An analysis method based on void defects in titanium alloys

Abstract

The present invention discloses an analysis method for void defects based on titanium alloys, which relates to the field of metal detection technology and includes the following steps: extracting initial microstructure features, establishing a mathematical model of grain morphology and void defect distribution, performing multi-scale distribution analysis to quantify void distribution characteristics, simulating the behavior of void expansion under stress in titanium alloys, obtaining void expansion laws, and developing intelligent prediction models. The analysis method for void defects based on titanium alloys obtains grain orientation and texture information of titanium alloys by setting advanced microscopic imaging technology, and quantifies grain size and shape characteristics to form a complete microstructure input; constructs a mathematical model describing void geometric features and distribution, integrates microscopic information to predict the impact of voids on overall material properties; combines stress triaxiality field with microscopic characteristics to reveal the driving force and direction law of void expansion.

Description

Technical Field

[0001] The invention relates to the technical field of metal detection, in particular to an analysis method based on void defects of titanium alloy. Background Art

[0002] Titanium alloys are widely used in aerospace, medical devices and industrial manufacturing due to their excellent specific strength, corrosion resistance and high temperature performance. However, during the manufacturing and use of titanium alloys, the presence of void defects may significantly reduce the mechanical properties of the material, thereby affecting its reliability and service life. Existing technologies mainly focus on the detection and static analysis of void defects, lacking in-depth research on void distribution characteristics, expansion laws and stress coupling, making it difficult to effectively predict failure behavior under complex working conditions. In addition, traditional analysis methods rely on single physical field data (such as stress fields or geometric features) and fail to fully integrate the multi-scale information of material microstructure characteristics (such as grain orientation, texture information) and dynamic behavior. Summary of the invention

[0003] In view of the deficiencies in the prior art, the present invention provides an analysis method for void defects based on titanium alloys to solve the problems raised in the above background technology.

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] In a first aspect, an embodiment of the present invention provides a method for analyzing void defects based on titanium alloys, comprising:

[0006] S1. Extracting the initial microstructure characteristics of titanium alloy samples;

[0007] S2. Based on the extracted microstructural features, a mathematical model of grain morphology and void defect distribution is established to predict the area where voids are concentrated;

[0008] S3, performing multi-scale distribution analysis to quantify void distribution characteristics according to the predicted void concentrated distribution areas;

[0009] S4. Using the quantitative void distribution characteristics, simulate the void expansion behavior of titanium alloy under stress and obtain the stress coupling results;

[0010] S5. Using the simulated stress coupling results, the dynamic process of void expansion and microcrack formation is observed in real time to obtain the void expansion law;

[0011] S6. Develop an intelligent prediction model based on the void expansion law as training data. The prediction results will provide a quantitative reference for material design and process optimization.

[0012] To further optimize the technical solution, in step S1, feature extraction includes:

[0013] Firstly, representative titanium alloy samples were selected and pretreated, including surface grinding, cleaning, and optical microscope observation;

[0014] Use a high-resolution scanning electron microscope to observe the microscopic morphology of the sample to obtain the grain morphology, grain boundary characteristics and potential void distribution of the titanium alloy material;

[0015] The grain orientation and texture information are analyzed by electron backscatter diffraction technology, the grain size and shape characteristics are quantified, and the equivalent diameter, major axis length, minor axis length, orientation angle of the titanium alloy grains, and the microstructural characteristics of the grain boundary energy density related to the grain orientation difference are obtained.

[0016] To further optimize the technical solution, in step S2, in the mathematical model of grain morphology and void defect distribution, the equivalent diameter, major axis length, minor axis length, orientation angle of the titanium alloy grains, and the microstructural characteristics of the grain boundary energy density related to the grain orientation difference are input, and the parameters of the microstructural characteristics are set:

[0017] :No. The equivalent diameter of a grain;

[0018] :No. The length of the major axis of each grain;

[0019] :No. The length of the minor axis of each grain;

[0020] :No. The orientation angle of each grain;

[0021] : grain boundary energy density;

[0022] : Void distribution probability density function.

[0023] To further optimize the technical solution, the mathematical model of grain morphology and void defect distribution is as follows:

[0024] ;

[0025] in,

[0026] : weight coefficient, which measures the effects of shape anisotropy, orientation difference and grain size on the distribution of void defects;

[0027] : Spatial weight function of void distribution.

[0028] To further optimize the technical solution, the spatial weight function of the gap distribution is as follows:

[0029] ;

[0030] in,

[0031] It is The center position of each grain, is the standard deviation of the grains, indicating how much influence the grain has on its surrounding area.

[0032] To further optimize the technical solution, in step S3, the multi-scale distribution analysis includes:

[0033] Based on the predicted areas where voids are concentrated, X-ray micro-CT technology is used to obtain a three-dimensional void distribution image inside the titanium alloy sample;

[0034] Combined with image segmentation algorithm, the size, shape and spatial position characteristics of each gap are extracted;

[0035] A multi-scale statistical model of void distribution is constructed to analyze the distribution characteristics of voids at different scales.

[0036] Further optimizing the technical solution, the input of the multi-scale statistical model includes the output of the mathematical model of grain morphology and void defect distribution. and data of three-dimensional void distribution images inside titanium alloy samples;

[0037] Set the parameters of the multiscale statistical model:

[0038] : The probability density function of the gap distribution output by the mathematical model;

[0039] : The scale radius of the observation window;

[0040] : At a radius of The number of gaps in the observation window;

[0041] : The total area of ​​gaps within the observation window;

[0042] : distribution dimension;

[0043] : Multi-scale distribution characteristic function, describing the change of void distribution with scale;

[0044] : Weight parameters, which respectively measure the contribution of void distribution probability density, area distribution density and distribution dimension to the overall distribution law.

[0045] To further optimize this technical solution, the multi-scale statistical model is as follows:

[0046] ;

[0047] in,

[0048] : Dynamically adjusted weight parameters to ensure adaptation to different material properties.

[0049] : Distribution dimension, used to describe the complexity of void distribution at the microscopic scale.

[0050] To further optimize the technical solution, in step S4, simulating the behavior of void expansion of titanium alloy under stress includes:

[0051] Construct three-dimensional finite element models;

[0052] Apply load conditions;

[0053] Nonlinear finite element calculation;

[0054] Obtain the stress triaxiality field.

[0055] To further optimize the technical solution, in step S5, the process of obtaining the gap expansion law includes:

[0056] Calculation of void expansion rate;

[0057] Direction vector calculation;

[0058] Dynamic evolution analysis generates the dynamic path of gap expansion and obtains the law of gap expansion.

[0059] In a second aspect, an embodiment of the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of an analysis method for void defects based on titanium alloys as described in the first aspect of the present invention are implemented.

[0060] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, the steps of an analysis method for void defects based on titanium alloys as described in the first aspect of the present invention are implemented.

[0061] Compared with the prior art, the present invention provides an analysis method for void defects based on titanium alloy, which has the following beneficial effects:

[0062] This analysis method based on void defects in titanium alloys uses advanced microscopic imaging technology to obtain the grain orientation and texture information of titanium alloys, and quantifies the grain size and shape characteristics to form a complete microstructure input; constructs a mathematical model that describes the geometric characteristics and distribution of voids, and integrates microscopic information to predict the impact of voids on overall material properties; combines the stress triaxiality field with microscopic characteristics to reveal the driving force and direction of void expansion. This system accurately quantifies the rate and direction of void expansion, significantly improving the accuracy and dynamic adaptability of defect prediction. This system can not only provide a new theoretical basis for the design and manufacture of materials, but also effectively improve the application performance of titanium alloys in the field of high reliability, and provide technical support for the development of high-performance engineering materials. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0064] Figure 1 A schematic flow chart of a method for analyzing void defects in titanium alloys proposed by the present invention;

[0065] Figure 2 This is a schematic flow chart of step S4 in a method for analyzing void defects in titanium alloys proposed by the present invention. DETAILED DESCRIPTION

[0066] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0067] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0068] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments.

[0069] Embodiment 1:

[0070] Reference Figure 1-2, which is the first embodiment of the present invention, provides an analysis method for void defects based on titanium alloy, comprising:

[0071] S1. Extract the initial microstructure features of titanium alloy samples.

[0072] This step is the basis of the entire analysis process. The extracted microstructural features will directly affect the subsequent quantification and identification of void defects.

[0073] In this embodiment, feature extraction includes:

[0074] Firstly, representative titanium alloy samples were selected and pretreated, including surface grinding, cleaning, and optical microscope observation;

[0075] Use a high-resolution scanning electron microscope to observe the microscopic morphology of the sample to obtain the grain morphology, grain boundary characteristics and potential void distribution of the titanium alloy material;

[0076] The grain orientation and texture information are analyzed by electron backscatter diffraction technology, the grain size and shape characteristics are quantified, and the equivalent diameter, major axis length, minor axis length, orientation angle of the titanium alloy grains, and the microstructural characteristics of the grain boundary energy density related to the grain orientation difference are obtained.

[0077] S2. Based on the extracted microstructural features, a mathematical model of grain morphology and void defect distribution is established to predict the area where voids are concentrated.

[0078] Identify areas where stress concentration or material inhomogeneity may exist, thereby pinpointing possible initial formation points of voids. Initial formation points are areas or locations where voids begin to form, usually in areas of high stress concentration or material inhomogeneity. By analyzing the shape, size, and orientation of the grains, the specific location in space can be determined.

[0079] When demarcating areas where stress concentrations or material inhomogeneities may exist, methods include:

[0080] Based on the grain size, shape and orientation information extracted by S1, the grain boundary energy gradient field is determined, reflecting the difference in energy distribution at the grain boundary at the microscopic level.

[0081] High gradient regions (i.e., large grain boundary energy gradient fields) are often potential areas of stress concentration or inhomogeneous material structure.

[0082] Calculation of stress concentration areas:

[0083] The internal stress distribution is calculated by finite element analysis (FEM) combining the grain boundary energy gradient field and stress triaxiality field.

[0084] Identify areas of high stress triaxiality values ​​as stress concentration points.

[0085] Material inhomogeneity determination:

[0086] By analyzing the grain shape (e.g. aspect ratio, shape complexity) and the inhomogeneity of size distribution, the inhomogeneous distribution areas are marked.

[0087] Simulated loading conditions were applied to these areas to observe the stress concentration effect and further verify the possibility of void formation.

[0088] Combined with Voronoi diagram simulation:

[0089] The grains are spatially divided based on the Voronoi diagram to simulate the grain morphology and arrangement.

[0090] At the grain boundary intersection or polycrystalline junction, the grain boundary energy difference and stress distribution are superimposed to analyze the local stress change trend.

[0091] Through the above analysis method, the initial formation points of voids caused by stress concentration or material inhomogeneity can be effectively calibrated. These points are usually located at the junction of grain boundaries, high stress gradients or locations where grain size changes suddenly.

[0092] The initial formation point determines the starting position of the gap, and the distribution of these points directly affects the path and rate of gap expansion. Combined with the gap expansion law in step S5, the dynamic evolution trend of the gap can be further analyzed.

[0093] The initial formation point serves as the starting point of the high-risk area and provides initial defect distribution data for the subsequent intelligent prediction model, enabling the model to more accurately predict the dynamic changes of the defect risk area.

[0094] In this embodiment, in the mathematical model of grain morphology and void defect distribution, the equivalent diameter, major axis length, minor axis length, orientation angle of the titanium alloy grains, and the microstructure characteristics of the grain boundary energy density related to the grain orientation difference are input, and the parameters of the microstructure characteristics are set:

[0095] :No. The equivalent diameter of each grain (based on grain size measurement);

[0096] :No. The length of the major axis of each grain (shape characteristic);

[0097] :No. The length of the minor axis of each grain (shape characteristic);

[0098] :No. Orientation angle of each grain (texture information);

[0099] : Grain boundary energy density (related to grain orientation differences);

[0100] : Void distribution probability density function.

[0101] Furthermore, the mathematical model of the grain morphology and void defect distribution is as follows:

[0102] ;

[0103] in,

[0104] : weight coefficient, which measures the effects of shape anisotropy, orientation difference and grain size on the distribution of void defects;

[0105] : Spatial weight function of void distribution.

[0106] Furthermore, the spatial weight function of the gap distribution is as follows:

[0107] ;

[0108] in,

[0109] It is The center position of each grain, is the standard deviation of the grains, indicating how much influence the grain has on its surrounding area.

[0110] Standard deviation of grain size It not only describes the uniformity of grain size, but also reflects the degree of influence of grains on the surrounding areas. When the standard deviation is small, the influence of grains on the surrounding areas is more uniform, and the distribution of voids and defects tends to be uniform. When the standard deviation is large, the influence of grains on the surrounding areas is uneven, stress concentration and void defects may form in certain areas, and affect the overall performance of the material.

[0111] When used, the model includes:

[0112] Data input: The grain feature data obtained from S1 is used as input. Combined with the spatial distribution of the grains, the center position of each grain is determined .

[0113] Model calculation: First, use the grain length-short axis ratio Reflects the effect of grain shape on void distribution. The closer the shape is to an ellipse (large difference between the major and minor axes), the higher the possibility of void formation. , describes the contribution of the orientation difference of adjacent grains to the grain boundary energy. The product of these two quantities reflects the coupling effect of orientation difference and grain boundary energy. The increase of orientation difference leads to the increase of grain boundary energy, and the change of grain boundary energy directly affects the distribution of voids and defects. Through the product form, we can quantify the contribution of grain orientation difference to grain boundary energy, so as to predict the distribution probability of voids at different grain boundaries. When the orientation difference is between 0° and 15°: this interval conforms to the grain boundary energy law of the Read-Shockley model. According to the model, when the orientation difference of the grains is small, the grain boundary energy increases with the increase of orientation difference. At this time, the structure of the grain boundary is more regular and the atomic arrangement is more orderly, so the energy will increase with the increase of orientation difference, resulting in an increase in the probability of void distribution. After the orientation difference is 40°: the structure of the grain boundary has changed significantly. At this time, the grain boundary gradually changes from the original continuous atomic layer to a discontinuous dislocation, resulting in the grain boundary energy decreasing with the increase of orientation difference. This is because a large number of dislocations begin to appear at high-angle grain boundaries (especially when they exceed 40°), so the grain boundary energy decreases and the probability of void concentration decreases. When the grain orientation difference is in the range of 15° to 40°, the change of grain boundary energy is more complicated, usually showing a tendency to stability or entering a saturation interval, and no longer strictly follows the law of the Read-Shockley model. The properties of the grain boundary gradually transition from low-angle grain boundaries to high-angle grain boundaries. In this process, the grain boundary energy tends to saturate and gradually approaches the average energy value of the high-angle grain boundary. Between 15° and 40°, the effect between dislocations gradually weakens, the grain boundary shows irregular atomic arrangement, and the energy increase rate slows down. When the orientation difference is close to 40°, the grain boundary energy is close to the upper limit of the high-angle grain boundary, showing characteristics similar to those of the high-angle grain boundary. At this time, the sensitivity of the grain boundary energy change is reduced, so in this interval, the energy change is relatively gentle. This interval can be regarded as the transition zone from low-angle grain boundaries to high-angle grain boundaries. When calculating the grain boundary energy, it is necessary to introduce nonlinear functions or piecewise functions to describe its change law.

[0114] Grain size The inverse of is used to emphasize the phenomenon that small grain regions are prone to void concentration.

[0115] Void distribution prediction: by Calculate the probability of void distribution at each position within the sample. Spatial weight function The influence range of each grain is described, ensuring that the model can predict the void distribution in local areas.

[0116] Result output: Mapped to the sample space, a two-dimensional heat map of void distribution is obtained. The high-value areas of the heat map correspond to the locations where voids are concentrated.

[0117] ( ) is a spatial coordinate used to describe the void distribution at each location inside the sample. In the model or calculation, it is assumed that the analysis of the sample can be simplified to a two-dimensional case. Especially when analyzing the shape, size and orientation of the grains, simplification is chosen within the plane to reduce the computational complexity. This is to more conveniently show the law of void distribution, especially in the case of cross-sections or thin slices, ignoring the third dimension.

[0118] Spatial weight function Describes the The influence of each grain on the void distribution. These functions map the influence of the grain to each spatial position within the sample ( This function ensures that the model can be located at a given position (e.g. a specific ( ) coordinates) predict the possible distribution of the gaps. Through calculations, the model can not only determine the initial location of the voids, but also analyze how the voids expand inside the sample.

[0119] S3. Based on the predicted areas where voids are concentrated, multi-scale distribution analysis is performed to quantify void distribution characteristics.

[0120] In this embodiment, the multi-scale distribution analysis includes:

[0121] Based on the predicted areas where voids are concentrated, X-ray micro-CT technology is used to obtain a three-dimensional void distribution image inside the titanium alloy sample;

[0122] Combined with image segmentation algorithm, the size, shape and spatial position characteristics of each gap are extracted;

[0123] A multi-scale statistical model of void distribution is constructed to analyze the distribution characteristics of voids at different scales.

[0124] Furthermore, the input of the multi-scale statistical model includes the output of the mathematical model of grain morphology and void defect distribution. and data of three-dimensional void distribution images inside titanium alloy samples;

[0125] Set the parameters of the multiscale statistical model:

[0126] : The probability density function of the gap distribution output by the mathematical model;

[0127] : The scale radius of the observation window;

[0128] : At a radius of The number of gaps in the observation window;

[0129] : The total area of ​​gaps in the observation window;

[0130] : Distribution dimension (used to describe the fractal characteristics of the void);

[0131] : Multi-scale distribution characteristic function, describing the change of void distribution with scale;

[0132] : Weight parameters, which respectively measure the contribution of void distribution probability density, area distribution density and distribution dimension to the overall distribution law.

[0133] Furthermore, the multi-scale statistical model is as follows:

[0134] ;

[0135] in,

[0136] : Dynamically adjusted weight parameters to ensure adaptation to different material properties.

[0137] : Distribution dimension, used to describe the complexity of void distribution at the microscopic scale.

[0138] When used, the model includes:

[0139] Data input: The input of the model includes the output of the previous model. And the three-dimensional void distribution data of the sample. Through image processing and segmentation technology, the spatial position of each void is extracted and area.

[0140] Multi-scale analysis: Divide the sample area into multiple circular observation windows and gradually adjust the radius , obtain statistical characteristics of different scales. Count the number of gaps in each window Sum of the area , as the basic data for multi-scale analysis. and The logarithmic relationship of , which is used to describe the microscopic complexity of void distribution.

[0141] Calculating different scales Next , and obtain the changing trend of the void distribution:

[0142] when Follow The increase decreases rapidly, indicating that the voids are more sparse on a large scale;

[0143] when right The change is small, indicating that the voids are evenly distributed at all scales;

[0144] High Dimensionality This suggests that the void distribution has a higher complexity, indicating that it is more strongly correlated with the microstructure.

[0145] In summary, this model combines the characteristics of multi-scale voids to evaluate their effects on mechanical properties. By controlling the formation of voids of specific scales, the manufacturing process can be optimized and the material quality can be improved.

[0146] S4. Using the quantitative void distribution characteristics, the void expansion behavior of titanium alloy under stress is simulated to obtain the stress coupling results.

[0147] In this embodiment, the behavior of void expansion of titanium alloy under stress is simulated, including:

[0148] Construct a three-dimensional finite element model.

[0149] Using the actual microstructure information of titanium alloy (such as grain orientation and void geometry distribution), the model needs to embed the void distribution information obtained in the previous step.

[0150] Apply loading conditions.

[0151] Apply external loading conditions that simulate real working conditions, including uniaxial tension, compression, shear, etc., to ensure that the stress field is consistent with the actual environment.

[0152] Nonlinear finite element calculations.

[0153] Finite element analysis software (such as ABAQUS, ANSYS) is used to perform nonlinear static calculations to obtain the stress tensor of each point.

[0154] Obtain the stress triaxiality field.

[0155] Calculate stress triaxiality based on stress tensor and verify the calculation results to ensure that the stress field is consistent with the actual test value or literature data. Use visualization technology to display the distribution law of stress triaxiality field and provide input for subsequent model analysis.

[0156] S5. Using the simulated stress coupling results, the dynamic process of void expansion and microcrack formation is observed in real time to obtain the void expansion law.

[0157] In this embodiment, the process of obtaining the gap expansion law includes:

[0158] Calculation of void expansion rate;

[0159] The calculation model is as follows:

[0160] ;

[0161] in,

[0162] : Void expansion rate field (void growth rate per unit area per unit time).

[0163] : Stress triaxiality field, indicating the stress ratio in the local area.

[0164] : Grain boundary weakening probability field, associated with grain orientation and texture information.

[0165] : The characteristic size of the void, which changes with time t and reflects the dynamic process of expansion.

[0166] : Indicates that at time t, the gap is in the spatial position ( ) is affected by the energy gradient at the grain boundary and the grain orientation difference.

[0167] : Local shear stress angle, describing the degree of directional deviation of void expansion.

[0168] : The intrinsic anti-fracture factor of the material, which is related to the intrinsic properties of the material.

[0169] : Scale factor, used for unit normalization.

[0170] : Nonlinear expansion factor, characterizing the nonlinear effect of stress triaxiality on the expansion rate.

[0171] Direction Vector calculate;

[0172] The calculation model is as follows:

[0173] ;

[0174] Indicates the optimal path direction for gap expansion.

[0175] Dynamic evolution analysis generates the dynamic path of gap expansion and obtains the gap expansion law;

[0176] The characteristic size of the void changes with time, and the expansion process is calculated iteratively.

[0177] Combined direction vector , generating a dynamic path for gap expansion.

[0178] S6. Develop an intelligent prediction model based on the void expansion law as training data. The prediction results will provide a quantitative reference for material design and process optimization.

[0179] In this embodiment, we design an intelligent prediction model based on the law of void expansion. The intelligent prediction model is used in this system to integrate microscopic characteristics, void expansion law and stress distribution data to predict potential high-risk defect areas inside titanium alloys. Compared with traditional statistical methods or simple physical field analysis, intelligent prediction models (such as neural networks, support vector machines and random forests) show higher flexibility and prediction accuracy when dealing with complex nonlinear relationships and multi-scale coupling problems.

[0180] The intelligent prediction model takes the mathematical model, void expansion law and analysis results of stress triaxiality field as the main input to comprehensively capture the potential risk factors of void defects inside titanium alloys.

[0181] When the output is a continuous indicator (such as gap expansion rate or failure time), a regression model (such as support vector regression, deep neural network) can be selected.

[0182] When defect classification or regional risk assessment is required, classification models (such as random forest or SVM) can be used.

[0183] By combining experimental feedback, the model can continuously update learning parameters, improve prediction accuracy, and provide dynamic support for titanium alloy design and manufacturing.

[0184] Embodiment 2:

[0185] This embodiment also provides a computer device, which is suitable for an analysis method of void defects based on titanium alloys, and includes a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement an analysis method of void defects based on titanium alloys as proposed in the above embodiment.

[0186] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, an analysis method for void defects based on titanium alloys as proposed in the above embodiment is implemented.

[0187] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0188] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0189] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0190] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk case (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0191] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit with a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit with a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0192] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for analyzing void defects in titanium alloys, characterized in that: include: S1. Extracting the initial microstructure characteristics of titanium alloy samples; S2. Based on the extracted microstructural features, a mathematical model of grain morphology and void defect distribution is established to predict the area where voids are concentrated. In the mathematical model of grain morphology and void defect distribution, the equivalent diameter, major axis length, minor axis length, orientation angle of the titanium alloy grains, and the microstructural features of grain boundary energy density related to the grain orientation difference are input; S3, performing multi-scale distribution analysis to quantify void distribution characteristics according to the predicted void concentrated distribution areas; Multiscale distribution analysis includes: Based on the predicted areas where voids are concentrated, X-ray micro-CT technology is used to obtain a three-dimensional void distribution image inside the titanium alloy sample; Combined with image segmentation algorithm, the size, shape and spatial position characteristics of each gap are extracted; Construct a multi-scale statistical model of void distribution and analyze the distribution characteristics of voids at different scales; The input of the multi-scale statistical model includes the output of the mathematical model of grain morphology and void defect distribution. and data of three-dimensional void distribution images inside titanium alloy samples; Set the parameters of the multiscale statistical model: : The probability density function of the gap distribution output by the mathematical model; : The scale radius of the observation window; : At a radius of The number of gaps in the observation window; : The total area of ​​gaps within the observation window; : distribution dimension; : Multi-scale distribution characteristic function, describing the change of void distribution with scale; : Weight parameter, which measures the contribution of void distribution probability density, area distribution density and distribution dimension to the overall distribution law; The multiscale statistical model is as follows: ； in, : Dynamically adjusted weight parameters to ensure adaptation to different material properties; : distribution dimension, used to describe the complexity of void distribution at the microscopic scale; S4. Using the quantitative void distribution characteristics, simulate the void expansion behavior of titanium alloy under stress and obtain the stress coupling results; S5. Using the simulated stress coupling results, the dynamic process of void expansion and microcrack formation is observed in real time to obtain the void expansion law; S6. Develop an intelligent prediction model based on the void expansion law as training data. The prediction results will provide a quantitative reference for material design and process optimization.

2. The method for analyzing void defects in titanium alloy according to claim 1, characterized in that: In step S1, feature extraction includes: Firstly, representative titanium alloy samples were selected and pretreated, including surface grinding, cleaning, and optical microscope observation; Use a high-resolution scanning electron microscope to observe the microscopic morphology of the sample to obtain the grain morphology, grain boundary characteristics and potential void distribution of the titanium alloy material; The grain orientation and texture information are analyzed by electron backscatter diffraction technology, the grain size and shape characteristics are quantified, and the equivalent diameter, major axis length, minor axis length, orientation angle of the titanium alloy grains, and the microstructural characteristics of the grain boundary energy density related to the grain orientation difference are obtained.

3. The method for analyzing void defects in titanium alloy according to claim 2, characterized in that: The mathematical model of grain morphology and void defect distribution is as follows: ； in, : weight coefficient, which measures the effects of shape anisotropy, orientation difference and grain size on the distribution of void defects; : spatial weight function of gap distribution; : No. The equivalent diameter of a grain; : No. The length of the major axis of each grain; : No. The length of the minor axis of each grain; : No. The orientation angle of each grain; : grain boundary energy density; : Void distribution probability density function.

4. The method for analyzing void defects in titanium alloy according to claim 3, characterized in that: The spatial weight function of the gap distribution is as follows: ； in, It is The center position of each grain, is the standard deviation of the grains, indicating how much influence the grain has on its surrounding area.

5. The method for analyzing void defects in titanium alloy according to claim 1, characterized in that: In the step S4, simulating the behavior of void expansion of titanium alloy under stress includes: Construct three-dimensional finite element models; Apply load conditions; Nonlinear finite element calculation; Obtain the stress triaxiality field.

6. The method for analyzing void defects in titanium alloy according to claim 1, characterized in that: In step S5, the process of obtaining the gap expansion rule includes: Calculation of void expansion rate; Direction vector calculation; Dynamic evolution analysis generates the dynamic path of gap expansion and obtains the law of gap expansion.

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