Product lightweight simulation optimization method, system, equipment and medium

Abstract

The invention belongs to the technical field of product forward design simulation optimization, and particularly relates to a product lightweight simulation optimization method, system, equipment and medium, which comprises the following steps: acquiring a 3D topological optimization result and a 2D topological optimization result, performing size optimization and section optimization, performing fusion interpretation on the optimization results to establish a reconstructed product, and performing simulation optimization on the reconstructed product. And carrying out functional and safety simulation check on the product after the structure reconstruction is completed. According to the method, free shape optimization based on a product reconstruction scheme is realized, design creativity, performance requirements and constraints of a manufacturing process can be comprehensively fused, weight, performance and production manufacturability are effectively balanced at the initial stage of product design, transition of product design engineers from conceptual product design to manufacturable products can be remarkably accelerated, and the product design efficiency is improved. And finally, an extremely light-weight product is designed.

Description

Technical Field

[0001] The present invention belongs to the technical field of forward design simulation optimization of products, and particularly relates to a method, system, device and medium for product lightweight simulation optimization. Background Art

[0002] As a crucial supporting technology, the forward design simulation optimization technology plays an irreplaceable role in the lightweight engineering. This technology can closely revolve around clear design goals, and through a series of rigorous and systematic simulation optimization processes and methods, layer by layer, ultimately create an extremely lightweight product that not only meets the requirements of use functionality and safety, but also takes into account the manufacturability of production.

[0003] However, it is worth noting that the design of hollow structure castings products in the current market often overly relies on traditional design experience and lacks a systematic and scientific technical guidance system. This design method often leads to problems such as weight redundancy and over-designed performance of products, which not only increases production costs but also causes waste of resources. Therefore, how to effectively solve this problem and achieve scientific design and reasonable weight reduction of hollow structure castings products has become an urgent problem to be solved. Summary of the Invention

[0004] The present invention proposes a method, system, device and medium for product lightweight simulation optimization to solve the problem of excessive design cost and product weight redundancy in the prior art.

[0005] To achieve the above object, the present invention proposes the following technical solutions: A method for product lightweight simulation optimization, comprising the following steps: Step 1, according to the installation space of the target product in the real service state, obtain the original 3D CAE model, and based on the original 3D CAE model, obtain the 3D topology optimization result; Step 2, transform the original 3D CAE model to obtain the original 2D CAE model, and based on the original 2D CAE model, obtain the 2D CAE modeling; Step 3, based on the 2D CAE model and product requirements, obtain the local structure to be optimized and the 2D topology optimization result; Step 4, define different thickness design variables, associate the thickness design variables with the local structure to be optimized, calculate and obtain the size optimization result, and obtain the detailed model; Step 5, according to the target product, generate a number of 1D units with the same complex cross-section, establish a connection between the 1D units and the detailed model, and based on the optimization direction of the target cross-section, obtain the cross-section optimization result; Step 6: Perform a fusion interpretation of the 3D topology optimization results, 2D topology optimization results, size optimization results, and cross-section optimization results to obtain a reconstructed product; Step 7: Conduct functional and safety simulation checks on the product after completing the structural reconstruction. If the reconstructed product fails the check, repeat all or selectively repeat Steps 1 - 6 to continue the optimization until the reconstructed product passes the check; if the reconstructed product passes the check, the extreme lightweight design of the product is completed.

[0006] Preferably, obtaining the 3D topology optimization results from the CAE model based on the original 3D in Step 1 specifically includes: Perform element division on the design space and non-design space models for 3D topology optimization, and define the design variables, responses, constraints, and objectives in the topology optimization based on the target requirements; define the mass response as the lightweight objective, and the target value is required to be minimized; activate CHECKER = 1 to control the checkerboard phenomenon, and define the DISCRETE parameter value to indirectly define the penalty coefficient P to control the number of semi-density elements, where P < 1; Submit the model for calculation to obtain the 3D topology optimization results.

[0007] Preferably, the CAE modeling in Step 2 includes CAE modal modeling, CAE stiffness modeling, and CAE strength modeling.

[0008] Preferably, in Step 2, it also includes performing 3D CAE modeling based on the CAE model of the original 3D, and repeatedly comparing the obtained 3D CAE modeling with the 2D CAE modeling until the modal, stiffness, and strength results are consistent.

[0009] Preferably, in Step 4, the definition of the thickness design variable includes a reference value, an upper limit value, and a lower limit value.

[0010] Preferably, in Step 5, it also includes establishing an RBE3 element and converting the RBE3 element into a CBUSH element; In Step 5, a connection is established between the 1D element and the detailed model through the CBUSH element.

[0011] Preferably, in Step 7, it also includes: If the reconstructed product passes the check, select further target regions and define them as single or multiple sets, and define the sets as design variables respectively, define the free-form optimization parameters, obtain the definitions of the responses, constraints, and objectives of the reconstructed product, and confirm that the definitions of the responses, constraints, and objectives of the reconstructed product are consistent with the corresponding definitions in Step 4 to complete the lightweight design of the product.

[0012] A product lightweight simulation optimization system, comprising a 3D topology optimization module, a CAE model calibration module, a 2D topology optimization module, a size optimization module, a cross-section optimization module, a product reconstruction module, and a verification module; The 3D topology optimization module is used to obtain the original 3D CAE model according to the installation space of the target product in the real service state, and based on the original 3D CAE model, obtain the 3D topology optimization result; The CAE model calibration module is used to convert the original 3D CAE model to obtain the original 2D CAE model, and based on the original 2D CAE model, obtain the 2D CAE modeling; The 2D topology optimization module is used to obtain the local structure to be optimized and the 2D topology optimization result based on the 2D CAE model and product requirements; The size optimization module is used to define different thickness design variables, associate the thickness design variables with the local structure to be optimized, calculate and obtain the size optimization result, and obtain the detailed model; The cross-section optimization module is used to generate a number of 1D units with the same complex cross-section according to the target product, establish a connection between the 1D units and the detailed model, and based on the optimization direction of the target cross-section, obtain the cross-section optimization result; The product reconstruction module is used to fuse and interpret the 3D topology optimization result, the 2D topology optimization result, the size optimization result, and the cross-section optimization result to obtain the reconstructed product; The verification module is used to perform functional and safety simulation verification on the product after the structure reconstruction is completed. If the reconstructed product fails the verification, continue to optimize until the reconstructed product passes the verification; if the reconstructed product passes the verification, complete the extreme lightweight design of the product.

[0013] An electronic device, comprising a memory and a processor; The memory is used to store a computer program; The processor is used to execute the computer program, and when the computer program is executed by the processor, the steps of the product lightweight simulation optimization method are implemented.

[0014] A computer-readable storage medium, in which a computer program is stored, and when the computer program is executed by a processor, the steps of the product lightweight simulation optimization method are implemented.

[0015] The advantages of the present invention are as follows: The present invention provides a method for optimizing product lightweight simulation, which includes completing 3D topology optimization to identify the global optimal structural layout, calibrating 3D CAE and 2D CAE models, performing 2D topology optimization to identify fine optimization of local structures, conducting local dimension optimization for 2D dimension optimization, and quickly optimizing the hollow section shape and parameters of a simplified model. Based on the results of the above optimizations, product structure reconstruction and verification of the performance of the reconstructed structure are carried out, realizing free-form optimization based on the product reconstruction scheme. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings forming a part of this specification are used to provide a further understanding of the present invention. The schematic embodiments and descriptions thereof of the present invention are used to explain the present invention and do not constitute an improper limitation to the present invention. In the drawings: Figure 1 is a flowchart of a method for optimizing product lightweight simulation; Figure 2 is a schematic diagram of the result of identifying the global optimal structural layout based on 3D topology optimization for a certain casting product in the embodiment; Figure 3 is a schematic diagram of the result of fine optimization of local structures based on 2D topology optimization for a certain casting product in the embodiment; Figure 4 is a schematic diagram of the result of local dimension optimization based on 2D dimension optimization for a certain casting product in the embodiment; Figure 5 is a schematic diagram of the result of quickly optimizing the hollow section shape and parameters based on a simplified model for a certain casting product in the embodiment; Figure 6 is a schematic diagram of an electronic device. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] The present invention will be described in detail below with reference to the drawings and in conjunction with the embodiments. It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other.

[0018] The following detailed descriptions are all exemplary descriptions, aiming to provide further detailed descriptions of the present invention. Unless otherwise specified, all technical terms used in the present invention have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs. The terms used in the present invention are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention. Embodiment 1

[0019] Please refer to Figure 1 as shown, the present invention provides a method for optimizing product lightweight simulation, which specifically includes the following steps: Step 1, according to product constraints, perform 3D-based topology optimization to identify local structures that require fine optimization, and obtain the 3D topology optimization results; specifically: Based on the installation space of the hollow-structured casting products in the actual service state, obtain the original 3D CAE model. Based on the original 3D CAE model, sort out the design space and non-design space for 3D topology optimization; Perform element division on the models including the design space and non-design space to ensure that the elements can accurately reflect the geometric characteristics of the design variables and non-design variables; According to the requirements, which include safety performance requirements, lightweight requirements, and manufacturing process requirements, complete the definition of design variables, responses, constraints, and objectives in topology optimization.

[0020] Define the mass response as the lightweight objective, and the objective value is required to be minimized.

[0021] Control the topology optimization results, activate CHECKER = 1 to control the checkerboard phenomenon, and ensure that the material distribution of the optimized structure is regular and the structure contour is clear; define the DISCRETE parameter value to indirectly define the penalty coefficient P to control the number of semi-density elements. The larger the P value, the better, but it cannot be greater than 1.

[0022] After the above process is completed, submit the model for calculation to obtain the 3D topology optimization results, as Figure 2 shown.

[0023] Step 2: Calibrate the 3D CAE and 2D CAE models to ensure that the functional and safety verification results of the 3D CAE and 2D CAE models are exactly the same; specifically: Perform CAE modeling based on the original 3D CAE model. Based on the safety performance, the CAE modeling includes at least three modelings: modal, stiffness, and strength. In this embodiment, taking modal modeling as an example, sequentially complete the steps of element division including surface elements and solid elements, define material properties, define load steps and boundary conditions, define output results such as displacements, etc., complete the 3D modal CAE modeling, submit for calculation, and output the results to obtain the 3D modal CAE modeling.

[0024] Offset the 3D surface elements of the original 3D CAE model inward by half of the wall thickness of the hollow structure to complete the conversion from 3D to 2D. Repeat the 3D CAE modeling process, complete the 2D modal, stiffness, and strength CAE modeling based on 2D, submit for calculation, and output the results to obtain the 2D modal CAE modeling.

[0025] Compare the 3D and 2D modal, stiffness, and strength results, and repeatedly adjust the 2D model until the comparison is consistent to ensure the credibility of the 2D CAE model.

[0026] Step 3: According to the product constraints, perform 2D-based topology optimization, identify the local structures that need to be finely optimized, and obtain the 2D topology optimization results; specifically: Based on the 2D CAE model obtained in Step 2, repeat the process of Step 1 to ensure that the elements can accurately reflect the geometric characteristics of the design variables and non-design variables. According to the requirements, which include safety performance requirements, lightweight requirements, and manufacturing process requirements, complete the definition of design variables, responses, constraints, and objectives in the topology optimization.

[0027] Define the mass response as the lightweight objective, and the objective value is required to be minimized.

[0028] Control the topology optimization result, activate CHECKER = 1 to control the checkerboard phenomenon, and ensure that the material distribution of the optimized structure is regular and the structure contour is clear; define the DISCRETE parameter value to indirectly define the penalty coefficient P to control the number of semi-density elements. The larger the P value, the better, but it cannot be greater than 1.

[0029] After the above process is completed, submit the model for calculation to obtain the 2D topology optimization result, complete the 2D-based topology optimization, and according to the safety performance requirements, lightweight requirements, and manufacturing process requirements, identify the local structures that can be finely optimized and record the number of structures to obtain the 2D topology optimization result, as Figure 3 shown.

[0030] Step 4, further perform size optimization based on the 2D topology optimization result of S3, that is, optimize the thickness parameters of the local structures; specifically: Define different thickness design variables, and the number of thickness design variables is consistent with the number of structures in Step 3, ensuring that each local structure corresponds to a thickness design variable, and the thickness design variables all need to define a reference value, an upper limit value, and a lower limit value.

[0031] Associate the thickness design variables with the local structures in Step 3, complete the definition of the size optimization implementation object, submit the model for calculation to obtain the size optimization result, and obtain the detailed model, as Figure 4 shown.

[0032] The definition of responses, constraints, and objectives in this step is consistent with that in Step 3.

[0033] Step 5, optimize the cross-section of the hollow structure, including the optimization of the cross-section shape and position; specifically: First, perform cross-section shape optimization. Select the target area, generate several 1D elements with the same complex cross-section, establish RBE3 elements, and then convert the RBE3 elements into CBUSH elements. Use CBUSH elements to establish connections between the 1D elements and the detailed model.

[0034] Select the target cross-section. Based on the optimization direction of the target cross-section, such as widening or heightening the cross-section to meet the safety usage requirements and with the lightest mass, further convert the 1D elements into BOX cross-sections with the geometric properties of the target cross-section, such as area, moment of inertia, polar moment of inertia, etc. At the same time, generate design variables and associate them with the corresponding cross-sections to complete the optimization of the cross-section shape.

[0035] Change the spatial positions of the nodes of the 1D elements, record the changing process, and define it as a shape variable to complete the optimization of the cross-section position.

[0036] The cross-section optimization result of this step is as Figure 5 shown.

[0037] The target area in this step is the area with relatively smooth surface transition in the detailed model.

[0038] The definitions of responses, constraints, and objectives in this step are the same as those in step 4.

[0039] Step 6, fuse and interpret the 3D topology optimization result, 2D topology optimization result, dimension optimization result, and cross-section optimization result, and complete the product structure reconstruction based on the interpretation result to obtain the reconstructed product.

[0040] Step 7, conduct functional and safety simulation checks on the product after completing the structure reconstruction, and select the subsequent process according to the check result. Specifically: If the reconstructed product fails the check, all or selectively repeat steps 1 - 6 to continue the optimization until the reconstructed product passes the check; If the reconstructed product passes the check, based on the reconstructed product that passes the check, select the local areas that may be further lightweighted and define them as single or multiple sets, then define the single or multiple sets as design variables respectively, define free-form optimization parameters, such as extension or compression in direction, boundaries, influence amounts, etc., to obtain the definitions of responses, constraints, and objectives of the reconstructed product, and confirm that the definitions of responses, constraints, and objectives of the reconstructed product are the same as those in step 4 to complete the extreme lightweight design of the product.

[0041] Based on the Altair Optistruct software, the present invention realizes 3D topology optimization to identify the global optimal structure layout, 3D CAE and 2D CAE model calibration, 2D topology optimization to identify the local structures that need to be finely optimized, local dimension optimization for local dimensions, and rapid optimization of the hollow cross-section shape and parameters of the simplified model. Further, based on the above optimization results, product structure reconstruction and product reconstructed structure performance verification are carried out to realize the optimization of the product.

[0042] The lightweight design of the hollow structure casting products realized by the present invention is scientific and reasonable, which can comprehensively integrate design creativity, performance requirements and manufacturing process constraints, effectively balance weight, performance and production manufacturability at the initial stage of product design, significantly accelerate the transition of product design engineers from conceptual product design to manufacturable products, and finally design extremely lightweight products. Embodiment 2

[0043] The present invention provides a product lightweight simulation optimization system, which specifically includes: a 3D topology optimization module, a CAE model calibration module, a 2D topology optimization module, a dimension optimization module, a section optimization module, a product reconstruction module and a verification module.

[0044] The 3D topology optimization module is used to implement step 1 in the product lightweight simulation optimization method. Specifically: Obtain the original 3D CAE model, sort out the design space and non-design space of 3D topology optimization; perform element division on the models including the design space and non-design space to ensure that the elements can accurately reflect the geometric characteristics of design variables and non-design variables; submit the model for calculation to obtain the 3D topology optimization result.

[0045] The CAE model calibration module is used to implement step 2 in the product lightweight simulation optimization method. Specifically: Based on the original 3D CAE model, perform CAE modeling, complete 3D modal CAE modeling, submit the calculation and output the results to obtain the 3D modal CAE modeling. Offset the 3D surface elements of the original 3D CAE model inward by half of the wall thickness of the hollow structure to complete the conversion from 3D to 2D. Repeat the 3D CAE modeling process, complete the modal, stiffness and strength CAE modeling based on 2D, submit the calculation and output the results to obtain the 2D modal CAE modeling; compare the modal, stiffness and strength results of 3D and 2D, and repeatedly adjust the 2D model until the comparison is consistent to ensure the credibility of the 2D CAE model.

[0046] The 2D topology optimization module is used to implement step 3 in the product lightweight simulation optimization method. Specifically: Based on the 2D CAE model obtained in step 3, repeat the process of the 3D topology optimization module to obtain the 2D topology optimization result, complete the topology optimization based on 2D, identify the local structures that can be finely optimized according to the safety performance requirements, lightweight requirements and manufacturing process requirements, record the number of structures, and obtain the 2D topology optimization result.

[0047] The dimension optimization module is used to implement step 4 in the product lightweight simulation optimization method. Specifically: Define different thickness design variables. The number of thickness design variables is consistent with the number of structures in step 3, ensuring that each local structure corresponds to one thickness design variable, and the thickness design variables all need to define reference values, upper limits, and lower limits. Associate the thickness design variables with the local structures in step 3 to complete the definition of the object for size optimization implementation. Submit the model for calculation to obtain the size optimization results and get a detailed model.

[0048] The cross-section optimization module is used to implement step 5 in the method for simulating and optimizing the lightweight of a product. Specifically: Conduct cross-section shape optimization. Select the target area, generate several 1D elements with the same complex cross-section, establish RBE3 elements, and then convert the RBE3 elements into CBUSH elements. Use the CBUSH elements to establish connections between the 1D elements and the detailed model. Select the target cross-section. Based on the optimization direction of the target cross-section, such as widening or heightening the cross-section to meet the safety usage requirements and have the strongest quality, further convert the 1D elements into BOX cross-sections with the geometric properties of the target cross-section, such as area, moment of inertia, polar moment of inertia, etc. At the same time, generate design variables and associate them with the corresponding cross-sections to complete the optimization of the cross-section shape.

[0049] Change the spatial positions of the 1D element nodes, record the change process, and define it as a shape variable to complete the optimization of the cross-section position.

[0050] The product reconstruction module is used to implement step 6 in the method for simulating and optimizing the lightweight of a product. Specifically: Fusion interpretation of the 3D topology optimization results, 2D topology optimization results, size optimization results, and cross-section optimization results, and complete the product structure reconstruction based on the interpretation results to obtain a reconstructed product.

[0051] The verification module is used to implement step 7 in the method for simulating and optimizing the lightweight of a product. If the verification of the reconstructed product fails, all or selectively repeat steps 1-6 and continue to optimize until the verification of the reconstructed product is qualified. If the verification of the reconstructed product is qualified, based on the verified reconstructed product, select the local areas that may be further lightweighted and define them as single or multiple sets, and then define the single or multiple sets as design variables respectively. Define free shape optimization parameters, such as extension or compression in direction, boundaries, influence amounts, etc., to obtain the definitions of the response, constraints, and objectives of the reconstructed product. Confirm that the definitions of the response, constraints, and objectives of the reconstructed product are consistent with the size optimization module to complete the extreme lightweight design of the product. Example 3

[0052] Please refer to Figure 3As shown, the present invention also provides an electronic device 100; the electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on the at least one processor 102, and at least one communication bus 104.

[0053] The memory 101 can be used to store the computer program 103. The processor 102 realizes the steps of the method for product lightweight simulation optimization described in Embodiment 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101. The memory 101 mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created according to the use of the electronic device 100 (such as audio data, etc.). In addition, the memory 101 can include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one magnetic disk storage device, a flash memory device, or other non-volatile solid-state storage devices.

[0054] The at least one processor 102 can be a central processing unit (CPU), or can also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 102 can be a microprocessor or the processor 102 can also be any conventional processor, etc. The processor 102 is the control center of the electronic device 100, and connects all parts of the entire electronic device 100 through various interfaces and lines.

[0055] The memory 101 in the electronic device 100 stores multiple instructions to implement a method for product lightweight simulation optimization. The processor 102 can execute the multiple instructions to achieve: Embodiment 4

[0056] If the modules / units integrated in the electronic device 100 are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above-described embodiment methods of the present invention, it can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-described method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, and read-only memory (ROM, Read-Only Memory).

[0057] As is known to those skilled in the art, the present invention can be implemented by other embodiments that do not depart from its spiritual essence or essential features. Therefore, the above-disclosed embodiments are illustrative in all aspects and are not exclusive. All changes within the scope of the present invention or within the scope equivalent to the present invention are encompassed by the present invention.

[0058] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0059] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of processes and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in one Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0060] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to work in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including an instruction device that implements the functions specified in one or more of the processes Figure 1 one or more processes and / or blocks Figure 1 the functions specified in one or more of the blocks.

[0061] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, such that a series of operational steps are performed on the computer or other programmable apparatus to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in one or more of the processes Figure 1 one or more processes and / or blocks Figure 1 the functions specified in one or more of the blocks.

[0062] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications or equivalent substitutions can still be made to the specific embodiments of the present invention. Any modification or equivalent substitution that does not depart from the spirit and scope of the present invention shall be covered by the protection scope of the claims of the present invention.

Claims

1. A method for simulating and optimizing product lightweighting, characterized in that, It includes the following steps: Step 1: Obtain the original 3D CAE model according to the installation space of the target product in its actual service state. Based on the original 3D CAE model, obtain the 3D topology optimization result; Step 2: Transform the original 3D CAE model to obtain the original 2D CAE model. Based on the original 2D CAE model, obtain the 2D CAE modeling; Step 3: Based on the 2D CAE model and product requirements, obtain the local structure to be optimized and the 2D topology optimization result; Step 4: Define different thickness design variables, associate the thickness design variables with the local structure to be optimized, calculate and obtain the size optimization result, and get the detailed model; Step 5: According to the target product, generate several 1D units with the same complex cross-section, establish a connection between the 1D units and the detailed model, and based on the optimization direction of the target cross-section, obtain the cross-section optimization result; Step 6: Conduct a fusion interpretation of the 3D topology optimization result, 2D topology optimization result, size optimization result, and cross-section optimization result to obtain the reconstructed product; Step 7: Conduct functional and safety simulation checks on the product after completing the structural reconstruction. If the reconstructed product fails the check, all or selectively repeat Steps 1-6 to continue the optimization until the reconstructed product passes the check; if the reconstructed product passes the check, complete the extreme lightweight design of the product.

2. The method for simulating and optimizing the lightweight of a product according to claim 1, wherein, The specific process of obtaining the 3D topology optimization result based on the original 3D CAE model in Step 1 is as follows: Divide the design space and non-design space models of the 3D topology optimization into elements, and define the design variables, responses, constraints, and objectives in the topology optimization based on the target requirements; define the mass response as the lightweight objective, and the target value is required to be minimized; Activate CHECKER = 1 to control the checkerboard phenomenon, and define the DISCRETE parameter value to indirectly define the penalty coefficient P to control the number of semi-density elements, where P < 1; Submit the model for calculation to obtain the 3D topology optimization result.

3. The method for simulating and optimizing the lightweight of a product according to claim 2, wherein The CAE modeling in Step 2 includes CAE modal modeling, CAE stiffness modeling, and CAE strength modeling.

4. The product lightweight simulation optimization method according to claim 1, characterized in that In Step 2, it also includes conducting 3D CAE modeling based on the original 3D CAE model, and repeatedly comparing the obtained 3D CAE modeling with the 2D CAE modeling until the modal, stiffness, and strength results are consistent.

5. The product lightweight simulation optimization method according to claim 1, wherein, In Step 4, the definition of the thickness design variable includes a reference value, an upper limit value, and a lower limit value.

6. The product lightweight simulation optimization method according to claim 1, wherein In Step 5, it also includes establishing an RBE3 element and converting the RBE3 element into a CBUSH element; In Step 5, a connection is established between the 1D unit and the detailed model through the CBUSH element.

7. The lightweight simulation optimization method for a product according to claim 1, characterized in that, In Step 7, it also includes: If the reconstructed product passes the check, select a further target area and define it as a single or multiple sets, and define the sets as design variables respectively, define the free-form optimization parameters, obtain the definitions of the responses, constraints, and objectives of the reconstructed product, and confirm that the definitions of the responses, constraints, and objectives of the reconstructed product are consistent with the corresponding definitions in Step 4 to complete the lightweight design of the product.

8. A product lightweight simulation and optimization system, characterized in that, It includes a 3D topology optimization module, a CAE model calibration module, a 2D topology optimization module, a size optimization module, a section optimization module, a product reconstruction module, and a verification module; The 3D topology optimization module is used to obtain the original 3D CAE model according to the installation space of the target product in the real service state, and obtain the 3D topology optimization result based on the original 3D CAE model; The CAE model calibration module is used to transform the original 3D CAE model to obtain the original 2D CAE model, and obtain the 2D CAE modeling based on the original 2D CAE model; The 2D topology optimization module is used to obtain the local structure to be optimized and the 2D topology optimization result based on the 2D CAE model and product requirements; The size optimization module is used to define different thickness design variables, associate the thickness design variables with the local structure to be optimized, calculate and obtain the size optimization result, and obtain the detailed model; The section optimization module is used to generate a number of 1D units with the same complex section according to the target product, establish a connection between the 1D units and the detailed model, and obtain the section optimization result based on the optimization direction of the target section; The product reconstruction module is used to fuse and interpret the 3D topology optimization result, 2D topology optimization result, size optimization result, and section optimization result to obtain the reconstructed product; The verification module is used to perform functional and safety simulation verification on the product after the structure reconstruction is completed. If the reconstructed product fails the verification, continue to optimize until the reconstructed product passes the verification; if the reconstructed product passes the verification, complete the extreme lightweight design of the product.

9. An electronic device, characterized in that, It includes a memory and a processor; The memory is used to store computer programs; The processor is used to execute the computer program. When the computer program is executed by the processor, the steps of a product lightweight simulation optimization method as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that The storage medium stores a computer program. When the computer program is executed by the processor, the steps of a product lightweight simulation optimization method as described in any one of claims 1 to 7 are implemented.

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