Lightweight forming optimization method for large-size shallow drawing part
By establishing a thickness-stiffness dynamic correlation and multi-objective optimization model, the springback control and structural performance problems of large-size shallow-drawn parts were solved, achieving efficient lightweight forming optimization, reducing costs and improving performance.
Patent Information
- Application Number
- CN202511584369.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-10-31
- Publication Date
- 2026-02-10
AI Technical Summary
Large-size shallow-drawn parts face challenges such as springback control, uneven thickness distribution, discrete structural performance, and NVH drift during the forming process. Traditional optimization methods rely on experience, and fragmented data leads to delayed verification, resulting in high costs and delayed performance verification.
By establishing a dynamic correlation between thickness and stiffness, using the Sobol sequence sampling method for global sensitivity analysis, constructing the NSGA-II optimization model, achieving multi-objective collaborative optimization, and combining LS-PrePost and LS-OPT software for automated data extraction and parameter optimization, a closed-loop mapping system of process parameters, forming results, and structural performance is established.
It achieved a reduction of springback by more than 40%, a 2-fold increase in fatigue life, a 4dB reduction in NVH vibration energy, a significant reduction in the number of physical prototypes, a 60% reduction in optimization cycle, and a 65% reduction in cost.
Smart Images
Figure CN121502938A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of shallow drawing forming technology, and in particular to an optimized method for lightweight forming of large-size shallow drawing parts. Background Technology
[0002] Large-sized shallow-drawn parts (such as body panels and chassis skid plates) have significant process bottlenecks due to their gentle curvature and small amount of plastic deformation, such as difficulty in controlling springback and uneven thickness distribution. Traditional optimization focuses only on meeting geometric dimensions, ignoring the hidden damage to structural performance caused by residual stress fields and thickness gradients generated during the forming process. This leads to inconsistent part stiffness, drift in NVH characteristics, and reduced fatigue life, resulting in high mold repair costs and delayed performance verification.
[0003] Traditional optimization methods for large-size shallow-drawn parts have fundamental flaws: First, they are single-point optimizations, with process adjustments relying on engineers' experience and no parameter-performance mapping chain established; second, verification is delayed, with performance evaluation requiring physical prototypes, and problems only being exposed in the later stages of development; and third, data is fragmented, with forming simulation and structural analysis using independent models, resulting in the loss of key data such as residual stress. Summary of the Invention
[0004] To address the aforementioned problems, the purpose of this invention is to provide an optimized method for lightweight forming of large-size shallow-drawn parts.
[0005] A method for optimizing the lightweight forming of large-size shallow-drawn parts includes:
[0006] Step 1: Extract the forming results of shallow-drawn parts;
[0007] Step 2: Establish a thickness-stiffness dynamic correlation in the forming results to calculate the stiffness reduction factor of shallow-drawn parts;
[0008] Step 3: Use the Sobol sequence sampling method to perform a global sensitivity analysis on all design variables and screen out the design variables that meet the criteria;
[0009] Step 4: Construct an NSGA-II optimization model based on the design variables that meet the conditions for multi-objective collaborative optimization;
[0010] Step 5: Evaluate the results of multi-objective collaborative optimization. When the evaluation value and the stiffness reduction factor of the shallow-drawn part are within the set range, the optimization is complete.
[0011] Preferably, in step 1, the forming results are automatically extracted using the LS-PrePost script.
[0012] Preferably, in step 2, the formula for the stiffness reduction factor is:
[0013] K adj=K0×[1-0.2(Δt / t0)2]
[0014] Among them, K adj The stiffness reduction factor is represented by K0, the original design stiffness is represented by Δt, the thickness deviation is represented by t0, and the original sheet thickness is represented by t0.
[0015] Preferably, in step 3, global sensitivity analysis is performed using the Sobol sequence sampling method in LS-OPT software.
[0016] Preferably, in step 3, the slope of the blank holder force curve and the drawbead coefficient are used as design variables, and the maximum springback angle and minimum thickness are used as response quantities. The sensitivity index of the design variables is calculated, and the design variables with sensitivity indices within the set range are used as design variables that meet the conditions.
[0017] The present invention also provides an electronic device, including a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, characterized in that the computer program, when executed by the processor, implements the steps in the above-described method for optimizing lightweight forming of large-size shallow-drawn parts.
[0018] The present invention also provides a computer-readable storage medium having a computer program stored thereon, characterized in that, when the computer program is executed by a processor, it implements the steps in the above-described method for optimizing the lightweight forming of large-size shallow-drawn parts.
[0019] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:
[0020] This invention relates to an optimized method for lightweight forming of large-size shallow-drawn parts. Compared with the prior art, this invention establishes a closed-loop mapping system of process parameters, forming results, and structural performance. This method ensures that the springback is reduced by more than 40%, while increasing the fatigue life by 2 times and reducing NVH vibration energy by 4dB, thus significantly reducing the number of physical prototypes.
[0021] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description
[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0023] Figure 1 The present invention provides a flowchart of an optimized method for lightweight forming of large-size shallow-drawn parts. Detailed Implementation
[0024] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.
[0025] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.
[0026] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.
[0027] Please see Figure 1 A method for optimizing lightweight forming of large-size shallow-drawn parts, comprising:
[0028] Step 1: Extract the forming results of shallow-drawn parts using the LS-PrePost script;
[0029] Step 2: Establish a thickness-stiffness dynamic correlation in the forming results to calculate the stiffness reduction factor of shallow-drawn parts;
[0030] In step 2, the formula for the stiffness reduction factor is:
[0031] K adj =K0×[1-0.2(Δt / t0)2]
[0032] Among them, K adjThe stiffness reduction factor is represented by K0, the original design stiffness is represented by Δt, the thickness deviation is represented by t0, and the original sheet thickness is represented by t0.
[0033] Step 3: Use the Sobol sequence sampling method to perform a global sensitivity analysis on all design variables and screen out the design variables that meet the criteria;
[0034] In step 3, the Sobol sequence sampling method is used in LS-OPT software. The slope of the blank holder force curve and the drawbead coefficient are used as design variables, and the maximum springback angle and minimum thickness are used as response quantities. The sensitivity index of the design variables is calculated, and the design variables with sensitivity indices within the set range are used as design variables that meet the conditions.
[0035] Step 4: Construct an NSGA-II optimization model based on the design variables that meet the conditions for multi-objective collaborative optimization;
[0036] Step 5: Evaluate the results of multi-objective collaborative optimization. When the evaluation value and the stiffness reduction factor of the shallow-drawn part are within the set range, the optimization is complete.
[0037] The optimization method of the present invention will be further explained below with reference to specific embodiments:
[0038] 1. Seamless transfer technology for residual stress
[0039] Automatically extract the forming results using the LS-PrePost script:
[0040] *DATABASE_EXTENT_BINARY sets STRFLG=1 to output shell element stress.
[0041] This step is the cornerstone of data extraction and transfer. Its purpose is to seamlessly and accurately transfer the detailed results (mainly residual stress field and thickness distribution field) obtained by the LS-DYNA explicit solver, which contains the actual deformation history of the part, from the field of forming simulation to the field of structural performance analysis.
[0042] By writing LS-PrePost post-processing scripts, the LS-DYNA result file (d3plot) is automatically read, and the stress data of the output shell elements is set using the *DATABASE_EXTENT_BINARY keyword.
[0043] 2. Thickness-Stiffness Dynamic Correlation
[0044] Create a thickness function in Mechanical:
[0045] ETABLE, THICK, THICK
[0046] SFE,ALL,PRES,%THICK_VAR%
[0047] Define the stiffness reduction factor formula:
[0048] K adj =K0×[1-0.2(Δt / t0)2]
[0049] Among them, K adj The stiffness reduction factor is represented by K0, the original design stiffness is represented by Δt, the thickness deviation is represented by t0, and the original sheet thickness is represented by t0.
[0050] This step is the core of performance mapping. Its purpose is to determine the impact of geometric changes (thickness non-uniformity) in the part caused by the quantitative forming process on its structural stiffness, a key performance indicator.
[0051] In ANSYS Mechanical, the thickness properties of each element in the finite element model are dynamically updated using APDL commands (such as ETABLE, THICK, THICK) based on the thickness distribution results. Simultaneously, the stiffness reduction factor is calculated using the stiffness reduction factor formula.
[0052] 3. Parameter Fine-tuning Optimization Engine – Sensitive Parameter Identification
[0053] Sobol sensitivity analysis was performed using LS-OPT software.
[0054] Design variables: slope of blank holder force curve ($k_p$), drawbead coefficient ($C_{dr}$)
[0055] Response parameters: Maximum springback angle ($\theta_b$), Minimum thickness ($t_{min}$)
[0056] RESPONSE_TYPE = STRESS_RATIO
[0057] SAMPLING_STRATEGY=SOBOL_SEQUENCE
[0058] This step acts as a navigator for optimization. Its purpose is to scientifically identify the few core parameters that have the greatest impact on key performance indicators from among numerous adjustable process parameters, thereby significantly narrowing the optimization search space and improving efficiency.
[0059] In the LS-OPT software, the Sobol sequence sampling method is used for global sensitivity analysis. The slope of the blank holder force curve and the drawbead coefficient are used as design variables, while the maximum springback angle and minimum thickness are used as response quantities to calculate the sensitivity index of each variable.
[0060] 4. Parameter Fine-tuning Optimization Engine – Multi-objective Collaborative Optimization
[0061] Constructing the NSGA-II optimization model:
[0062] %LS-OPT script core section
[0063] Objective:min(max_rebound),max(min_thickness)
[0064] Constraint:modal_freq>=48Hz,fatigue_cycles>1e6
[0065] Variable Range: 0.92*C_dr0 <C_dr<1.08*C_dr0
[0066] This step is the intelligent engine that seeks the optimal solution. Its purpose is to automatically find the best combination of process parameters that can simultaneously satisfy multiple, often conflicting, performance objectives (such as minimum springback and maximum fatigue life) within the identified key parameter range.
[0067] Implementation details: An NSGA-II multi-objective genetic algorithm optimization model is constructed in LS-OPT. This model aims to minimize springback and maximize minimum thickness, and uses modal frequency and fatigue life as constraints to perform global optimization within a fine-tuning range of ±8% for key parameters.
[0068] 5. Real-time performance prediction technology
[0069] The evaluation results are obtained by using NVH rapid evaluation. The optimization is completed when the evaluation value and the stiffness reduction factor of the shallow drawn part are within the set range.
[0070] In the optimization of a floor component (2000×1500mm) for an electric vehicle:
[0071] - The drawbead resistance coefficient at the rear was adjusted from 0.25 to 0.28.
[0072] - Front blank holder force reduced by 8%
[0073] Optimization of the roof (1500×1200mm) of a certain new energy vehicle:
[0074]
[0075]
[0076] Results: The rebound amount decreased from 2.1mm to 0.9mm, the first mode increased from 47Hz to 49.1Hz, and the vibration in the key frequency band of road noise (80-120Hz) decreased by 3.5dB. This solution compresses the traditional separate process development and performance verification process into a unified digital closed-loop system.
[0077] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:
[0078] (1) Full-process LS-DYNA kernel. A unified solver is used for forming → springback → structural analysis to avoid data conversion loss.
[0079] (2) Workbench features an automated closed-loop system with strong engineering applicability. Automatic parameter iteration via Parameter Set reduces the optimization cycle by 60%.
[0080] (3) Fine-tuning and precise control. Verification shows that adjusting the drawbead coefficient by ±8% can simultaneously improve springback and NVH performance.
[0081] (4) Cross-scale data lossless transfer, development of stress / thickness field mapping interface, breaking through the bottleneck of traditional simulation data fragmentation, virtual interception of performance red line, prediction of NVH / fatigue risks in the process stage, reducing the cost by 65% compared with physical verification, fine adjustment of combined effect quantification, confirming that the blank holder force ±5% adjustment can improve stiffness consistency by 40%, while controlling springback ≤0.8mm.
[0082] The present invention also provides an electronic device, including a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor. The transceiver, the memory, and the processor are connected via the bus. When the computer program is executed by the processor, it implements the steps in the above-described method for optimizing the lightweight forming of large-size shallow-drawn parts. Compared with the prior art, the beneficial effects of the electronic device provided by the present invention are the same as those of the above-described method for optimizing the lightweight forming of large-size shallow-drawn parts, and will not be elaborated here.
[0083] The present invention also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, it implements the steps in the above-described method for optimizing the lightweight forming of large-size shallow-drawn parts. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the present invention are the same as the beneficial effects of the above-described method for optimizing the lightweight forming of large-size shallow-drawn parts, and will not be elaborated here.
[0084] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.
Claims
1. An optimized method for lightweight forming of large-size shallow-drawn parts, characterized in that, include: Step 1: Extract the forming results of shallow-drawn parts; Step 2: Establish a thickness-stiffness dynamic correlation in the forming results to calculate the stiffness reduction factor of shallow-drawn parts; Step 3: Use the Sobol sequence sampling method to perform a global sensitivity analysis on all design variables and screen out the design variables that meet the criteria; Step 4: Construct an NSGA-II optimization model based on the design variables that meet the conditions for multi-objective collaborative optimization; Step 5: Evaluate the results of multi-objective collaborative optimization. When the evaluation value and the stiffness reduction factor of the shallow-drawn part are within the set range, the optimization is complete.
2. The method for optimizing lightweight forming of large-size shallow-drawn parts according to claim 1, characterized in that, In step 1, the forming results are automatically extracted using the LS-PrePost script.
3. The method for optimizing lightweight forming of large-size shallow-drawn parts according to claim 2, characterized in that, In step 2, the formula for the stiffness reduction factor is: K adj =K0×[1-0.2(Δt / t0)2] Among them, K adj The stiffness reduction factor is represented by K0, the original design stiffness is represented by Δt, the thickness deviation is represented by t0, and the original sheet thickness is represented by t0.
4. The method for optimizing lightweight forming of large-size shallow-drawn parts according to claim 3, characterized in that, In step 3, global sensitivity analysis is performed using the Sobol sequence sampling method in the LS-OPT software.
5. The method for optimizing lightweight forming of large-size shallow-drawn parts according to claim 4, characterized in that, In step 3, the slope of the blank holder force curve and the drawbead coefficient are used as design variables, and the maximum springback angle and minimum thickness are used as response quantities. The sensitivity index of the design variables is calculated, and the design variables with sensitivity indices within the set range are used as design variables that meet the conditions.
6. An electronic device comprising a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, characterized in that, When the computer program is executed by the processor, it implements the steps in the lightweight forming optimization method for large-size shallow-drawn parts as described in any one of claims 1-5.
7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps in the lightweight forming optimization method for large-size shallow-drawn parts as described in any one of claims 1-5.