Adaptive fake leader configuration control method
By calculating the fragility index of the cantilever structure and generating the optimal dummy fork design scheme, the deformation and fracture problems of the mold cantilever structure during high-pressure extrusion were solved, thereby improving the mold service life and production efficiency and reducing costs.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- FOSHAN SANSHUIFENGLV ALUMINIUMINDUSTRY CO LTD
- Filing Date
- 2026-03-24
- Publication Date
- 2026-07-21
AI Technical Summary
In aluminum profile processing, the cantilever structure of the mold is prone to elastic or plastic deformation, collapse, swaying or even breakage during high pressure extrusion, resulting in a short mold life. Existing solutions increase the post-processing steps of the profile and are costly, affecting production efficiency.
By acquiring the cavity geometry data and material property data of the mold, the geometric feature vector and vulnerability index of the cantilever structure are calculated. Based on the vulnerability index, the protection requirements are determined, and the optimal dummy fork design scheme is generated. This avoids designing tear-opening structures and directly configures dummy forks on the cantilever structure to provide additional support.
It improves the service life and production efficiency of molds, reduces production costs, and avoids additional post-processing procedures and labor input.
Smart Images
Figure CN122433281A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of aluminum profile processing technology, and in particular to an adaptive dummy foreman configuration control method. Background Technology
[0002] With the gradual development of the aluminum profile processing industry, the requirements for precision mold production are becoming increasingly stringent. This is especially true for complex cross-section profiles with small openings but large cantilever lengths, where molds face extremely severe challenges. During high-pressure extrusion, the flow of aluminum metal generates significant local compressive stress and shear force on the cantilever portion of the mold opening, making the cantilever highly susceptible to elastic or plastic deformation, collapse, swaying, or even direct breakage and scrapping. This failure mode not only occurs frequently but also directly restricts the mold's service life, typically only able to withstand extrusion volumes of several hundred to several thousand tons, far below the durability level of conventional profile molds. Currently, the mainstream industry approach is to design the profile opening with a tear-open structure to significantly reduce the stress area and length of the mold cantilever, thereby mitigating the risk of collapse. However, this traditional solution adds post-processing steps, requiring additional manual labor or equipment to tear off the opening residue, significantly increasing production costs and labor input, ultimately leading to a decrease in production efficiency.
[0003] The above content is only used to help understand the technical solution of the present invention and does not represent an admission that the above content is prior art. Summary of the Invention
[0004] The main objective of this invention is to provide an adaptive dummy fork configuration control method, aiming to improve mold lifespan while increasing production efficiency. To achieve the above objective, this invention provides an adaptive dummy fork configuration control method, which includes the following steps: Acquire the cavity geometry data and material property data of the target mold, and determine the geometric feature vector and vulnerability index of each cantilever structure based on the cavity geometry data and the material property data; Protection requirement information is determined based on the vulnerability index and a preset threshold. This protection requirement information is used to mark whether each cantilever structure needs to be configured with a dummy foreman. The initial set of parameters for the fake foreman is determined based on the geometric feature vector, the protection requirement information, and the preset generation model. The optimal dummy foreman design scheme is determined based on the initial dummy foreman parameter set.
[0005] Optionally, the step of determining the geometric feature vector and vulnerability index of each cantilever structure based on the cavity geometry data and the material property data includes: The cantilever information of each cantilever structure is extracted based on the cavity geometry data. The cantilever information includes: cantilever length, cantilever width, cantilever thickness, and root fillet radius. The equivalent cantilever length is calculated based on the cantilever length, the cantilever width, the cantilever thickness, and the root fillet radius. The fragility index is determined based on the equivalent cantilever length, the material property data, and the extrusion process parameters.
[0006] Optionally, the step of determining protection requirement information based on the vulnerability index and a preset threshold includes: When the vulnerability index is greater than or equal to the preset threshold, the protection requirement information is determined to require protection from a fake foreman. When the vulnerability index is less than the preset threshold, it is determined that the protection requirement is not required by the fake foreman.
[0007] Optionally, the preset generation model includes a conditional diffusion model, and the step of determining the initial fake foreman parameter set based on the geometric feature vector, the protection requirement information, and the preset generation model includes: The cavity geometry data is input into the geometry encoder to obtain the overall geometry code of the mold; The geometric feature vector, the protection requirement information, and the overall geometric code of the mold are input into the conditional diffusion model to obtain the initial dummy foreman parameter vector; The initial fake foreman parameter set is generated based on the initial fake foreman parameter vector.
[0008] Optionally, the step of inputting the geometric feature vector, the protection requirement information, and the overall geometric code of the mold into the conditional diffusion model to obtain the initial dummy foreman parameter vector includes: Based on the protection requirements information, determine the set of cantilever structures that need to be configured with dummy foremen; Construct conditional inputs based on the set of cantilever structures and the geometric feature vectors; The conditional input and the overall geometric code of the mold are input into the conditional diffusion model to obtain the output result of the conditional diffusion model; The output result is used as the initial dummy foreman parameter vector.
[0009] Optionally, the step of determining the optimal dummy foreman design scheme based on the initial dummy foreman parameter set includes: Input the initial set of parameters of the fake foreman into the fluid-structure interaction simulation engine to obtain the cantilever stress distribution and the outlet velocity field; A multi-objective loss function is constructed based on the cantilever stress distribution and the outlet velocity field. The initial set of dummy foremen parameters is iteratively optimized using the multi-objective loss function and optimization algorithm to obtain the optimal dummy foremen design scheme.
[0010] Optionally, the step of constructing a multi-objective loss function based on the cantilever stress distribution and the outlet velocity field includes: Calculate the stress loss term based on the cantilever stress distribution and material yield strength; Calculate the velocity equilibrium loss term based on the described outlet velocity field; Calculate the processing complexity penalty term based on the initial set of fake foremen parameters; The multi-objective loss function is constructed based on the stress loss term, the flow rate equalization loss term, and the processing complexity penalty term.
[0011] Furthermore, to achieve the above objectives, the present invention also provides an adaptive dummy foreman configuration control system, the adaptive dummy foreman configuration control system comprising: The acquisition module is used to acquire the cavity geometry data and material property data of the target mold, and determine the geometric feature vector and vulnerability index of each cantilever structure based on the cavity geometry data and the material property data; The analysis module is used to determine protection requirement information based on the vulnerability index and a preset threshold. The protection requirement information is used to mark whether each cantilever structure needs to be configured with a dummy foreman. The generation module is used to determine the initial set of fake foreman parameters based on the geometric feature vector, the protection requirement information, and the preset generation model; The optimization module is used to determine the optimal dummy foreman design scheme based on the initial dummy foreman parameter set.
[0012] Furthermore, to achieve the above objectives, the present invention also provides an adaptive fake foreman configuration control device, the adaptive fake foreman configuration control device comprising: a memory, a processor, and an adaptive fake foreman configuration control program stored in the memory and executable on the processor, the adaptive fake foreman configuration control program being configured to implement the steps of the adaptive fake foreman configuration control method described in any of the above claims.
[0013] In addition, to achieve the above objectives, the present invention also provides a storage medium storing an adaptive dummy foreman configuration control program, wherein the adaptive dummy foreman configuration control program, when executed by a processor, implements the steps of the adaptive dummy foreman configuration control method described in any of the above claims.
[0014] This invention proposes an adaptive dummy fork configuration control method. This method acquires the cavity geometry data and material property data of the target mold, and determines the geometric feature vector and vulnerability index of each cantilever structure based on the cavity geometry data and material property data. It then determines protection requirement information based on the vulnerability index and a preset threshold. This protection requirement information is used to mark whether each cantilever structure needs to be configured with a dummy fork. An initial dummy fork parameter set is determined based on the geometric feature vector, the protection requirement information, and a preset generation model. Finally, an optimal dummy fork design scheme is determined based on the initial dummy fork parameter set. Compared to designing a tear-open structure, this method can accurately design suitable dummy forks for cantilever structures that require protection, thereby reducing production risks while improving production efficiency. Attached Figure Description
[0015] Figure 1 This is a schematic diagram of the structure of the adaptive fake foreman configuration control device for the hardware operating environment involved in the embodiments of the present invention; Figure 2 This is a flowchart illustrating the first embodiment of the adaptive fake foreman configuration control method of the present invention; Figure 3 This is a flowchart illustrating a second embodiment of the adaptive dummy foreman configuration control method of the present invention.
[0016] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation
[0017] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
[0018] Reference Figure 1 , Figure 1 This is a schematic diagram of the adaptive dummy foreman configuration control device structure for the hardware operating environment involved in the embodiments of the present invention.
[0019] like Figure 1As shown, the adaptive dummy foreman configuration control device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, an interaction device 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to enable communication between these components. The interaction device 1003 may include a display screen or an input unit such as a keyboard. Optionally, the interaction device 1003 may also be connected to the communication bus via standard wired or wireless interfaces. The network interface 1004 may optionally include standard wired or wireless interfaces (such as a Wi-Fi interface). The memory 1005 may be a high-speed random access memory (RAM) or a stable non-volatile memory (NVM), such as a disk drive. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.
[0020] Those skilled in the art will understand that Figure 1 The structure shown does not constitute a limitation on the adaptive dummy foreman configuration control device, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.
[0021] like Figure 1 As shown, the memory 1005, which serves as a storage medium, may include an operating system, a data storage module, a network communication module, a user interface module, and an adaptive dummy foreman configuration control program.
[0022] exist Figure 1 In the adaptive fake foreman configuration control device shown, the network interface 1004 is mainly used for data communication with other devices; the interaction device 1003 is mainly used for data interaction with the user; the processor 1001 and memory 1005 in the adaptive fake foreman configuration control device of the present invention can be set in the adaptive fake foreman configuration control device, and the adaptive fake foreman configuration control device calls the adaptive fake foreman configuration control program stored in the memory 1005 through the processor 1001 and executes the adaptive fake foreman configuration control method provided in the embodiment of the present invention.
[0023] This invention provides an adaptive dummy foreman configuration control method, referring to... Figure 2 , Figure 2 This is a flowchart illustrating the first embodiment of an adaptive dummy foreman configuration control method according to the present invention.
[0024] In this embodiment, the adaptive fake foreman configuration control method includes: Step S1: Obtain the cavity geometry data and material property data of the target mold, and determine the geometric feature vector and vulnerability index of each cantilever structure based on the cavity geometry data and the material property data; In this embodiment, the cavity geometry data is determined by obtaining the original design drawing of the target mold. Original design Figure 1 Typically, a CAD model is used, including the cantilever structure's length, cross-sectional moment of inertia, wall thickness distribution, root fillet radius, and spacing between adjacent cavities. Furthermore, material property data such as the mold material's elastic modulus, yield strength, coefficient of thermal expansion, and Poisson's ratio are obtained as the material property data. Specifically, the geometric eigenvector is calculated using the cantilever structure's slenderness ratio and cross-sectional characteristics. Optionally, a vulnerability index is determined based on thermal stress analysis. This vulnerability index reflects the risk of deflection, as molds often experience high pressure and high temperature during aluminum profile production, and the cantilever structure makes the mold susceptible to damage.
[0025] Step S2: Determine protection requirement information based on the vulnerability index and the preset threshold. The protection requirement information is used to mark whether each cantilever structure needs to be configured with a dummy foreman. The vulnerability index is compared with a preset threshold. When the index exceeds the threshold, the cantilever is determined to have a risk of breakage or excessive deformation, and is marked as a protection requirement that requires the configuration of a dummy foreman. Optionally, the threshold can be dynamically adjusted according to the mold material grade and product precision requirements, and over-configuration can be avoided in low-risk areas, thus taking into account both risk and cost requirements.
[0026] Step S3: Determine the initial set of fake foreman parameters based on the geometric feature vector, the protection requirement information, and the preset generation model; Optionally, the geometric feature vector and protection requirement information are input into a preset generation model. This model is trained based on historical data and can generate a set of candidate dummy foremen parameters within the feasible region at the root of the cantilever. Optionally, it can include parameters such as support position coordinates, top contact surface shape, height compensation amount, and transition fillet design to ensure the compatibility of the dummy foremen with the bottom surface of the cantilever.
[0027] Step S4: Determine the optimal dummy foreman design scheme based on the initial dummy foreman parameter set.
[0028] Specifically, the initial set of dummy foremen parameters is simulated, and multiple dummy foremen design schemes are generated by adjusting the schemes. The optimal dummy foremen design scheme is then determined from among these schemes.
[0029] In this embodiment, the cavity geometry data and material property data of the target mold are acquired, and the geometric feature vector and vulnerability index of each cantilever structure are determined based on the cavity geometry data and the material property data. Protection requirement information is determined based on the vulnerability index and a preset threshold. The protection requirement information is used to mark whether each cantilever structure needs to be configured with a dummy fork. An initial dummy fork parameter set is determined based on the geometric feature vector, the protection requirement information, and a preset generation model. The optimal dummy fork design scheme is determined based on the initial dummy fork parameter set. Compared with designing a tear-open structure, it can accurately design suitable dummy forks for cantilever structures that need protection, thereby reducing production risks and improving production efficiency.
[0030] Furthermore, based on the first embodiment, a second embodiment of the adaptive dummy foreman configuration control method of the present invention is proposed. In this embodiment, reference is made to... Figure 3 The step of determining the geometric feature vector and vulnerability index of each cantilever structure based on the cavity geometry data and the material property data includes: Step S11: Extract the cantilever information of each cantilever structure based on the cavity geometry data. The cantilever information includes: cantilever length, cantilever width, cantilever thickness, and root fillet radius. In this embodiment, the root fillet radius refers to the radius of the arc in the transition area between the cantilever structure and the base wall it connects to. Optionally, it is generally the radius of the inner corner rounding at the abrupt change in cross-section.
[0031] Step S12: Calculate the equivalent cantilever length based on the cantilever length, the cantilever width, the cantilever thickness, and the root fillet radius; Specifically, the formula for calculating the equivalent cantilever length is as follows:
[0032] Here Here, is the width of the opening segment. L represents the width of the root, where L is the cantilever length. The dynamic load factor is calculated as follows: f The extrusion process parameters can include a variety of parameters, here The dynamic load factor is σ, where σ is the yield strength of aluminum, v is the extrusion speed, and f is the billet temperature. Optionally, the default aluminum temperature set during the extrusion process can be used as the billet temperature.
[0033] Step S13: Determine the fragility index based on the equivalent cantilever length, the material property data, and the extrusion process parameters.
[0034] The vulnerability index is calculated based on the equivalent cantilever length, the cantilever thickness, the root fillet radius, and the dynamic load factor.
[0035] The cantilever fragility index, or CVI, is calculated as follows: CVI =
[0036] Here, R is the radius of the root fillet. It should be noted that for the stress concentration factor, the closer the fillet radius R is to the thickness T, the smaller the denominator and the lower the overall fragility index.
[0037] In this embodiment, the cantilever fragility index quantifies the risk level of plastic deformation or fracture of the cantilever under specific process conditions. The higher the index, the more fragile the structure, providing a quantitative mechanical basis for the protection requirement determination in step S2, and realizing a leap from geometric static description to dynamic process risk assessment.
[0038] Furthermore, based on the first or second embodiment, a third embodiment of the adaptive fake foreman configuration control method of the present invention is proposed. In this embodiment, the step of determining protection requirement information based on the vulnerability index and the preset threshold includes: When the vulnerability index is greater than or equal to the preset threshold, the protection requirement information is determined to require protection from a fake foreman. When the vulnerability index is less than the preset threshold, it is determined that the protection requirement is not required by the fake foreman.
[0039] In this embodiment, the vulnerability index is compared with a preset threshold set by engineering experience. When the vulnerability index is greater than or equal to the threshold, it indicates that the stress level or deformation risk of the corresponding cantilever structure under the extrusion process load has exceeded the safety margin. At this time, the protection requirement information is marked as requiring dummy foreman protection, and the subsequent dummy foreman design process is forcibly triggered to ensure that the cantilever receives additional support under the high pressure impact of the melt. When the vulnerability index is less than the threshold, it is determined that the stiffness of the cantilever itself is sufficient to withstand the process load, and the protection requirement information is marked as not requiring dummy foreman protection, thereby avoiding the setting of redundant support structures in low-risk areas, saving material costs and simplifying mold maintenance.
[0040] Furthermore, based on any of the above embodiments, a fourth embodiment of the adaptive fake foreman configuration control method of the present invention is proposed. In this embodiment, the preset generation model includes: a conditional diffusion model, and the step of determining the initial fake foreman parameter set according to the geometric feature vector, the protection requirement information, and the preset generation model includes: The cavity geometry data is input into the geometry encoder to obtain the overall geometry code of the mold; The geometric feature vector, the protection requirement information, and the overall geometric code of the mold are input into the conditional diffusion model to obtain the initial dummy foreman parameter vector; The initial fake foreman parameter set is generated based on the initial fake foreman parameter vector.
[0041] In this embodiment, an initial dummy fork parameter vector generated by a conditional diffusion model is optionally received. This vector represents the geometric configuration and layout features of the dummy fork in the latent space. A preset parameter decoder performs inverse normalization and dimension mapping operations on this vector, converting it into a set of engineering parameters including the three-dimensional coordinates of the support points, the surface coefficients of the top envelope surface, the height compensation amount, and the transition fillet radius. Furthermore, the initial dummy fork parameter set satisfies both the conformal fit constraint with the cantilever bottom surface and retains design freedom for subsequent optimization and adjustment, achieving a cross-domain transformation from the neural network latent space to the physical mold design space. Optionally, the conditional diffusion model here includes a geometric encoder, a conditional encoder, and a denoising network. It should be noted that the geometric encoder compresses the three-dimensional data of the mold cavity into a complete geometric code, and the conditional encoder maps the cantilever geometric features and protection requirements into conditional latent vectors. The denoising U-Net or Transformer takes noisy parameters as input, learns the parameter topology through self-attention, and fuses the geometric code and conditional vector through a cross-attention layer in each denoising step.
[0042] Furthermore, the step of inputting the geometric feature vector, the protection requirement information, and the overall geometric code of the mold into the conditional diffusion model to obtain the initial dummy foreman parameter vector includes: Based on the protection requirements information, determine the set of cantilever structures that need to be configured with dummy foremen; Construct conditional inputs based on the set of cantilever structures and the geometric feature vectors; The conditional input and the overall geometric code of the mold are input into the conditional diffusion model to obtain the output result of the conditional diffusion model; The output result is used as the initial dummy foreman parameter vector.
[0043] In this embodiment, after the conditional input and the overall geometric encoding of the mold are injected into the conditional diffusion model, the model fuses the global geometric context and local cantilever features through a cross-attention mechanism, driving the denoising network to gradually eliminate random noise and converge to the latent space point that meets the engineering constraints; the tensor output after the denoising is completed is the initial fake foreman parameter vector, and its numerical distribution contains the optimal geometric shape and spatial pose of the fake foreman.
[0044] Furthermore, based on any of the above embodiments, a fifth embodiment of the adaptive dummy foreman configuration control method of the present invention is proposed. In this embodiment, the step of determining the optimal dummy foreman design scheme according to the initial dummy foreman parameter set includes: Input the initial set of parameters of the fake foreman into the fluid-structure interaction simulation engine to obtain the cantilever stress distribution and the outlet velocity field; A multi-objective loss function is constructed based on the cantilever stress distribution and the outlet velocity field. The initial set of dummy foremen parameters is iteratively optimized using the multi-objective loss function and optimization algorithm to obtain the optimal dummy foremen design scheme.
[0045] In this embodiment, optionally, the initial dummy fork parameter set generated in the fourth embodiment is received, converted into a three-dimensional solid model, and imported into a fluid-structure interaction simulation engine. The simulation engine simultaneously solves the structural mechanics equations and fluid dynamics equations to obtain the stress distribution cloud map of the cantilever structure and the velocity field distribution at the mold exit during the melt filling process. Based on these simulation results, a multi-objective loss function that comprehensively considers strength safety, flow balance, and manufacturing economy is constructed. Sequential quadratic programming or genetic algorithms are used to iteratively optimize parameters such as the support position and envelope curvature of the dummy fork. The parameters are continuously corrected until the loss function converges to its minimum value, ultimately outputting the optimal dummy fork geometry and layout scheme that satisfies multiple constraints. In this embodiment, the fluid dynamics equations generally refer to the Navier-Stokes equations describing the flow behavior of high-temperature, high-pressure aluminum melt in the mold cavity. Specifically, the simulation parameters can be set in the ANSYS Workbench software.
[0046] Furthermore, the step of constructing a multi-objective loss function based on the cantilever stress distribution and the outlet velocity field includes: Calculate the stress loss term based on the cantilever stress distribution and material yield strength; Calculate the velocity equilibrium loss term based on the described outlet velocity field; Calculate the processing complexity penalty term based on the initial set of fake foremen parameters; The multi-objective loss function is constructed based on the stress loss term, the flow rate equalization loss term, and the processing complexity penalty term.
[0047] In this embodiment, a stress loss term is constructed by extracting the ratio of the maximum stress at the root of the cantilever obtained from simulation to the material's yield strength, in order to quantify the strength safety margin of the cantilever. Simultaneously, a flow velocity equilibrium loss term is constructed by analyzing the standard deviation or range of the flow velocity field at the outlet of each flow branch of the mold, in order to measure the filling consistency. Furthermore, a processing complexity penalty term is constructed based on the surface order, cantilever height, and number of dummy fork envelope surfaces, in order to assess manufacturing difficulty. The above three terms are then fused using a weighted summation method to obtain the multi-objective loss function.
[0048] Furthermore, this invention also proposes an adaptive dummy foreman configuration control system, which includes: The acquisition module is used to acquire the cavity geometry data and material property data of the target mold, and determine the geometric feature vector and vulnerability index of each cantilever structure based on the cavity geometry data and the material property data; The analysis module is used to determine protection requirement information based on the vulnerability index and a preset threshold. The protection requirement information is used to mark whether each cantilever structure needs to be configured with a dummy foreman. The generation module is used to determine the initial set of fake foreman parameters based on the geometric feature vector, the protection requirement information, and the preset generation model; The optimization module is used to determine the optimal dummy foreman design scheme based on the initial dummy foreman parameter set.
[0049] Furthermore, this invention also proposes an adaptive fake foreman configuration control device, which includes: a memory, a processor, and an adaptive fake foreman configuration control program stored in the memory and executable on the processor. The adaptive fake foreman configuration control program is configured to implement the steps of an embodiment of the adaptive fake foreman configuration control method described above.
[0050] Furthermore, embodiments of the present invention also propose a storage medium storing an adaptive fake foreman configuration control program, wherein when the adaptive fake foreman configuration control program is executed by a processor, it implements the steps of the embodiments of the adaptive fake foreman configuration control method described above.
[0051] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.
[0052] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.
[0053] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.
[0054] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.
Claims
1. An adaptive dummy foreman configuration control method, characterized in that, The adaptive fake foreman configuration control method includes the following steps: Acquire the cavity geometry data and material property data of the target mold, and determine the geometric feature vector and vulnerability index of each cantilever structure based on the cavity geometry data and the material property data; Protection requirement information is determined based on the vulnerability index and a preset threshold. This protection requirement information is used to mark whether each cantilever structure needs to be configured with a dummy foreman. The initial set of parameters for the fake foreman is determined based on the geometric feature vector, the protection requirement information, and the preset generation model. The optimal dummy foreman design scheme is determined based on the initial dummy foreman parameter set.
2. The adaptive dummy foreman configuration control method as described in claim 1, characterized in that, The step of determining the geometric feature vector and vulnerability index of each cantilever structure based on the cavity geometry data and the material property data includes: The cantilever information of each cantilever structure is extracted based on the cavity geometry data. The cantilever information includes: cantilever length, cantilever width, cantilever thickness, and root fillet radius. The equivalent cantilever length is calculated based on the cantilever length, the cantilever width, the cantilever thickness, and the root fillet radius. The fragility index is determined based on the equivalent cantilever length, the material property data, and the extrusion process parameters.
3. The adaptive dummy foreman configuration control method as described in claim 1, characterized in that, The step of determining protection requirement information based on the vulnerability index and the preset threshold includes: When the vulnerability index is greater than or equal to the preset threshold, the protection requirement information is determined to require protection from a fake foreman. When the vulnerability index is less than the preset threshold, it is determined that the protection requirement is not required by the fake foreman.
4. The adaptive dummy foreman configuration control method as described in claim 1, characterized in that, The preset generation model includes a conditional diffusion model, and the step of determining the initial fake foreman parameter set based on the geometric feature vector, the protection requirement information, and the preset generation model includes: The cavity geometry data is input into the geometry encoder to obtain the overall geometry code of the mold; The geometric feature vector, the protection requirement information, and the overall geometric code of the mold are input into the conditional diffusion model to obtain the initial dummy foreman parameter vector; The initial fake foreman parameter set is generated based on the initial fake foreman parameter vector.
5. The adaptive dummy foreman configuration control method as described in claim 4, characterized in that, The step of inputting the geometric feature vector, the protection requirement information, and the overall geometric code of the mold into the conditional diffusion model to obtain the initial fake foreman parameter vector includes: Based on the protection requirements information, determine the set of cantilever structures that need to be configured with dummy foremen; Construct conditional inputs based on the set of cantilever structures and the geometric feature vectors; The conditional input and the overall geometric code of the mold are input into the conditional diffusion model to obtain the output result of the conditional diffusion model; The output result is used as the initial dummy foreman parameter vector.
6. The adaptive dummy foreman configuration control method as described in any one of claims 1 to 5, characterized in that, The step of determining the optimal dummy foreman design scheme based on the initial dummy foreman parameter set includes: Input the initial set of parameters of the fake foreman into the fluid-structure interaction simulation engine to obtain the cantilever stress distribution and the outlet velocity field; A multi-objective loss function is constructed based on the cantilever stress distribution and the outlet velocity field. The initial set of dummy foremen parameters is iteratively optimized using the multi-objective loss function and optimization algorithm to obtain the optimal dummy foremen design scheme.
7. The adaptive dummy foreman configuration control method as described in claim 6, characterized in that, The step of constructing a multi-objective loss function based on the cantilever stress distribution and the outlet velocity field includes: Calculate the stress loss term based on the cantilever stress distribution and material yield strength; Calculate the velocity equilibrium loss term based on the described outlet velocity field; Calculate the processing complexity penalty term based on the initial set of fake foremen parameters; The multi-objective loss function is constructed based on the stress loss term, the flow rate equalization loss term, and the processing complexity penalty term.
8. An adaptive dummy foreman configuration control system, characterized in that, The adaptive fake foreman configuration control system includes: The acquisition module is used to acquire the cavity geometry data and material property data of the target mold, and determine the geometric feature vector and vulnerability index of each cantilever structure based on the cavity geometry data and the material property data; The analysis module is used to determine protection requirement information based on the vulnerability index and a preset threshold. The protection requirement information is used to mark whether each cantilever structure needs to be configured with a dummy foreman. The generation module is used to determine the initial set of fake foreman parameters based on the geometric feature vector, the protection requirement information, and the preset generation model; The optimization module is used to determine the optimal dummy foreman design scheme based on the initial dummy foreman parameter set.
9. An adaptive dummy foreman configuration control device, characterized in that, The adaptive fake foreman configuration control device includes: a memory, a processor, and an adaptive fake foreman configuration control program stored in the memory and executable on the processor, wherein the adaptive fake foreman configuration control program is configured to implement the steps of the adaptive fake foreman configuration control method as described in any one of claims 1 to 7.
10. A storage medium, characterized in that, The storage medium stores an adaptive fake foreman configuration control program, which, when executed by a processor, implements the steps of the adaptive fake foreman configuration control method as described in any one of claims 1 to 7.