equiaxed crystals are generated using the Voronoi algorithm. The initial equiaxed crystals are stretched and rotated to generate columnar crystal geometric regions that match the geometric parameters of the n-th grain. The n-th columnar crystal geometric region is cut into the same shape as the n-th feature region and assembled according to the macroscopic shape of the weld to form a complete weld geometric model. The grain orientation data from I representative orientation libraries are converted into elastic stiffness matrices and assigned to the weld geometry model according to the positions of I scanning regions. The simulation module is used to import the assigned weld geometry model into finite element software for ultrasonic testing simulation.

[0010] Thirdly, the present invention proposes a computer-readable storage medium storing a computer program / instruction thereon, which, when executed by a processor, implements the steps of the weld ultrasonic simulation modeling method based on partition statistics and model reconstruction in the first aspect.

[0011] The beneficial effects of this invention are as follows: 1. This invention organically combines the regional EBSD scanning strategy with the Voronoi algorithm for reconstructing geometric structures, and innovatively proposes a "polar-based" approach. Figure 1 The "consistent statistical sampling" method not only ensures that the simulation model has realistic acoustic anisotropy characteristics, but also significantly reduces the amount of data processing. At the same time, it assigns and reconstructs the geometric structure, realizing an efficient combination of macroscopic geometric morphology and microscopic physical properties, significantly improving the computational efficiency and accuracy of ultrasonic testing simulation of coarse-grained welds, and achieving a balance between simulation computational efficiency and microstructure reproduction.

[0012] 2. This invention addresses the differences in grain growth direction and size in different depth regions of the weld by employing a regional EBSD scanning strategy, which effectively solves the problems of not being able to cover the entire weld in a single scan and the difficulty in acquiring data for large-sized welds, thus overcoming the limitations of field of view and size.

[0013] 3. This invention reconstructs the geometric structure using the Voronoi algorithm and assigns physical properties using EBSD sampling data, achieving efficient fusion of geometric and physical properties, which significantly improves the accuracy and practicality of ultrasonic testing simulation of coarse-grained welds. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 This is a flowchart of a weld ultrasonic simulation modeling method based on partitioned statistics and model reconstruction; Figure 2 This is a macroscopic metallographic image of the weld and a scanned area partitioning diagram in the embodiment; Figure 3 This is the EBSD map of scan region #1; Figure 4 This is the EBSD map of scan region #2; Figure 5 This is the EBSD map of scan region #3; Figure 6 It is the pole figure of the original dataset of scan region #1; Figure 7 It is the pole figure of the sampled dataset of scan region #1; Figure 8 It is the pole figure of the original dataset of scan region #2; Figure 9 It is the pole figure of the sampled dataset from scan region #2; Figure 10 It is the pole figure of the original dataset of scan region #3; Figure 11 It is the pole figure of the sampled dataset from scan region #3; Figure 12 This is a schematic diagram of the weld feature area division; Figure 13 This is a schematic diagram of geometric modeling of different feature areas of the weld (Voronoi generation and assembly); Figure 14 This is a schematic diagram of the weld orientation assignment zone; Figure 15 This is a model diagram of ultrasonic testing of weld seams; Figure 16 This is a comparison of simulation and experimental results. Detailed Implementation

[0016] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the specification of this invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "or / and" as used herein includes any and all combinations of one or more of the associated listed items.

[0018] This invention addresses the limitations of EBSD scanning field of view and the high computational cost of full-spectrum modeling for large-size welds. It proposes a weld ultrasonic simulation modeling method based on regional EBSD statistical sampling and Voronoi reconstruction. This method organically combines a regional EBSD scanning strategy with a Voronoi algorithm-based geometric structure reconstruction strategy, significantly improving the computational efficiency, accuracy, and practicality of ultrasonic testing simulation for coarse-grained welds. For details, please refer to... Figure 1 The ultrasonic simulation modeling method for welds based on partitioned statistics and model reconstruction proposed in this embodiment includes the following steps: S1. EBSD scanning of weld seam partitions to acquire grain orientation data.

[0019] The weld seam is divided into I scanning regions, and each region is scanned using EBSD. The scan results are then stitched together to obtain complete weld grain orientation data. In this step, the scanning region can be set to a rectangle. Depending on the weld seam size, a matching number of scanning regions is set based on the coverage area of ​​a single scan, balancing the number of scans and the weld seam scanning range. In this embodiment, please refer to... Figure 2 First, the longitudinal section of the target weld (e.g., a nickel-based alloy 800HT weld) is ground, polished, and etched to obtain macroscopic metallographic images. Due to the large size of the weld, approximately 40 mm deep, a single EBSD scan cannot cover the entire area. Therefore, as... Figure 2 As shown, the weld area was divided into three rectangular scanning areas (1#, 2#, and 3#) along the depth direction, and EBSD scans were performed on each area (step size 8.5μm, with each scanning area covering the entire area in a single scan). The scan results are shown below. Figure 3 , Figure 4 , Figure 5 As shown, the grain orientation data of each scanning area is obtained (which can be saved as .cpr format), and the complete weld grain orientation data is obtained after splicing.

[0020] S2. Statistical sampling and verification of grain orientation data.

[0021] This step is one of the core designs of this invention. Since the EBSD full map contains millions of pixels, directly importing it into finite element software would result in an excessively large computational model that is difficult to converge. Therefore, to avoid computational redundancy caused by directly importing large amounts of data, this embodiment adopts a statistical sampling strategy: randomly sampling the grain orientation data of the i-th scan region to generate the i-th sampling dataset. The pole figure of the i-th sampling dataset is compared with the pole figure of the original dataset. If the texture intensity center position and distribution pattern of the two match, the i-th sampling dataset is retained as the representative orientation library of the i-th scan region. i∈[1,I]. Specifically, taking scan region #1 as an example, such as... Figure 3 As shown, L (e.g., 292) pixels of grain orientation Euler angle data are randomly selected from the EBSD map information of scan region #1. These L Euler angle data are used as a sampling dataset. Subsequently, extreme... Figure 1 Consistency verification: Use the MTEX ​​toolbox to draw a pole figure of the original dataset (the original dataset is the data of all pixels in the scan area) of the #1 scan region (e.g., Figure 6 As shown), and plotting the pole figures of the sampled dataset (as shown). Figure 7 (As shown). By comparing the polar density distribution characteristics of the two, if the center position and distribution pattern of texture intensity in the polar plots match, then the sampling data is considered representative. Comparison Figure 6 , Figure 7 It can be observed that the distribution positions and intensities of the high-density regions on the (100) and (110) pole figures are highly consistent, indicating that the sampled dataset of 292 pixels is sufficient to represent the texture features of the 1# scan region. This validated sampled dataset is used as the representative orientation library for the 1# scan region. If the texture features of the sampled dataset pole figure are inconsistent with those of the original dataset pole figure, i.e., the intensity center position and distribution pattern do not match, random sampling is performed again or the number of extracted pixels is increased before random sampling is performed until the texture features of both are consistent. Similarly, the above process is repeated for the 2# and 3# scan regions. Figure 8 , Figure 9 As shown, it is a comparison of the original dataset of the 2# scan area and the sampled dataset of 320 pixels. Figure 10 , Figure 11 By comparing the original dataset of the 3# scan area with the sampled dataset of 342 pixels, and verifying the results, a representative orientation library was obtained, and a grain orientation database for the entire weld was constructed.

[0022] S3, Weld geometry model reconstruction.

[0023] Based on grain growth characteristics, the weld seam is divided into N characteristic regions, and the grain geometric parameters of the nth characteristic region are statistically analyzed. n∈[1,N]. Specifically, observation of the weld metallography reveals a gradient change in grain growth direction and size at different depths within the weld. This gradient change is used as a grain growth characteristic and the regions are divided along their boundaries. Please refer to... Figure 12 In this embodiment, to more precisely characterize this grain morphology change, the complete weld was subdivided into four feature regions, 1#, 2#, 3#, and 4#, during modeling. The grain geometry parameters of feature regions 1#, 2#, 3#, and 4# were obtained through EBSD scanning: maximum Ferrette diameter, fitted ellipse angle, and grain width. The results are shown in Table 1.

[0024] Table 1: Grain geometric parameters of characteristic regions 1#, 2#, 3#, and 4#.

[0025]

[0026] Subsequently, the Voronoi algorithm was used on the MATLAB platform to generate initial equiaxed crystals. By introducing an anisotropic scaling factor, the initial equiaxed crystals were stretched and rotated to generate columnar crystal geometric regions that match the geometric parameters of the nth grain. Figure 13 As shown, taking feature region #1 as an example, firstly, a columnar crystal geometric region matching the grain geometry parameters of feature region #1 is generated. Then, the boundary of this columnar crystal geometric region is cut using the truncation method to make it cut into the same shape as feature region #1. Following the same logic, columnar crystal geometric regions with the shapes of feature regions #2, #3, and #4 are cut out in sequence. Finally, they are assembled according to the macroscopic shape of the weld to form a complete weld geometric model.

[0027] S4. Assign material properties to the weld geometry model.

[0028] The Bond transformation method is used to convert the Euler angles in the representative orientation library into an elastic stiffness matrix, which is then assigned to the weld geometry model according to the positions of the three scanned regions. Specifically, for cubic materials, the constitutive stiffness matrix consists of three independent elastic constants C0. 11 C 12 C 44 Composition. For example, for nickel-based alloys, C can be defined as... 11 =235GPa, C 12 =145 GPa, C 44 =126 GPa. For any scanning region, the Bond transformation method is used to calculate the rotation matrix R based on the Euler angles in its representative orientation library. Then, the elastic stiffness matrix C' of each grain in the global coordinate system is calculated: C'=RCR -1 .

[0029] C represents the constitutive stiffness matrix of the part material in the principal axis coordinate system. Similarly, the elastic stiffness matrices C' for scanned regions 1#, 2#, and 3# are calculated. Finally, through the joint simulation interface between MATLAB and COMSOL Multiphysics (LiveLink for MATLAB), the three calculated elastic stiffness matrices C' are assigned to the weld geometry model according to the positions of the three scanned regions. For example... Figure 14 As shown, weld areas 1#, 2#, and 3# correspond to scan areas 1#, 2#, and 3#, respectively. They are assigned values ​​along the weld depth direction to achieve accurate assignment of material microstructure properties.

[0030] S5, Finite Element Simulation and Imaging.

[0031] Import the assigned weld geometry model into COMSOL finite element software to create an ultrasonic testing model. For example... Figure 15 As shown, the excitation signal is set as a five-cycle Hanning window modulated sine wave with a center frequency of 5MHz. Low reflection boundary conditions are set at the boundary of the ultrasonic testing model to reduce sidewall reflection interference. The ultrasonic testing simulation is performed using the A-scan method.

[0032] To verify the accuracy of the ultrasonic testing model for weld microstructure established in this invention, a verification and comparison were conducted. For example... Figure 16 As shown, Figure 16 Figure (a) shows a bottom surface echo signal acquired by experiment and simulation (this invention). The phase of the simulated signal is basically consistent with that of the experimental signal. Figure 16 Figure (b) shows the spectrum of the simulated (invention) and experimental bottom wave signals. It can be observed that both the experimental and simulated signals exhibit a shift in the dominant frequency towards lower frequencies, and the trends are consistent. The comparison between the experiment and the simulation fully verifies the accuracy of the ultrasonic testing model for weld microstructure established in this invention.

[0033] In another embodiment, the present invention also proposes a weld ultrasonic simulation modeling system based on partitioned statistics and model reconstruction, which uses the weld ultrasonic simulation modeling method based on partitioned statistics and model reconstruction described in the above embodiments. The weld ultrasonic simulation modeling system includes: a data acquisition module, a model reconstruction module, and a simulation module.

[0034] The data acquisition module is used to divide the weld into I scanning regions and perform EBSD scanning on each region to obtain weld grain orientation data. Grain orientation data from the i-th scanning region is randomly sampled to generate the i-th sampled dataset. The pole figure of the i-th sampled dataset is compared with the pole figure of the original dataset. If the texture intensity center position and distribution pattern match, the i-th sampled dataset is retained as the representative orientation library for the i-th scanning region. i∈[1,I]. The model reconstruction module is used to divide the weld into N feature regions based on grain growth characteristics and to statistically analyze the grain geometric parameters of the n-th feature region. n∈[1,N]. Initial equiaxed crystals are generated using the Voronoi algorithm. The initial equiaxed crystals are stretched and rotated to generate columnar crystal geometric regions that match the geometric parameters of the n-th grain. The n-th columnar crystal geometric region is cut into the same shape as the n-th feature region and assembled according to the macroscopic shape of the weld to form a complete weld geometric model. The grain orientation data from I representative orientation libraries are converted into elastic stiffness matrices and assigned to the weld geometry model according to the positions of I scanning regions. The simulation module is used to import the assigned weld geometry model into finite element software for ultrasonic testing simulation.

[0035] In another embodiment, a computer-readable storage medium is also proposed, which stores a computer program / instructions. When the computer program / instructions are executed by a processor, they implement the steps of the weld ultrasonic simulation modeling method based on partition statistics and model reconstruction described in the above embodiments. The computer-readable storage medium may include, but is not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0036] In another embodiment, a computer program product is also proposed, comprising a computer program / instructions. This computer program / instructions are used to cause a computer to perform the steps of the weld ultrasonic simulation modeling method based on partitioned statistics and model reconstruction described in the above embodiments. The computer program / instructions exist in a computer-readable medium in forms including, but not limited to, source files, executable files, and installation package files. Correspondingly, the computer program / instructions can be executed by a computer in ways including, but not limited to: the computer directly executing the instructions; or the computer compiling the instructions and then executing the corresponding compiled program; or the computer reading and executing the instructions; or the computer reading and installing the instructions and then executing the corresponding installed program.

[0037] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0038] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

Claims

1. A method for ultrasonic simulation modeling of welds based on partitioned statistics and model reconstruction, characterized in that, It includes: The weld was divided into I scanning regions and EBSD scans were performed on each region to obtain weld grain orientation data. Randomly sample the grain orientation data of the i-th scan region to generate the i-th sampled dataset; compare the pole figure of the i-th sampled dataset with the pole figure of the original dataset; if the texture intensity center position and distribution pattern of the two match, retain the i-th sampled dataset as the representative orientation library of the i-th scan region; i∈[1,I]; the original dataset is the set of all pixel data points in the scan region. The weld is divided into N characteristic regions based on grain growth characteristics, and the grain geometric parameters of the nth characteristic region are statistically analyzed; n∈[1,N]; The initial equiaxed crystal is generated using the Voronoi algorithm; the initial equiaxed crystal is stretched and rotated to generate a columnar crystal geometric region that matches the geometric parameters of the nth grain; the nth columnar crystal geometric region is cut into the same shape as the nth feature region and assembled according to the macroscopic shape of the weld to form a complete weld geometric model. The grain orientation data in I representative orientation libraries are converted into elastic stiffness matrices and assigned to the weld geometry model according to the positions of I scanning regions; the assigned weld geometry model is then imported into finite element software for ultrasonic testing simulation.

2. The ultrasonic simulation modeling method for welds based on partitioned statistics and model reconstruction according to claim 1, characterized in that, If the texture features of the sampled dataset pole map are inconsistent with those of the original dataset pole map, random sampling will be performed again until the texture features of the two datasets are consistent.

3. The ultrasonic simulation modeling method for welds based on partitioned statistics and model reconstruction according to claim 1, characterized in that, The grain geometry parameters include: maximum Ferete diameter, fitted ellipse angle, and grain width; these parameters are obtained by EBSD scanning.

4. The ultrasonic simulation modeling method for welds based on partitioned statistics and model reconstruction according to claim 1, characterized in that, The grain growth characteristics are the changes in grain growth direction and size gradient at different depths of the weld, which are observed by metallographic analysis of the weld.

5. The ultrasonic simulation modeling method for welds based on partitioned statistics and model reconstruction according to claim 1, characterized in that, Methods for assigning weld geometry models include: Calculate the rotation matrix R based on Euler angles from grain orientation data; Calculate the elastic stiffness matrix C' of each grain in the global coordinate system: C'=RCR -1 ; C is the constitutive stiffness matrix of the part material; The simulation software assigns C' to the weld geometry model according to the positions of I scanning areas.

6. The ultrasonic simulation modeling method for welds based on partitioned statistics and model reconstruction according to claim 1, characterized in that, After importing the assigned weld geometry model into the finite element software, boundary conditions and excitation signals are set; the excitation signal is a sine wave modulated by a Hanning window. And / or, the scanning method for ultrasonic testing simulation is A-scan.

7. The ultrasonic simulation modeling method for welds based on partitioned statistics and model reconstruction according to claim 1, characterized in that, The scanning area is set to a rectangle and divided along the depth direction on the longitudinal section of the weld.

8. The ultrasonic simulation modeling method for welds based on partitioned statistics and model reconstruction according to claim 1, characterized in that, The original dataset includes grain orientation Euler angle data for all pixels in the scanned area; The sampling dataset includes grain orientation Euler angle data of L randomly sampled pixels in the scanned area; L < the total number of pixels in the scanned area.

9. A weld ultrasonic simulation modeling system based on partitioned statistics and model reconstruction, characterized in that, It uses the ultrasonic simulation modeling method for welds based on partition statistics and model reconstruction as described in any one of claims 1 to 8; The ultrasonic simulation modeling system for welds includes: The data acquisition module is used to divide the weld into I scanning regions and perform EBSD scanning on each region to obtain weld grain orientation data; randomly sample the grain orientation data of the i-th scanning region to generate the i-th sampling dataset; compare the pole figure of the i-th sampling dataset with the pole figure of the original dataset; if the texture intensity center position and distribution pattern of the two match, retain the i-th sampling dataset as the representative orientation library of the i-th scanning region; i∈[1,I]; The model reconstruction module is used to divide the weld into N feature regions based on grain growth characteristics and to statistically analyze the grain geometric parameters of the nth feature region; n∈[1,N]; generate initial equiaxed crystals using the Voronoi algorithm; stretch and rotate the initial equiaxed crystals to generate columnar crystal geometric regions that match the geometric parameters of the nth grain; cut the nth columnar crystal geometric region into the same shape as the nth feature region and assemble them according to the macroscopic shape of the weld to form a complete weld geometric model; convert the grain orientation data in I representative orientation libraries into elastic stiffness matrices and assign them to the weld geometric model according to the positions of I scan regions; The simulation module is used to import the assigned weld geometry model into finite element software for ultrasonic testing simulation.

10. A computer-readable storage medium having a computer program / instructions stored thereon, characterized in that, When the computer program / instruction is executed by the processor, it implements the steps of the ultrasonic simulation modeling method for weld seams based on partition statistics and model reconstruction as described in any one of claims 1 to 8.

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