Iterative optimization method and equipment for molded surface of automobile part mold and medium

Abstract

The invention discloses an automobile part mold profile iterative optimization method and device and a medium, and relates to the technical field of mold profile optimizing.The automobile part mold profile iterative optimization method comprises the following steps that a mold finite element model is established based on design parameters of automobile parts, and initial profile data are obtained; carrying out analogue simulation analysis by utilizing computer software based on the finite element model of the mold to obtain springback molded surface data; based on the initial molded surface data and the rebound molded surface data, solving a molded surface optimization path by using a least square support vector machine to obtain molded surface optimization iteration data; based on the molded surface optimization iteration data, performing optimization iteration on the molded surface of the mold to obtain a final optimized molded surface of the mold; the method is used for solving the problems of low precision, too many times and low efficiency of mold profile optimization iteration caused by only considering the node displacement difference before and after springback and not considering the springback path when mold profile optimization is carried out on the automobile part springback problem in an existing mold profile optimization technology.

Description

Technical Field

[0001] The invention relates to the technical field of mold surface optimization, and in particular to a method, equipment and medium for iterative optimization of a mold surface of an automobile component. Background Art

[0002] Mold surface optimization technology is a method to optimize the mold geometry through precise simulation and calculation during the mold design stage; this technology aims to improve mold manufacturing accuracy, reduce production cycle, reduce costs, and ensure that the quality of the final product meets the design requirements.

[0003] Some auto parts, such as covers, will inevitably have springback problems during the production process, resulting in unqualified auto parts. However, changing process parameters and material parameters alone cannot fundamentally solve the springback problem. Therefore, mold surface optimization is crucial. Mold surface optimization can fundamentally solve the springback problem of parts. The idea of ​​optimization is to use springback to allow parts to transition during deformation, so that the shape accuracy of the formed parts after springback just meets the requirements, truly avoiding the occurrence of springback. The traditional optimization method is to use repeated trial and error and mold repair, which is time-consuming and labor-intensive, and overly dependent on manual labor. The existing mold surface optimization method is to establish a finite model of the mold through modeling software, and then optimize the mold surface through the geometric compensation method, that is, first obtain the geometric offset before and after the springback of a certain node according to calculation, and according to the obtained offset, apply a vector opposite to the geometric offset vector before the node springs back, then the position of the node after springback compensation is the end point of the vector, and this method is applied to each node to obtain the springback compensation of the entire surface. , complete the surface optimization; however, in the process of forming automobile parts, springback is a continuous process. During the springback process, each node will displace according to its own path, and finally form the result of springback, which is reflected in the change of curvature and can be regarded as a rotation of a certain point. Using only the node displacement difference before and after springback for surface optimization will cause excessive springback of automobile parts. This is because in the process of reverse compensation, the curvature of the compensation surface is too large due to reverse compensation, and the springback amount will increase accordingly. Usually, many iterations of optimization are required to achieve a qualified mold surface, and the accuracy is not high. If the path of springback is considered in the process of mold surface optimization, the accuracy of mold surface optimization can be improved, the number of mold surface optimization iterations can be reduced, and the efficiency of mold surface optimization can be improved. Therefore, the existing mold surface optimization technology only considers the node displacement difference before and after springback, and does not consider the springback path, which leads to the problems of low accuracy, excessive number and low efficiency of mold surface optimization iteration. Summary of the invention

[0004] The present invention aims to solve one of the technical problems in the prior art to at least a certain extent, by establishing a mold finite element model, using computer software to perform simulation analysis, and using a least squares support vector machine to solve the surface optimization path, and optimizing the mold surface iteratively; to solve the problem that the existing mold surface optimization technology only considers the node displacement difference before and after the springback, and does not consider the springback path, resulting in low precision, too many times and low efficiency of mold surface optimization iterations.

[0005] To achieve the above objectives, in a first aspect, the present application provides a method for iterative optimization of a mold surface of an automobile component, comprising the following steps:

[0006] Based on the design parameters of automobile parts, the mold finite element model is established to obtain the initial surface data;

[0007] Based on the finite element model of the mold, computer software is used to perform simulation analysis to obtain the springback surface data;

[0008] Based on the initial profile data and the springback profile data, the profile optimization path is solved by using the least squares support vector machine to obtain the profile optimization iteration data;

[0009] The mold surface is optimized and iterated based on the surface optimization iteration data to obtain the final optimized mold surface.

[0010] Furthermore, based on the design parameters of the automobile parts, a mold finite element model is established to obtain initial surface data, including the following sub-steps:

[0011] According to the design parameters of automobile parts, the geometric model of automobile parts mold is constructed by using CAD software, and then exported and saved to obtain the mold geometric model file;

[0012] Based on DEFORM software, using the pre-processing module of DEFORM software, the mold geometry model file is imported to obtain the DDEFORM mold model;

[0013] DEFORM software is used to mesh the DEFORM mold model using tetrahedrons, the number of mesh units is set to A1, and the mesh of the contact area between the mold and the part in the DEFORM mold model is refined. After completion, the mold finite element model is obtained;

[0014] The mold finite element model is placed in a rectangular coordinate system, and the coordinates of the mesh unit nodes of the mold finite element model surface are obtained and marked as initial surface data. The mold surface corresponding to the initial surface data is the initial surface.

[0015] Furthermore, based on the mold finite element model, computer software is used to perform simulation analysis to obtain the springback profile data, including the following sub-steps:

[0016] Using DEFORM software, according to the part design parameters, the material parameters, material properties and elastic modulus of the part are input, and the material properties of the mold are input, and then the first simulation production is performed to obtain the first simulated automobile parts finite element model, which is marked as the first part simulation model, and the elastic modulus is E;

[0017] The coordinates of the mesh unit nodes on the surface of the simulation model of the first part are extracted and marked as springback surface data. The mold surface corresponding to the springback surface data is the springback surface.

[0018] Furthermore, based on the initial profile data and the springback profile data, the profile optimization path is solved by using the least squares support vector machine to obtain the profile optimization iteration data, which includes the following sub-steps:

[0019] The input elastic modulus is adjusted to 0.5*E, and the second simulation production is performed to obtain the finite element model of the second simulated automobile parts, which is marked as the second part simulation model; the coordinates of the grid unit nodes on the surface of the second part simulation model are extracted and marked as the springback intermediate profile data, and the mold profile corresponding to the springback intermediate profile data is the springback intermediate profile;

[0020] The least squares support vector machine is used to solve the surface springback path.

[0021] Furthermore, solving the springback path of the profile using the least squares support vector machine includes the following sub-steps:

[0022] Based on the initial profile data, the springback profile data and the springback intermediate profile data, the mesh unit nodes where the profile springback occurs are screened out, and the coordinates of the mesh unit nodes where the profile springback occurs in the initial profile data are marked as B0(x0i, y0i, z0i), the coordinates of the mesh unit nodes corresponding to the springback profile data are marked as B1(x1i, y1i, z1i), and the coordinates of the mesh unit nodes corresponding to the springback intermediate profile data are marked as B2(x2i, y2i, z2i), where i represents the coordinates of the i-th mesh unit node, and the i in B0(x0i, y0i, z0i), B1(x1i, y1i, z1i) and B2(x2i, y2i, z2i) correspond to each other;

[0023] Based on the least squares support vector machine algorithm, x0i, y0i, x1i, y1i, x2i and y2i are used as the input of the least squares support vector machine, z0i, z1i and z2i are used as the output of the least squares support vector machine, and the least squares support vector machine is used to perform curve fitting to obtain the curve path from B0 to B1, which is marked as the surface springback path B0~B1;

[0024] The surface optimization path is solved based on the compensation coefficient to obtain the surface optimization iteration data.

[0025] Furthermore, solving the profile optimization similarity path based on the compensation coefficient to obtain the profile optimization iteration data includes the following sub-steps:

[0026] Assume that the compensation coefficient is α, and the coordinates of the mesh unit node on the initial surface corresponding to the mesh unit node coordinates B1(x1i, y1i, z1i) on the surface where the springback occurs are C1(xui, yui, zui) when the compensation coefficient is α;

[0027] Solve C1(xui, yui, zui): draw a tangent to the straight line B0B1 through B1(x1i, y1i, z1i) to get the first equation. Based on B0(x0i, y0i, z0i) and B1(x1i, y1i, z1i), get the length of the straight line B0B1, marked as LB01. Draw a circle with B0(x0i, y0i, z0i) as the center and α*LB01 as the radius to get the second equation.

[0028] Solve xui and yui by combining the first and second equations. If there are two sets of solutions, calculate the vertical distances between the two sets of solutions and the springback surface based on the springback surface data, and select the one with the shorter vertical distance to the springback surface as the final solution; zui=z0i+α*(z1i-z0i); obtain the solved C1, marked as C1(xci, yci, zci);

[0029] If the compensation coefficient α<1, the similar path of profile optimization is: starting point B0 (x0i, y0i, z0i) along the profile springback path B0~B1 to C1 (xci, yci, zci) marked as B0~C1;

[0030] If the compensation coefficient α>1, the surface optimization similarity path is: the surface springback path B0~B1 plus the straight line B1C1, marked as B0~B1C1;

[0031] If the compensation coefficient α=1, then C1=B1, and the surface optimization similarity path is: surface springback path B0~C1;

[0032] The surface optimization path is solved based on the surface optimization similarity path to obtain the surface optimization iteration data.

[0033] Furthermore, solving the profile optimization path based on the profile optimization similarity path and obtaining the profile optimization iteration data includes the following sub-steps:

[0034] Based on B0(x0i, y0i, z0i), the normal vector of point B0(x0i, y0i, z0i) is obtained, marked as B0H; based on C1(xci, yci, zci), the normal vector of C1(xci, yci, zci) is obtained, marked as C1H;

[0035] Move the profile optimization similarity path so that the end point of the profile optimization similarity path coincides with the starting point position C1 of the profile optimization similarity path, and then rotate the profile optimization similarity path around the starting point position C1 until B0H coincides with C1H, mark the profile optimization similarity path at this time as the profile optimization path, obtain the position coordinates D (xdi, ydi, zdi) of the end point of the profile optimization path, and mark it as the profile optimization iteration data.

[0036] Furthermore, the mold surface is optimized and iterated based on the surface optimization iteration data to obtain the final optimized mold surface, which includes the following sub-steps:

[0037] Modify the profile of the mold finite element model based on the profile optimization iteration data to obtain the optimized profile of the mold, perform a third simulation production, obtain the third simulated automotive parts finite element model, mark it as the third part simulation model, output the parameters of the third part simulation model, and judge whether the parameters of the third part simulation model are within the error range based on the design parameters of the automotive parts. If they are within the error range, mark the profile of the modified mold finite element model as the final optimized profile of the mold;

[0038] If it is not within the error range, extract the coordinates of the grid unit nodes on the surface of the simulation model of the third part as new springback surface data, and again use the least squares support vector machine to solve the surface optimization path based on the initial surface data and the springback surface data to obtain a new surface optimization path. Then, optimize the mold optimization surface again based on the new surface optimization path to obtain a new mold optimization surface. Perform simulation production again and determine whether the parameters of the simulated model are within the error range. Until the parameters of the simulated model are within the error range, mark the surface of the mold finite element model after the last modification as the final optimized mold surface.

[0039] In a second aspect, the present application provides an electronic device, including a processor and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the steps in the above method are performed.

[0040] In a third aspect, the present application provides a storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps in the above method are performed.

[0041] The beneficial effects of the present invention are as follows: the present invention first establishes a mold finite element model based on the design parameters of the automobile parts to obtain initial profile data; then uses computer software to perform simulation analysis based on the mold finite element model to obtain springback profile data; then uses a least squares support vector machine to solve the profile optimization path based on the initial profile data and the springback profile data to obtain profile optimization iteration data; then optimizes and iterates the mold profile based on the profile optimization iteration data to obtain the final optimized mold profile; optimizes the mold profile for the springback problem of automobile parts, calculates and fits the profile optimization path based on the node displacement difference before and after the springback, thereby increasing the accuracy of mold profile optimization, reducing the number of profile optimization iterations, and improving the profile optimization iteration efficiency;

[0042] The present invention establishes a finite element model of the mold and accurately simulates the forging process through finite element simulation technology. It can quickly iterate and verify different mold surface solutions, shorten the process preparation cycle, reduce material waste due to improper design, improve production efficiency, and reduce production costs. The least squares support vector machine is used to solve the surface optimization path. The least squares support vector machine has good performance for complex curve fitting tasks. When faced with data with nonlinear relationships, it can more accurately capture its internal laws. Compared with the support vector machine, it no longer needs to solve quadratic programming, which improves the calculation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 is a flow chart of the steps of the method of the present invention;

[0044] Figure 2 It is a schematic diagram of the rebound intermediate profile of the present invention;

[0045] Figure 3 It is a schematic diagram of the profile springback path of the present invention;

[0046] Figure 4 It is a schematic diagram of profile optimization of the present invention;

[0047] Figure 5 It is a schematic diagram of secondary surface optimization of the present invention;

[0048] Figure 6 It is a schematic structural diagram of the electronic device of the present invention. DETAILED DESCRIPTION

[0049] 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.

[0050] Example 1, please refer to Figure 1 As shown, in the first aspect, the present application provides a method for iterative optimization of a mold surface of an automobile component, comprising the following steps:

[0051] Step S1, based on the design parameters of the automobile parts, a mold finite element model is established to obtain initial surface data; Step S1 includes the following sub-steps:

[0052] Step S101, constructing a geometric model of an automobile component mold using CAD software according to automobile component design parameters, and exporting and saving the model to obtain a mold geometric model file;

[0053] Step S102, based on DEFORM software, using the pre-processing module of DEFORM software, importing the mold geometry model file to obtain the DDEFORM mold model; when performing finite element processing, DEFORM software is mainly composed of a pre-processing module, a solver module and a post-processing module, wherein the pre-processing module can import the CAD geometry model established by other modeling software, and then perform relevant settings, mainly including: dividing the grid, selecting the material model, selecting the solver and iterative algorithm, defining boundary conditions and other simulation parameters; the solver module is a finite element solution calculation module, and generally does not require user intervention in the process of solving the calculation task, but can observe the information of the intermediate results of the solution calculation in real time, and can terminate the process of the solution calculation at any time; the post-processing module can conveniently and intuitively display the results of the solution calculation, so that users can analyze and process the material simulation process;

[0054] Step S103, using DEFORM software, using tetrahedron to mesh the DEFORM mold model, the number of mesh units is set to A1, in this embodiment, the number of mesh units is set to 128000, and the mesh of the contact area between the mold and the part in the DEFORM mold model is refined to improve the accuracy of subsequent production simulation results. After completion, a finite element model of the mold is obtained;

[0055] Step S104, placing the mold finite element model in a rectangular coordinate system, obtaining the coordinates of the mesh unit nodes of the mold finite element model surface, marking them as initial surface data, and the mold surface corresponding to the initial surface data is the initial surface;

[0056] In the specific implementation process, since the finite element software DEFORM does not have the ability of 3D solid modeling, but it has the ability to connect with the data of other 3D solid modeling software, it can easily import the 3D geometric model generated by other modeling software into DEFORM to complete the finite element analysis simulation. Therefore, the geometric model of the automobile parts mold is constructed by CAD software and then imported into DEFORM for subsequent processing; the number of grid cells needs to be set according to the actual application scenario. Setting the number of grid cells too small will affect the accuracy of the production simulation results. Setting the number of grid cells too large will cause the production simulation time to be too long, affecting the efficiency of the surface optimization iteration.

[0057] Step S2, performing simulation analysis based on the mold finite element model using computer software to obtain springback profile data; Step S2 includes the following sub-steps:

[0058] Step S201, using DEFORM software, according to the part design parameters, input the material parameters, material properties and elastic modulus of the part, input the material properties of the mold, and the elastic modulus is E;

[0059] Material parameters include: density, yield strength, tensile strength, hardness, thermal expansion coefficient, Poisson's ratio, shear modulus and bulk modulus; material properties include rigid body and plastic body; elastic modulus is a physical quantity that describes the ability of a material to resist elastic deformation under external force; also known as Young's modulus, it is the ratio of normal stress to the corresponding normal strain in the elastic deformation stage of the material; it reflects the ability of the material to resist elastic deformation; from a macroscopic perspective, the elastic modulus measures the size of the object's ability to resist elastic deformation; from a microscopic perspective, it reflects the bond strength between atoms, ions or molecules, and the unit of elastic modulus is usually Pascal Pa; in this real-time example, the elastic modulus size E is 70GPa;

[0060] Step S202, then performing the first simulation production to obtain the first simulated automobile parts finite element model, which is marked as the first part simulation model;

[0061] Step S203, extracting the coordinates of the grid unit nodes on the surface of the first part simulation model, marking them as springback profile data, and the mold profile corresponding to the springback profile data is the springback profile;

[0062] In the specific implementation process, a rigid body refers to an ideal object whose shape and size remain unchanged under the action of external force; in practice, any solid will have a certain degree of deformation after being subjected to force, but this deformation is extremely small compared to the geometric dimensions of the object itself, so it can be ignored in many applications. In the actual production process, the mold surface should remain unchanged to ensure the accuracy of the produced parts, so the material properties of the mold should be set to a rigid body; a plastic body refers to an object that will undergo permanent deformation under the action of external force; unlike a rigid body, the shape of a plastic body will not completely return to its original state after the external force is removed; in the actual production process, the blank must be shaped by the mold to become a suitable part, so the material properties of the part must be set to a plastic body.

[0063] Step S3, based on the initial profile data and the springback profile data, using the least squares support vector machine to solve the profile optimization path to obtain the profile optimization iteration data; Step S3 includes the following sub-steps:

[0064] Step S301, see Figure 2 As shown, the input elastic modulus is adjusted to 2*E, and the second simulation production is performed to obtain the second simulated automobile parts finite element model, which is marked as the second part simulation model;

[0065] When solving the surface optimization path, in addition to the starting point and the end point, an intermediate point needs to be known; and the elastic modulus of the part is inversely proportional to the springback amount, and the elastic modulus affects the springback degree of the part; therefore, when solving the surface optimization path, when the elastic modulus of the material is increased, the intermediate surface obtained is located between the design surface and the springback surface; however, in order to ensure the accuracy of solving the surface optimization path, the intermediate surface cannot be too close to the design surface or the springback surface, so the elastic modulus after adjustment cannot be too close to the elastic modulus before adjustment, nor can it be too large or close to the elastic modulus before adjustment;

[0066] Step S302, extracting the coordinates of the grid unit nodes on the surface of the second part simulation model, marking them as springback intermediate profile data, and the mold profile corresponding to the springback intermediate profile data is the springback intermediate profile;

[0067] Step S303, using the least squares support vector machine to solve the surface springback path; Step S303 includes the following sub-steps:

[0068] Step S3031, filtering out the mesh unit nodes where the profile springback occurs based on the initial profile data, the springback profile data and the springback intermediate profile data;

[0069] Step S3032, the grid unit node coordinates of the surface springback in the initial surface data are marked as B0 (x0i, y0i, z0i), the corresponding grid unit node coordinates in the springback surface data are marked as B1 (x1i, y1i, z1i), and the corresponding grid unit node coordinates in the springback intermediate surface data are marked as B2 (x2i, y2i, z2i), where i represents the coordinates of the i-th grid unit node, and the i in B0 (x0i, y0i, z0i), B1 (x1i, y1i, z1i) and B2 (x2i, y2i, z2i) correspond to each other; for example, the point (x02, y03, z01) on the initial surface corresponds to the point (x12, y13, z11) on the springback surface, and corresponds to the point (x22, y23, z21) on the springback intermediate surface;

[0070] Step S3033, please refer to Figure 3 As shown, based on the least squares support vector machine algorithm, x0i, y0i, x1i, y1i, x2i and y2i are used as the input of the least squares support vector machine, z0i, z1i and z2i are used as the output of the least squares support vector machine, and the least squares support vector machine is used to perform curve fitting to obtain the curve path from B0 to B1, which is marked as the surface springback path B0~B1; in this embodiment, the curve fitting is performed by LS-SVMlab-v1.8 in the MATLAB software toolbox to obtain the curve path from B0 to B1;

[0071] Step S304, solving the profile optimization path based on the compensation coefficient to obtain profile optimization iteration data; Step S304 includes the following sub-steps:

[0072] Step S3041, set the compensation coefficient to α, and set the mesh unit node coordinates B1 (x1i, y1i, z1i) on the initial surface corresponding to the mesh unit node coordinates on the springback surface to C1 (xui, yui, zui) when the compensation coefficient is α; because the part springback problem itself is a nonlinear problem, the springback amount of the part may increase to a certain extent after the mold surface compensation, so it is necessary to consider the problem that the springback amount will increase after compensation, redesign the compensation amount, and the compensation amount should also be adaptively increased or decreased, so the compensation coefficient will be introduced for the optimization iteration of the mold surface, usually in actual production, the compensation coefficient value range is [0.8, 1.2]; after adding the compensation coefficient, the springback path will also change with the change of the compensation coefficient, so the change of the springback path must be determined;

[0073] Step S3042, solve C1(xui, yui, zui): draw a tangent line to the straight line B0B1 through B1(x1i, y1i, z1i) to obtain the first equation, which is as follows:

[0074] yui=[f(x0i,x2i)+f(x0i,x2i,x1i,)(2x1i-x2i-x0i)](xui-x1i)+y1i, where f(x0i,x2i)+f(x0i,x2i,x1i,)(2x1i-x2i-x0i) is the slope of the springback path B0~B1 at point B1;

[0075] Step S3043, based on B0(x0i, y0i, z0 i ) and B1(x1i, y1i, z1i), we get the length of the straight line B0B1, marked as LB01, and make a circle with B0(x0i, y0i, z0i) as the center and α*LB01 as the radius to get the second equation, which is as follows: α*LB01=(xui-x0i) 2 +(yui-y0i) 2 ;

[0076] Step S3044, solving xui and yui by combining the first equation and the second equation. If there are two sets of solutions, the vertical distances between the two sets of solutions and the springback surface are calculated based on the springback surface data, and the one with the shorter vertical distance to the springback surface is selected as the final solution.

[0077] Step S3045, solve zui=z0i+α*(z1i-z0i); obtain the solved C1, marked as C1(xci, yci, zci);

[0078] Step S3046, if the compensation coefficient α<1, C1 is located on the springback path B0-B1, so the springback path becomes B0-C1, and the surface optimization similarity path is: starting point B0 (x0i, y0i, z0i) along the surface springback path B0-B1 to C1 (xci, yci, zci) marked as B0-C1;

[0079] Step S3047, if the compensation coefficient α>1, then C1 is outside the springback path B0-B1, and the profile optimization similarity path is: the profile springback path B0-B1 plus the straight line B1C1, marked as B0-B1C1;

[0080] Step S3048, if the compensation coefficient α=1, then C1=B1, and the profile optimization similarity path is: profile springback path B0-C1;

[0081] Step S305, see Figure 4 As shown, Figure 4 The compensation coefficient α>1, the profile optimization path is solved based on the profile optimization similarity path, and the profile optimization iteration data is obtained; step S305 includes the following sub-steps:

[0082] Step S3051, based on B0(x0i, y0i, z0i), obtain the normal vector of point B0(x0i, y0i, z0i), marked as B0H;

[0083] Step S3052, based on C1(xci, yci, zci), obtain the normal vector of C1(xci, yci, zci), marked as C1H;

[0084] Step S3053, moving the profile optimization similar path so that the end point of the profile optimization similar path coincides with the starting point position C1 of the profile optimization similar path;

[0085] Step S3054, then rotate the profile optimization similar path around the starting position C1 until B0H and C1H coincide, and mark the profile optimization similar path at this time as the profile optimization path;

[0086] Step S3055, obtaining the position coordinates D (xdi, ydi, zdi) of the end point of the profile optimization path, and marking them as profile optimization iteration data;

[0087] During the implementation process, the least squares support vector machine is a machine learning algorithm based on statistical theory. Its main advantages are high computational efficiency, easy implementation and strong generalization ability. The least squares support vector machine has good performance for complex curve fitting tasks. When faced with data with nonlinear relationships, it can more accurately capture its internal laws, and compared with the support vector machine, it no longer requires the solution of quadratic programming, which improves the computational efficiency and fitting accuracy. Because the parts produced before and after the optimization of the mold surface only change in curvature, their complexity has not changed; so the shape of the mold surface springback path is similar before and after the surface optimization, so in this embodiment, the shape of the surface optimization similar path is used instead of the surface optimization path.

[0088] Step S4, iteratively optimizing the mold surface based on the surface optimization iteration data to obtain the final optimized mold surface; Step S4 includes the following sub-steps:

[0089] Step S401, modifying the profile of the mold finite element model based on the profile optimization iteration data to obtain the mold optimized profile, and performing a third simulation production to obtain a third simulated automobile parts finite element model, which is marked as a third part simulation model;

[0090] Step S402, outputting the parameters of the third part simulation model, and judging whether the parameters of the third part simulation model are within the error range based on the design parameters of the automobile parts; for example, if the maximum design length of a certain automobile cover is 520 mm ± 3 mm, the error range is [517 mm, 523 mm], that is, less than 517 mm or greater than 523 mm is unqualified;

[0091] Step S403, if it is within the error range, marking the profile of the modified mold finite element model as the final optimized profile of the mold;

[0092] Step S404, if it is not within the error range, extracting the coordinates of the grid unit nodes on the surface of the simulation model of the third part as new springback profile data;

[0093] Step S405, see Figure 5 If it is not within the error range, the coordinates of the mesh unit nodes on the surface of the simulation model of the third part are extracted as new springback surface data, and the surface optimization path is solved by the least squares support vector machine based on the initial surface data and the springback surface data to obtain a new surface optimization path. The mold optimization surface is optimized again based on the new surface optimization path to obtain a new mold optimization surface. Simulation production is performed again to determine whether the parameters of the simulated model are within the error range, until the parameters of the simulated model are within the error range, and the surface of the mold finite element model after the last modification is marked as the final optimized surface of the mold.

[0094] In the specific implementation process, it is often difficult to achieve the ideal effect with one surface optimization iteration, so a second and more surface optimization iterations are required. However, compared with the traditional surface optimization iteration based on geometric displacement, surface optimization iteration based on the surface optimization path can reduce the number of surface optimization iterations, increase the accuracy of single surface optimization, and improve the efficiency of surface optimization iteration.

[0095] Example 2, please refer to Figure 6 As shown, Figure 6 The structural schematic diagram of an electronic device is illustrated, and the electronic device may include: a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus. The memory stores computer-readable instructions, and the processor may call the instructions in the memory. When the computer-readable instructions are executed by the processor, the steps in a method for iterative optimization of the mold surface of an automobile part are executed to achieve the following functions: based on the design parameters of the automobile part, a mold finite element model is established to obtain initial surface data; based on the mold finite element model, a simulation analysis is performed using computer software to obtain springback surface data; based on the initial surface data and the springback surface data, a least squares support vector machine is used to solve the surface optimization path to obtain surface optimization iteration data; based on the surface optimization iteration data, the mold surface is optimized and iterated to obtain the final optimized mold surface.

[0096] In addition, the logic instructions in the above-mentioned memory can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art, and the computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk.

[0097] Embodiment 3, the present application also provides a computer-readable storage medium, the present application provides a storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps in the above method for iterative optimization of an automobile component mold surface are executed to achieve the following functions: based on the design parameters of the automobile component, a mold finite element model is established to obtain initial surface data; based on the mold finite element model, simulation analysis is performed using computer software to obtain springback surface data; based on the initial surface data and the springback surface data, a least squares support vector machine is used to solve the surface optimization path to obtain surface optimization iterative data; based on the surface optimization iterative data, the mold surface is optimized and iterated to obtain the final optimized mold surface.

[0098] Through the description of the above implementation methods, the embodiments of the present invention can be provided as methods, systems or computer program products. Based on such an understanding, the above technical solutions can be essentially or partly contributed to the prior art in the form of software products, which can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and include several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0099] In the embodiments provided in the present application, it should be understood that the disclosed system or method can be implemented in other ways. The embodiments described above are merely schematic. For example, the division of modules or units is only a logical function division. There may be other division methods in actual implementation. For example, multiple modules or units can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of systems, modules and units can be electrical, mechanical or other forms.

[0100] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for iterative optimization of automobile parts mold surface, characterized in that: The steps include: Based on the design parameters of automobile parts, the mold finite element model is established to obtain the initial surface data; Based on the finite element model of the mold, computer software is used to perform simulation analysis to obtain the springback surface data; Based on the initial profile data and the springback profile data, the profile optimization path is solved by using the least squares support vector machine to obtain the profile optimization iteration data; The mold surface is optimized and iterated based on the surface optimization iteration data to obtain the final optimized mold surface.

2. The method for iterative optimization of automobile component mold surface according to claim 1, characterized in that: Based on the design parameters of automobile parts, the mold finite element model is established to obtain the initial surface data, which includes the following sub-steps: According to the design parameters of automobile parts, the geometric model of automobile parts mold is constructed by using CAD software, and then exported and saved to obtain the mold geometric model file; Based on DEFORM software, using the pre-processing module of DEFORM software, the mold geometry model file is imported to obtain the DDEFORM mold model; DEFORM software is used to mesh the DEFORM mold model using tetrahedrons, the number of mesh units is set to A1, and the mesh of the contact area between the mold and the part in the DEFORM mold model is refined. After completion, the mold finite element model is obtained; The mold finite element model is placed in a rectangular coordinate system, and the coordinates of the mesh unit nodes of the mold finite element model surface are obtained and marked as initial surface data. The mold surface corresponding to the initial surface data is the initial surface.

3. The method for iterative optimization of automobile component mold surface according to claim 2, characterized in that: Based on the mold finite element model, computer software is used to perform simulation analysis to obtain the springback surface data, including the following sub-steps: Using DEFORM software, according to the part design parameters, the material parameters, material properties and elastic modulus of the part are input, and the material properties of the mold are input, and then the first simulation production is performed to obtain the first simulated automobile parts finite element model, which is marked as the first part simulation model, and the elastic modulus is E; The coordinates of the mesh unit nodes on the surface of the simulation model of the first part are extracted and marked as springback surface data. The mold surface corresponding to the springback surface data is the springback surface.

4. The method for iterative optimization of automobile component mold surface according to claim 3, characterized in that: Based on the initial profile data and the springback profile data, the profile optimization path is solved using the least squares support vector machine to obtain the profile optimization iterative data, which includes the following sub-steps: The input elastic modulus is adjusted to 0.5*E, and the second simulation production is performed to obtain the finite element model of the second simulated automobile parts, which is marked as the second part simulation model; the coordinates of the grid unit nodes on the surface of the second part simulation model are extracted and marked as the springback intermediate profile data, and the mold profile corresponding to the springback intermediate profile data is the springback intermediate profile; The least squares support vector machine is used to solve the surface springback path.

5. The method for iterative optimization of automobile component mold surface according to claim 4, characterized in that: Solving the springback path of the profile using the least squares support vector machine includes the following sub-steps: Based on the initial profile data, the springback profile data and the springback intermediate profile data, the mesh unit nodes where the profile springback occurs are screened out, and the coordinates of the mesh unit nodes where the profile springback occurs in the initial profile data are marked as B0(x0i, y0i, z0i), the coordinates of the mesh unit nodes corresponding to the springback profile data are marked as B1(x1i, y1i, z1i), and the coordinates of the mesh unit nodes corresponding to the springback intermediate profile data are marked as B2(x2i, y2i, z2i), where i represents the coordinates of the i-th mesh unit node, and the i in B0(x0i, y0i, z0i), B1(x1i, y1i, z1i) and B2(x2i, y2i, z2i) correspond to each other; Based on the least squares support vector machine algorithm, x0i, y0i, x1i, y1i, x2i and y2i are used as the input of the least squares support vector machine, z0i, z1i and z2i are used as the output of the least squares support vector machine, and the least squares support vector machine is used to perform curve fitting to obtain the curve path from B0 to B1, which is marked as the surface springback path B0~B1; The surface optimization path is solved based on the compensation coefficient to obtain the surface optimization iteration data.

6. The method for iterative optimization of automobile component mold surface according to claim 5, characterized in that: Solving the similar path of profile optimization based on the compensation coefficient and obtaining the profile optimization iteration data includes the following sub-steps: Assume that the compensation coefficient is α, and the coordinates of the mesh unit node on the initial surface corresponding to the mesh unit node coordinates B1(x1i, y1i, z1i) on the surface where the springback occurs are C1(xui, yui, zui) when the compensation coefficient is α; Solve C1(xui, yui, zui): draw a tangent to the straight line B0B1 through B1(x1i, y1i, z1i) to get the first equation. Based on B0(x0i, y0i, z0i) and B1(x1i, y1i, z1i), get the length of the straight line B0B1, marked as LB01. Draw a circle with B0(x0i, y0i, z0i) as the center and α*LB01 as the radius to get the second equation. Solve xui and yui by combining the first and second equations. If there are two sets of solutions, calculate the vertical distances between the two sets of solutions and the springback surface based on the springback surface data, and select the one with the shorter vertical distance to the springback surface as the final solution; zui=z0i+α*(z1i-z0i); obtain the solved C1, marked as C1(xci, yci, zci); If the compensation coefficient α<1, the similar path of profile optimization is: starting point B0 (x0i, y0i, z0i) along the profile springback path B0~B1 to C1 (xci, yci, zci) marked as B0~C1; If the compensation coefficient α>1, the surface optimization similarity path is: the surface springback path B0~B1 plus the straight line B1C1, marked as B0~B1C1; If the compensation coefficient α=1, then C1=B1, and the surface optimization similarity path is: surface springback path B0~C1; The surface optimization path is solved based on the surface optimization similarity path to obtain the surface optimization iteration data.

7. The method for iterative optimization of automobile component mold surface according to claim 6, characterized in that: Solving the profile optimization path based on the profile optimization similarity path and obtaining the profile optimization iteration data includes the following sub-steps: Based on B0(x0i, y0i, z0i), the normal vector of point B0(x0i, y0i, z0i) is obtained, marked as B0H; based on C1(xci, yci, zci), the normal vector of C1(xci, yci, zci) is obtained, marked as C1H; Move the profile optimization similarity path so that the end point of the profile optimization similarity path coincides with the starting point position C1 of the profile optimization similarity path, and then rotate the profile optimization similarity path around the starting point position C1 until B0H coincides with C1H, mark the profile optimization similarity path at this time as the profile optimization path, obtain the position coordinates D (xdi, ydi, zdi) of the end point of the profile optimization path, and mark it as the profile optimization iteration data.

8. The method for iterative optimization of automobile component mold surface according to claim 7, characterized in that: The mold surface is optimized and iterated based on the surface optimization iteration data to obtain the final optimized mold surface, which includes the following sub-steps: Modify the profile of the mold finite element model based on the profile optimization iteration data to obtain the optimized profile of the mold, perform a third simulation production, obtain the third simulated automotive parts finite element model, mark it as the third part simulation model, output the parameters of the third part simulation model, and judge whether the parameters of the third part simulation model are within the error range based on the design parameters of the automotive parts. If they are within the error range, mark the profile of the modified mold finite element model as the final optimized profile of the mold; If it is not within the error range, extract the coordinates of the grid unit nodes on the surface of the simulation model of the third part as new springback surface data, and again use the least squares support vector machine to solve the surface optimization path based on the initial surface data and the springback surface data to obtain a new surface optimization path. Then, optimize the mold optimization surface again based on the new surface optimization path to obtain a new mold optimization surface. Perform simulation production again and determine whether the parameters of the simulated model are within the error range. Until the parameters of the simulated model are within the error range, mark the surface of the mold finite element model after the last modification as the final optimized mold surface.

9. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores computer-readable instructions. When the computer-readable instructions are executed by the processor, the steps in the method according to any one of claims 1 to 8 are executed.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps in the method according to any one of claims 1 to 7 are executed.

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