Stress Analysis Method and System for Inorganic Mineral Castings Based on Virtual Simulation System

Abstract

This invention relates to the field of mechanical property analysis technology, specifically to a method and system for stress analysis of inorganic mineral castings based on a virtual simulation system. The method includes the following steps: acquiring grain voxel density differences to screen boundary points and generate boundary curve datasets; fitting defect shapes to form a defect region mesh model; extracting fatigue path node distribution maps; screening thermo-co-current risk nodes; and drawing concentrated stress region annotation results. In this invention, density abrupt change features are introduced as an identification basis when acquiring grain structure boundaries, avoiding boundary offsets caused by traditional geometric approximation modeling. A closed curve network is established through the spatial continuity connection of boundary points, improving the refinement of grain structure modeling. For the fatigue path calibration stage, the continuous change relationship of node displacement trends is used instead of concentrated strain point acquisition, allowing fatigue path identification to break free from single-point measurement dependence and enhancing the completeness of stress judgment under multi-field conditions in the virtual simulation system.

Description

Technical Field

[0001] This invention relates to the field of mechanical property analysis technology, and in particular to a method and system for stress analysis of inorganic mineral castings based on a virtual simulation system. Background Technology

[0002] The field of mechanical property analysis technology is an engineering technology that studies the stress-strain distribution and deformation / failure mechanisms of materials or components under stress. Its core aspects include the determination of material mechanical properties, prediction of mechanical behavior, modeling of stressed structures, and calculation of mechanical responses. Relying on theoretical mechanics, materials science, and computer simulation technology, it establishes predictive and verifiable mechanical models by acquiring and processing information such as material parameters, load conditions, and boundary constraints. This enables quantitative analysis and evaluation of the mechanical properties of materials or components under different working conditions. Traditional methods for stress analysis of inorganic mineral castings involve using sample mechanical property test data and physical model experiments in casting process design and quality control, combined with manual calculations and two-dimensional static analysis, to estimate and evaluate the stress state of castings under service conditions. This is achieved by placing strain gauges on the sample for strain measurement or simulating external forces through physical loading experiments, and by referring to static equilibrium equations and material mechanics formulas to complete stress calculation and distribution judgment.

[0003] Existing methods for stress analysis of inorganic mineral castings rely on actual measurements and geometric simplification when constructing grain structure boundaries. They lack three-dimensional continuity when dealing with internal defect boundaries, leading to local deviations in structural modeling. In fatigue risk identification, they rely on the local response of strain gauges or the performance of specimens from physical loading experiments, which cannot accurately pinpoint crack paths under virtual working conditions. In high-temperature gradient environments, they rely solely on static formulas to calculate thermal stress response, lacking an analytical basis for the coupling relationship between heat flow direction and load direction. This makes it difficult to achieve accurate location of failure areas in heated and stressed structures. When facing the need for structural risk assessment under multiple working conditions, they suffer from operational defects such as a single response method and limited predictive content. Summary of the Invention

[0004] To address the technical problems existing in the prior art, embodiments of the present invention provide a method for stress analysis of inorganic mineral castings based on a virtual simulation system, comprising the following steps:

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a stress analysis method for inorganic mineral castings based on a virtual simulation system, comprising the following steps:

[0006] S1: Obtain the voxel density distribution data of inorganic mineral casting grains in the virtual simulation system in three dimensions, filter the voxel positions where the density difference exceeds the material interface identification, connect adjacent boundary points into a closed boundary curve network, and obtain the grain boundary curve dataset.

[0007] S2: Call the grain boundary curve dataset, perform three-dimensional surface fitting on the region enclosed by the curve to form a closed defect shape, and use the spatial difference between the defect shape and the overall grain structure to construct a local independent region. Arrange nodes at equal intervals in the virtual simulation system mesh to construct a defect region mesh model.

[0008] S3: Based on the defect area mesh model, apply a preset cyclic load in the virtual simulation system, call the three-dimensional displacement values ​​of the mesh nodes at the time step, calculate the displacement change between adjacent time steps, and generate a fatigue path node distribution map;

[0009] S4: Invoke the fatigue path node distribution map, obtain the node temperature value and the temperature value of adjacent nodes, calculate the temperature difference and form a thermal gradient direction vector, compare the angle between the thermal gradient direction vector and the node equivalent stress direction vector, and generate a set of thermally coordinated risk nodes.

[0010] As a further embodiment of the present invention, the grain boundary curve dataset includes boundary point coordinates, boundary curve topological relationships, and boundary curve closure; the defect region mesh model includes mesh cell size, mesh node spatial distribution, and defect region volume morphology; the fatigue path node distribution map includes node number sequence, node spatial path, and path change trend; and the thermo-coordinated risk node set includes risk node coordinates, thermal gradient direction, and equivalent stress direction.

[0011] As a further aspect of the present invention, the specific steps of S1 are as follows:

[0012] S101: Obtain the voxel density distribution data of inorganic mineral casting grains in the three-dimensional scanning of the virtual simulation system. For the voxel position, call the density values ​​of the six adjacent voxels in the three-dimensional direction. Combine the real-time voxel density value with the density values ​​of the six adjacent voxels and perform subtraction operation respectively. Record the difference obtained. Identify the difference between the obtained difference and the real-time voxel density value and generate a set of density mutation voxel coordinates.

[0013] S102: Call the density mutant voxel coordinate set, combine the spatial positions of adjacent voxels in the three-dimensional grid, connect the voxel coordinates that satisfy the spatial connection relationship in sequence, and determine the continuity of adjacent positions in the direction of the three-dimensional coordinate axis. Group and classify the coordinate set of voxel points according to the continuity, and obtain the boundary point coordinate combination sequence.

[0014] S103: Based on the boundary point coordinate combination sequence, connect the spatial positions between adjacent boundary points to construct a three-dimensional curve unit, and cyclically connect the boundary points to form a spatial closed structure. Remove non-closed point pairs with abnormal coordinate intervals, and connect a complete and continuous boundary line segment network to obtain a grain boundary curve dataset.

[0015] As a further aspect of the present invention, the specific steps of S2 are as follows:

[0016] S201: Call the grain boundary curve dataset, select the spatial coordinates of three consecutive boundary points in each group according to the arrangement order of the grain boundary curves in the three-dimensional coordinate system, construct the initial triangular patch in the three-point surface construction method, generate a continuously distributed triangular mesh after iterating through the boundary point set, remove the mesh patches with abnormal vertex spacing, and generate a continuous three-dimensional fitting patch group.

[0017] S202: Based on the continuous three-dimensional fitted patch group, connect the edge positions of the fitted patches, detect whether the boundary line segments form a closed path, interpolate to generate missing patches for the non-closed parts, and close the overall curved surface contour to form a defect three-dimensional shape boundary body.

[0018] S203: Call the three-dimensional shape boundary of the defect and the density distribution data of the original grain voxel, compare the volume spatial overlap of the two at their corresponding positions in the three-dimensional coordinate system, extract the set of coordinate points in the defect boundary but not covered by the original grain voxel, define the set of coordinate points as the local difference space position, and mark the independent region number according to the three-dimensional boundary closure to obtain the difference space region inside the grain.

[0019] S204: Based on the difference space region inside the grain, divide the mesh nodes at equal intervals within the three-dimensional coordinate boundary, number the nodes in sequence to construct a three-dimensional octahedral unit, remove the boundary overlapping nodes and fill the heterogeneous unit to construct the defect region mesh model.

[0020] As a further aspect of the present invention, the specific steps of S3 are as follows:

[0021] S301: Based on the defect area mesh model, apply the set cyclic load boundary conditions according to the time axis in the virtual simulation system, record the three-dimensional coordinate values ​​of the mesh nodes at multiple time steps, call the three-dimensional coordinate differences of the nodes between adjacent time steps, calculate the three-dimensional displacement change value of the nodes between adjacent time steps, and obtain the time step displacement change data.

[0022] S302: Call the displacement change data of the time step, arrange the displacement change values ​​under the time step in order according to the node number, construct the time series data corresponding to the node, count the numerical difference between the change values ​​of adjacent time steps in the node sequence, and determine whether there is a continuous increasing trend. Mark the node number segment that satisfies the continuous increasing feature to obtain the continuously increasing node number group.

[0023] S303: Based on the continuously growing node number group, extract the spatial coordinates of the corresponding node in the defect area mesh model, connect adjacent nodes in sequence according to the node number arrangement order, construct path segments representing the direction of change trend, and output the path segments in three-dimensional space after labeling them by number to obtain the fatigue path node distribution map.

[0024] As a further aspect of the present invention, the specific steps of S4 are as follows:

[0025] S401: Call the node number in the fatigue path node distribution map, extract the corresponding temperature value according to the node number, synchronously obtain the temperature values ​​of the six spatially adjacent nodes of the node, combine the temperature difference between the real-time node and the adjacent nodes, identify the distribution direction of the temperature difference value in the three-dimensional coordinate system, integrate the direction vectors and perform vector superposition to obtain the thermal gradient direction vector group.

[0026] S402: Based on the thermal gradient direction vector group, extract the equivalent stress direction vector data recorded by the corresponding node in the fatigue path, calculate the angle between each group of thermal gradient direction vectors and equivalent stress direction vectors, record the node number corresponding to the angle, and obtain the thermal stress angle distribution list.

[0027] S403: Call the thermal stress angle distribution list, compare the angle values ​​with the set thermal coordination threshold values ​​in turn, calculate the thermal coordination deviation value, filter the node numbers with thermal coordination deviation values ​​lower than the threshold, and extract the spatial coordinates in the defect area mesh model to obtain the thermal coordination risk node set.

[0028] As a further aspect of the present invention, the thermodynamic coordination deviation value refers to the difference between the reference node and the average angle and the thermodynamic fluctuation of the node group.

[0029] As a further aspect of the present invention, the method further includes step S5:

[0030] S5: Using the aforementioned thermally coordinated risk node set, draw the outline of the risk area according to the spatial coordinates of the nodes in the three-dimensional visualization interface of the virtual simulation system, cluster and connect the nodes with the same orientation consistency in the same area to form a continuous concentrated stress area, and mark the location and range of the area to generate the concentrated stress area marking result.

[0031] The labeling results of the concentrated stress area include the spatial range of the area, the location identifier of the area, and the clustering results of the node orientation consistency.

[0032] As a further aspect of the present invention, the specific steps of S5 are as follows:

[0033] S501: Call the spatial coordinate values ​​of the nodes in the thermal collaborative risk node set, connect adjacent nodes sequentially according to the coordinate point distribution in the three-dimensional visualization interface of the virtual simulation system, extract boundary points to form a continuous edge structure, gradually close the boundary line segments to form a polygonal region, and obtain the three-dimensional risk area outline boundary layer.

[0034] S502: Based on the three-dimensional risk area contour boundary layer, extract the risk node number in the area, call the equivalent stress direction vector corresponding to the risk node, calculate the angle between any two node direction vectors in the area, filter the node pairs with the angle value lower than the direction consistency limit angle, and connect them in ascending order to form clustering line segments, and construct a continuous concentrated stress clustering unit group.

[0035] S503: Based on the continuous concentrated stress clustering unit group, extract the spatial node coordinates, calculate the center point position and three-axis projection size of each unit in the three-dimensional space, and mark the spatial position of the center point of the stress region and the coverage range of the three-axis direction respectively to obtain the concentrated stress region marking result.

[0036] An inorganic mineral casting stress analysis system based on a virtual simulation system includes:

[0037] The grain boundary extraction module obtains the voxel density distribution data of inorganic mineral casting grains in the three-dimensional scanning of the virtual simulation system, calls the density value of the voxel and the density value of the six adjacent voxels to perform the subtraction operation, compares the difference with the material interface recognition threshold, and then filters the voxel positions where the difference exceeds the threshold to generate a grain boundary curve dataset.

[0038] The defect region construction module, based on the grain boundary curve dataset, performs three-dimensional surface fitting on the region enclosed by the curve to form a closed defect shape, calculates the spatial difference between the defect shape and the overall grain structure, and arranges grid nodes at equal intervals to construct a defect region grid model.

[0039] The fatigue path identification module calls the defect area mesh model, applies a preset cyclic load in the virtual simulation system, obtains the three-dimensional displacement value of each mesh node at each time step, calculates the displacement change between adjacent time steps, and generates a fatigue path node distribution map.

[0040] The thermal synergy screening module extracts the node temperature value and the adjacent node temperature value according to the fatigue path node distribution map, calculates the temperature difference and forms a thermal gradient direction vector, calculates the angle between the vector and the node equivalent stress direction vector, and compares it with the thermal synergy threshold to generate a thermal synergy risk node set.

[0041] The concentrated stress annotation module calls the thermally coordinated risk node set, draws the risk area outline according to the node spatial coordinates in the three-dimensional visualization interface, and performs clustering calculation on the consistency of node orientation in the same area to generate concentrated stress area annotation results.

[0042] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0043] In this invention, density abrupt change features are introduced as the identification basis when acquiring grain structure boundaries to avoid boundary offset caused by traditional geometric approximation modeling. A closed curve network is established by connecting the spatial continuity of boundary points to improve the refinement of grain structure modeling. For the fatigue path calibration, the continuous change relationship of node displacement trend is used to replace the acquisition of concentrated strain points. The relationship between the node direction vector and the crack path angle is used for screening, so that fatigue path identification is freed from the dependence on single-point measurement. By using the synergistic identification method of the angle between the thermal gradient direction and the stress direction, the direction judgment standard of thermally driven failure region is established. The spatial labeling and clustering of concentrated stress region is completed in the simulation environment to ensure that stress analysis has the ability to identify structural hierarchy in thermo-mechanical coupling scenarios, and enhances the integrity of stress judgment of virtual simulation system under multi-field conditions. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying 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.

[0045] Figure 1 This is a schematic diagram of the steps of the present invention;

[0046] Figure 2 This is a detailed schematic diagram of S1 of the present invention;

[0047] Figure 3 This is a detailed schematic diagram of S2 of the present invention;

[0048] Figure 4 This is a detailed schematic diagram of S3 of the present invention;

[0049] Figure 5 This is a detailed schematic diagram of S4 of the present invention;

[0050] Figure 6 This is a detailed schematic diagram of S5 of the present invention;

[0051] Figure 7 This is a system module diagram of the present invention. Detailed Implementation

[0052] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0053] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0054] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0055] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0056] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0057] Please see Figure 1 This invention provides a method for stress analysis of inorganic mineral castings based on a virtual simulation system, comprising the following steps:

[0058] S1: Obtain the voxel density distribution data of inorganic mineral casting grains in the virtual simulation system three-dimensional scanning, call the voxel and perform the subtraction operation with the density values ​​of six adjacent voxels, filter the voxel positions with density differences exceeding the material interface identification, take the voxel positions as structural boundary points, connect adjacent boundary points into a closed boundary curve network, and obtain the grain boundary curve dataset.

[0059] S2: Call the grain boundary curve dataset, perform three-dimensional surface fitting on the region enclosed by the curve to form a closed defect shape, and use the spatial difference between the defect shape and the overall grain structure to construct a local independent region. Arrange nodes at equal intervals in the virtual simulation system mesh to construct a mesh model of the defect region.

[0060] S3: Based on the defect area mesh model, a preset cyclic load is applied in the virtual simulation system, the three-dimensional displacement values ​​of the mesh nodes at the time step are called, the displacement change of adjacent time steps is calculated, and a displacement change sequence is constructed according to the node number. The node segments with continuously increasing changes are selected to generate a fatigue path node distribution map.

[0061] S4: Call the fatigue path node distribution map, obtain the node temperature value and the temperature value of adjacent nodes, calculate the temperature difference and form a thermal gradient direction vector, compare the angle between the thermal gradient direction vector and the node equivalent stress direction vector, filter out risk nodes with an angle lower than the thermal synergy threshold, and generate a thermal synergy risk node set.

[0062] S5: Using the thermally coordinated risk node set, draw the outline of the risk area according to the spatial coordinates of the nodes in the 3D visualization interface of the virtual simulation system, cluster and connect the nodes with the same orientation consistency in the same area to form a continuous concentrated stress area, and mark the location and range of the area to generate the concentrated stress area labeling result.

[0063] The grain boundary curve dataset includes boundary point coordinates, boundary curve topology, and boundary curve closure. The defect region mesh model includes mesh cell size, mesh node spatial distribution, and defect region volume morphology. The fatigue path node distribution map includes node number sequence, node spatial path, and path change trend. The thermo-coordinated risk node set includes risk node coordinates, thermal gradient direction, and equivalent stress direction. The concentrated stress region annotation results include region spatial range, region location identifier, and node orientation consistency clustering results.

[0064] Please see Figure 2 The specific steps of S1 are as follows:

[0065] S101: Obtain the voxel density distribution data of inorganic mineral casting grains in the three-dimensional scanning of the virtual simulation system. For the voxel position, call the density values ​​of the six adjacent voxels in the three-dimensional direction. Combine the real-time voxel density value with the density values ​​of the six adjacent voxels and perform subtraction operation respectively. Record the difference obtained. Identify the difference between the obtained difference and the real-time voxel density value and generate a set of density mutation voxel coordinates.

[0066] The voxel resolution parameters need to be set during scanning, with the voxel side length set to 0.01 mm to ensure high-precision scanning data. After acquiring the 3D scanning data, voxel reconstruction is performed. The scanned point cloud data is mapped to 3D mesh voxel cells through spatial mapping. For each voxel point V(x, y, z), a 3D adjacency structure is constructed, and the density values ​​of the six adjacent voxel points in the positive and negative directions of the x, y, and z axes are extracted sequentially, denoted as... to Simultaneously record the current voxel density value. Execute respectively minus to The operation is denoted as , For any voxel point where the value of Δi is greater than the set density change threshold τ, a density abrupt change is considered to exist. The density change threshold τ can be set according to the actual density change range of the material. For example, for inorganic mineral castings, if the average density is 3.5 g / cm³ and the fluctuation range is ±0.2 g / cm³, τ can be set to 0.3 g / cm³. A specific example is a voxel... With a density of 3.6 g / cm³, and six adjacent voxels having densities of 3.2, 3.4, 3.5, 3.6, 3.7, and 3.3 g / cm³, the Δ values ​​are 0.4, 0.2, 0.1, 0, -0.1, and 0.3. Based on τ = 0.3, only... and If the conditions are met, record the current voxel. The coordinates are the density mutation points, generating a set of density mutant element coordinates.

[0067] S102: Call the density mutant voxel coordinate set, combine the spatial positions of adjacent voxels in the 3D mesh, connect the voxel coordinates that satisfy the spatial connection relationship in sequence, and determine the continuity of adjacent positions in the 3D coordinate axis direction. Group and classify the voxel point coordinate set according to the continuity, and obtain the boundary point coordinate combination sequence.

[0068] The positions of each voxel point in 3D space are analyzed. An eight-adjacency relationship is established based on the voxel point coordinates. An adjacency matrix is ​​constructed by determining whether the changes in x, y, and z coordinates between adjacent voxels are ±1. Adjacent voxels are connected using a breadth-first search algorithm, forming connected subsets. Each subset represents a potential grain boundary region. For example, voxels V(10, 10, 10), V(10, 11, 10), and V(11, 11, 10) satisfy the adjacency condition and are classified into the same subset. The continuity of the voxel point distribution along the 3D coordinate axes in each connected subset is then determined. If there is a discontinuity in a certain direction, that is, if there is a gap between adjacent voxels, it is considered continuous. Voxel points are classified according to continuity. For example, if the set of voxel coordinates in a connected region is {(10, 10, 10), (10, 11, 10), (10, 12, 10), (10, 14, 10)}, since (10, 13, 10) is missing, it is considered as two continuous regions and split into two boundary point sequences {(10, 10, 10), (10, 11, 10), (10, 12, 10)} and {(10, 14, 10)}, forming a boundary point coordinate combination sequence.

[0069] S103: Based on the boundary point coordinate combination sequence, the spatial positions between adjacent boundary points are connected to construct a three-dimensional curve unit, and the boundary points are cyclically connected to form a spatial closed structure. Non-closed point pairs with abnormal coordinate intervals are eliminated, and a complete and continuous boundary line segment network is connected to obtain the grain boundary curve dataset.

[0070] The coordinates of each pair of adjacent points are sequentially extracted and their Euclidean distance is calculated. These are then connected to form line segments. Using a three-dimensional linear interpolation method, a three-dimensional curve unit is constructed connecting adjacent boundary points. For example, to connect points A(10, 10, 10) and B(10, 11, 10), the line segment L = =1, forming basic boundary segments. Connect the segments in a loop to form a closed-loop structure. If the distance between two boundary points exceeds the set threshold δ, the point pair is removed. The δ value needs to be set according to the actual grain size. For example, if the average grain diameter is 30μm, set δ=2μm. If the distance between boundary points P(20,20,20) and Q(25,20,20) is 5μm, it is judged as an abnormal point pair and removed. Only the points that meet the continuous connection condition are retained, completing the construction of the closed boundary curve and obtaining a network structure containing complete continuous boundary segments, forming a grain boundary curve dataset.

[0071] Please see Figure 3 The specific steps of S2 are as follows:

[0072] S201: Call the grain boundary curve dataset, select the spatial coordinates of three consecutive boundary points in each group according to the arrangement order of the grain boundary curves in the three-dimensional coordinate system, construct the initial triangular patch in the three-point surface construction method, generate a continuously distributed triangular mesh after iterating through the boundary point set, remove the mesh patch with abnormal vertex spacing, and generate a continuous three-dimensional fitting patch group.

[0073] Following the arrangement order of curves in the 3D coordinate system, the boundary points are sequentially arranged and combined. Each time, three sets of adjacent boundary points are selected, and an initial triangular patch is generated by connecting the three-point coordinates. During the construction process, the system uses incremental traversal of adjacent point indices to achieve continuous combination. Utilizing the planar rules defined by the boundary point coordinates in 3D space, a series of spatially continuous patch units are gradually connected and constructed. During the generation of patches, the system makes a preliminary judgment on the spatial distance relationship between each set of three points. If the distance between two points is much greater than the average distance between the other boundary points, it is considered to have an abnormal mutation and is filtered out using the distance difference judgment method. If the average boundary point distance is 5μm, and the distance on one side of the three points exceeds 15μm, it is considered to have a coordinate outlier phenomenon, and the patch is discarded and not included in the fitting range. During the traversal, all boundary point combinations that meet the conditions participate in the construction of the triangular mesh, and the set of adjacent three points is continuously updated to form a continuous curved surface structure. The three-point update is continuously performed until the boundary point set is traversed, generating a continuous 3D fitting patch group.

[0074] S202: Based on a continuous three-dimensional fitted patch group, connect the edge positions of the fitted patches, detect whether the boundary line segments form a closed path, interpolate to generate missing patches for non-closed parts, and close the overall curved surface contour to form a defect three-dimensional shape boundary body.

[0075] The edge information of each facet is extracted and integrated into a set of overall boundary segments. By recording the three boundary coordinates of each triangular facet and merging the boundary segments, the spatial connection status between the endpoints of each segment is determined. For boundary segments that cannot form a closed loop structure, a position interpolation method is used to construct the missing facet in the middle, so that it forms spatial continuity with the connected facets. During the interpolation process, parameters such as the relative position, direction change, and curvature trend between segments are considered. For parts where the spacing between segments is small but they are not connected into a closed loop, reasonable interpolation points are generated according to the direction of adjacent points to construct triangular facets to fill the gaps. If the spatial distance between two boundary segments is less than the set limit distance, a bridging edge will be automatically generated, and a new triangular facet will be formed by combining with the surrounding points to close the area. The non-closed boundary is gradually filled in. The facet filling operation maintains the continuity of adjacency and the natural transition of the surface, forming a structurally rigorous and closed three-dimensional defect boundary body structure, providing a reliable basic model for subsequent structural difference identification and spatial analysis, and forming a complete three-dimensional closed surface contour.

[0076] S203: Call the three-dimensional shape boundary volume of the defect and the density distribution data of the original grain voxels, compare the volumetric spatial overlap of the two at their corresponding positions in the three-dimensional coordinate system, extract the set of coordinate points in the defect boundary volume but not covered by the original grain voxels, define the set of coordinate points as the local difference spatial location, and mark the independent region number according to the three-dimensional boundary closure to obtain the difference spatial region inside the grain.

[0077] The system maps the spatial location of the defect's 3D boundary volume to the original grain voxel density distribution data. Each voxel coordinate is used as a detection unit. The system checks each spatial point within the defect's boundary volume to see if there is a matching item in the original grain density data voxel set. By traversing the coordinate points in the boundary volume and comparing them with the original voxel set, if a coordinate in the defect's boundary volume does not correspond to an original grain voxel, the point is identified as a difference spatial location. The system archives and records the difference point set and performs grouping processing based on spatial location continuity. A spatial clustering method is used to construct the difference spatial structure. Each difference segment is segmented and labeled according to its connection relationship with adjacent difference points in 3D space. By progressively traversing the adjacent directions of the difference points, if adjacent coordinate points also belong to the difference point set, they are grouped into the same region until no further expansion is possible. After labeling, each spatial difference structure is assigned a unique number for subsequent identification, visualization, and analysis to obtain the difference spatial region inside the grain.

[0078] S204: Based on the spatial differences within the grain, divide the mesh nodes at equal intervals within the three-dimensional coordinate boundary, number the nodes sequentially, construct three-dimensional octahedral elements, remove overlapping boundary nodes and fill with heterogeneous elements to construct a mesh model of the defect region.

[0079] A 3D region mesh model is established with a defined coordinate range. During the meshing process, an equal-interval interpolation method is used to generate mesh nodes. The boundary range is evenly divided in the X, Y, and Z directions to form a regular 3D mesh point array. Each direction is equally divided into several segments. The generated nodes are numbered sequentially according to the coordinate order to facilitate the subsequent construction of structural units. Every eight adjacent nodes constitute an octahedral unit. The positions of the eight corner points forming the unit body are determined according to the spatial arrangement rules, and the edges are connected to construct the mesh surface to avoid node duplication or unit overlap. If there are duplicate nodes with the remaining units in the boundary region, the node positions are automatically removed or offset to form a set of independent nodes without duplication. In the differential region, a material property inhomogeneity marker is set, and some units are assigned different label values ​​for subsequent multi-property simulation. For example, a region with unit numbers from 100 to 120 will be designated as a high-density differential region, and the remaining regions will be set as ordinary units. The entire differential region mesh model is constructed by mapping node numbers to units, so that the structure has complete topology, uniform spatial filling, and the feasibility of subsequent analysis. A defect region mesh model is constructed.

[0080] Please see Figure 4 The specific steps of S3 are as follows:

[0081] S301: Based on the defect region mesh model, the set cyclic load boundary conditions are applied according to the time axis in the virtual simulation system, the three-dimensional coordinate values ​​of the mesh nodes are recorded at multiple time steps, the three-dimensional coordinate differences of the nodes between adjacent time steps are called, the three-dimensional displacement change value of the nodes between adjacent time steps is calculated, and the time step displacement change data is obtained.

[0082] In the virtual simulation system, time axis parameters are loaded, the total loading time, time step interval, and cyclic load characteristics are set, and the load frequency, amplitude, and direction of action are set. The parameters are then input into the system boundary condition module. The system applies the load step by step according to the set cyclic boundary conditions. After each time step is completed, the simulation module records the changes in the three-dimensional coordinate values ​​of the current grid node, forming a sequence of node coordinate evolution over time. For each node number, the coordinate difference between time steps is calculated, that is, the vector difference between the current time step coordinate and the previous time step coordinate is processed to obtain the displacement change vector of the current time step. The displacement difference of the nodes is uniformly stored as time step displacement change data. This dataset covers the displacement change results between simulation nodes and time steps, forming a multi-dimensional data set of the spatial position response of the node during loading, and obtaining the time step displacement change data.

[0083] S302: Call the displacement change data of the time step, arrange the displacement change values ​​under the time step in order according to the node number, construct the time series data corresponding to the node, count the numerical difference between the change values ​​of adjacent time steps in the node sequence, and determine whether there is a continuous increasing trend. Mark the node number segment that satisfies the continuous increasing feature to obtain the continuously increasing node number group.

[0084] The system organizes the time-series displacement changes of each node according to its node number, forming a continuous sequence of data based on the displacement changes at each time step. This sequence data undergoes trend detection processing, and the system progressively calculates the numerical difference between each pair of adjacent time steps in the sequence. and The displacement values ​​at each time step are subtracted to obtain a continuous difference change sequence. Then, it is determined whether there are three or more adjacent time steps in the difference sequence whose displacement changes continuously increase, that is, satisfying the condition that the difference is positive and increasing. If the displacement sequence of a certain node is set as [0.02, 0.03, 0.05, 0.08], then its difference sequence is [+0.01, +0.02, +0.03], which belongs to a continuous growth trend. The node number is identified and recorded. The node numbers that satisfy this rule are grouped and organized according to the number range to obtain the continuously increasing node number group.

[0085] S303: Based on the continuously growing node number group, extract the spatial coordinates of the corresponding node in the defect area mesh model, connect adjacent nodes in sequence according to the node number arrangement order, construct path segments representing the direction of change trend, and output the path segments in three-dimensional space after labeling them by number to obtain the fatigue path node distribution map.

[0086] The three-dimensional coordinates corresponding to the node numbers are extracted from the defect area mesh model. The spatial positions of each node are read sequentially according to the numbering order, and line segment paths are constructed according to the coordinate relationship between adjacent nodes. The continuous connection of line segments represents the evolution direction of the displacement trend in space. The set of path line segments is marked as the main path of fatigue response. During the output process, each path line segment is uniquely identified by its start and end points according to the node number, and its position coordinates in three-dimensional space are recorded. The set of path line segments can be output to the visualization module in the form of three-dimensional graphics. The path is marked with different colors or numbers to indicate its increasing trend segments in order to distinguish the degree of evolution between path segments. The fatigue path extraction and graphic construction are completed, and the accurate expression of the node spatial response path is realized, resulting in a fatigue path node distribution map.

[0087] Please see Figure 5 The specific steps of S4 are as follows:

[0088] S401: Call the node number in the fatigue path node distribution map, extract the corresponding temperature value according to the node number, synchronously obtain the temperature values ​​of the six spatially adjacent nodes of the node, combine the temperature difference between the real-time node and the adjacent nodes, identify the distribution direction of the temperature difference value in the three-dimensional coordinate system, integrate the direction vectors and perform vector superposition to obtain the thermal gradient direction vector group.

[0089] Based on the spatial location index of each node, the temperature records of that node during the simulation are extracted sequentially. Simultaneously, the adjacent node numbers in the six directions (±X, ±Y, ±Z) of the node in the 3D mesh structure are retrieved to obtain the corresponding temperature values ​​of each adjacent node, forming a temperature data set containing the current node and its six adjacent nodes. The temperature difference between each group of adjacent points is then processed by direction assignment, that is, the temperature difference value in each adjacent direction is converted into a temperature gradient component along that direction. The temperature of the current node is set to... The temperature of adjacent points in the +X direction is Then the gradient component in the X direction is The temperature gradient components in six directions are calculated respectively. The directional components are combined into gradient vectors according to the corresponding coordinate axes. The six gradient vectors are then merged and weighted or directly linearly superimposed to form the total thermal gradient direction vector at the node. This method is used to traverse the nodes on the fatigue path to obtain the thermal gradient direction vector group.

[0090] S402: Based on the thermal gradient direction vector group, extract the equivalent stress direction vector data of the corresponding node in the fatigue path, calculate the angle between each group of thermal gradient direction vector and equivalent stress direction vector, record the node number corresponding to the angle, and obtain the thermal stress angle distribution list.

[0091] By comparing the equivalent stress direction vector data recorded in the fatigue path nodes, the system matches each node number one by one to ensure that each set of direction vector data corresponds in the same spatial position. The system uses a three-dimensional vector angle formula to calculate the angle between each set of thermal gradient vectors and stress direction vectors. The formula calculates the angle cosθ by the ratio of the vector dot product to the modulus, and then calculates the angle θ in degrees. The system performs this angle calculation once for each node and records the obtained angle value and the corresponding node number. Each row contains the node number, thermal gradient vector, stress vector, and angle value. The system supports sorting, filtering, and graphical output of the list. The angle value reflects the spatial coupling direction between heat flow and mechanical stress, providing a direct and quantifiable directional deviation index basis for subsequent node risk identification, and obtaining a list of thermal stress angle distributions.

[0092] S403: Call the thermal stress angle distribution list, compare the angle values ​​with the set thermal coordination threshold values ​​in turn, calculate the thermal coordination deviation value, filter the node numbers with thermal coordination deviation values ​​lower than the threshold, and extract the spatial coordinates in the defect area mesh model to obtain the thermal coordination risk node set;

[0093] The thermal coordination deviation value is calculated using the following formula:

[0094] ;

[0095] in, For the first Thermodynamic coordination deviation value of each node Representing the The thermal stress angle value of each node Representing the The thermal stress angle value of each node The average value of the included angle between the nodes. This indicates the set threshold. Represents the set of angles The sample variance;

[0096] The calculation logic of the formula: absolute value term This represents the deviation between the current node angle and the average thermal stress angle, used to measure its individual variability; the square root term. It is the square root of the mean square deviation of the angle between the nodes, which represents the overall dispersion trend of the set and reflects the volatility of global risk; the sum of the two constitutes the numerical basis of the deviation; this result is divided by the thermal threshold. Normalize the results to standardize the degree of difference; then multiply by a correction factor. The variance ratio measures the relative weight of the degree of thermal stress fluctuation to the overall mean, and is used to adjust the response strength of the previous calculated value to the risk.

[0097] The thermodynamic coordination deviation value is a quantitative indicator that measures the degree of deviation between the thermal stress angle of a certain node and the average state of the overall thermodynamic field. Taking into account the difference between the node and the average angle as well as the thermodynamic fluctuation of the entire node group, the larger the value, the higher the risk of coordination instability or damage of the node under the action of thermodynamics.

[0098] Parameter meaning and calculation process:

[0099] In thermal stress simulation, the first The included angle value of each node, in degrees (°);

[0100] The arithmetic mean of the included angles at the nodes is given by the formula: It is calculated from the set of angles between all simulation nodes;

[0101] The variance term of the included set, used to represent global volatility. The total number of nodes;

[0102] The thermo-mechanical synergy threshold is set to 25° with reference to the mechanical safety margin of the reference material. This value is calculated based on the empirical value of the critical angle of thermal load failure.

[0103] Angle set The sample variance is calculated as follows: This item shares the same data source as the previous one but is used for internal calculation of the correction factor.

[0104] To verify the applicability of the formula, the simulation angles of six key thermal nodes in a local region are shown below:

[0105] Table 1: Data Table of Thermal Stress Angle Monitoring

[0106]

[0107] As shown in Table 1, let the current node be node 1, and its included angle value be... Based on this, the parameter calculations are carried out:

[0108] Calculate the average value :

[0109] ;

[0110] Calculate the variance term :

[0111] ;

[0112] ;

[0113] Calculate the square root of the difference of squares :

[0114] ;

[0115] Calculate the absolute value difference term :

[0116] ;

[0117] Combined construction deviation calculation:

[0118] ;

[0119] First calculate the fractional terms:

[0120] ;

[0121] ;

[0122] Substitute into the formula to calculate:

[0123] ;

[0124] The results indicate that the thermodynamic coordination deviation at node 1 is 0.1884, relative to the baseline range (empirically, the low-risk standard is set at...). This indicates that the current node is in a low-risk range, and nodes with values ​​higher than this can be retained or prioritized for analysis in the future.

[0125] The advantage of the formula is that by combining and superimposing the individual angle deviation term with the global mean squared error term, and introducing the ratio correction of the angle variance to the square of the mean, it takes into account both single-point abnormal fluctuations and overall trend deviations. In the collaborative risk detection of thermal fields, it can simultaneously identify isolated anomalies and disturbances, thereby improving the sensitivity and accuracy of node screening.

[0126] Please see Figure 6 The specific steps of S5 are as follows:

[0127] S501: Call the spatial coordinate values ​​of the nodes in the thermal collaborative risk node set, connect adjacent nodes sequentially according to the coordinate point distribution in the three-dimensional visualization interface of the virtual simulation system, extract boundary points to form a continuous edge structure, gradually close the boundary line segments to form a polygonal region, and obtain the three-dimensional risk area outline boundary layer.

[0128] According to the node number, nodes are loaded one by one in the 3D visualization environment of the virtual simulation system. Adjacent nodes are connected according to the spatial proximity relationship between coordinate points to form an initial line segment network. It is determined whether the connection between nodes meets the spatial adjacency condition, that is, whether the Euclidean distance between nodes is lower than the set connection threshold. If the condition is met, the node pair is constructed as a line segment. Multiple boundary point closed loop structures are constructed through continuous connection. The coordinates of the boundary points forming the line segments are identified. The boundary points are connected in spatial order to form a continuous polygon boundary line. During the process, the distance between the first and last points is verified. If the distance between two points is lower than the set end tolerance, it is determined that the boundary is closed. If it is not closed, interpolation is performed to connect and supplement the points to ensure that the boundary line is closed and forms a closed polygon region. The closed polygon contour is combined through 3D coordinate mapping to obtain the 3D risk area contour boundary layer.

[0129] S502: Based on the three-dimensional risk area contour boundary layer, extract the risk node number within the area, call the equivalent stress direction vector corresponding to the risk node, calculate the angle between any two node direction vectors within the area, filter node pairs with angle values ​​lower than the direction consistency limit angle, and connect them in ascending order to form clustering segments, thus constructing a continuous concentrated stress clustering unit group.

[0130] The layer filters the risk node numbers within the enclosed spatial region and retrieves the equivalent stress direction vectors corresponding to the nodes. It then combines and arranges the direction vector data formed by the nodes within the region. For each pair of nodes, it calculates the angle between the direction vectors. After calculating the angle using the vector angle formula, it compares it with the set direction consistency limit angle. If the angle is less than the limit angle (e.g., 15°), the node pair is considered to have a high stress direction consistency, and the node pair number is recorded. Node pairs meeting this condition are sorted in ascending order of number, and clustering segments are constructed one by one. Node pairs with consistent directions and close positions are connected to form continuous segments. If the segments have continuity and consistency with the node number order, they are merged into the same cluster unit. Within the layer, based on the continuity of the connections between nodes, multiple continuous concentrated stress cluster unit groups are automatically divided. Each group contains several nodes and their corresponding direction-consistent segment sets, used for subsequent spatial center analysis and size extraction processing, thus constructing continuous concentrated stress cluster unit groups.

[0131] S503: Based on the continuous concentrated stress clustering unit group, extract the spatial node coordinates, calculate the center point position and three-axis projection size of each unit group in the three-dimensional space, and mark the spatial position of the center point of the stress region and the coverage range of the three-axis direction respectively to obtain the concentrated stress region marking result;

[0132] The spatial coordinates of the nodes contained in each unit group are extracted, and a geometric distribution model of the point set in three-dimensional space is constructed based on this. The geometric center point of each group of points is calculated, and the coordinates of the center point are the average of the spatial coordinates of the nodes of the cluster unit. The three-dimensional center position of each concentrated stress region is obtained accordingly. The maximum and minimum coordinate values ​​of each cluster unit in the X, Y, and Z directions are statistically analyzed to determine the coverage range of the unit group in the three-axis directions. That is, the range is [minX, maxX] in the X-axis direction, [minY, maxY] in the Y-axis direction, and [minZ, maxZ] in the Z-axis direction. The three-axis dimension projection constitutes the bounding box representation of the cluster unit in three-dimensional space. The center position of each cluster region is marked, and the three-axis coverage range is marked to form a complete concentrated stress region labeling result.

[0133] Please see Figure 7 An inorganic mineral casting stress analysis system based on a virtual simulation system includes:

[0134] The grain boundary extraction module obtains the voxel density distribution data of inorganic mineral casting grains in the three-dimensional scanning of the virtual simulation system, calls the density value of the voxel and the density value of the six adjacent voxels to perform the subtraction operation, compares the difference with the material interface recognition threshold, and then filters the voxel positions where the difference exceeds the threshold to generate a grain boundary curve dataset.

[0135] The defect region construction module is based on the grain boundary curve dataset. It performs three-dimensional surface fitting on the region enclosed by the curve to form a closed defect shape, calculates the spatial difference between the defect shape and the overall grain structure, and arranges grid nodes at equal intervals to construct a mesh model of the defect region.

[0136] The fatigue path identification module calls the defect area mesh model, applies a preset cyclic load in the virtual simulation system, obtains the three-dimensional displacement value of each mesh node at each time step, calculates the displacement change between adjacent time steps, and generates a fatigue path node distribution map.

[0137] The thermal synergy screening module extracts the node temperature value and the temperature value of adjacent nodes based on the fatigue path node distribution map, calculates the temperature difference and forms a thermal gradient direction vector, calculates the angle between the vector and the node equivalent stress direction vector, and compares it with the thermal synergy threshold to generate a thermal synergy risk node set.

[0138] The concentrated stress annotation module calls the thermal-coordinated risk node set, draws the risk area outline according to the node spatial coordinates in the 3D visualization interface, and performs cluster calculation on the consistency of node orientation in the same area to generate concentrated stress area annotation results.

[0139] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for stress analysis of inorganic mineral castings based on a virtual simulation system, characterized in that, Includes the following steps: S1: Obtain the voxel density distribution data of inorganic mineral casting grains in the virtual simulation system in three dimensions, filter the voxel positions where the density difference exceeds the material interface identification, connect adjacent boundary points into a closed boundary curve network, and obtain the grain boundary curve dataset. S2: Call the grain boundary curve dataset, perform three-dimensional surface fitting on the region enclosed by the curve to form a closed defect shape, and use the spatial difference between the defect shape and the overall grain structure to construct a local independent region. Arrange nodes at equal intervals in the virtual simulation system mesh to construct a defect region mesh model. S3: Based on the defect area mesh model, apply a preset cyclic load in the virtual simulation system, call the three-dimensional displacement values ​​of the mesh nodes at the time step, calculate the displacement change between adjacent time steps, and generate a fatigue path node distribution map; S4: Invoke the fatigue path node distribution map, obtain the node temperature value and the temperature value of adjacent nodes, calculate the temperature difference and form a thermal gradient direction vector, compare the angle between the thermal gradient direction vector and the node equivalent stress direction vector, and generate a set of thermally coordinated risk nodes.

2. The method for stress analysis of inorganic mineral castings based on a virtual simulation system according to claim 1, characterized in that, The grain boundary curve dataset includes boundary point coordinates, boundary curve topology, and boundary curve closure. The defect region mesh model includes mesh cell size, mesh node spatial distribution, and defect region volume morphology. The fatigue path node distribution map includes node number sequence, node spatial path, and path change trend. The thermo-coordinated risk node set includes risk node coordinates, thermal gradient direction, and equivalent stress direction.

3. The method for stress analysis of inorganic mineral castings based on a virtual simulation system according to claim 1, characterized in that, The specific steps of S1 are as follows: S101: Obtain the voxel density distribution data of inorganic mineral casting grains in the three-dimensional scanning of the virtual simulation system. For the voxel position, call the density values ​​of the six adjacent voxels in the three-dimensional direction. Combine the real-time voxel density value with the density values ​​of the six adjacent voxels and perform subtraction operation respectively. Record the difference obtained. Identify the difference between the obtained difference and the real-time voxel density value and generate a set of density mutation voxel coordinates. S102: Call the density mutant voxel coordinate set, combine the spatial positions of adjacent voxels in the three-dimensional grid, connect the voxel coordinates that satisfy the spatial connection relationship in sequence, and determine the continuity of adjacent positions in the direction of the three-dimensional coordinate axis. Group and classify the coordinate set of voxel points according to the continuity, and obtain the boundary point coordinate combination sequence. S103: Based on the boundary point coordinate combination sequence, connect the spatial positions between adjacent boundary points to construct a three-dimensional curve unit, and cyclically connect the boundary points to form a spatial closed structure. Remove non-closed point pairs with abnormal coordinate intervals, and connect a complete and continuous boundary line segment network to obtain a grain boundary curve dataset.

4. The method for stress analysis of inorganic mineral castings based on a virtual simulation system according to claim 3, characterized in that, The specific steps of S2 are as follows: S201: Call the grain boundary curve dataset, select the spatial coordinates of three consecutive boundary points in each group according to the arrangement order of the grain boundary curves in the three-dimensional coordinate system, construct the initial triangular patch in the three-point surface construction method, generate a continuously distributed triangular mesh after iterating through the boundary point set, remove the mesh patches with abnormal vertex spacing, and generate a continuous three-dimensional fitting patch group. S202: Based on the continuous three-dimensional fitted patch group, connect the edge positions of the fitted patches, detect whether the boundary line segments form a closed path, interpolate to generate missing patches for the non-closed parts, and close the overall curved surface contour to form a defect three-dimensional shape boundary body. S203: Call the three-dimensional shape boundary of the defect and the density distribution data of the original grain voxel, compare the volume spatial overlap of the two at their corresponding positions in the three-dimensional coordinate system, extract the set of coordinate points in the defect boundary but not covered by the original grain voxel, define the set of coordinate points as the local difference space position, and mark the independent region number according to the three-dimensional boundary closure to obtain the difference space region inside the grain. S204: Based on the difference space region inside the grain, divide the mesh nodes at equal intervals within the three-dimensional coordinate boundary, number the nodes in sequence to construct a three-dimensional octahedral unit, remove the boundary overlapping nodes and fill the heterogeneous unit to construct the defect region mesh model.

5. The method for stress analysis of inorganic mineral castings based on a virtual simulation system according to claim 4, characterized in that, The specific steps for S3 are as follows: S301: Based on the defect area mesh model, apply the set cyclic load boundary conditions according to the time axis in the virtual simulation system, record the three-dimensional coordinate values ​​of the mesh nodes at multiple time steps, call the three-dimensional coordinate differences of the nodes between adjacent time steps, calculate the three-dimensional displacement change value of the nodes between adjacent time steps, and obtain the time step displacement change data. S302: Call the displacement change data of the time step, arrange the displacement change values ​​under the time step in order according to the node number, construct the time series data corresponding to the node, count the numerical difference between the change values ​​of adjacent time steps in the node sequence, and determine whether there is a continuous increasing trend. Mark the node number segment that satisfies the continuous increasing feature to obtain the continuously increasing node number group. S303: Based on the continuously growing node number group, extract the spatial coordinates of the corresponding node in the defect area mesh model, connect adjacent nodes in sequence according to the node number arrangement order, construct path segments representing the direction of change trend, and output the path segments in three-dimensional space after labeling them by number to obtain the fatigue path node distribution map.

6. The method for stress analysis of inorganic mineral castings based on a virtual simulation system according to claim 5, characterized in that, The specific steps of S4 are as follows: S401: Call the node number in the fatigue path node distribution map, extract the corresponding temperature value according to the node number, synchronously obtain the temperature values ​​of the six spatially adjacent nodes of the node, combine the temperature difference between the real-time node and the adjacent nodes, identify the distribution direction of the temperature difference value in the three-dimensional coordinate system, integrate the direction vectors and perform vector superposition to obtain the thermal gradient direction vector group. S402: Based on the thermal gradient direction vector group, extract the equivalent stress direction vector data recorded by the corresponding node in the fatigue path, calculate the angle between each group of thermal gradient direction vectors and equivalent stress direction vectors, record the node number corresponding to the angle, and obtain the thermal stress angle distribution list. S403: Call the thermal stress angle distribution list, compare the angle values ​​with the set thermal coordination threshold values ​​in turn, calculate the thermal coordination deviation value, filter the node numbers with thermal coordination deviation values ​​lower than the threshold, and extract the spatial coordinates in the defect area mesh model to obtain the thermal coordination risk node set.

7. The method for stress analysis of inorganic mineral castings based on a virtual simulation system according to claim 6, characterized in that, The thermal coordination deviation value refers to the difference between the reference node and the average angle and the thermal fluctuation of the node group.

8. The method for stress analysis of inorganic mineral castings based on a virtual simulation system according to claim 1, characterized in that, The method further includes step S5: S5: Using the aforementioned thermally coordinated risk node set, draw the outline of the risk area according to the spatial coordinates of the nodes in the three-dimensional visualization interface of the virtual simulation system, cluster and connect the nodes with the same orientation consistency in the same area to form a continuous concentrated stress area, and mark the location and range of the area to generate the concentrated stress area marking result. The labeling results of the concentrated stress area include the spatial range of the area, the location identifier of the area, and the clustering results of the node orientation consistency.

9. The method for stress analysis of inorganic mineral castings based on a virtual simulation system according to claim 8, characterized in that, The specific steps of S5 are as follows: S501: Call the spatial coordinate values ​​of the nodes in the thermal collaborative risk node set, connect adjacent nodes sequentially according to the coordinate point distribution in the three-dimensional visualization interface of the virtual simulation system, extract boundary points to form a continuous edge structure, gradually close the boundary line segments to form a polygonal region, and obtain the three-dimensional risk area outline boundary layer. S502: Based on the three-dimensional risk area contour boundary layer, extract the risk node number in the area, call the equivalent stress direction vector corresponding to the risk node, calculate the angle between any two node direction vectors in the area, filter the node pairs with the angle value lower than the direction consistency limit angle, and connect them in ascending order to form clustering line segments, and construct a continuous concentrated stress clustering unit group. S503: Based on the continuous concentrated stress clustering unit group, extract the spatial node coordinates, calculate the center point position and three-axis projection size of each unit in the three-dimensional space, and mark the spatial position of the center point of the stress region and the coverage range of the three-axis direction respectively to obtain the concentrated stress region marking result.

10. A stress analysis system for inorganic mineral castings based on a virtual simulation system, characterized in that, The system is used to implement the stress analysis method for inorganic mineral castings based on a virtual simulation system as described in any one of claims 1-9, and the system comprises: The grain boundary extraction module obtains the voxel density distribution data of inorganic mineral casting grains in the three-dimensional scanning of the virtual simulation system, calls the density value of the voxel and the density value of the six adjacent voxels to perform the subtraction operation, compares the difference with the material interface recognition threshold, and then filters the voxel positions where the difference exceeds the threshold to generate a grain boundary curve dataset. The defect region construction module, based on the grain boundary curve dataset, performs three-dimensional surface fitting on the region enclosed by the curve to form a closed defect shape, calculates the spatial difference between the defect shape and the overall grain structure, and arranges grid nodes at equal intervals to construct a defect region grid model. The fatigue path identification module calls the defect area mesh model, applies a preset cyclic load in the virtual simulation system, obtains the three-dimensional displacement value of each mesh node at each time step, calculates the displacement change between adjacent time steps, and generates a fatigue path node distribution map. The thermal synergy screening module extracts the node temperature value and the adjacent node temperature value according to the fatigue path node distribution map, calculates the temperature difference and forms a thermal gradient direction vector, calculates the angle between the vector and the node equivalent stress direction vector, and compares it with the thermal synergy threshold to generate a thermal synergy risk node set. The concentrated stress annotation module calls the thermally coordinated risk node set, draws the risk area outline according to the node spatial coordinates in the three-dimensional visualization interface, and performs clustering calculation on the consistency of node orientation in the same area to generate concentrated stress area annotation results.

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