High-end motor core mold assembly precise digital twin method and system

Abstract

The application discloses a high-end motor core mold assembly accurate digital twin method and system; the method is: point cloud data collection is carried out on all template holes and inserts in the mold. According to the point cloud data of each template hole and insert, a curved surface is formed respectively. According to the curved surface corresponding to each template hole and insert, and the ideal position of each insert on the mold, an accurate digital twin assembly body model of the template and each insert is established. Through interference assembly simulation and multi-objective parameter optimization scheme, the design position of the template hole is iteratively optimized. The application takes the distance between the hole center position of the template hole after assembly simulation and the ideal position of the corresponding insert on the mold as the fitness function, and combines a multi-objective genetic algorithm to realize rapid iteration of the design position of the template hole, and improve the design optimization efficiency of the high-end motor core mold.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of high-end motor core mold precision assembly, and particularly relates to a high-end motor core mold assembly precision digital twinning method and system. BACKGROUND

[0002] In the mold industry, especially in the high-end mold manufacturing industry of new energy automobile motor core, the mold assembly link is crucial to production and manufacturing. Due to the large number of holes on the mold and the multi-axis hole interference assembly, which is a highly nonlinear problem, the interference assembly deformation of the same size but different structures cannot be directly adjusted by the deformation measurement and reverse compensation method. At the same time, the structural differences between different molds are huge, which makes the analysis of the assembly force caused by interference fit extremely difficult, and further leads to the difficulty in controlling the position degree of the mold plate hole, inevitably leading to low assembly efficiency, prolonged assembly cycle, and increased labor cost.

[0003] With the continuous improvement of mold processing and manufacturing precision requirements, future mold assembly will tend to be high precision, low damage and high efficiency. This means that the precise prediction of the position change caused by the stress deformation of the high-end motor core mold interference assembly becomes increasingly important. The accuracy of this prediction is directly related to the precision and stability of the mold assembly, and is the key to achieving high-precision, low-damage and high-efficiency assembly. However, as the assembly requirements improve, the difficulty of this prediction also increases. Although in-depth study of mold assembly principles can provide theoretical support for high-precision assembly, in the actual factory environment, faced with a large number of molds of different shapes and sizes, it is still one of the biggest challenges for the high-end motor core mold full-life manufacturing cycle to achieve precise prediction of the subtle changes of these molds. SUMMARY

[0004] To solve the problems of the prior art, the embodiment provides a prediction and optimization method for anti-assembly deformation in the high-end motor core mold industry, based on point cloud data processing, establishing an accurate model containing geometric distribution error and simulating and optimizing it, to solve the actual problems of low assembly efficiency and high labor cost in the high-end motor core mold industry.

[0005] In the first aspect, the application provides a high-end motor core mold assembly precision digital twinning method, comprising the following steps:

[0006] Point cloud data of all mold plate holes and corresponding inserts in the mold are collected.

[0007] Curved surfaces are respectively fitted according to the point cloud data of each mold plate hole and insert.

[0008] Based on the corresponding surfaces of each template hole and insert, as well as the ideal position of each insert on the mold, an accurate digital twin assembly model of the template and each insert is established.

[0009] The interference fit between the inserts and the template holes is simulated on the precise digital twin assembly model to obtain the center position of each template hole after assembly deformation. The design position of each template hole in the precise digital twin assembly model is iteratively updated so that the simulated center position of each template hole after assembly deformation approaches the ideal position of each insert on the mold.

[0010] The design position of the template hole is the processing position of the template hole in the mold indicated in the specific drawing.

[0011] Preferably, the design position of each template hole in the precise digital twin assembly model is iteratively updated using a multi-objective genetic method. The fitness function of the multi-objective genetic method is set as the distance between the simulated center position of the template hole after assembly deformation and the ideal position of the corresponding insert on the mold.

[0012] Preferably, the multi-target genetic method comprises:

[0013] (1) Initialize population parameters: Take the set of all template hole design position coordinates on the template as individuals and establish the initial population.

[0014] (2) Perform interference fit simulation for each individual in the current population and calculate the fitness function based on the simulation results.

[0015] (3) Generate a new generation of population through selection, crossover, and mutation.

[0016] (4) Repeat steps (2) to (3) until the convergence condition is reached or the maximum number of iterations is reached.

[0017] Preferably, the convergence condition is that the fitness function values ​​corresponding to all template hole processing position coordinates in the same body are less than or equal to a preset allowable error threshold.

[0018] Preferably, the horizontal and vertical coordinate deviations between the designed positions of the template holes and the corresponding ideal positions of the inserts in all generated individuals are less than a preset displacement threshold, which is preferably 20 μm.

[0019] Preferably, the point cloud data is subjected to elimination of gross errors, systematic errors and random errors.

[0020] Preferably, the process for establishing a precise digital twin assembly model is as follows: using the fitted template hole assembly surface as the assembly surface on the mold to establish a precise digital twin 3D model of the mold. Using the fitted insert assembly surface as the assembly surface on the insert, establish a precise digital twin 3D model of the insert. The precise digital twin 3D model of the insert and the precise digital twin 3D model of the mold are combined to form a precise digital twin assembly model.

[0021] Preferably, the process of establishing an accurate digital twin 3D model of the mold is: first establish a basic 3D model of the mold without template holes; use the assembly surfaces of each template hole to perform Boolean cutting operations on the template hole design positions on the basic 3D model of the mold to obtain an accurate digital twin 3D model of the mold.

[0022] Preferably, the interference fit simulation process includes: assigning material properties, contact relationships, and boundary conditions to a meshed, precise digital twin assembly model; performing assembly simulation on each template hole in the precise digital twin assembly model and the corresponding insert; and outputting the coordinates of the center of each template hole after assembly deformation.

[0023] In a second aspect, the present invention provides a precise digital twin system for assembling high-end motor core molds, which is used to perform the aforementioned method. The system comprises a point cloud acquisition device, preferably a coordinate measuring machine, for collecting point cloud data, a modeling and simulation module, and a position optimization module. The modeling and simulation module is used to establish a precise digital twin assembly model. The position optimization module is used to iteratively optimize the design position of the template holes on the mold within the precise digital twin assembly model.

[0024] The beneficial effects of the present invention are:

[0025] 1. This invention addresses the widespread assembly deformation problem in the high-end motor core mold industry by proposing a solution using digital twin technology. This solution establishes a precise digital twin model, combines simulation technology with optimization algorithms, and optimizes the design position of the mold's template holes. This ensures that the template holes, after interference fit deformation, reach the ideal position for the insert, thereby improving the dimensional accuracy of the assembled high-end motor core mold.

[0026] 2. The present invention uses the distance between the simulated template hole center position after assembly and the ideal position of the corresponding insert on the mold as the fitness function, and combines it with a multi-objective genetic method to achieve rapid iteration of the template hole design position, thereby improving the design optimization efficiency of high-end motor core molds.

[0027] 3. The present invention uses a high-precision three-dimensional coordinate measuring machine to obtain point cloud data of the assembly surface of the assembly, and uses the point cloud data to fit and generate assembly surfaces. Based on the assembly surfaces, a digital twin model with precise geometric information of the assembly surfaces is constructed in computer-aided engineering (CAE) software. The digital twin model integrates the geometric information, material properties, assembly posture and contact relationship of the mold, and can obtain more accurate assembly deformation prediction results in simulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solution of the present invention, the following is a brief introduction to the drawings required for use in the description of the specific implementation methods. 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 paying any creative work.

[0029] Figure 1 It is a schematic diagram of the interference fit between the mold template hole and the insert.

[0030] Figure 2 It is an overall flow chart of an embodiment of the present invention.

[0031] Figure 3 This is a flow chart of point cloud measurement of template holes and inserts in step S100 of an embodiment of the present invention.

[0032] Figure 4 This is a flowchart of point cloud data processing in step S200 of an embodiment of the present invention.

[0033] Figure 5 This is a flowchart of basic three-dimensional model building in step S300 of an embodiment of the present invention.

[0034] Figure 6 This is a flowchart of generating an accurate digital twin assembly model in step S400 of an embodiment of the present invention.

[0035] Figure 7 It is a flowchart of the iterative optimization of the template hole design position simulation in step S500 of an embodiment of the present invention. DETAILED DESCRIPTION

[0036] To make the purpose, technical solution, and advantages of this embodiment more clear, the technical solution of this embodiment will be described more clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiment is only a part of the embodiment of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0037] Note that the ideal surface or ideal model described in this embodiment is a surface or model that does not contain geometric distribution errors, and the precise surface or precise model is a surface or model that contains geometric distribution errors.

[0038] The present invention will be further described below with reference to the accompanying drawings.

[0039] See also Figure 1 , Figure 1 This is a schematic diagram of the interference fit of the template hole and insert of the high-end motor core mold of this embodiment. It is particularly important to note that this embodiment can also be applied to high-end mold assembly occasions of different sizes, shapes and high precision requirements. The process and results of the prediction and optimization method for the anti-assembly deformation of the high-end motor core mold provided by this embodiment are not affected by any external conditions of the mold.

[0040] See also Figure 2 , Figure 2 The flowchart of the overall technical solution of this embodiment is a method for assembling a precise digital twin of a high-end motor core mold, including the following steps:

[0041] Step S100: point cloud measurement of template holes and inserts; Figure 3 As shown, a high-precision three-dimensional coordinate measuring machine is used to measure point cloud data for the inner cylindrical surfaces of all mold holes and the outer cylindrical surfaces of the corresponding inserts. A set of point cloud data is obtained for each mold hole and insert assembly surface. This embodiment is applicable to the assembly of any number of mold holes and inserts, and is not limited by the shape of the mold hole structure. For example, any shape of mold hole and corresponding insert, such as cylindrical holes, square holes, and special-shaped holes, can be used.

[0042] Before point cloud acquisition, a statistical analysis of the number and size of template holes and inserts is required to obtain the optimal point cloud data measurement plan; the point cloud data measurement plan includes the layer spacing of the sampled points and the spacing between adjacent points in each layer; the point cloud data measurement plan is determined based on the results of the Pearson correlation coefficient calculation to ensure the efficiency and rationality of the measurement plan.

[0043] Step S200: point cloud data processing; sequentially eliminating gross errors in the measured point cloud data, systematic errors of the high-precision three-dimensional coordinate measuring machine, and random errors in the measurement process.

[0044] In this embodiment, it should be noted that in step S200, based on the assembly surface point cloud data measured in step S100, gross errors and random errors are eliminated in Matlab software, and the measured point cloud data are reversely corrected for the three coordinates of X, Y and Z based on the systematic error correction test.

[0045] like Figure 4 As shown, step S200 specifically includes:

[0046] Step S210: gross error elimination: performing gross error elimination on the measured point cloud data of the mold template hole and the corresponding insert assembly surface based on the Laida rule.

[0047] In this embodiment, it should be noted that in step S210, the gross error is the straight-line distance from the point cloud to the center of the fitting circle. Distance to average The difference is greater than 3 times the standard deviation ( ) points, the gross error points are eliminated to complete the gross error elimination step of the point cloud data, and the average distance and standard deviation The calculation formula is:

[0048]

[0049]

[0050] Step S220: System error correction: a system error measurement experiment is performed on the measuring machine, and the experimental results are used to perform reverse correction of the system error on the point cloud data that has completed the gross error elimination step.

[0051] In this embodiment, it should be noted that in step S220, the system error correction experiment is as follows: using the high-precision surface of the standard rectangular block as the reference surface, making the nominal length direction of the block parallel to the X, Y and Z directions of the three-dimensional coordinate measuring machine, repeating the dot measurement, and calculating the difference with the nominal distance to obtain the system error of the high-precision three-dimensional coordinate measuring machine used for measurement. The X, Y and Z coordinates of the point cloud data are compensated and corrected respectively, and the point cloud data after the system error is corrected is output.

[0052] Step S230: random error filtering: performing random error filtering on the point cloud data that has completed the system error correction based on median filtering to obtain processed point cloud data for subsequent calculations.

[0053] In this embodiment, it should be noted that in step S230, the point cloud data that has completed the system error correction is filtered out of random errors using the median filter method through Matlab software, thereby obtaining the point cloud data of the template hole and insert assembly surface that has completed all point cloud data processing for subsequent calculations.

[0054] Step S300: Modeling a basic three-dimensional model of the mold template; based on the CAD drawing of the high-end motor core mold template, establishing the basic three-dimensional model of the mold template in a three-dimensional modeling software.

[0055] In this embodiment, it should be noted that in step S300, the 2D CAD drawing of a high-end motor core mold (excluding the template holes) is processed in AutoCAD software, and an ideal 3D model of the template is created in Solidworks 3D modeling software. It should be noted that this embodiment does not restrict the model type or file format, and the resulting basic 3D model does not include the template holes to be assembled.

[0056] like Figure 5 As shown, step S300 specifically includes:

[0057] Step S310: mold template drawing processing: processing the CAD drawing of the high-end motor core mold template.

[0058] In this embodiment, it should be noted that, in step S310, the specific drawing processing method is to retain only the structural part on the CAD drawing, and remove the template holes to be assembled and the remaining annotations or data.

[0059] Step S320: IGS file output; the CAD drawing after template drawing processing is output in IGS format.

[0060] In this embodiment, it should be noted that, in step S320, the CAD drawing processed in step S310 is converted into the IGS format.

[0061] Step S330: establishing a basic three-dimensional model; reading the IGS format drawing based on the Solidwords three-dimensional modeling software, performing a stretching operation on it, and realizing the modeling of the basic three-dimensional model without the template hole.

[0062] In this embodiment, it should be noted that in step S330, the basic three-dimensional model is a three-dimensional model without geometric distribution errors. In the Solidworks three-dimensional modeling software, the IGS file output in step S320 is read, thereby stretching the two-dimensional drawing into a basic three-dimensional model without the surface template holes with geometric errors.

[0063] Step S400: Modeling a precise digital twin assembly model; generating a precise three-dimensional assembly model with the geometric distribution errors based on the basic three-dimensional model without geometric distribution errors, NURBS surface modeling theory, and Boolean cut operations; adding physical conditions to the precise assembly three-dimensional model to generate the precise digital twin model.

[0064] like Figure 6 As shown, step S400 specifically includes:

[0065] Step S410: Construct NURBS surface according to the point cloud data corresponding to each assembly surface; the process of constructing NURBS surface includes: obtaining node parameters and curve node vectors corresponding to the measured data points in the point cloud data by cumulative chord length method, and generating NURBS surface from the node parameters and curve node vectors; the specific process belongs to a conventional means, and thus is not described herein.

[0066] Step S420: Generate the precise assembly surface with the geometric distribution error based on the NURBS surface, including the precise assembly surface of the mold template hole and the precise assembly surface of the corresponding insert. It should be noted that, in the process of generating the precise assembly surface, the uppermost and lowermost point cloud Z coordinates are changed, so that the generated assembly surface with the geometric distribution error is slightly higher than the mold thickness, and the purpose is to ensure that the subsequent Boolean operation cutting can form a template hole penetrating through both ends on the basis of the three-dimensional model.

[0067] Step S430: Import the basis three-dimensional model without the template hole generated in step S300 and the precise assembly surface of the template hole with the geometric distribution error generated in step S420 into the Comsol software; the import position of the central axis of the precise assembly surface of the template hole is the design position coordinate of the template hole on the mold; perform Boolean cutting operation on the template three-dimensional model without the assembly hole by using the precise assembly surface with the geometric distribution error, and then delete the separated part and the precise assembly surface of the template hole; at this time, the template hole with the precise geometric distribution error information is generated on the basis three-dimensional model, and thus the precise digital twin three-dimensional model of the mold is obtained.

[0068] Perform Boolean cutting operation on the cuboid three-dimensional entity model with the same thickness as the mold thickness by using the precise assembly surface of the insert, and retain the separated part as the precise digital twin three-dimensional model of the insert.

[0069] Step S440: Import the precise digital twin three-dimensional model of the mold and the precise digital twin three-dimensional model of the insert generated in step S430 into the CAE simulation software for assembly, so as to generate the precise digital twin three-dimensional assembly model of the mold with the geometric distribution error.

[0070] Step S450: Perform mesh division on the precise digital twin assembly model by using the sweeping mesh method. The mesh unit is C3D8R.

[0071] Step S460: Assign the corresponding physical conditions of the assembly to the precise digital twin assembly model; assign the material properties based on the material properties of the mold template and the insert respectively, set the assembly surface contact relationship of the assembly, and apply the boundary conditions. The precise digital twin assembly model can accurately represent the actual mold template hole and the insert assembly in terms of physical performance and geometric shape.

[0072] In the present embodiment, it is to be noted that in step S450, the corresponding physical conditions of the assembly body to be imparted include material properties, boundary conditions, temperature environment, contact relationship between the mold template hole and the insert, assembly pose, etc.

[0073] In some other embodiments, the way to build the precise digital twin assembly body model is as follows: first, an ideal three-dimensional model containing ideal mold holes is established according to the drawing, and the ideal three-dimensional model is meshed; the surface of the ideal mold hole is a cylindrical surface; then, a NURBS surface is established according to the point cloud data; then, all surface mesh nodes on the assembly surface of the ideal three-dimensional model are offset to the NURBS surface built in step S410; each surface mesh node is offset to the nearest interpolation point or extreme point, so that the assembly surface of the ideal three-dimensional model is converted from the ideal cylindrical surface to the NURBS surface. In some further embodiments, the interpolation points with a distance less than a threshold value from the extreme points are deleted in order to ensure that each extreme point can be offset by a surface mesh node, so that the offset surface mesh can better express the topography of the NURBS surface. Finally, according to the offset amount of all surface mesh nodes, the internal nodes of the ideal three-dimensional model are adjusted according to the smoothness principle to ensure the convergence of the mesh quality, and the precise digital twin assembly body model is obtained. This way can avoid the difficulty of directly meshing the precise digital twin assembly body model with a complex surface.

[0074] Step S500: Template hole design position simulation iterative optimization; using the precise digital twin assembly body model obtained in step S400, the interference assembly simulation of the mold template hole and the corresponding insert is performed in the CAE simulation software; combining the simulation results, through the interaction of the multi-objective genetic method and the simulation of the precise digital twin assembly body model, the design position of the template hole on the mold is iteratively optimized, so that the result after the optimized mold template hole and the corresponding insert interference assembly meets the requirements of high precision and high efficiency of actual high-end motor core mold manufacturing and assembly.

[0075] As shown in Figure 6 , step S500 specifically includes:

[0076] Step S510: Template hole initial position variable setting; the ideal position coordinates of each insert on the mold are identified by the ideal hole center coordinates of each template hole on the mold drawing; the ideal hole center coordinates of each template hole on the mold drawing are taken as the initial template hole design position coordinates.

[0077] In this embodiment, it should be noted that in step S510, the initial simulation is performed according to the ideal position of the template hole in the CAD drawing, that is, when the multi-objective optimization algorithm is combined subsequently, the initial population is the X and Y coordinates of each template hole on the template. It should be noted that the variable modifies the position of the Boolean cutting operation of the accurate surface of the template hole and the ideal three-dimensional entity of the mold template in step S420 and the assembly position of step S430, and then the python code is imported into the CAE simulation software.

[0078] Step S520: interference assembly simulation of the accurate digital twin assembly model; the interference assembly of each template hole and its corresponding insert in the accurate digital twin assembly model is preprocessed and simulated in the CAE software.

[0079] In this embodiment, it should be noted that in step S520, the simulation preprocessing is performed based on the CAE simulation software. The simulation preprocessing includes setting the node set for the grid nodes of all template holes and insert assembly surfaces in the accurate digital twin model. After simulation preprocessing, the accurate digital twin assembly model is simulated for interference assembly of the template hole and the insert.

[0080] Step S530: reading and outputting the coordinates of the deformed hole center of each template hole obtained by simulation.

[0081] In this embodiment, it should be noted that in step S530, the coordinates of all nodes in the node set after interference assembly simulation calculation are read and output, and the least squares fitting is performed based on the node coordinates to obtain the hole center position of the template hole on the mold after simulation.

[0082] Step S540: multi-objective genetic method; based on the multi-objective optimization algorithm, the optimization iteration of the template hole center position is performed, and after each iteration, steps S520 and S530 are executed again to obtain the updated hole center coordinates after interference assembly, realizing the combination of simulation and optimization algorithm; during the iteration process, the hole center coordinates after interference assembly deformation gradually approach the ideal hole center coordinates. In this embodiment, the fitness function of the multi-objective genetic method is set as the distance between the hole center coordinates after interference assembly deformation obtained by simulation and the ideal hole center coordinates. When the fitness functions of all template holes reach the set permissible error threshold, the convergence condition of the optimization algorithm can be reached in advance, and the multi-objective genetic algorithm process is shown in the figure.

[0083] The specific process of the multi-objective genetic method is as follows:

[0084] (1) Initialize population parameters: A set of values ​​for the coordinates of all the template hole design positions on the template is defined as an individual, and multiple individuals are set to establish an initial population. In this embodiment, the number of populations is set to 20, and the maximum number of iterations is set to 50. The range of the horizontal and vertical coordinates of the hole center of each template hole is set to ±20 μm.

[0085] (2) Simulate each individual in the current population according to the process of steps S520 and S530, and calculate the fitness function based on the simulation results; if there is no individual in the current population that can meet the convergence conditions, then perform non-dominated sorting on all individuals according to the fitness function of the current population; stratify the individuals in the current population according to the dominance relationship between different individuals in the current population, and find the non-dominated solution in the current population.

[0086] In this embodiment, the convergence condition is that the fitness function values ​​corresponding to the design position coordinates of all template holes in the same individual are less than or equal to a preset allowable error threshold; the value of the allowable error threshold is set according to the accuracy requirements, and is set to 5μm in this embodiment; in other embodiments, the allowable error threshold can also be set to 2μm, 6μm, 8μm, 10μm or other values.

[0087] (3) Select parent individuals based on the results of the non-dominated sorting in step (2), and generate a new generation of sub-populations based on the parent individuals through selection, crossover, and mutation.

[0088] (4) For the new generation population, re-execute steps (2) to (3) and continue iterating until the convergence condition is reached or the maximum number of iterations is reached.

[0089] This embodiment combines the precise digital twin model interference fit simulation with a multi-objective optimization algorithm. The design position of the template hole is modified iteratively through a multi-objective genetic method. The simulated position is read and output through a node set to calculate the fitness function, thereby realizing the combination of simulation and optimization algorithms. This greatly improves the optimization efficiency of the template hole design position coordinates and scientifically guides the process parameters of high-end motor core mold companies.

[0090] This embodiment addresses the problem of difficulty in analyzing assembly deformation in high-end motor core molds, and proposes a prediction and optimization method based on a precise digital twin model. This method can accurately predict the position deviation of the extrusion deformation after interference fit of the template hole, and optimize the initial design position of the template hole through a multi-objective genetic optimization algorithm, so that the position deviation of each template hole after interference fit is within the allowable error range. The present invention can scientifically guide the process parameters of the high-end motor core mold industry, and effectively solves the current largest assembly deformation problem in the mold industry.

[0091] In summary, the embodiment provides a prediction and optimization method for anti-assembly deformation for high-end motor core mold industry, uses high-precision accurate digital twin model, combines CAE simulation software and multi-objective optimization algorithm to predict the interference assembly deformation of the mold, and scientifically guides the initial design position of the mold plate hole. It can greatly reduce the scrap rate of high-end motor core mold production and manufacturing, improve the assembly efficiency, greatly reduce the labor cost and time cost of mold enterprises, and also provide precision guarantee for high-end motor core mold production and manufacturing core products, greatly promoting the development of high-precision, high-efficiency and low-cost of mold manufacturing industry.

Claims

1. A precise digital twin method for assembling high-end motor core molds, characterized by: The method includes: Collect point cloud data of all template holes in the mold and their corresponding inserts; NURBS surfaces are formed by fitting the point cloud data of each template hole and insert; Based on the NURBS surfaces corresponding to each template hole and insert, as well as the ideal position of each insert on the mold, an accurate digital twin assembly model of the template and each insert is established; The method for constructing a precise digital twin assembly model is as follows: first, an ideal 3D model containing an ideal mold hole is created according to the drawing and meshed. The surface of the ideal mold hole is a cylindrical surface. Then, all surface mesh nodes on the assembly surface of the ideal 3D model are offset to a NURBS surface. Each surface mesh node is offset to the nearest interpolation point or extreme point, so that the assembly surface of the ideal 3D model is transformed from an ideal cylindrical surface to a NURBS surface. Based on the offset of all surface mesh nodes, the internal nodes of the ideal 3D model are adjusted according to the principle of smoothness to obtain a precise digital twin assembly model. The interference fit between the inserts and the template holes is simulated on the precise digital twin assembly model to obtain the center position of each template hole after assembly deformation. The designed position of each template hole in the precise digital twin assembly model is iteratively updated so that the simulated center position of each template hole after assembly deformation approaches the ideal position of each insert on the mold. The design position of each template hole in the digital twin assembly model is iteratively updated through a multi-objective genetic method; the fitness function of the multi-objective genetic method is set as the distance between the center position of the template hole after assembly deformation obtained by simulation and the ideal position of the corresponding insert on the mold.

2. The high-end motor core mold assembly precise digital twin method according to claim 1 is characterized by: The multi-target genetic method comprises: (1) Initialize population parameters: Take the set of all template hole design position coordinates on the template as individuals and establish the initial population; (2) Perform interference fit simulation for each individual in the current population and calculate the fitness function based on the simulation results; (3) Generate a new generation of population through selection, crossover, and mutation; (4) Repeat steps (2) to (3) until the convergence condition is reached or the maximum number of iterations is reached.

3. The high-end motor core mold assembly precise digital twin method according to claim 2, characterized in that: The convergence condition is that the fitness function values ​​corresponding to the coordinates of all template hole processing positions in the same body are less than or equal to a preset allowable error threshold.

4. The method for assembling a high-end motor core mold with precise digital twinning according to claim 2, characterized in that: The horizontal and vertical coordinate deviations between the designed positions of the template holes and the corresponding ideal positions of the inserts in all generated individuals are less than the preset displacement threshold.

5. The method for assembling a high-end motor core mold with precise digital twinning according to claim 1, characterized in that: The point cloud data is subjected to elimination of gross errors, systematic errors, and random errors.

6. The high-end motor core mold assembly precise digital twin method according to claim 1, characterized in that: The interference fit simulation process is as follows: assigning material properties, contact relationships, and boundary conditions to the precise digital twin assembly model divided into grids; assembling each template hole in the precise digital twin assembly model with the corresponding inserts through simulation, and outputting the coordinates of the hole centers of each template hole after assembly deformation.

7. A high-end motor core mold assembly precision digital twin system, characterized by: Used to execute the method as claimed in claim 1, characterized in that: the high-end motor core mold assembly precise digital twin system includes a point cloud acquisition device, a modeling and simulation module and a position optimization module; the point cloud acquisition device is used to collect point cloud data; the modeling and simulation module is used to establish a precise digital twin assembly model; the position optimization module is used to iteratively optimize the design position of the template hole on the mold in the precise digital twin assembly model.

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