Rapid forming method for RV reducer complex structural part based on 3D printing technology

Abstract

The invention relates to the technical field of computer-aided forming, and discloses an RV reducer complex structural part rapid forming method based on a 3D printing technology, and the method comprises the steps: receiving three-dimensional model data of an RV reducer, and carrying out topological optimization; performing feature segmentation on the three-dimensional model to obtain differentiated sub-regions; filling each sub-region to generate an initial filling path; obtaining first printing deviation data, and correcting the initial filling path; acquiring second printing deviation data, and comparing the second printing deviation data with a preset threshold to judge whether to trigger a path correction mechanism; if the filling path needs to be corrected, dynamically adjusting the filling path; according to the method, the three-dimensional model data is processed through the computer, the filling path is optimized, the printing deviation is analyzed and dynamically adjusted, and the precision and efficiency of 3D printing of the RV reducer complex structural part are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of computer-aided prototyping, and more specifically, to a rapid prototyping method for complex structural parts of an RV reducer based on 3D printing technology. Background Art

[0002] With the rapid development of 3D printing technology, 3D printing has been widely used in various fields, especially in rapid prototyping. At present, 3D printing technology is increasingly used in the field of industrial manufacturing, especially in the field of mechanical transmission, such as the design and manufacture of complex structural parts such as RV reducers, 3D printing technology shows great application potential.

[0003] The patent application with publication number CN114536766A discloses a 3D printing rapid prototyping method. The method first obtains the three-dimensional data of the object to be printed and establishes a three-dimensional model, then prepares the raw materials and screens the raw materials, and then introduces the raw materials into the 3D printer trough to finally obtain the 3D printed product. The method is simple and reasonable, does not require laser forming, does not require the raw materials to be limited, can achieve rapid prototyping at room temperature and pressure, shortens the prototyping cycle, saves costs, and the prototyping rate is controllable and has good use effect. However, the patent failed to propose a special optimization solution for complex structural parts such as RV reducers, nor did it consider the problem of deviation control during the printing process.

[0004] The patent application with the authorization announcement number CN214788854U discloses an RV reducer based on 3D printing. The device includes a primary reduction mechanism and a secondary reduction mechanism, and realizes two-stage reduction through specially designed planetary gear transmission and cycloid pinwheel transmission. The use of 3D printing technology to manufacture key components can achieve lightweight design and rapid manufacturing of RV reducers. However, the patent mainly focuses on the mechanism innovation of RV reducers, and lacks a systematic explanation of how to use 3D printing technology to achieve molding optimization and quality control of complex structural parts.

[0005] The patent application with publication number CN112026166A discloses a method for preparing a rapid prototyping 3D printed workpiece. This method uses a photosensitive resin and a binder to fully mix as a composite printing material, and uses fluid filling printing technology to achieve 3D printing, which can effectively overcome the problems of long 3D printing time and unstable structure. However, this method focuses on the modification of printing materials and process optimization, and lacks consideration of how to optimize the three-dimensional model and path planning based on the structural characteristics of the RV reducer.

[0006] In summary, the existing technology lacks a 3D printing optimization solution for the complex structural characteristics of the RV reducer, lacks effective control measures for printing deviations, and is difficult to ensure printing accuracy and efficiency. Summary of the invention

[0007] In order to overcome the above-mentioned defects of the prior art, the present invention provides a rapid prototyping method for complex structural parts of an RV reducer based on 3D printing technology, performs topology optimization and feature segmentation on the three-dimensional model, generates differentiated sub-region filling schemes, and adaptively corrects and dynamically adjusts the filling path by acquiring historical and real-time printing deviation data, so as to improve the accuracy and efficiency of 3D printing of the RV reducer.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] Receive the three-dimensional model data of the RV reducer, perform topological optimization on the three-dimensional model, and generate first filling path data; perform feature segmentation on the three-dimensional model of the RV reducer to obtain differentiated sub-regions; fill each sub-region to generate an initial filling path for the complex structural parts of the RV reducer;

[0010] Acquire first printing deviation data, and based on the first printing deviation data, correct the initial filling path data to generate third filling path data; the first printing deviation data includes size deviation data, shape deviation data and surface quality deviation data;

[0011] Obtain the second printing deviation data, compare the second printing deviation data with the preset deviation threshold, and determine whether it is necessary to trigger the path correction mechanism; if the path correction mechanism is triggered, dynamically adjust the third filling path data according to the second printing deviation data to generate fourth filling path data; and execute the 3D printing process of the complex structural parts of the RV reducer according to the fourth filling path data.

[0012] Further, the differentiated sub-regions include sub-region A, sub-region B, sub-region C and sub-region D;

[0013] The feature segmentation of the three-dimensional model of the RV reducer to obtain differentiated sub-regions includes:

[0014] A three-dimensional finite element model of the RV reducer is established, and a structural mechanics analysis of the RV reducer is performed under rated working conditions to obtain stress and displacement cloud maps;

[0015] Extract high stress areas from stress cloud maps, and extract deformation concentration areas from displacement cloud maps;

[0016] Perform wall thickness analysis on the 3D model of the RV reducer and set the wall thickness greater than the preset wall thickness threshold T thick The area is extracted as the wall thickness area;

[0017] According to the high stress area, the deformation concentration area and the wall thickness area, sub-area A, sub-area B, sub-area C and sub-area D are obtained.

[0018] Further, the sub-region A, sub-region B, sub-region C and sub-region D are obtained according to the high stress region, the deformation concentration region and the wall thickness region, including:

[0019] Subtract the deformation concentration area and the thick wall area from the high stress area to obtain sub-area A;

[0020] Subtract the high stress area and thick wall area from the deformation concentration area to obtain sub-area B;

[0021] Subtract the high stress area and the deformation concentration area from the thick wall area to obtain the sub-area C;

[0022] The area other than sub-area A, sub-area B, and sub-area C is referred to as sub-area D.

[0023] Furthermore, the filling of each sub-region to generate an initial filling path of the complex structural parts of the RV reducer includes:

[0024] A high-intensity filling mode including but not limited to a cross and a spiral line and a filling density ρ1 are adopted for the sub-region A to generate a filling path of the sub-region A and obtain the filling path data of the sub-region A;

[0025] A high-rigidity filling mode including but not limited to a honeycomb and hexagonal grid and a filling density ρ2 are adopted for the sub-region B to generate a filling path for the sub-region B and obtain filling path data for the sub-region B;

[0026] A low filling mode including but not limited to a triangle or a square grid and a filling density ρ3 is adopted for the sub-region C to generate a filling path of the sub-region C and obtain filling path data of the sub-region C;

[0027] A filling density ρ4 is used for the sub-region D to generate a filling path for the sub-region D, and the filling path data for the sub-region D is obtained; wherein ρ2>ρ1>ρ3>ρ4;

[0028] Combining the filling path data of each sub-area to generate second filling path data;

[0029] The first filling path data and the second filling path data are merged to generate the initial filling path of the complex structural parts of the RV reducer.

[0030] Further, the obtaining of the first printing deviation data includes:

[0031] Compare the 3D models of all historical printing tasks with the current RV reducer 3D model to calculate the similarity of shape features;

[0032] The similarity between the extracted shape features and the current RV reducer 3D model is greater than the preset similarity threshold T simThe historical printing tasks are marked as similar historical printing tasks; and the printing parameters of the similar historical printing tasks are extracted as the first historical printing data;

[0033] Extracting the size parameters of each similar historical printing task in the first historical printing data, comparing the size parameters with the design size parameters of the historical three-dimensional model corresponding to the similar historical printing task, and calculating the size deviation data;

[0034] Extracting shape parameters of each similar historical printing task in the first historical printing data, comparing the shape parameters with design shape parameters of the historical three-dimensional model corresponding to the similar historical printing task, and calculating shape deviation data;

[0035] Surface quality parameters of each similar historical printing task in the first historical printing data are extracted to obtain surface quality deviation data.

[0036] Further, the obtaining of the second printing deviation data includes:

[0037] During the 3D printing process of complex structural parts of RV reducers, the printing environment parameters and material characteristic parameters are collected in real time;

[0038] Inputting the printing environment parameters and the material characteristic parameters into the pre-trained printing quality prediction model to obtain second printing deviation data;

[0039] The second printing deviation data includes second size deviation data, second shape deviation data and second surface quality deviation data.

[0040] Further, the deviation thresholds include a size deviation threshold Td, a shape deviation threshold Ts and a surface quality deviation threshold Tq;

[0041] The path correction mechanism includes size deviation correction, shape deviation correction and surface quality correction;

[0042] The comparing the second printing deviation data with the preset deviation threshold to determine whether the path correction mechanism needs to be triggered includes:

[0043] If the second size deviation data exceeds Td, size deviation correction is triggered;

[0044] If the second shape deviation data exceeds Ts, the shape deviation correction is triggered;

[0045] If the second surface quality deviation data exceeds Tq, a surface quality correction is triggered.

[0046] The RV reducer complex structural parts rapid prototyping system based on 3D printing technology is used to implement the above-mentioned RV reducer complex structural parts rapid prototyping method based on 3D printing technology, and the system includes:

[0047] Region segmentation module: used to receive the three-dimensional model data of the RV reducer, perform topological optimization on the three-dimensional model, and generate first filling path data; perform feature segmentation on the three-dimensional model of the RV reducer to obtain differentiated sub-regions;

[0048] Path filling module: used to fill each sub-area and generate the initial filling path of the complex structural parts of the RV reducer;

[0049] A first correction module: used for acquiring first printing deviation data, and based on the first printing deviation data, correcting the initial filling path data to generate third filling path data; the first printing deviation data includes size deviation data, shape deviation data and surface quality deviation data;

[0050] The second correction module is used to obtain the second printing deviation data, compare the second printing deviation data with the preset deviation threshold, and determine whether the path correction mechanism needs to be triggered; if the path correction mechanism is triggered, the third filling path data is dynamically adjusted according to the second printing deviation data to generate the fourth filling path data; according to the fourth filling path data, the 3D printing process of the complex structural parts of the RV reducer is executed.

[0051] An electronic device comprises a memory, a central processing unit, and a computer program stored in the memory and executable on the central processing unit. When the central processing unit executes the computer program, the above-mentioned method for rapid prototyping of complex structural parts of an RV reducer based on 3D printing technology is implemented.

[0052] A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed, implements the above-mentioned method for rapid prototyping of complex structural parts of an RV reducer based on 3D printing technology.

[0053] Compared with the prior art, the present invention has the following beneficial effects:

[0054] By performing feature segmentation and differential filling on the RV reducer 3D model, and using historical printing data and real-time printing parameters for path correction, the 3D printing accuracy of complex structural parts has been greatly improved, especially for key areas such as high stress areas and deformation concentration areas. Differentiated filling strategies are adopted to effectively ensure the mechanical properties and geometric accuracy of these areas; topology optimization and intelligent partition filling methods are used to rationally plan the printing path, reducing unnecessary material consumption and printing time. At the same time, the mechanism of dynamically adjusting printing parameters also avoids repeated printing due to printing deviations, further improving production efficiency; by real-time monitoring of environmental parameters and material properties during the printing process, combined with pre-trained quality prediction models, deviations in the printing process can be discovered and corrected in a timely manner, effectively ensuring the dimensional accuracy, shape accuracy and surface quality of the final product; by optimizing the filling path and material usage, raw material waste is reduced, and at the same time, the one-time molding rate is improved, the rework and scrap rates are reduced, thereby reducing production costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. 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 creative work.

[0056] Figure 1 It is a principle flow chart of the rapid prototyping method of complex structural parts of RV reducer based on 3D printing technology in the present invention;

[0057] Figure 2 A flowchart of a method for performing feature segmentation on a three-dimensional model of an RV reducer to obtain differentiated sub-regions in a rapid prototyping method for complex structural parts of an RV reducer based on 3D printing technology of the present invention;

[0058] Figure 3 A flow chart of a method for extracting high stress areas from stress cloud maps in a rapid prototyping method for complex structural parts of an RV reducer based on 3D printing technology of the present invention;

[0059] Figure 4 It is a flow chart of a method for extracting a deformation concentration area from a displacement cloud map in a rapid prototyping method for complex structural parts of an RV reducer based on 3D printing technology of the present invention;

[0060] Figure 5 A method flow chart of obtaining sub-region A, sub-region B, sub-region C and sub-region D according to high stress region, deformation concentration region and wall thickness region in the rapid prototyping method of complex structural parts of RV reducer based on 3D printing technology of the present invention;

[0061] Figure 6 A flow chart of a method for filling each sub-area and generating second filling path data in a rapid prototyping method for complex structural parts of an RV reducer based on 3D printing technology of the present invention;

[0062] Figure 7 A flow chart of a method for obtaining first printing deviation data of each similar historical printing task in the rapid prototyping method of complex structural parts of an RV reducer based on 3D printing technology of the present invention;

[0063] Figure 8 A flow chart of a method for determining whether a path correction mechanism needs to be triggered in a rapid prototyping method for complex structural parts of an RV reducer based on 3D printing technology of the present invention;

[0064] Fig. 9 This is a functional module diagram of the rapid prototyping system for complex structural parts of an RV reducer based on 3D printing technology in the present invention. DETAILED DESCRIPTION

[0065] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments 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 creative work are within the scope of protection of the present invention.

[0066] Example 1

[0067] See also Figure 1 As shown, this embodiment provides a rapid prototyping method for complex structural parts of an RV reducer based on 3D printing technology, including:

[0068] Step S1000, receiving the three-dimensional model data of the RV reducer, performing topological optimization on the three-dimensional model, and generating first filling path data; establishing a three-dimensional finite element model of the RV reducer according to the three-dimensional model data of the RV reducer, and performing feature segmentation on the three-dimensional model of the RV reducer to obtain differentiated sub-regions; filling each sub-region to generate an initial filling path of a complex structural part of the RV reducer;

[0069] Furthermore, step S1000 includes:

[0070] Step S1100, receiving three-dimensional model data of the RV reducer, performing topology optimization on the three-dimensional model, and generating first filling path data;

[0071] Furthermore, step S1100 includes:

[0072] Step S1110, establishing a topology optimization mathematical model based on the three-dimensional model data of the RV reducer;

[0073] Step S1120, solving the topology optimization mathematical model to obtain optimized filling material distribution information, and generating first filling path data according to the filling material distribution information.

[0074] Specifically, first receive the 3D CAD model of the RV reducer, which contains detailed geometric information of the RV reducer, such as external dimensions, internal structure, wall thickness changes, etc. At the same time, it is also necessary to input the material property parameters of the RV reducer, such as material density, elastic modulus, Poisson's ratio, yield strength, etc. These parameters are important bases for structural analysis and optimization. In addition, it is necessary to clarify the stress state of the RV reducer, including the size, direction, and action position of the load. Based on the above input information, a topology optimization model is built in the CAE software, and the topology optimization model includes objective functions, constraints, and design variables; the objective function is to minimize the amount of filling material under the premise of meeting the strength and stiffness requirements, and the constraints include stress constraints, displacement constraints, etc.; the purpose of topology optimization is to optimize the distribution of materials in the design domain under the premise of meeting the structural performance requirements, remove redundant or low-stress materials, and obtain a lightweight structure. By defining design variables (pseudo-density of each unit material), objective functions (such as minimizing volume), constraints (such as stress constraints, displacement constraints), etc., a topology optimization mathematical model is established.

[0075] For example, taking minimizing volume as an example, the design variable is the pseudo-density of each unit, and the objective function is to minimize the sum of (pseudo-density × unit volume) of all units. The constraints generally include:

[0076] 1) Overall static equilibrium equation to ensure that the optimized structure can withstand external loads;

[0077] 2) Local stress constraint, to ensure that the optimized structure does not fail under load, and Von-Mises stress is often used as a criterion;

[0078] 3) Displacement constraint, ensuring that the deformation of key positions after optimization meets the use requirements and avoiding excessive deformation that affects transmission accuracy;

[0079] 4) Volume fraction constraint: Control the volume of material removed. Too much will result in insufficient strength, and too little will not achieve the weight reduction effect. Generally, 20% to 50% is taken.

[0080] After the constraints are defined, the last step is to use the optimization algorithm for numerical solution. Topology optimization is a huge nonlinear programming problem involving a large number of design variables, which requires the use of mathematical programming methods. Existing topology optimization methods mainly include:

[0081] 1) The homogenization method: First, calculate the equivalent properties of the porous material using the homogenization theory, and then optimize the material distribution using the optimization criterion method.

[0082] 2) The solid isotropic material with penalization (SIMP) method: Describe the non-linear relationship between the pseudo-density and the material properties using a power function. The higher the power exponent, the closer it approaches the "0-1" solution.

[0083] 3) The level set method: Implicitly describe the structural boundary using a level set function and evolve the boundary shape through the Hamilton-Jacobi equation.

[0084] 4) The evolutionary structural optimization (ESO) method: Iteratively remove low-stress elements or add high-stress elements based on heuristic criteria.

[0085] 5) The bi-directional evolutionary structural optimization (BESO) method: Introduce a material addition criterion on the basis of ESO to accelerate the convergence rate.

[0086] These methods have their own advantages and disadvantages. The SIMP method is the most widely used, which can better ensure the volume constraint and obtain the "black and white" solution. The algorithm iterates continuously to decrease the objective function value. When the change in the objective function value between two adjacent iterations is less than a certain threshold or the maximum number of iterations is reached, the optimization process ends.

[0087] Exemplarily, taking the SIMP method as an example, the basic steps are as follows:

[0088] 1) Given the initial design variable value \(x_e\), for example, when fully filled, \(x_e = 1\).

[0089] 2) Calculate the element stiffness matrix, stress, and sensitivity.

[0090] 3) Assemble the global stiffness matrix and solve for the displacements.

[0091] 4) Calculate the objective function value and its sensitivity with respect to \(x_e\), and construct an approximate optimization model.

[0092] 5) Solve the approximate optimization model using MMA and update \(x_e\).

[0093] 6) Repeat steps 2)-5) until convergence.

[0094] Finally, obtain the element pseudo-density distribution \(x_e\). \(x_e = 1\) represents the material element, \(x_e = x\) min represents the void element, and \(x\) min represents the minimum relative density value in the SIMP topology optimization method, which is a small positive number close to 0. \(0 < x_e < 1\) represents the intermediate transition zone (gray-scale element). The gray-scale element has no physical meaning. Set a pseudo-density threshold. Those greater than the threshold are defined as materials, and those less than the threshold are defined as voids. By introducing \(x\) min , the numerical problem of singularity of the global stiffness matrix during the optimization process can be avoided.

[0095] After processing the grayscale units, a relatively clear material distribution area is obtained, thereby obtaining the optimal material distribution result. In the result, the area with higher material pseudo-density (red) indicates that the material is indispensable and bears the main load; on the contrary, the area with low pseudo-density (blue) is redundant material that can be removed. For reducers, stress concentration areas such as the root of the gear and the rim cannot usually be optimized, while low stress areas such as the center of the gear and the middle of the shaft can optimize most of the material.

[0096] Based on this, the first filling path is generated. The path parameters include filling direction (such as X direction, Y direction), filling density (such as 80%) and filling speed (such as 60mm / s). The filling direction is generally along the direction of the principal stress, consistent with the force; the filling density is proportional to the pseudo-density value at that location. The higher the pseudo-density, the denser the filling; the filling speed takes into account the printing efficiency and forming quality. Too fast means poor strength, and too slow means low efficiency. These parameters provide initial values ​​for subsequent further optimization.

[0097] Step S1100 uses topology optimization to find the optimal material distribution of the RV reducer, which not only meets the strength and stiffness requirements of the structure, but also minimizes the material usage, which can effectively shorten the printing time and reduce the printing cost. The optimized filling path parameters are relatively macroscopic, and a unified filling method is defined for the entire structure, which is convenient for rapid prototyping. However, for parts such as the RV reducer with complex structures and large spans, the force characteristics of different areas are significantly different. If a single filling method is used, problems such as poor local printing quality may occur. Therefore, it is also necessary to carry out targeted filling optimization for different areas on this basis.

[0098] Step S1200, establishing a three-dimensional finite element model of the RV reducer according to the three-dimensional model data of the RV reducer, and performing feature segmentation on the three-dimensional model of the RV reducer to obtain differentiated sub-regions;

[0099] Furthermore, if Figure 2 As shown, step S1200 includes:

[0100] Step S1210, establishing a three-dimensional finite element model of the RV reducer according to the three-dimensional model data of the RV reducer, performing structural mechanics analysis on the reducer under rated working conditions, and obtaining a stress cloud map and a displacement cloud map; the three-dimensional finite element model includes n1 nodes;

[0101] Specifically, for the RV reducer, we must first create a finite element model based on its 3D CAD model and perform finite element analysis. Finite element analysis refers to discretizing the continuous structure into several units, establishing a local coordinate system on the unit, and then using the node as the link between the units to establish the overall coordinate system. On this basis, the stiffness matrix, mass matrix, etc. of the unit are established and assembled into a total stiffness and total mass matrix. Then, combined with the boundary conditions, initial conditions and load conditions, the static equilibrium equation or dynamic equilibrium equation is solved to obtain the displacement, strain, stress and other response quantities of the structure under the load. Due to the complex structure of the RV reducer, it is necessary to mesh different characteristic areas. For example, the mesh of stress concentration areas such as gear tooth surface and wheel rim should be relatively dense, and the general mesh size is not more than 0.5mm; while the mesh of stress horizontal areas such as wheel body and box body can be slightly sparse, and the mesh size can be relaxed to 1-2mm. The mesh unit type can be tetrahedron or hexahedron unit. Mesh encryption is also required on the tooth surface, mating surface and other parts. After the meshing is completed, define material properties such as elastic modulus, Poisson's ratio, density, etc. Then apply boundary conditions, such as fixed constraint bearing seat hole, simply supported constraint output end; apply loads, such as torque on input shaft, counter torque on output shaft, etc. Loads and boundary conditions should reflect actual working conditions as much as possible. Finally, submit the solution to obtain the stress field and displacement field distribution of the reducer under load, which are intuitively presented in the form of cloud maps. The stress cloud map reflects the force level of each part, and the displacement cloud map reflects the degree of deformation of each part.

[0102] Finite element analysis can be used to simulate the stress characteristics of the RV reducer efficiently and at low cost, predict its mechanical behavior under actual working conditions, and discover weak links in the design early. Compared with physical tests, simulation analysis is not limited by site, tooling, and external factors, and can be carried out quickly, providing a reliable basis for subsequent structural optimization and reinforcement. Stress cloud maps and displacement cloud maps intuitively present the complex stress state inside the RV reducer, and are important inputs for filling optimization.

[0103] Step S1220, extracting high stress areas from the stress cloud map;

[0104] Furthermore, if Figure 3 As shown, step S1220 includes:

[0105] Step S1221, converting the stress cloud image into a grayscale image, wherein the grayscale value of the grayscale image is inversely proportional to the stress value;

[0106] Step S1222, traverse the grayscale image and select grayscale values ​​less than a preset grayscale threshold T stress The pixels are classified as high stress areas.

[0107] Specifically, since the cloud map usually represents the numerical value by continuous color gradient, it is not convenient to directly identify and extract the area. Therefore, the color stress cloud map must be converted into a grayscale map, that is, the color information is mapped to a grayscale value. The grayscale value is inversely proportional to the stress at that location. The greater the stress, the lower the grayscale value. Then set the grayscale threshold T stress Traverse each pixel in the grayscale image and convert the grayscale value below the threshold T stress The pixel points are classified as high stress areas. The selection of thresholds needs to be determined according to the material's yield limit, fatigue limit and other parameters. It can be taken as 50% of the yield limit, or 1.5 times the fatigue limit, etc., to ensure the safety margin of the high stress area. The stress cloud map contains a lot of stress numerical information, and it is not easy to directly extract the area. Through grayscale and threshold segmentation, the continuous stress field can be converted into discrete high and low stress areas, which is convenient for clustering and extraction.

[0108] Step S1230, extracting the deformation concentration area from the displacement cloud map;

[0109] Furthermore, if Figure 4 As shown, step S1230 includes:

[0110] Step S1231, calculating the displacement gradient of the node according to the displacement cloud map;

[0111] Step S1232: select the node with the largest displacement gradient as the seed point, and select the node with the largest displacement gradient greater than the preset gradient threshold T as the seed point. disp The pixels are classified into the deformation area;

[0112] Step S1233, repeat S1232 until all nodes meeting the conditions are classified into the deformation area, and a deformation concentration area is obtained.

[0113] Specifically, when extracting areas with large deformation, if only the absolute value of displacement is considered, areas with large rigid body displacement may also be extracted, but the deformation of these areas is not significant. Therefore, the severity of deformation is judged by calculating the displacement gradient of the node. The displacement gradient reflects the rapid rate of change of displacement. On the displacement cloud map, first select the node with the largest gradient as the seed point, and then search around it with the gradient as the center, and select the nodes with gradients greater than the preset gradient threshold T in the adjacent nodes. disp The points are classified into the deformation area. Here, the region growing method is used for region segmentation, which can effectively gather points with similar deformation degrees together. dispIt can be set according to the gradient distribution characteristics of the displacement cloud map, such as taking twice the gradient mean, etc. Repeat the expansion until there are no new nodes that meet the conditions and can be included in the deformation zone, and the deformation concentration zone is obtained. Excessive deformation will affect the transmission accuracy of the RV reducer, and the deformation concentration area must also be filled in order to reduce the deformation. The displacement gradient combines the absolute value and relative rate of change of the displacement, which can better reflect the significance of the deformation. The regional growing algorithm is used to extract the deformation zone, which avoids the subjectivity of the gradient threshold selection, and can adaptively extract the area with more concentrated deformation according to the displacement distribution, thereby improving the pertinence of the filling optimization.

[0114] Step S1240, performing wall thickness analysis on the three-dimensional model of the RV reducer, and classifying the wall thickness greater than the preset wall thickness threshold T thick The area is extracted as the wall thickness area;

[0115] Specifically, in addition to stress level and deformation trend, the selection of filling method for RV reducer also needs to consider the influence of wall thickness. Generally speaking, the thicker the wall, the more likely it is to use a low filling density to save material; on the contrary, in places with thinner wall thickness, in order to ensure surface quality, the filling density is usually increased. Therefore, it is necessary to perform wall thickness analysis on the 3D model. Using the wall thickness analysis command in CAD software, the distance from the surface to the inside of the model can be quickly calculated. Set the wall thickness threshold T thick， The area greater than the threshold is extracted as the wall thickness area. The value of Tthick needs to take into account both filling density and molding quality. If it is too large, the filling density is too low and the strength is insufficient; if it is too small, the purpose of reducing material cannot be achieved. The average wall thickness of this type of parts can be taken, or the recommended value in the 3D printing process guide can be referred to. Most of the existing filling methods are based on experience, and the impact of wall thickness on filling quality is rarely considered. Through wall thickness analysis, thick wall areas can be found and appropriate filling parameters can be matched for them, which can save costs while ensuring local strength and surface quality, making the filling scheme more scientific and reasonable.

[0116] Step S1250, obtaining sub-region A, sub-region B, sub-region C and sub-region D according to the high stress region, the deformation concentration region and the wall thickness region.

[0117] Furthermore, if Figure 5 As shown, step S1250 includes:

[0118] Step S1251, subtracting the deformation concentration area and the thick wall area from the high stress area to obtain a sub-area A;

[0119] Step S1252, subtracting the high stress area and the thick wall area from the deformation concentration area to obtain a sub-area B;

[0120] Step S1253, subtracting the high stress area and the deformation concentration area from the thick wall area to obtain a sub-area C;

[0121] Step S1254: The area other than sub-area A, sub-area B, and sub-area C is defined as sub-area D.

[0122] Specifically, after the above analysis, the RV reducer is divided into high stress area, deformation concentration area, and thick wall area, but there is often overlap between these areas. For example, the high stress area may also be a large deformation area, and the thick wall area may also be a low stress area. In order to simplify the area division, it is necessary to eliminate the coupling between the areas.

[0123] Specific:

[0124] Subtract the deformation concentration area and thick wall area from the high stress area to obtain the sub-area A caused solely by the high stress level;

[0125] Subtract the high stress area and thick wall area from the deformation concentration area to obtain the sub-area B caused by large deformation alone;

[0126] Subtract the high stress area and deformation concentration area from the thick wall area to obtain the sub-area C caused by the thick wall alone;

[0127] The area other than sub-area A, sub-area B, and sub-area C is defined as sub-area D, which has the lowest filling density.

[0128] In this way, decoupling is achieved between the sub-areas, and the filling properties are more independent, which facilitates subsequent targeted filling optimization.

[0129] Through the above steps, the overlap between different feature areas is eliminated, so that each sub-area has a unique filling attribute and filling redundancy is avoided. For complex parts, this divide-and-conquer method can greatly reduce the difficulty of optimization, carry out in parallel, and improve optimization efficiency. Comprehensive consideration from different angles such as high stress areas, deformation concentration areas, and thick wall areas makes the filling plan more comprehensive and less likely to miss key areas.

[0130] Step S1200 comprehensively considers factors such as the structural mechanical properties of the RV reducer and the 3D printing process requirements, and divides the complex overall model into multiple sub-regions with obvious differences, which is a prerequisite for achieving regional heterogeneous filling. The selection of the number of sub-regions requires a balance between the fineness of the filling and the computational cost. Too many sub-regions can certainly better match the local structural characteristics, but will also significantly increase the difficulty of the optimization solution. This embodiment recommends dividing the RV reducer into 5-8 sub-regions, which not only meets the needs of differentiated filling, but also does not bring too high a computational burden.

[0131] Step S1300, filling each sub-region, generating filling path data corresponding to each sub-region, combining the filling path data of each sub-region, generating second filling path data;

[0132] Furthermore, if Figure 6 As shown, step S1300 includes:

[0133] Step S1310, using a high-intensity filling mode including but not limited to a cross and a spiral line and a filling density ρ1 for the sub-region A, generating a filling path for the sub-region A, and obtaining filling path data for the sub-region A;

[0134] Specifically, sub-area A is a high-stress area of ​​the RV reducer. This area bears a large load and is prone to stress concentration, which can cause failures such as fatigue and yield. Therefore, the filling of area A should focus on ensuring strength and stiffness. Commonly used high-strength filling patterns are: cross-crossing, that is, filling with alternating parallel lines in two directions, and the crossing angle is usually 90°, which can provide more uniform mechanical properties; spiral filling, that is, continuous filling from the inside to the outside in a spiral manner, with a smooth path, small stress concentration, and high strength. In addition, there are high-strength modes such as concentric circle filling and radial filling to choose from. The filling angle must be consistent with the direction of the principal stress at that location, so that the material will be more smoothly stressed and less prone to stress mutations. The filling angle is consistent with the force direction, and the filling spacing is controlled below 1mm, so that a higher filling density can be obtained, generally reaching more than 80%, thereby ensuring higher local strength.

[0135] Sub-area A adopts a high-strength filling mode, which can maximize the mechanical properties of 3D printed materials, resist large external loads, and extend the service life of high-stress areas. Directed filling can make the material bear force more smoothly, and dense filling can improve local strength. This is especially important for key stress areas such as tooth roots and journals. The strength of these areas directly determines the transmission performance and reliability of RV reducers. Compared with traditional overall filling, the directional dense filling of additive manufacturing can increase the strength of key areas by more than 30%.

[0136] Step S1320, using a high-rigidity filling mode including but not limited to a honeycomb or hexagonal grid and a filling density ρ2 for the sub-region B, generating a filling path for the sub-region B, and obtaining filling path data for the sub-region B;

[0137] Specifically, sub-area B is the deformation concentration area of ​​the RV reducer. The deformation in this area is relatively large, and the deformation gradient is steep, which can easily cause a decrease in transmission accuracy. Therefore, the filling of area B should focus on improving stiffness and controlling deformation. Commonly used high-rigidity filling patterns are: honeycomb filling, that is, nested filling of hexagonal honeycomb structures. This structure is widely used in thin-walled, plate and shell parts. It has high bending stiffness while being lightweight; hexagonal grid, that is, the entire filling area is divided into regular hexagonal grids, and the grids are filled, similar to triangular grids, which are used to improve structural stiffness. The filling angle should be perpendicular to the deformation direction. If the deformation is mainly radial, the filling line is mainly circumferential, so that the displacement in the deformation direction can be constrained to the greatest extent. The filling spacing should be small, generally less than 0.8mm, and the corresponding filling density can reach more than 60% to obtain higher deformation resistance.

[0138] Sub-area B adopts a high-rigidity filling mode, which can effectively improve the deformation resistance of this area and ensure the operating accuracy of the RV reducer. Selective filling can maximize the constraint of the material and inhibit the development of deformation. Dense filling can further enhance the rigidity. Deformation is a common quality hazard of RV reducers. Slight deformation can cause poor gear meshing, and severe deformation can even cause jamming. The use of partitioned optimized filling can increase the rigidity of the deformation-sensitive area by more than 50%, greatly improving the transmission quality of the reducer.

[0139] Step S1330, using a low filling mode including but not limited to a triangle or square grid and a filling density ρ3 for the sub-region C, generating a filling path for the sub-region C, and obtaining filling path data for the sub-region C;

[0140] Specifically, sub-area C is the thick-walled area of ​​the RV reducer, which has a large wall thickness, a high moment of inertia, and relatively small force. Therefore, the filling density of area C can be appropriately reduced to save printing materials while ensuring strength. Commonly used low filling modes are: triangular grid, that is, dividing the filling area into regular triangles, filling along the edges of the triangles to build a stable frame structure; square grid, that is, dividing the filling area into regular quadrilaterals, filling along the quadrilaterals, similar to the triangular grid, further reducing the filling density. For thick-walled areas, the filling density is generally controlled at 30% to 50%, which can not only meet the strength requirements, but also effectively shorten the printing time. The filling spacing can be appropriately increased, which is acceptable within 2mm, further increasing the sparseness of the filling.

[0141] Sub-area C adopts a low filling mode, which saves printing materials to the maximum extent and reduces manufacturing costs while meeting the strength requirements. Grid-like filling can form a skeleton support at key nodes to ensure the minimum strength requirements of thick-walled areas. Reducing invalid filling can also shorten printing time and improve production efficiency. Thick-walled areas are usually non-critical stress-bearing areas such as the wheel body and end cover of the reducer, which allows for a moderate reduction in the filling rate. After partition optimization filling, the material reduction rate can reach more than 20%, with significant cost reduction and efficiency improvement.

[0142] Step S1340, using a filling density ρ4 for the sub-region D, generating a filling path for the sub-region D, and obtaining filling path data for the sub-region D; wherein ρ2>ρ1>ρ3>ρ4;

[0143] Sub-region B is the deformation concentration area of ​​the RV reducer, which is usually located at the weak section, such as the tooth root, transition fillet, etc., with a large stress-strain gradient, which is prone to stress concentration. High filling density means that the volume fraction of the filling material inside this area is higher, the force is more fully applied, and it can resist deformation better, avoiding early failure caused by stress concentration. When the RV reducer transmits torque, the torsional force is transmitted along the normal direction of the key section. The high filling density of sub-region B helps to improve the continuity of the material in this area, make the force transmission path smoother, and the torsional force can be transmitted from one side to the other side evenly and orderly, reducing the strain mismatch caused by stress mutation. Sub-region B may also involve the tooth surface contact area, which needs to withstand greater contact stress. High filling density provides more solid material support, higher contact stiffness, can effectively alleviate the contact stress peak, and improve the fatigue life of the tooth surface. Sub-region B gathers a large amount of deformation energy and is more prone to thermal deformation when running at high speed. High filling density means that the overall thermal conductivity of the material in the area is higher, which helps to diffuse the deformation energy in time, reduce local temperature rise, and maintain dimensional stability. In contrast, although sub-area A is a high-stress area, its deformation is relatively uniform, the heat dissipation conditions are better, and the filling density can be appropriately reduced. Areas C and D mainly play a supporting and connecting role, and the filling density can be further reduced to save material usage. Sub-area B comprehensively considers multiple factors such as strength, stiffness, deformation, and force transmission. It is the weak link of the RV reducer. The use of the highest filling density is the result of a compromise optimization of mechanics, thermals, and processes. It can maximize the flexibility of additive manufacturing and achieve lightweight design.

[0144] Sub-area D is a non-critical area of ​​the RV reducer with low stress level, small deformation and thin wall thickness. The filling pattern and angle of this area have little effect on the performance of the reducer. In order to maximize the printing efficiency, the D area should adopt the lowest filling density, generally between 15% and 20%, and only the necessary skeleton needs to be formed. At the same time, the filling spacing can be relaxed to more than 3mm to further reduce the printing time. After the treatment, the filling density and spacing of the D area will be significantly different from those of the A, B, and C areas, which fully reflects the optimization effect of the partition filling. The sparsest filling of sub-area D can minimize the filling amount and shorten the printing time in the area with the lowest mechanical performance requirements, reflecting the flexible production characteristics of additive manufacturing. If this minimum requirement filling area is filled in a traditional homogeneous manner, a large amount of redundancy will often be generated, wasting printing resources. The partition optimization filling can save this part of unnecessary consumption, and the filling amount of the D area can be reduced by more than 50%, and the printing efficiency can be improved by more than 30%. The existence of the D area brings greater optimization space for the filling scheme of the entire reducer.

[0145] Step S1350: combining the filling path data of each sub-region to generate second filling path data.

[0146] Specifically, after the above-mentioned filling path generation by region, each sub-region of the RV reducer has a corresponding optimal filling solution. At this time, it is necessary to assemble the filling path data of each region to generate a filling solution representing the entire reducer. During assembly, it is necessary to pay attention to the smooth transition of the filling paths in different regions at the interface, and the filling lines should be as continuous as possible without leaving gaps. The filling angle changes of adjacent areas should be as small as possible to reduce stress mutations. The overall filling path after assembly shows obvious partition characteristics in density and angle from a macroscopic perspective, and the continuity and transition of the path are achieved in local details, reflecting the unity of integrity and locality.

[0147] Assembling each area to form the overall filling path of the reducer makes the partition optimization plan a CNC program that can be used to guide printing, completing a key step from digital twin to physical manufacturing. The assembly plan achieves a smooth transition of the filling path at the interface. While enjoying the benefits of partition filling, it ensures the integrity and consistency of the formed parts, avoids sudden changes in physical properties between regions, and thus improves the overall performance of the reducer. This is another advantage of partition filling over overall filling. Through assembly, the optimization results of each area can be superimposed and amplified, greatly improving the applicability of additive manufacturing in the field of reducers.

[0148] In summary, step S1300 adopts a regional differentiated filling optimization strategy, making full use of the differences in force characteristics of different regions, and adopts high strength, high stiffness, low filling rate, and minimum filling rate filling methods for regions A, B, C, and D, respectively, to meet the requirements of strength, precision, lightness, and high efficiency. This is difficult to achieve in overall filling and homogeneous filling. After the schemes of each zone are assembled, a smooth transition of the path at the interface is achieved, ensuring the integrity and consistency of the formed part. Partition filling is an inevitable requirement for the flexible and intelligent development of additive manufacturing, conforming to the trend of subtractive structures towards functional gradient and composite development, and realizing the integrated optimization design of "shape-material-force", which has broad application prospects.

[0149] Step S1400: merge the first filling path data and the second filling path data to generate an initial filling path for the complex structural part of the RV reducer.

[0150] Specifically, after obtaining the first filling path and the second filling path respectively, the two need to be further fused to form the initial filling path parameters representing the entire process of additive manufacturing. The first filling path is a macro filling scheme obtained based on topology optimization, which reflects the optimal distribution of materials in the entire design domain and has the characteristics of global optimality; the second filling path is a refined filling scheme obtained based on stress-strain analysis, which further refines the filling methods of different internal regions and has the characteristics of local optimality. Only by organically combining the results obtained at two scales and two stages and taking advantage of each other's strengths and weaknesses can the best solution that takes both the overall and local into account be obtained. The fusion process needs to consider the following factors: First, the difference in filling density between the two schemes can be taken as a weighted average of the two, and the weight is proportional to the degree of influence of the area on the performance of the reducer. Second, the difference in filling angle between the two schemes is mainly based on the force analysis results, while taking into account the printing direction. Third, the continuity of the fused filling path at the junction should be smooth. In addition, the total filling rate of the fusion scheme should be checked, which should not be too high or too low, and is generally controlled at 20% to 30%. After verification, parameters such as filling density, filling angle, and filling spacing are converted into numerical control codes to guide the actual filling process of the 3D printer, and ultimately the optimal filling configuration of the RV reducer is obtained that meets the use requirements and saves printing materials.

[0151] The fusion of the two-stage filling scheme takes the advantages of global optimization and local optimization to form a unified final filling scheme with multiple scales and multiple objectives. On the one hand, the fusion filling scheme retains the optimization results of the macroscopic distribution of materials by topology optimization, which meets the overall requirements of load transmission; on the other hand, it incorporates the differentiated treatment of local structures by regional filling, which meets the local requirements of force. And it realizes the continuous transition of the two filling paths at the interface, avoiding the sudden change of density and angle. The filling rate verification further balances the gains and losses of material reduction and strength from the perspectives of cost and quality. The obtained NC code has fully considered the printing process requirements and can be directly used to guide printing, saving the manual programming link. Multi-stage fusion optimization makes up for the shortcomings of the current filling method that focuses on a single goal, and represents the mainstream direction of future additive manufacturing filling optimization technology.

[0152] The step of fusing the first filling path data with the second filling path data to generate an initial filling path for the complex structural part of the RV reducer includes:

[0153]

[0154] in:

[0155] : Initial filling path data after fusion;

[0156] : First fill path data;

[0157] : The second filling path data;

[0158] : The power index of the first filling path, used to adjust the weight of the first filling path in the fusion;

[0159] : The power index of the second filling path, used to adjust the weight of the second filling path in the fusion; and It is obtained based on experience or by fitting historical data. It is usually used to balance the needs of global and local optimization. It is also used to adjust the importance of global and local filling paths. When local needs are more important, Can be compared larger, to enhance the impact of local paths;

[0160] : Attenuation factor, reflecting the distance attenuation effect in the first filling path; Represents the distance from the path node to the structure center, is the attenuation parameter, which is the parameter that controls the influence range of the global path; Each node in the model can be The Euclidean distance of the reducer geometric center is calculated; Determined by experiment or simulation analysis, it usually reflects the rate at which the path fill decays; It can effectively weaken the influence of the paths far away from the central area on the final filling paths, ensuring the centralization of the paths;

[0161] : An enhancement of the local path, where is the stress intensity, is the enhancement factor, reflecting the importance of the area with high local stress to the path correction; Extract local stress values ​​through stress cloud diagram in finite element analysis; It is set according to the requirement of local material strength and is used to control the weight of local stress on path enhancement; the higher the local stress, The larger the value is, the greater the filling density and complexity of the path in the area can be, and the local structural performance can be improved.

[0162] Path power index , attenuation parameter And the enhancement factor are the main variables, which together determine the global and local optimization balance of the final filling path and the distribution characteristics of the path. As these variables are adjusted, When it increases, the paths with long distances decay faster and the effect of the global path decreases; when As increases, the region with large local stress will obtain stronger path reinforcement.

[0163] By parameters and By adjusting the weights of the global and local paths, the formula can automatically balance global optimization and local strengthening according to design requirements. This is especially important in complex structures, which can not only ensure the overall structural integrity, but also strengthen the local areas that require high stress or high stiffness. It can effectively weaken the path influence of distant areas, making the printing path more concentrated and reducing material waste away from the core area. This helps improve printing efficiency and material utilization. Local path enhancement This allows high stress areas to receive additional path reinforcement, ensuring the mechanical properties of these areas while avoiding material redundancy. Through this formula, the printing path achieves a more refined dynamic balance between global and local optimization, adapting to different stress distributions and structural characteristics.

[0164] Step S2000, obtaining n2 similar historical printing tasks with similar structural features to the current RV reducer three-dimensional model, extracting printing parameters of n2 similar historical printing tasks as first historical printing data; analyzing the first historical printing data, obtaining first printing deviation data of each similar historical printing task, and based on the first printing deviation data, correcting the initial filling path data to generate third filling path data; the first printing deviation data includes size deviation data, shape deviation data and surface quality deviation data; n2 is a positive integer;

[0165] Furthermore, step S2000 includes:

[0166] Step S2100, obtaining n2 similar historical printing tasks with similar structural features to the current RV reducer three-dimensional model, and extracting printing parameters of the n2 similar historical printing tasks as the first historical printing data; n2 is a positive integer;

[0167] Furthermore, step S2100 includes:

[0168] Step S2110, comparing the 3D models of all historical printing tasks with the current RV reducer 3D model, and calculating the shape feature similarity;

[0169] Step S2120, extracting the shape feature similarity with the current RV reducer three-dimensional model is greater than a preset similarity threshold T sim The historical printing tasks are marked as similar historical printing tasks.

[0170] Specifically, historical printing tasks refer to all printing jobs completed by 3D printing equipment, including three-dimensional models of parts, printing parameter settings, printing effect evaluation and other contents. The three-dimensional model reflects the geometric shape, size and structural characteristics of the printed object; printing parameters include filling method, filling density, printing direction, layer thickness, printing speed, etc.; printing effect evaluation generally quantifies the quality characteristics of the printed parts through precision detection, mechanical testing, surface roughness measurement and other means. These data are usually stored in the database of the device or uploaded to the cloud server. In order to find the historical printing tasks that are most similar to the current RV reducer three-dimensional model, their shape features need to be compared first. The three-dimensional shape description algorithm can be used to extract the feature vectors of the model, such as volume, surface area, convex hull, Fourier descriptor, etc., and then calculate the similarity between the current model and the feature vectors of each historical model, such as Euclidean distance, Mahalanobis distance, angle cosine, etc. Set the similarity threshold T sim, extract the printing tasks corresponding to the historical printing models with similarity higher than the threshold. The selection of the threshold needs to take into account the number and degree of similarity of similar models. If it is too high, it may not be possible to find enough reference cases, and if it is too low, the reference value is not great. Generally, the mean or median of the similarity of historical models can be taken. After extracting similar historical printing tasks, their printing parameters are used as the first historical printing data for subsequent path correction. Printing parameters are the key factors affecting the forming quality. Drawing on the experience of similar cases can avoid some known problems and provide better initial values ​​for parameter optimization. The more similar models there are, the richer the historical data obtained, and the better the optimization effect.

[0171] The process of using historical data to guide the current printing task is essentially an example-based learning method. Compared with blind exploration, referring to solutions to similar problems can greatly improve optimization efficiency and reduce trial and error costs. Especially for parts with complex structures and high process requirements, the selection of printing parameters requires the accumulation and inheritance of experience. Through big data analysis and similarity matching, this method can automatically find relevant cases and avoid the repetitive work of manual retrieval. Models with similar shape features often have similar stress and deformation characteristics, and the correlation of the forming process is very high. Therefore, using shape similarity as a retrieval condition can quickly lock in potential best practices. Although printing parameters cannot be copied directly, as first-hand information, they can make the starting point of optimization higher and the direction clearer.

[0172] Step S2200, analyzing the first historical printing data to obtain first printing deviation data of each similar historical printing task, wherein the first printing deviation data includes size deviation data, shape deviation data and surface quality deviation data;

[0173] Furthermore, if Figure 7 As shown, step S2200 includes:

[0174] Step S2210, extracting the size parameters of each similar historical printing task in the first historical printing data, comparing the size parameters with the design size parameters of the historical three-dimensional model corresponding to the similar historical printing task, and calculating the size deviation data;

[0175] Step S2220, extracting shape parameters of each similar historical printing task in the first historical printing data, comparing the shape parameters with the design shape parameters of the historical three-dimensional model corresponding to the similar historical printing task, and calculating shape deviation data;

[0176] Step S2230 , extracting the surface quality parameter of each similar historical printing task in the first historical printing data to obtain surface quality deviation data.

[0177] Specifically, the first printing deviation data reflects the difference between the actual effect of the historical print and the design intent of the 3D model. A large deviation indicates that the printing scheme still has room for improvement, while a small deviation indicates that the scheme is relatively mature. To quantitatively evaluate the printing deviation, it is necessary to examine it from three aspects: dimensional accuracy, shape accuracy, and surface quality. Dimensional deviation refers to the difference between the actual processing size and the size marked on the drawing, and is often measured by indicators such as runout and coaxiality. Tools such as coordinate measuring machines, vernier calipers, and micrometers can be used to measure the key dimensions of the print, and then compared with the design dimensions to calculate the deviation value. For mating surfaces with high dimensional accuracy requirements, such as bearing holes and stop surfaces, the dimensional deviation should be controlled within 0.05mm. Shape deviation refers to the difference between the actual processing contour and the theoretical design contour, which is used to evaluate the shape fit between the print and the 3D model. Structured light scanners, laser trackers, etc. can be used to obtain the point cloud data of the print, and then compare it with the CAD model to calculate the shape deviation. It is generally expressed by indicators such as hausdorff distance and surface reconstruction error. For RV reducers, the tooth profile deviation requirements are relatively high, and if it exceeds 0.03mm, it will affect the meshing performance. Surface quality deviation refers to the gap between the actual surface roughness, color, texture and other characteristics and the design requirements. The surface profile curve and roughness parameters can be measured by a profiler or roughness meter, or the surface color can be measured by a spectrophotometer, and visual inspection can be performed to see if there are defects such as layer traces, reticulation, and wrinkles. The defect area ratio and surface roughness grade are commonly used quantitative indicators. Especially in areas with high appearance requirements such as tooth surfaces and stop surfaces, surface quality is particularly important. The above three types of deviation data reflect the advantages and disadvantages of historical printing schemes from different aspects. Dimensional accuracy is related to part assembly and interchangeability, and is a basic element for measuring printing level; shape accuracy affects the force deformation and kinematic pair coordination of parts, and is a guarantee of mechanical properties; surface quality determines the appearance and feel, and is the beauty of the printed part. Only when all standards are met can it be considered a successful practice. Comparing the deviation data of each historical scheme to find a high-quality scheme with small deviation and good stability can provide a reliable reference system for the path design of the new model.

[0178] In general, the first printing deviation data objectively records the actual effect of historical printing and is the direct basis for judging the quality of printing parameters. Similar models use similar parameters, and the printing results will naturally be similar. By analyzing the deviation data, high-quality parameter combinations can be identified, and while inheriting excellent practices, lessons from failure can also be learned, and detours can be avoided. And those parameter values ​​with small deviations will naturally become the preferred targets for path correction. The systematic mining of deviation data makes the selection of parameters traceable, and it is no longer a matter of luck. The success rate and stability of printing will be greatly improved. In addition, the accumulation of deviation data also provides data support for the continuous improvement of printing technology, helping companies summarize laws, accumulate knowledge, and create their own core process know-how.

[0179] Step S2300 , based on the first printing deviation data, correct the initial filling path data to generate third filling path data.

[0180]

[0181] in:

[0182] : The third fill path data, indicating the coordinate position The corrected path value on ;

[0183] : Initial fill path data, indicating the coordinate position The initial path value of

[0184] : With the current The number of similar historical printing tasks with similar structural features of the reducer 3D model;

[0185] : Dimensional deviation influence coefficient, corresponding to The dimension deviation in a similar historical printing task reflects the influence of the dimension deviation on the path correction. The coefficient value is determined by technicians in this field through multiple experiments or optimization; 1≤i≤n2;

[0186] : No. Dimension deviation data of similar historical printing tasks;

[0187] : Shape deviation influence coefficient, corresponding to the The shape deviation in a similar historical printing task; the weight reflecting the shape deviation on the correction of the filling path, obtained by fitting or optimizing the historical data of the shape deviation, usually determined by the multidimensional least squares method;

[0188] : No. Shape deviation data of similar historical printing tasks;

[0189] : Surface quality deviation influence coefficient, corresponding to The surface quality deviation in a similar historical printing task reflects the influence of the surface quality deviation on the final path correction, which is determined by fitting the deviation between the surface quality and the design data or by empirical data;

[0190] : No. Surface quality deviation data of similar historical printing tasks;

[0191] : Weight factor, measuring the The similarity between a similar historical printing task and the current printing task; generally, the higher the similarity, the greater the weight, and the influence of each historical printing task on the third filling path data is adjusted by the weight factor.

[0192] With deviation data The larger the deviation is during the historical printing process, the more significant the fill path correction is. The increase in will increase the impact of similar historical tasks on path correction. Therefore, if a historical task is very similar to the current model, the corresponding deviation correction will have a greater impact.

[0193] The formula combines the deviation information (including size, shape, and surface quality) of multiple similar historical printing tasks with the current initial filling path data, and uses weight factors to accurately adjust the impact of different deviations on path correction, thereby improving the accuracy of the printing path. By introducing multiple deviation coefficients and weights, the formula can dynamically adapt to the data of different historical tasks, ensuring that the corrected path data is closer to the ideal printing result and reducing printing errors.

[0194] When correcting the initial filling path, it is necessary to fine-tune the paths of each area through an iterative algorithm or a model based on historical data to compensate for these deviations. For example, if the shape deviation of a certain area is large, the path of the area can be encrypted, that is, the density of the printing layer can be increased to improve the local shape accuracy. In addition, "filling path" refers to the movement trajectory of the nozzle or laser when the structure is built layer by layer in 3D printing. Different areas can adopt different filling modes (such as spiral, honeycomb, etc.) to adapt to material and mechanical requirements. By correcting the initial path, the defects of the finished product caused by errors during the printing process can be effectively reduced, and the geometric accuracy, mechanical properties and surface quality of the final RV reducer structural parts can be guaranteed. The logical reasoning of this correction step is based on the feedback of prior data. By dynamically adjusting the filling strategy, the material distribution and path planning are further optimized, thereby improving the consistency of the finished product. For example, if a certain area often has surface unevenness problems in historical tasks, the corrected path will reduce the occurrence of similar problems by increasing density or adjusting the printing order. In summary, the core of step S2300 is to use the feedback of historical data to optimize the printing path through the correction algorithm, so as to ensure the high-precision molding of complex structural parts of the RV reducer.

[0195] Step S3000, real-time collection of printing environment parameters and material characteristic parameters during the 3D printing process, input of the printing environment parameters and material characteristic parameters into a pre-trained printing quality prediction model, and acquisition of second printing deviation data; comparison of the second printing deviation data with a preset deviation threshold to determine whether a path correction mechanism needs to be triggered; if the path correction mechanism is triggered, dynamic adjustment of the third filling path data is performed according to the second printing deviation data to generate fourth filling path data; and execution of the 3D printing process of the complex structural parts of the RV reducer is performed according to the fourth filling path data.

[0196] Furthermore, step S3000 includes:

[0197] Step S3100, during the 3D printing process of the complex structural parts of the RV reducer, real-time collection of printing environment parameters and material characteristic parameters;

[0198] Specifically, the printing environment parameters mainly include temperature, humidity and airflow speed. If the temperature is too high, the viscosity of the material will decrease, the fluidity will increase, and the collapse and deformation will occur; if the temperature is too low, the viscosity of the material will increase, the fluidity will deteriorate, and the extrusion will be discontinuous. Excessive humidity can easily cause the material to absorb moisture, produce bubbles, and affect the forming quality. Airflow disturbance can cause the molten material to cool rapidly and produce warping deformation. Therefore, the printing environment needs to be strictly controlled. By installing temperature and humidity sensors and wind speed sensors in the printing chamber, environmental parameters can be collected in real time. Material characteristic parameters mainly include melting temperature, fluidity and shrinkage. The melting temperature directly determines the setting of the printing temperature. If it is too high, the material will be degraded, and if it is too low, it will be difficult to melt. Fluidity is an important indicator for evaluating the forming performance of the material. Materials with good fluidity are easier to fill complex structures. The shrinkage rate affects the dimensional accuracy of the final part. The larger the shrinkage rate, the easier it is to deform. By installing an infrared thermometer at the print head, the melting temperature of the material can be monitored in real time with an accuracy of ±1°C. A capillary rheometer is installed at the feed point to test the melt flow rate (MFR) of the material. The larger the MFR, the better the material fluidity. In addition, the dilatometer can measure the linear expansion coefficient of the material when heated, from which the shrinkage rate of the material can be estimated. These sensors are connected to the feeding system of the 3D printer and upload the measured material parameters to the control system in real time. Real-time collection of printing parameters is the basis for dynamic optimization of the path. Only by accurately grasping the changes in materials and environment can we judge whether the current filling method is reasonable and provide a reliable basis for subsequent path correction. The collection accuracy of environmental parameters and material parameters is high. It is necessary to select suitable sensors and integrate them well with the printing equipment to minimize the impact on the normal printing process. At the same time, the sensor should be equipped with corresponding signal processing circuits to complete filtering, amplification, A / D conversion, etc., and convert analog signals into digital signals for the control system to read and analyze.

[0199] Step S3200, inputting the printing environment parameters and the material characteristic parameters into the pre-trained printing quality prediction model to obtain second printing deviation data; the second printing deviation data includes second size deviation data, second shape deviation data and second surface quality deviation data;

[0200] Specifically, the print quality prediction model uses the gradient boosting decision tree (GBDT) algorithm. GBDT can build a powerful integrated prediction model by combining the prediction results of multiple weak learners (decision trees). It can flexibly handle various types of features (such as continuous and discrete), and has strong nonlinear modeling capabilities and noise resistance. First, a large number of known printing parameters (such as printing speed, printing temperature, material type, etc.) and corresponding multi-dimensional deviation data (such as size deviation, shape deviation, surface quality deviation) are collected as training samples. Then, through iterative training of multiple rounds of decision trees, each round learns the residual of the previous round, so that the overall prediction result continues to approach the true value. In the generation process of each decision tree, the optimal features and splitting points are selected by the greedy algorithm for node splitting to maximize the information gain. After all decision trees are generated, they are linearly combined to obtain the final strong learner as the prediction model of printing deviation. When new printing environment parameters and material characteristic parameters are input, the GBDT model can comprehensively consider various factors and predict the most likely size deviation, shape deviation and surface quality deviation values. The GBDT prediction model can obtain the printing parameters collected by the sensor in real time, quickly estimate the deviation level of the current filling path, and provide an important basis for subsequent path optimization. Through big data-driven deviation prediction, combined with expert experience and machine learning algorithms, it can continuously accumulate and optimize 3D printing process knowledge, providing strong support for the realization of intelligent production.

[0201] Step S3300, comparing the second printing deviation data with a preset deviation threshold, and determining whether a path correction mechanism needs to be triggered; the deviation threshold includes a size deviation threshold Td, a shape deviation threshold Ts, and a surface quality deviation threshold Tq; the path correction mechanism includes size deviation correction, shape deviation correction, and surface quality correction;

[0202] Furthermore, if Figure 8 As shown, step S3300 includes:

[0203] Step S3310: if the second size deviation data exceeds Td, size deviation correction is triggered;

[0204] Step S3320: if the second shape deviation data exceeds Ts, triggering shape deviation correction;

[0205] Step S3330: If the second surface quality deviation data exceeds Tq, surface quality correction is triggered.

[0206] Specifically, after the print deviation prediction value (second print deviation data) is obtained, it is necessary to evaluate the severity of the deviation. It is necessary to trigger the path correction only when it exceeds a certain range. Therefore, a series of deviation thresholds should be set in advance as a criterion. The dimensional deviation threshold Td can refer to the dimensional tolerance band marked in the RV reducer drawing, such as ±0.1mm. If the predicted dimensional deviation exceeds the tolerance band, it means that the current filling path cannot meet the design requirements and needs to be adjusted. The shape deviation threshold Ts can refer to the form and position tolerance requirements of the RV reducer, such as flatness ≤0.05mm and roundness ≤0.03mm. If the predicted shape deviation exceeds the tolerance range, it means that the filling path may introduce large deformation and needs to be optimized. The surface quality threshold Tq generally refers to the 3D printing process specifications. Different printing methods and materials will have different roughness requirements. For example, the surface roughness requirement for PA12 printed by the selective laser sintering (SLS) process is Ra1.6. If the predicted surface defect level exceeds the roughness limit, it means that the filling method will affect the surface performance and needs to be improved. The setting of the threshold needs to take into account both printing efficiency and quality. If it is too high, corrections will be triggered frequently, extending the printing cycle; if it is too low, the purpose of defect control cannot be achieved. A set of threshold standards commonly used in the industry can be formulated based on process test results and production experience. In actual applications, the system will automatically compare the predicted deviation with the threshold. Once it is found to exceed the standard, an alarm will be issued to prompt the operator to intervene. At the same time, the deviation data and threshold conditions will be recorded in detail for technicians to analyze the reasons and improve the process parameters. The printing deviation early warning mechanism can avoid the accumulation of defects and correct unreasonable filling paths in time, thereby ensuring the yield rate of RV reducers. On the one hand, the early warning is based on objective deviation thresholds, and on the other hand, it is inseparable from the guidance of expert experience. Printing companies should establish a complete deviation response plan, formulate corresponding correction strategies for deviations caused by different reasons, and be targeted. Deviation early warning makes path optimization more forward-looking, rather than a last-ditch effort, and is an important part of ensuring the quality of 3D printing.

[0207] Step S3400: if the path correction mechanism is triggered, the third filling path data is dynamically adjusted according to the second printing deviation data to generate fourth filling path data; and the 3D printing process of the complex structural part of the RV reducer is executed according to the fourth filling path data;

[0208] Specifically, for dimensional deviation, it is mainly achieved by adjusting the filling spacing and angle. If the dimensional deviation correction is triggered, the filling spacing and filling angle in the third filling path data are adjusted to generate the fourth filling path data; the smaller the filling spacing, the closer the size is to the design value; the smaller the angle between the filling angle and the long side, the less the size shrinks; the filling spacing refers to the distance between adjacent filling lines. The smaller the spacing, the higher the filling rate and the closer the size is to the design value, but the printing efficiency will be reduced. In order to find a balance between efficiency and accuracy, a variable spacing strategy can be adopted, with small spacing near the outer contour and large spacing in the internal area. The filling angle refers to the angle between the filling line and the long side of the part. Vertical filling will aggravate the thermal expansion and contraction effect, resulting in dimensional shrinkage. To reduce shrinkage, you can choose to fill at a small angle to the long side, such as 15° or 30°. For shape deviation, it is mainly achieved by optimizing the filling direction and form. If the shape deviation correction is triggered, the filling direction and filling form in the third filling path data are adjusted to generate the fourth filling path data; the filling direction is consistent with the force direction to reduce deformation; the filling form is selected as grid filling or honeycomb filling, etc., which can improve the support stiffness; the filling direction should be consistent with the force direction as much as possible, so that the filling line bears tensile stress and prevents deformation. When the filling direction is difficult to match the force direction, cross filling can be used, and the mutually perpendicular filling lines form a grid support. The filling form also affects the shape stability, and common ones include straight line filling, grid filling, honeycomb filling, etc. Straight line filling has low density and is prone to deformation; grid filling has high strength, but stress concentration; honeycomb filling has high strength and uniform stress distribution. A reasonable filling form can be selected based on the force characteristics.

[0209] For the surface quality problem, it is mainly achieved by changing the filling density and speed. If the surface quality correction is triggered, the filling density and filling speed in the third filling path data are adjusted to generate the fourth filling path data; the higher the filling density, the denser the surface; the slower the filling speed, the smoother the surface. The filling density is usually expressed by the filling rate, that is, the percentage of the filling line in the cross section. The higher the filling rate, the denser the surface and the fewer defects, but the longer the printing time. A gradient filling strategy can be adopted, with a high filling rate on the surface and a low filling rate inside, taking into account both surface quality and cost. The filling speed also affects the surface quality. The faster the speed, the thicker the filling line and the rougher the surface. To obtain a smooth surface, the filling speed can be reduced near the outer surface to improve the resolution. Too slow speed will cause poor welding, which requires a trade-off. The fourth filling path comprehensively considers real-time deviation feedback and can better adapt to changes in printing status. Dynamic adjustment of filling parameters is a complex optimization problem that requires balancing multiple objectives, such as accuracy, efficiency, strength, cost, etc. It can be modeled as a multi-objective optimization, solved by an evolutionary algorithm, and the optimal combination is searched in the parameter space. The optimization process should make full use of expert knowledge and design reasonable optimization variables and constraints. For example, the angle should not be too large, and the speed should not exceed the equipment limit. In addition, it is necessary to evaluate the sensitivity of the parameters, identify key parameters, and reduce the optimization dimension. Dynamic path optimization is a closed-loop feedback control that continuously corrects the filling parameters by monitoring the deviation in real time, and finally converges to the best printing solution. The optimized path can be adaptively adjusted to compensate for various interferences in the printing process, making the quality of parts more stable and reliable.

[0210] Step S3500, repeating steps S3100 to S3400 until the RV reducer is printed;

[0211] Specifically, since 3D printing is a progressive manufacturing process, dynamic path optimization needs to be implemented throughout the printing process. After printing begins, it enters the real-time monitoring mode and continuously collects printing parameters. At regular intervals (such as 1 minute), a path evaluation is triggered to predict the current deviation. If the deviation exceeds the threshold, the path correction program is started, the optimization algorithm is called to generate a new path, and it is converted into process parameters for distribution. During the printing process, path optimization and printing execution are carried out simultaneously, and the two interact continuously to form dynamic feedback. After multiple iterations, the printing parameters will be continuously corrected until the deviation converges to the allowable range. This real-time closed-loop control can correct the deviation in time, dynamically compensate for process errors, and avoid defect diffusion. The frequency of repeated optimization needs to be determined according to the specific situation. Too high a frequency will increase the computational burden, and too low a frequency will make it difficult to capture the printing dynamics. Dynamic path optimization runs through the entire printing cycle of the RV reducer, making the printing process more transparent and controllable. By monitoring the printing status in real time, evaluating the risk of deviation, and automatically adjusting the process parameters, the quality of the parts is more stable and uniform. Dynamic optimization must strictly control the time cost to avoid affecting the printing progress. Efficient algorithms, such as differential algorithms for image processing, can be used to quickly extract deviation information. Heuristic methods, such as genetic algorithms, should be used for optimization search to avoid falling into local optimum. Process parameter adjustment should be smoothly transitioned to avoid oscillation caused by step changes. Dynamic path optimization makes 3D printing an intelligent manufacturing process, which can ultimately complete high-quality parts manufacturing through real-time perception, dynamic decision-making, and online execution. This is the development trend of modern advanced manufacturing technology, representing a major leap in 3D printing technology from experience to science, from offline to online, and from open loop to closed loop.

[0212] Step S3600, after printing is completed, the RV reducer is post-processed and quality inspected.

[0213] Specifically, after the printing task is completed, the RV reducer needs to be post-processed and quality tested. Post-processing mainly includes substrate removal, surface polishing, heat treatment, etc., which can improve the mechanical properties and appearance quality of the parts. Common substrate removal methods include solvent immersion, ultrasonic cleaning, etc., which can quickly dissolve the support material. Surface polishing can be done by sandpaper grinding, sandblasting, etc. to improve the surface finish. Heat treatment such as annealing or quenching can eliminate residual stress and stabilize the size. The post-processing effect directly affects the performance of the parts. The process parameters must be strictly controlled and included in the quality control system. Quality inspection mainly includes size inspection, shape inspection and performance inspection. Size inspection can use three-coordinate measuring machines, vernier calipers, etc. to measure the key dimensions of the parts and compare them with the drawing tolerances. Shape inspection can use optical scanners, etc. to measure surface profile, flatness, etc., and analyze shape deviations. Performance inspection can test the strength, hardness, conductivity, etc. of parts to evaluate performance. Correlating the dynamically optimized path parameters with the test results can evaluate the optimization effect and correct the optimization direction. Summarize successful optimization cases as best practices and apply them to subsequent tasks. Analyze the causes of failed optimization cases, learn lessons, and avoid repeating mistakes. Post-processing and quality inspection are important links to ensure the performance of RV reducers, and are also important means to evaluate the effect of dynamic optimization. Through closed-loop optimization, data can be continuously accumulated, models can be improved, and the level of intelligent path design can be improved. Dynamic path optimization combines online detection and offline optimization throughout the entire product life cycle, and ultimately achieves high-quality and efficient manufacturing of RV reducers. This is an important milestone in the application of 3D printing in the industrial field, marking the leap of 3D printing from prototype manufacturing to large-scale production.

[0214] Example 2

[0215] This embodiment provides a rapid prototyping system for complex structural parts of an RV reducer based on 3D printing technology on the basis of Embodiment 1. Fig. 9 As shown, including:

[0216] Region segmentation module: used to receive the three-dimensional model data of the RV reducer, perform topological optimization on the three-dimensional model, and generate first filling path data; perform feature segmentation on the three-dimensional model of the RV reducer to obtain differentiated sub-regions;

[0217] Path filling module: used to fill each sub-area and generate the initial filling path of the complex structural parts of the RV reducer;

[0218] A first correction module: used for acquiring first printing deviation data, and based on the first printing deviation data, correcting the initial filling path data to generate third filling path data; the first printing deviation data includes size deviation data, shape deviation data and surface quality deviation data;

[0219] The second correction module is used to obtain the second printing deviation data, compare the second printing deviation data with the preset deviation threshold, and determine whether the path correction mechanism needs to be triggered; if the path correction mechanism is triggered, the third filling path data is dynamically adjusted according to the second printing deviation data to generate the fourth filling path data; according to the fourth filling path data, the 3D printing process of the complex structural parts of the RV reducer is executed.

[0220] In the region segmentation module, the topology optimization of the three-dimensional model to generate the first filling path data includes:

[0221] Step S1110, establishing a topology optimization mathematical model based on the three-dimensional model data of the RV reducer;

[0222] Step S1120, solving the topology optimization mathematical model to obtain optimized filling material distribution information, and generating first filling path data according to the filling material distribution information.

[0223] In the region segmentation module, the three-dimensional model of the RV reducer is feature segmented to obtain differentiated sub-regions including:

[0224] Step S1210, establishing a three-dimensional finite element model of the RV reducer according to the three-dimensional model data of the RV reducer, performing structural mechanics analysis on the reducer under rated working conditions, and obtaining a stress cloud map and a displacement cloud map; the three-dimensional finite element model includes n1 nodes;

[0225] Step S1220, extracting high stress areas from the stress cloud map;

[0226] Step S1230, extracting the deformation concentration area from the displacement cloud map;

[0227] Step S1240, performing wall thickness analysis on the three-dimensional model of the RV reducer, and classifying the wall thickness greater than the preset wall thickness threshold T thick The area is extracted as the wall thickness area;

[0228] Step S1250, obtaining sub-region A, sub-region B, sub-region C and sub-region D according to the high stress region, the deformation concentration region and the wall thickness region.

[0229] In the path filling module, filling each sub-area to generate the initial filling path of the complex structural parts of the RV reducer includes:

[0230] Step S1310, using a high-intensity filling mode including but not limited to a cross and a spiral line and a filling density ρ1 for the sub-region A, generating a filling path for the sub-region A, and obtaining filling path data for the sub-region A;

[0231] Step S1320, using a high-rigidity filling mode including but not limited to a honeycomb or hexagonal grid and a filling density ρ2 for the sub-region B, generating a filling path for the sub-region B, and obtaining filling path data for the sub-region B;

[0232] Step S1330, using a low filling mode including but not limited to a triangle or square grid and a filling density ρ3 for the sub-region C, generating a filling path for the sub-region C, and obtaining filling path data for the sub-region C;

[0233] Step S1340, using a filling density ρ4 for the sub-region D, generating a filling path for the sub-region D, and obtaining filling path data for the sub-region D; wherein ρ2>ρ1>ρ3>ρ4;

[0234] Step S1350, combining the filling path data of each sub-region to generate second filling path data;

[0235] Step S1400: merge the first filling path data and the second filling path data to generate an initial filling path for the complex structural part of the RV reducer.

[0236] The first correction module comprises:

[0237] The first deviation acquisition unit is used to acquire n2 similar historical printing tasks with similar structural features to the current RV reducer three-dimensional model, and extract the printing parameters of the n2 similar historical printing tasks as the first historical printing data; n2 is a positive integer; analyze the first historical printing data to obtain the first printing deviation data of each similar historical printing task, wherein the first printing deviation data includes size deviation data, shape deviation data and surface quality deviation data;

[0238] The initial path correction unit corrects the initial filling path data based on the first printing deviation data to generate third filling path data.

[0239] In the first deviation acquisition unit, the acquisition of n2 similar historical printing tasks with similar structural features to the current RV reducer three-dimensional model includes:

[0240] Step S2110, comparing the 3D models of all historical printing tasks with the current RV reducer 3D model, and calculating the shape feature similarity;

[0241] Step S2120, extracting the shape feature similarity with the current RV reducer three-dimensional model is greater than a preset similarity threshold T sim The historical printing tasks are marked as similar historical printing tasks.

[0242] In the first deviation acquisition unit, analyzing the first historical printing data to obtain the first printing deviation data of each similar historical printing task includes:

[0243] Step S2210, extracting the size parameters of each similar historical printing task in the first historical printing data, comparing the size parameters with the design size parameters of the historical three-dimensional model corresponding to the similar historical printing task, and calculating the size deviation data;

[0244] Step S2220, extracting shape parameters of each similar historical printing task in the first historical printing data, comparing the shape parameters with the design shape parameters of the historical three-dimensional model corresponding to the similar historical printing task, and calculating shape deviation data;

[0245] Step S2230 , extracting the surface quality parameter of each similar historical printing task in the first historical printing data to obtain surface quality deviation data.

[0246] The second correction module comprises:

[0247] The second deviation acquisition unit is used to collect printing environment parameters and material characteristic parameters in real time during the 3D printing process of the complex structural parts of the RV reducer; input the printing environment parameters and material characteristic parameters into the pre-trained printing quality prediction model to obtain second printing deviation data; the second printing deviation data includes second size deviation data, second shape deviation data and second surface quality deviation data;

[0248] The third path correction unit is used to compare the second printing deviation data with the preset deviation threshold to determine whether the path correction mechanism needs to be triggered; the deviation threshold includes a size deviation threshold Td, a shape deviation threshold Ts and a surface quality deviation threshold Tq; the path correction mechanism includes a size deviation correction, a shape deviation correction and a surface quality correction; if the path correction mechanism is triggered, the third filling path data is dynamically adjusted according to the second printing deviation data to generate fourth filling path data; according to the fourth filling path data, the 3D printing process of the complex structural parts of the RV reducer is executed.

[0249] Example 3

[0250] This embodiment discloses an electronic device, which may include one or more processors and one or more memories. The memories store computer-readable codes, which, when executed by the one or more processors, may execute the 3D printing technology-based rapid prototyping method for complex structural parts of an RV reducer.

[0251] The method or system according to the embodiment of the present application can also be implemented with the help of the architecture of the electronic device disclosed in the present application. The electronic device may include a bus, one or more CPUs, a read-only memory (ROM), a random access memory (RAM), a communication port connected to a network, an input / output component, a hard disk, etc. A storage device in the electronic device, such as a ROM or a hard disk, can store the rapid prototyping method for complex structural parts of an RV reducer based on 3D printing technology provided in the present application. The rapid prototyping method of complex structural parts of an RV reducer based on 3D printing technology may, for example, include: receiving three-dimensional model data of an RV reducer, performing topological optimization on the three-dimensional model, and generating first filling path data; performing feature segmentation on the three-dimensional model of the RV reducer to obtain differentiated sub-regions; filling each sub-region to generate an initial filling path for the complex structural parts of the RV reducer; obtaining first printing deviation data, and based on the first printing deviation data, correcting the initial filling path data to generate third filling path data; the first printing deviation data includes size deviation data, shape deviation data, and surface quality deviation data; obtaining second printing deviation data, comparing the second printing deviation data with a preset deviation threshold, and determining whether a path correction mechanism needs to be triggered; if the path correction mechanism is triggered, dynamically adjusting the third filling path data according to the second printing deviation data to generate fourth filling path data; and executing the 3D printing process of the complex structural parts of the RV reducer according to the fourth filling path data.

[0252] Furthermore, the electronic device may also include a user interface. Of course, the architecture disclosed in the present invention is only exemplary, and when implementing different devices, one or more components in the electronic device disclosed in the present invention may be omitted according to actual needs.

[0253] Example 4

[0254] This embodiment discloses a computer-readable storage medium, on which computer-readable instructions are stored. When the computer-readable instructions are executed by a processor, the rapid prototyping method for complex structural parts of an RV reducer based on 3D printing technology according to the embodiment of the present application described with reference to the above figures can be executed. The storage medium includes, but is not limited to, for example, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and cache memory (cache). Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0255] In addition, according to the implementation of the present application, the process described in the above reference flowchart can be implemented as a computer software program. For example, the present application provides a non-temporary machine-readable storage medium, the non-temporary machine-readable storage medium stores machine-readable instructions, and the machine-readable instructions can be executed by a processor to execute instructions corresponding to the method steps provided in the present application, for example: receiving three-dimensional model data of an RV reducer, topologically optimizing the three-dimensional model, and generating first filling path data; performing feature segmentation on the three-dimensional model of the RV reducer to obtain differentiated sub-regions; filling each sub-region to generate an initial filling path of a complex structural part of the RV reducer; obtaining first printing deviation data, and based on the first printing deviation data, correcting the initial filling path data to generate third filling path data; the first printing deviation data includes size deviation data, shape deviation data, and surface quality deviation data; obtaining second printing deviation data, comparing the second printing deviation data with the size of a preset deviation threshold, and determining whether a path correction mechanism needs to be triggered; if the path correction mechanism is triggered, dynamically adjusting the third filling path data according to the second printing deviation data to generate fourth filling path data; and executing the 3D printing process of the complex structural part of the RV reducer according to the fourth filling path data. When the computer program is executed by a central processing unit (CPU), the above functions defined in the method of the present application are performed.

[0256] The methods, systems, and devices of the present application may be implemented in many ways. For example, the methods, systems, and devices of the present application may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above order of steps for the method is for illustration only, and the steps of the method of the present application are not limited to the order specifically described above unless otherwise specified. In addition, in some embodiments, the present application may also be implemented as programs recorded in a recording medium, which include machine-readable instructions for implementing the method according to the present application. Therefore, the present application also covers recording media storing programs for executing the method according to the present application.

[0257] In addition, the parts of the above-mentioned technical solutions provided in the embodiments of the present application that are consistent with the implementation principles of the corresponding technical solutions in the prior art are not described in detail to avoid excessive redundancy.

[0258] The specific implementation modes as described above further describe the purpose, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation mode of the present invention and is not intended to limit the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A rapid prototyping method for complex structural parts of RV reducer based on 3D printing technology, characterized in that: The method comprises: Receive the three-dimensional model data of the RV reducer, perform topological optimization on the three-dimensional model, and generate first filling path data; perform feature segmentation on the three-dimensional model of the RV reducer to obtain differentiated sub-regions; fill each sub-region to generate an initial filling path for the complex structural parts of the RV reducer; Acquire first printing deviation data, and based on the first printing deviation data, correct the initial filling path data to generate third filling path data; the first printing deviation data includes size deviation data, shape deviation data and surface quality deviation data; Obtain the second printing deviation data, compare the second printing deviation data with the preset deviation threshold, and determine whether it is necessary to trigger the path correction mechanism; if the path correction mechanism is triggered, dynamically adjust the third filling path data according to the second printing deviation data to generate fourth filling path data; and execute the 3D printing process of the complex structural parts of the RV reducer according to the fourth filling path data.

2. The rapid prototyping method for complex structural parts of RV reducer based on 3D printing technology according to claim 1 is characterized in that: The differentiated sub-regions include sub-region A, sub-region B, sub-region C and sub-region D; The feature segmentation of the three-dimensional model of the RV reducer to obtain differentiated sub-regions includes: A three-dimensional finite element model of the RV reducer is established, and a structural mechanics analysis of the RV reducer is performed under rated working conditions to obtain stress and displacement cloud maps; Extract high stress areas from stress cloud maps, and extract deformation concentration areas from displacement cloud maps; Perform wall thickness analysis on the 3D model of the RV reducer and set the wall thickness greater than the preset wall thickness threshold T thick The area is extracted as the wall thickness area; According to the high stress area, the deformation concentration area and the wall thickness area, sub-area A, sub-area B, sub-area C and sub-area D are obtained.

3. The rapid prototyping method for complex structural parts of RV reducer based on 3D printing technology according to claim 2 is characterized in that: The sub-region A, sub-region B, sub-region C and sub-region D obtained according to the high stress region, the deformation concentration region and the wall thickness region include: Subtract the deformation concentration area and the thick wall area from the high stress area to obtain sub-area A; Subtract the high stress area and thick wall area from the deformation concentration area to obtain sub-area B; Subtract the high stress area and the deformation concentration area from the thick wall area to obtain the sub-area C; The area other than sub-area A, sub-area B, and sub-area C is referred to as sub-area D.

4. The rapid prototyping method for complex structural parts of RV reducer based on 3D printing technology according to claim 3 is characterized in that: The filling of each sub-area to generate an initial filling path for the complex structural parts of the RV reducer includes: A high-intensity filling mode including a cross and a spiral line and a filling density ρ1 are adopted for the sub-region A to generate a filling path of the sub-region A and obtain the filling path data of the sub-region A; A high-rigidity filling mode including honeycomb and hexagonal grids and a filling density ρ2 are adopted for sub-region B to generate a filling path for sub-region B and obtain filling path data for sub-region B; A low filling mode including triangle and square grids and a filling density ρ3 are adopted for the sub-region C to generate a filling path of the sub-region C and obtain the filling path data of the sub-region C; A filling density ρ4 is used for the sub-region D to generate a filling path for the sub-region D, and the filling path data for the sub-region D is obtained; wherein ρ2>ρ1>ρ3>ρ4; Combining the filling path data of each sub-area to generate second filling path data; The first filling path data and the second filling path data are merged to generate the initial filling path of the complex structural parts of the RV reducer.

5. The rapid prototyping method for complex structural parts of RV reducer based on 3D printing technology according to claim 1 is characterized in that: The obtaining of the first printing deviation data comprises: The first printing deviation data includes size deviation data, shape deviation data and surface quality deviation data; Compare the 3D models of all historical printing tasks with the current RV reducer 3D model to calculate the similarity of shape features; The similarity between the extracted shape features and the current RV reducer 3D model is greater than the preset similarity threshold T sim The historical printing tasks are marked as similar historical printing tasks; and the printing parameters of the similar historical printing tasks are extracted as the first historical printing data; Extracting the size parameters of each similar historical printing task in the first historical printing data, comparing the size parameters with the design size parameters of the historical three-dimensional model corresponding to the similar historical printing task, and calculating the size deviation data; Extracting shape parameters of each similar historical printing task in the first historical printing data, comparing the shape parameters with design shape parameters of the historical three-dimensional model corresponding to the similar historical printing task, and calculating shape deviation data; Surface quality parameters of each similar historical printing task in the first historical printing data are extracted to obtain surface quality deviation data.

6. The rapid prototyping method for complex structural parts of RV reducer based on 3D printing technology according to claim 1 is characterized in that: The obtaining of the second printing deviation data comprises: During the 3D printing process of complex structural parts of RV reducers, the printing environment parameters and material characteristic parameters are collected in real time; Inputting the printing environment parameters and the material characteristic parameters into the pre-trained printing quality prediction model to obtain second printing deviation data; The second printing deviation data includes second size deviation data, second shape deviation data and second surface quality deviation data.

7. The rapid prototyping method for complex structural parts of RV reducer based on 3D printing technology according to claim 6 is characterized in that: The deviation thresholds include a size deviation threshold Td, a shape deviation threshold Ts and a surface quality deviation threshold Tq; The path correction mechanism includes size deviation correction, shape deviation correction and surface quality correction; The comparing the second printing deviation data with the preset deviation threshold to determine whether the path correction mechanism needs to be triggered includes: If the second size deviation data exceeds Td, size deviation correction is triggered; If the second shape deviation data exceeds Ts, the shape deviation correction is triggered; If the second surface quality deviation data exceeds Tq, a surface quality correction is triggered.

8. A rapid prototyping system for complex structural parts of an RV reducer based on 3D printing technology, which is used to implement the rapid prototyping method for complex structural parts of an RV reducer based on 3D printing technology as described in any one of claims 1 to 7, characterized in that: The system comprises: Region segmentation module: used to receive the three-dimensional model data of the RV reducer, perform topological optimization on the three-dimensional model, and generate first filling path data; perform feature segmentation on the three-dimensional model of the RV reducer to obtain differentiated sub-regions; Path filling module: used to fill each sub-area and generate the initial filling path of the complex structural parts of the RV reducer; A first correction module: used for acquiring first printing deviation data, and based on the first printing deviation data, correcting the initial filling path data to generate third filling path data; the first printing deviation data includes size deviation data, shape deviation data and surface quality deviation data; The second correction module is used to obtain the second printing deviation data, compare the second printing deviation data with the preset deviation threshold, and determine whether the path correction mechanism needs to be triggered; if the path correction mechanism is triggered, the third filling path data is dynamically adjusted according to the second printing deviation data to generate the fourth filling path data; according to the fourth filling path data, the 3D printing process of the complex structural parts of the RV reducer is executed.

9. An electronic device comprising a memory, a central processing unit, and a computer program stored in the memory and executable on the central processing unit, characterized in that: When the central processing unit executes the computer program, the method for rapid prototyping of complex structural parts of an RV reducer based on 3D printing technology described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which, when executed, implements the method for rapid prototyping of complex structural parts of an RV reducer based on 3D printing technology as described in any one of claims 1 to 7.

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