Simulation-based non-slip mirror support compression molding process method and device

Abstract

The invention relates to the technical field of process simulation, and discloses an anti-slip mirror support compression molding process method and device based on simulation. The method comprises the following steps: inputting zero shear viscosity, relaxation time, shear thinning index, reference temperature and temperature sensitivity coefficient of a silica gel material into a Polyflow material database, and applying time nodes and pressure values of a quick charging stage, a slow charging stage and a pressure maintaining stage to an injection port of an anti-slip mirror holder mold cavity; setting a self-adaptive encrypted grid model, and solving phase volume fraction field distribution data of each grid unit in the self-adaptive encrypted grid model; and extracting a first filling depth value at the end moment of the quick filling stage, a second filling depth value at the end moment of the slow filling stage and a third filling depth value at the end moment of the pressure maintaining stage from the phase volume fraction field distribution data, and calculating a filling quality evaluation value. The problem that a traditional fixed viscosity model is low in prediction precision is solved, and the prediction precision of the filling depth is improved.

Description

Technical Field

[0001] This invention relates to the field of process simulation technology, and in particular to a simulation-based anti-slip mirror holder molding process method and apparatus. Background Technology

[0002] In the molding process of anti-slip mirror holders, the quality of micro-texture filling directly affects the anti-slip performance of the product. However, traditional process parameter optimization relies on trial and error, requiring the fabrication of multiple molds for experimental verification, resulting in long development cycles and high costs. Existing simulation methods use fixed viscosity values ​​or simple power-law models to describe the rheological properties of silicone, which cannot accurately reflect the dual nonlinear changes in viscosity with temperature and shear rate during molding, leading to large errors in the prediction of injection pressure and filling depth. Traditional simulations use uniform mesh generation and constant pressure loading, but the mesh accuracy in the micro-textured region is insufficient to capture the material climbing behavior, and it cannot accurately control the flow rate of the material at different filling stages, resulting in a high bubble entrapment rate at the root of the texture and large deviations in the predicted filling depth. Summary of the Invention

[0003] The main objective of this invention is to provide a simulation-based anti-slip mirror holder molding process method and apparatus. This invention solves the problem of low prediction accuracy of traditional fixed viscosity models and improves the prediction accuracy of filling depth.

[0004] To achieve the above objectives, the present invention provides a simulation-based molding process for anti-slip mirror holders, comprising the following steps: The zero-shear viscosity, relaxation time, shear thinning index, reference temperature and temperature sensitivity coefficient of the silicone material were input into the Polyflow material database, and the time nodes and pressure values ​​of the fast charging stage, slow charging stage and holding pressure stage were applied to the injection port of the anti-slip mirror mold cavity. The three-dimensional geometry of the microtextured cavity is divided into the texture root region, the texture sidewall region, and the flat bottom region, and the mesh size is set to obtain an adaptive densified mesh model. The phase volume fraction field distribution data of each mesh element in the adaptive densified mesh model is then solved. Extract the first filling depth value at the end of the fast charging stage, the second filling depth value at the end of the slow charging stage, and the third filling depth value at the end of the pressure holding stage from the phase volume fraction field distribution data, and calculate the filling quality evaluation value.

[0005] Optionally, in a first implementation of the first aspect of the present invention, the step of inputting the zero-shear viscosity, relaxation time, shear thinning index, reference temperature, and temperature sensitivity coefficient of the silicone material into the Polyflow material database, and applying the time points and pressure values ​​of the fast charging stage, slow charging stage, and holding pressure stage to the injection port of the anti-slip mirror mold cavity includes: Rheological tests were conducted on the silicone material at multiple temperature points and multiple shear rate points to obtain test data; The test data were fitted to the Cross-WLF viscoelastic constitutive equation to calculate the zero-shear viscosity, relaxation time, shear thinning index, reference temperature and temperature sensitivity coefficient. The zero-shear viscosity, relaxation time, shear thinning index, reference temperature, and temperature sensitivity coefficient are input into the Polyflow material database as custom material properties. Establish time nodes and pressure values ​​that include fast charging, slow charging and pressure holding stages, and apply the time nodes and pressure values ​​to the injection port of the anti-slip mirror mold cavity.

[0006] Optionally, in a second implementation of the first aspect of the present invention, the construction includes time points and pressure values ​​for a fast charging stage, a slow charging stage, and a pressure holding stage, and applying the time points and pressure values ​​to the injection port of the anti-slip mirror mold cavity includes: The fast charging phase is set to increase from zero pressure to the first pressure value in the first time interval with the first pressure gradient, the slow charging phase is set to increase to the second pressure value in the second time interval with the second pressure gradient, and the pressure holding phase is set to maintain a constant third pressure value in the third time interval. The time nodes and pressure values ​​of the fast charging phase, slow charging phase and pressure holding phase are obtained respectively. In the Polyflow boundary conditions module, the time node and pressure value are applied as time-dependent boundary conditions to the injection port of the anti-slip mirror mold cavity.

[0007] Optionally, in a third implementation of the first aspect of the present invention, the step of dividing the three-dimensional geometry of the micro-textured cavity into a texture root region, a texture sidewall region, and a flat bottom region, and setting the mesh size to obtain an adaptive refined mesh model, and solving for the phase volume fraction field distribution data of each mesh element in the adaptive refined mesh model, includes: The three-dimensional geometry of the microtextured cavity imported into the Polyflow geometry module is divided into a texture root region, a texture sidewall region, and a flat bottom region. The texture root region is set to a first mesh size, the texture sidewall region is set to a second mesh size, and the flat bottom region is set to a third mesh size. A tetrahedral mesh generation algorithm is used to generate meshes in the texture root region, texture sidewall region and flat bottom region, and gradient transition parameters are set at the subdomain boundary to make the mesh size transition smoothly, thus obtaining an adaptive refined mesh model. The adaptive encrypted mesh model is iteratively solved according to a preset time step to obtain the pressure field distribution data, velocity field distribution data, temperature field distribution data, and phase volume fraction field distribution data of each mesh element.

[0008] Optionally, in a fourth implementation of the first aspect of the present invention, the iterative solution of the adaptive densified mesh model at a preset time step to obtain the pressure field distribution data, velocity field distribution data, temperature field distribution data, and phase volume fraction field distribution data of each mesh cell includes: The viscosity field distribution data of each grid cell in the adaptive fine mesh model is calculated based on the Cross-WLF viscoelastic constitutive equation, and the viscosity field distribution data is substituted into the momentum conservation equation and the energy conservation equation. According to the preset time step, the momentum conservation equation and the energy conservation equation are subjected to pressure-velocity coupled iterative calculation, and the pressure field distribution data, velocity field distribution data and temperature field distribution data of each grid cell are updated until the convergence criterion is met; The volume fraction method was used to track the free surface positions of the silicone material and air, and to calculate the pressure field distribution data, velocity field distribution data, temperature field distribution data, and phase volume fraction field distribution data of each grid cell.

[0009] Optionally, in a fifth implementation of the first aspect of the present invention, the step of using the volume fraction method to track the free surface positions of the silicone material and air and calculating the pressure field distribution data, velocity field distribution data, temperature field distribution data, and phase volume fraction field distribution data of each grid cell includes: Define the phase volume fraction function for each mesh element, and label elements with a phase volume fraction of 1 as silicone material, elements with a phase volume fraction of 0 as air, and elements with a phase volume fraction between 0 and 1 as interface elements. The phase volume fraction transport equation is solved based on the velocity field distribution data, and the phase volume fraction function value of each grid cell is updated to track the free surface position of the silicone material and air. The spatial coordinates of the interface unit in the micro-texture region are identified, and pressure field distribution data, velocity field distribution data, temperature field distribution data, and phase volume fraction field distribution data are output.

[0010] Optionally, in a sixth implementation of the first aspect of the present invention, the step of extracting the first filling depth value at the end of the fast charging stage, the second filling depth value at the end of the slow charging stage, and the third filling depth value at the end of the pressure holding stage from the phase volume fraction field distribution data, and calculating the filling quality evaluation value, includes: At the end of the fast charging phase, the end of the slow charging phase, and the end of the pressure holding phase, the spatial position of the interface unit is identified from the phase volume fraction field distribution data, and the first filling depth value, the second filling depth value, and the third filling depth value are extracted respectively. The target cells with a phase volume fraction less than a preset threshold and a pressure lower than atmospheric pressure are identified by traversing each grid cell, and the total volume of the target cells is calculated to obtain the bubble defect volume value. Calculate the depth ratio of the third filling depth value to the texture design depth, calculate the volume ratio of the bubble defect volume value to the total texture volume, and multiply the depth ratio and the volume ratio to obtain the filling quality evaluation value.

[0011] Optionally, in the seventh implementation of the first aspect of the present invention, the simulation-based anti-slip mirror holder molding process further includes: The pressure rise gradient during the slow charging phase and the pressure value during the holding phase are fixed at a fixed time point and pressure value. The pressure rise gradient during the fast charging phase is changed, and multiple first value levels are set within the first gradient range for simulation calculation. The filling quality evaluation value corresponding to each first value level is extracted. The pressure rise gradient during the fast charging phase and the pressure value during the holding phase are fixed at a fixed time point and pressure value. The pressure rise gradient during the slow charging phase is changed, and multiple second value levels are set within the second gradient range for simulation calculation. The filling quality evaluation value corresponding to each second value level is extracted. The pressure rise gradient at each first value level during the fast charging phase is fitted with the corresponding filling quality evaluation value to obtain a first mapping function. The pressure rise gradient at each second value level during the slow charging phase is fitted with the corresponding filling quality evaluation value to obtain a second mapping function. Based on the first and second mapping functions, a quantitative mapping relationship between the injection pressure curve and the microtexture filling quality is obtained.

[0012] Optionally, in the eighth implementation of the first aspect of the present invention, the step of fitting a first mapping function by combining the pressure rise gradient at each first value level during the fast charging phase with the corresponding filling quality evaluation value, and fitting a second mapping function by combining the pressure rise gradient at each second value level during the slow charging phase with the corresponding filling quality evaluation value, and obtaining a quantitative mapping relationship between the injection pressure curve and the microtexture filling quality based on the first and second mapping functions, includes: The pressure rise gradient at each first value level during the fast charging phase is used as the independent variable and the corresponding filling quality evaluation value is used as the dependent variable to perform a quadratic function fitting to obtain the first mapping function. The pressure rise gradient at each second value level during the slow charging stage is used as the independent variable, and the corresponding filling quality evaluation value and bubble defect volume value are used as the dependent variables to perform quadratic function fitting to obtain the second mapping function. Based on the first mapping function, the optimal value of the first pressure gradient in the fast charging stage when the filling quality evaluation value is maximized is obtained. Based on the second mapping function, the optimal value of the second pressure gradient in the slow charging stage when the bubble defect volume value is minimized is obtained. The optimal values ​​of the first and second pressure gradients are combined to obtain the quantitative mapping relationship between the injection pressure curve and the microtexture filling quality.

[0013] The present invention also provides a simulation-based anti-slip mirror holder molding process apparatus, comprising: The input module is used to input the zero shear viscosity, relaxation time, shear thinning index, reference temperature and temperature sensitivity coefficient of the silicone material into the Polyflow material database, and to apply the time nodes and pressure values ​​of the fast charging stage, slow charging stage and holding pressure stage to the injection port of the anti-slip mirror mold cavity. The solver module is used to divide the three-dimensional geometry of the microtextured cavity into the texture root region, the texture sidewall region and the flat bottom region, and set the mesh size to obtain an adaptive densified mesh model, and solve the phase volume fraction field distribution data of each mesh element in the adaptive densified mesh model. The calculation module is used to extract the first filling depth value at the end of the fast charging stage, the second filling depth value at the end of the slow charging stage, and the third filling depth value at the end of the pressure holding stage from the phase volume fraction field distribution data, and to calculate the filling quality evaluation value.

[0014] In summary, this invention accurately describes the dual nonlinear characteristics of silicone material viscosity with respect to temperature and shear rate by establishing a Cross-WLF viscoelastic constitutive equation. It couples this constitutive equation to the momentum and energy conservation equations to form a multiphysics closed-loop solution system, enabling the viscosity value of each grid cell in the simulation to be updated in real time with the local temperature and shear rate fields, thus solving the problem of low prediction accuracy in traditional fixed viscosity models. By designing a three-stage pressure sequence of fast charging, slow charging, and holding pressure and applying it as a time-dependent boundary condition, precise control of the material's flow behavior at different filling stages in the microtexture is achieved. The fast charging stage rapidly overcomes flow resistance, the slow charging stage controls the ramp-up speed to avoid bubble entrainment, and the holding pressure stage compensates for curing shrinkage. A regional adaptive mesh refinement strategy is adopted, focusing on the texture root, sidewalls, and flat areas. Refined mesh sizes are set in the critical texture root region to capture free surface propagation and bubble nucleation behavior, improving the accuracy of filling depth prediction. By dynamically extracting characteristic parameters such as the material front position, filling depth, and bubble volume at the end of the three stages of fast charging, slow charging, and pressure holding, a comprehensive evaluation function for filling quality was established, enabling quantitative assessment of the filling process. Through multiple simulations with varying pressure curve parameters and function fitting between the filling quality evaluation value and the pressure gradient, a quantitative mapping relationship from process parameters to product quality was established. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the simulation-based anti-slip mirror holder molding process steps in one embodiment of the present invention; Figure 2 This is a structural block diagram of a simulation-based anti-slip mirror holder molding process device according to an embodiment 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] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0018] Reference Figure 1 This embodiment provides a simulation-based molding process for anti-slip mirror holders, including the following steps: S1, input the zero shear viscosity, relaxation time, shear thinning index, reference temperature and temperature sensitivity coefficient of the silicone material into the Polyflow material database, and apply the time nodes and pressure values ​​of the fast charging stage, slow charging stage and holding pressure stage to the injection port of the anti-slip mirror mold cavity. Among these tests, a systematic rheological property test was conducted on the viscoelastic behavior of the selected anti-slip silicone material for mirror holders within the processing temperature range. Multiple temperature points were set, such as 140℃, 150℃, 160℃, 170℃, and 180℃, and at each temperature point, a shear rate from 10... -2 s -1 Up to 10 3 s -1 Twenty-five test points were arranged at logarithmic intervals to form a two-dimensional test matrix covering the temperature-shear coupling effect, obtaining an experimental dataset of viscosity variations with shear rate and temperature. The experimental data were fitted to the Cross-WLF viscoelastic constitutive equation using a nonlinear least squares algorithm. During this process, the zero-shear viscosity η0 and relaxation time λ, which accurately describe the shear-thinning effect of the material, the shear-thinning exponent n, which describes the flow index characteristics, and the reference temperature T_ref and temperature sensitivity coefficients A1 and A2, reflecting the viscosity-temperature dependence, were calculated using the coefficient of determination R0. 2A value greater than 0.98 is used as the criterion to ensure that the fitting quality meets engineering accuracy standards. Zero-shear viscosity, relaxation time, shear thinning index, reference temperature, and temperature sensitivity coefficient are input as viscoelastic material properties into the material database module of the Polyflow simulation platform, and a unique material identification number is established. This allows the local viscosity value of each finite volume element to dynamically change according to the real-time temperature field and shear rate field, thus forming a numerical model with physical accuracy. After completing the material model configuration, a multi-stage pressure evolution function was designed based on the molding requirements at different stages of the molding process. A piecewise linear function was constructed, comprising three stages: fast charging, slow charging, and holding pressure. In the fast charging stage, the pressure rapidly increases from 0 to 5 MPa to overcome the initial resistance. In the slow charging stage, the pressure increases at a relatively gentle slope to 6.5 MPa to control the leading edge advance rate and prevent bubble entrainment. In the holding pressure stage, a constant pressure of 7 MPa is maintained to compensate for material crosslinking shrinkage. The time node array [0, 0.8, 2.5, 8] and the corresponding pressure value array [0, 5, 6.5, 7] are loaded as pressure boundary conditions onto the injection port boundary in the Polyflow model. Automatic interpolation and update of pressure within each time step are achieved by selecting "piecewise linear interpolation" as the time function.

[0019] S2, the three-dimensional geometry of the microtextured cavity is divided into the texture root region, the texture sidewall region and the flat bottom region, and the mesh size is set to obtain the adaptive densified mesh model, and the phase volume fraction field distribution data of each mesh element in the adaptive densified mesh model is solved; Specifically, the 3D geometric model of the microtextured cavity was imported into the geometry module of the Polyflow simulation platform. Based on the internal structural characteristics of the cavity, the geometric model space was divided into three functional sub-regions: the texture root region, which contacts the material-air interface and directly affects the filling accuracy; the texture sidewall region, which controls the change of the flow direction at the leading edge; and the flat bottom region, which has stable overall flow and low gradient. Different mesh sizes were set for these three sub-regions. The texture root region was set to the first mesh size of 0.015 mm to ensure interface tracking accuracy; the texture sidewall region was set to the second mesh size of 0.05 mm to cope with the surge in velocity gradient caused by abrupt changes in flow direction; and the flat bottom region was set to the third mesh size of 0.2 mm to reduce the total computational resource consumption while meeting the flow field resolution requirements. After completing the region division and setting the mesh size, the Delaunay tetrahedral mesh generation algorithm was used to generate unstructured 3D meshes for each of the three subdomains. Gradient transition parameters were set at the boundaries of all subdomains to ensure a smooth transition of mesh sizes in each region, avoiding non-convergence issues caused by mesh distortion or abrupt changes. This generated an adaptive refined mesh model containing approximately 8.5 million mesh elements, with the texture root region accounting for over 65% of the mesh. A multiphysics coupled iterative solution process was initiated, dynamically solving the adaptive refined mesh model at a preset time step Δt = 0.01s. Within each time step, the momentum conservation equation, energy conservation equation, and VOF free surface transport equation were simultaneously iterated. The material viscosity value was updated in real-time based on the velocity and temperature fields of the current time step. The SIMPLE algorithm was used to solve for the pressure field correction and update the velocity vector and temperature distribution, simultaneously advancing the interface evolution of the phase volume fraction. This completed the solution for the four types of physical field distribution data (pressure, velocity, temperature, and phase volume fraction) for each mesh element at a given time step.

[0020] S3. Extract the first filling depth value at the end of the fast charging stage, the second filling depth value at the end of the slow charging stage, and the third filling depth value at the end of the pressure holding stage from the phase volume fraction field distribution data, and calculate the filling quality evaluation value.

[0021] Specifically, based on the preset key moments of the process stage t1=0.8 seconds, t2=2.5 seconds, and t3=8 seconds, the positional changes of the material-air interface are identified from the phase volume fraction distribution at the corresponding time points. At each key moment, all cells in the adaptively refined mesh are traversed to extract the set of cells whose phase volume fraction F value is in the interface state (i.e., F=0.5), and the centroid spatial coordinates of these cells are obtained, especially the depth distribution in the z-axis direction. The centerline position of each textured groove is searched along the z-direction to determine the interface cell position where the phase volume fraction first drops to 0.5. The difference between the interface cell position and the groove bottom reference coordinate is calculated to obtain the local filling depth value of the textured groove. Then, the average value of the local filling depth value of all 200 textured grooves is calculated as the first filling depth value d1, the second filling depth value d2, and the third filling depth value d3, respectively, to describe the filling and advancement of the material at different stages. All mesh elements are traversed, and target elements that simultaneously satisfy a phase volume fraction F less than a preset threshold (e.g., F < 0.95) and a local pressure P lower than the ambient atmospheric pressure (e.g., P < 0.1 MPa) are selected. These elements represent unfilled areas or areas where bubble nucleation occurs. The volume values ​​of the target elements are summed to obtain the bubble defect volume. Simultaneously, based on the mold cavity design parameters, the theoretical total volume of the entire texture structure is calculated, for example, the sum of the volumes of 200 slots, as a reference volume benchmark. By calculating the ratio between the final stage d3 and the texture design depth, the depth compliance coefficient is obtained. The ratio of the bubble defect volume to the theoretical total volume is expressed as the bubble defect rate. The filling quality evaluation value Q is constructed as: Q = Depth Compliance Coefficient × (1 / 2) * ... (Bubble defect rate).

[0022] In one example, the zero-shear viscosity, relaxation time, shear thinning index, reference temperature, and temperature sensitivity coefficient of the silicone material are input into the Polyflow material database. The time points and pressure values ​​for the fast-charge, slow-charge, and holding phases are applied to the injection port of the anti-slip mirror mold cavity, including: Rheological tests were conducted on the silicone material at multiple temperature points and multiple shear rate points to obtain test data; The test data were fitted to the Cross-WLF viscoelastic constitutive equation to calculate the zero-shear viscosity, relaxation time, shear thinning index, reference temperature and temperature sensitivity coefficient. Input zero-shear viscosity, relaxation time, shear thinning index, reference temperature, and temperature sensitivity coefficient as custom material properties into the Polyflow material database; Establish time nodes and pressure values ​​for fast charging, slow charging, and pressure holding stages, and apply the time nodes and pressure values ​​to the injection port of the anti-slip mirror mold cavity.

[0023] In this example, a stable polymer silicone material sample suitable for compression molding was selected, and systematic rheological performance tests were conducted using a high-precision rotational rheometer under laboratory conditions. Five representative temperature gradient points were set during the testing process, such as 140℃, 150℃, 160℃, 170℃, and 180℃, and a range covering 10℃ was applied at each temperature point. -2 s -1 Up to 10 3 s -1 The shear rate was used to form a two-dimensional test matrix. A logarithmic interval design was used for 25 discrete points in the shear rate calculation. A set of viscosity-shear rate data was obtained at each temperature point, resulting in a total of 125 experimental data points across five temperature points. The experimental data were then fitted nonlinearly in logarithmic form using least squares fitting and substituted into the Cross-WLF viscoelastic constitutive equation model. The Cross term describes the shear-thinning properties of silica gel, while the WLF term characterizes the exponential effect of temperature on viscosity. The optimal fitting parameters, including zero-shear viscosity η0, relaxation time λ, shear-thinning exponent n, reference temperature T_ref, and temperature sensitivity coefficients A1 and A2, were obtained through iterative convergence. The fitting was completed when the sum of squared residuals was minimized, and the coefficient of determination R was required to be within acceptable limits. 2 A value greater than 0.98 is used to ensure the accuracy of the viscoelastic model. After model fitting is completed, the above six parameters are input into the Polyflow material database system, and the material number and type are defined in the database. The Cross-WLF constitutive relation is registered as the default constitutive model of the material, so that the viscosity value can be updated in real time according to the local temperature and shear rate during subsequent simulations, realizing a continuous mapping of material properties from experimental measurements to numerical simulations. After establishing the material properties, to ensure the driving force in the simulation process possesses dynamic characteristics matching the actual process, an injection port pressure boundary condition curve was designed. Specifically, a piecewise linear pressure function with three stages was constructed. The fast charging stage was set to a time of 0 to 0.8 seconds, with the pressure linearly increasing from 0 to 5 MPa to overcome the initial flow resistance of the material. The slow charging stage was 0.8 to 2.5 seconds, with the pressure slowly increasing to 6.5 MPa to guide the material to smoothly climb into the microtexture. The holding pressure stage was 2.5 to 8 seconds, with the pressure maintained constant at 7 MPa to compensate for the volume loss due to silicone crosslinking shrinkage and stabilize the texture filling depth. The piecewise pressure curve was plotted as a time node array [0, 0.8, 2.5, 8] and a corresponding pressure value array [0, 5, 6.5, 8]. 7] Input the data into the Polyflow boundary condition module, set the injection port to pressure boundary type, and select piecewise linear interpolation as the time function type. This allows the solver to calculate the current pressure value based on the set node interpolation in each time step and apply it to the simulation model, thus achieving a dynamic loading process consistent with the actual molding conditions.

[0024] In one example, time points and pressure values ​​are constructed, including fast charging, slow charging, and pressure holding phases. These time points and pressure values ​​are then applied to the injection port of the anti-slip mirror mold cavity, including: The fast charging phase is set to increase from zero pressure to the first pressure value in the first time interval with the first pressure gradient, the slow charging phase is set to increase to the second pressure value in the second time interval with the second pressure gradient, and the pressure holding phase is set to maintain a constant third pressure value in the third time interval. The time nodes and pressure values ​​of the fast charging phase, slow charging phase and pressure holding phase are obtained respectively. In the Polyflow boundary conditions module, time nodes and pressure values ​​are applied as time-dependent boundary conditions to the injection port of the anti-slip mirror mold cavity.

[0025] In this example, a phased pressurization process is designed based on the physical characteristics of the filling behavior of the microtexture structure in the mold cavity of the anti-slip mirror. The main goal of the fast-charging stage is to quickly overcome the initial viscosity of the material and the inlet resistance of the mold cavity with a high pressure gradient. The first time interval is set to 0 to 0.8 seconds. Within the first time interval, the injection pressure is linearly increased from 0 to the first pressure value at the first pressure gradient. For example, an increase rate of 6.25 MPa / s corresponds to a pressure of 5 MPa at 0.8 seconds. The slow-charging stage is set to 0.8 to 2.5 seconds. In order to prevent the material from causing air bubbles or interface instability due to high-speed impact, a smaller pressure gradient, such as 0.88 MPa / s, is selected to increase the pressure from 5 MPa to 6.5 MPa. During this stage, the advance speed of the leading edge is controlled to be kept below 0.12 mm / s. The holding pressure stage corresponds to a time interval of 2.5 to 8 seconds. During this stage, the silicone begins to cross-link and undergoes volume shrinkage. It is necessary to keep the pressure at the injection port stable to replenish the material and prevent the formation of voids in the texture. Therefore, the pressure is set to a constant 7 MPa, forming a three-stage injection pressure timing curve. The endpoint times of the three stages were marked as 0.8 seconds, 2.5 seconds, and 8 seconds, respectively, with corresponding pressure values ​​of 5 MPa, 6.5 MPa, and 7 MPa. These were combined to obtain the time node array [0, 0.8, 2.5, 8] and the pressure value array [0, 5, 6.5, 7]. These arrays were then input into the boundary condition module of the Polyflow simulation system. In the boundary condition module, the injection port region was selected as "inlet_boundary", the boundary type was set to "pressure_boundary", and the time function type was selected as "piecewise_linear", i.e., piecewise linear interpolation function. This enabled the solver to automatically interpolate based on the time nodes in each time step and update the current injection port pressure value in real time, thereby ensuring that the driving boundary in the simulation process could accurately reproduce the multi-stage loading logic under real working conditions.

[0026] In one example, the three-dimensional geometry of the microtextured cavity is divided into a texture root region, a texture sidewall region, and a flat bottom region, and the mesh size is set to obtain an adaptive refinement mesh model. The phase volume fraction field distribution data of each mesh element in the adaptive refinement mesh model is then solved, including: The 3D geometry of the microtextured cavity imported into the Polyflow geometry module is divided into a texture root region, a texture sidewall region, and a flat bottom region. The texture root region is set to the first mesh size, the texture sidewall region is set to the second mesh size, and the flat bottom region is set to the third mesh size. A tetrahedral mesh generation algorithm is used to generate meshes in the texture root region, texture sidewall region, and flat bottom region. Gradient transition parameters are set at the subdomain boundary to make the mesh size transition smoothly, resulting in an adaptive refined mesh model. The adaptive densified grid model is iteratively solved according to a preset time step to obtain the pressure field distribution data, velocity field distribution data, temperature field distribution data, and phase volume fraction field distribution data of each grid cell.

[0027] In this example, the 3D geometric model of the anti-slip mirror holder microtextured cavity is imported into the Polyflow geometry processing module. During the import process, functional regions in the cavity are identified and marked. Based on the flow behavior characteristics and local geometric change trends during the silicone filling process, the entire cavity is divided into three sub-regions: the texture root region, which characterizes the free surface evolution behavior at the material-air interface; the texture sidewall region, which controls the material flow direction turning point; and the flat bottom region, where the flow is relatively stable. Targeted mesh scale parameters are set for these three sub-regions. The texture root region is set to the first mesh size, for example, 0.015 mm, to capture interface details and ensure the accuracy of the VOF method. The texture sidewall region is set to the second mesh size, for example, 0.05 mm, to cope with the velocity gradient increase caused by the rapid change in flow direction. The flat bottom region is set to the third mesh size, for example, 0.2 mm, to reduce the overall computational resource consumption. After completing the partitioning and size configuration, the tetrahedral mesh generation algorithm based on the Delaunay 3D partitioning principle is selected in the Polyflow mesh generation module to construct unstructured meshes in the three sub-regions. At the same time, a continuous gradient transition function is set between the sub-domains to make the mesh size transition smoothly from large to small or from small to large, preventing problems such as non-convergence or numerical oscillation caused by abrupt mesh changes. The adaptive refinement mesh model is obtained. The adaptive refinement mesh model can control the number of elements within a reasonable range (such as about 8.5 million elements) while ensuring local accuracy requirements in key texture areas. After spatial discretization, the Coupled momentum-energy-VOF joint solution template is loaded into the Polyflow solver module, and the time step Δt is set to 0.01s. The SIMPLEC algorithm is selected as the main iterative framework to construct a strongly coupled multiphysics calculation process that includes velocity field, pressure field, temperature field, and phase volume fraction field. In each time step, the shear viscosity is updated in real time based on the temperature and velocity state of the previous step, and then the stress term in the momentum control equation is corrected to update the velocity vector and pressure distribution. Simultaneously, the temperature rise and heat conduction terms in the energy equation are solved to obtain the material temperature evolution process. Based on the velocity field, the VOF transport equation is solved to update the phase volume fraction distribution, so as to achieve a complete solution of the physical state data of all grid cells in each time step, including the spatially distributed pressure value, velocity component, temperature value, and the position of the silica gel-air interface.

[0028] In one example, the adaptive fine mesh model is iteratively solved at a preset time step to obtain the pressure field distribution data, velocity field distribution data, temperature field distribution data, and phase volume fraction field distribution data of each mesh cell, including: The viscosity field distribution data of each grid cell in the adaptive fine mesh model were calculated based on the Cross-WLF viscoelastic constitutive equation, and the viscosity field distribution data were substituted into the momentum conservation equation and the energy conservation equation. An iterative calculation of pressure-velocity coupling is performed on the momentum and energy conservation equations according to the preset time step to update the pressure field distribution data, velocity field distribution data, and temperature field distribution data of each grid unit until the convergence criterion is satisfied. The volume fraction method is used to track the free surface position between the silica gel material and air and calculate the pressure field distribution data, velocity field distribution data, temperature field distribution data, and phase volume fraction field distribution data of each grid unit.

[0029] In this example, a complete multi-physics coupling solution framework is established based on the adaptive refined grid model loaded into the Polyflow platform. In this framework, the Cross-WLF viscoelastic constitutive equation that has been fitted through rheological experiments and imported into the material database is used as the viscosity field generation model. By reading the local temperature value and shear rate value of each grid unit in real time, the viscosity field distribution data at the current moment is calculated within each time step. The viscosity field distribution reflects the change in the flow resistance of the material under non-isothermal and non-Newtonian conditions. Substituting the viscosity field as a variable term into the momentum conservation equation makes the pressure gradient and shear viscosity term form a non-linear partial differential equation system, and at the same time substituting it into the energy conservation equation to calculate the power density of the shear heating term, forming a strong coupling relationship between the momentum equation and the energy equation. During the time integration process, a fixed time step Δt, such as 0.01 seconds, is set, and through the SIMPLEC pressure-velocity iteration algorithm, within each time step, first calculate the pressure correction term in the momentum equation according to the current viscosity field, then correct the velocity field components and pressure values, and then substitute the updated velocity vector into the energy equation to solve the temperature distribution, and use the corrected temperature field to feedback and update the viscosity value again, forming a non-linear coupling iteration chain among viscosity - pressure - velocity - temperature. The iteration process continues until the relative error of the velocity field within the current step is lower than 10 -4 and the relative error of the temperature field is lower than 10 -5 . After each successful coupling iteration, the VOF (Volume of Fluid) volume fraction method is used to track the flow interface between the silica gel material and air. Define the phase volume fraction F = 1 to represent the silica gel region, F = 0 to represent the air region, and 0 < F < 1 to represent the interface unit. By solving the transport equation F / t + v· F = 0 to update the interface position. When calculating the F value, judge whether the bubble nucleation condition is satisfied near the interface, that is, the local pressure is lower than the atmospheric pressure and the interface unit has a negative pressure gradient structure, and further synchronously record the distribution of the F value and the P, v, T values in the spatial domain, so as to obtain a complete four-field data output at each time step, that is, the pressure field, velocity field, temperature field, and phase volume fraction field driven by viscosity.

[0030] In one example, the volume fraction method is used to track the free surface positions of the silicone material and air, and to calculate the pressure field distribution data, velocity field distribution data, temperature field distribution data, and phase volume fraction field distribution data for each grid cell, including: Define the phase volume fraction function for each mesh element, and label elements with a phase volume fraction of 1 as silicone material, elements with a phase volume fraction of 0 as air, and elements with a phase volume fraction between 0 and 1 as interface elements. The phase volume fraction transport equation is solved based on the velocity field distribution data, and the phase volume fraction function value of each grid cell is updated to track the free surface position of the silicone material and air. The system identifies the spatial coordinates of the interface unit in the micro-texture region and outputs pressure field distribution data, velocity field distribution data, temperature field distribution data, and phase volume fraction field distribution data.

[0031] In this example, the Volume Fraction (VOF) method is introduced into the Polyflow simulation framework as a method for tracking multiphase flow interfaces. A phase volume fraction function F(x, y, z, t) that evolves over time is defined for each mesh cell in the entire adaptive refined mesh model. The phase volume fraction function expresses the proportion of silica gel and air in each cell, where F=1 represents that the cell is completely filled with silica gel, F=0 represents that the cell is filled with air, and F values ​​between 0 and 1 indicate that the cell is at the interface between material and air, i.e., an interface cell. During the simulation, the phase volume fraction is updated over time, and its evolution depends on the change in the velocity field. Therefore, at each time step, the latest updated velocity field distribution v(x, y, z, t) is substituted into the VOF transport equation. F / t + v· When F = 0, the spatial derivative term of F is discretely calculated and time-advanced to update the instantaneous value of F in each grid cell, ensuring the accurate evolution of the interface morphology during dynamic flow. To ensure interface tracking stability, an interface reconstruction algorithm is used to numerically correct the F value in the critical region and maintain its monotonicity, avoiding non-physical diffusion or oscillation phenomena. After updating the F function value, cells with F values ​​in the range of (0,1) are extracted by traversing all grid cells; these are the interface cells. The geometric center coordinates or centroid coordinates of these cells are recorded, thereby establishing a distribution map of the material front and air interface in three-dimensional space. In particular, in the micro-textured region, coordinate filtering is used to retain only the interface cells inside the texture grooves, achieving real-time positioning of the silicone propulsion into the microstructure. The local pressure value, velocity vector, temperature value, and F function value corresponding to the interface cell are output as a set of multiphysics distribution data.

[0032] In one example, the first filling depth value at the end of the fast charging stage, the second filling depth value at the end of the slow charging stage, and the third filling depth value at the end of the pressure holding stage are extracted from the phase volume fraction field distribution data, and the filling quality evaluation value is calculated, including: At the end of the fast charging stage, the end of the slow charging stage, and the end of the pressure holding stage, the spatial positions of the interface units are identified from the phase volume fraction field distribution data, and the first filling depth value, the second filling depth value, and the third filling depth value are extracted respectively; Traverse each grid unit to identify the target units with a phase volume fraction less than a preset threshold and a pressure lower than the atmospheric pressure, and statistically sum the volumes of the target units to obtain the bubble defect volume value; Calculate the depth ratio of the third filling depth value to the texture design depth, calculate the volume ratio of the bubble defect volume to the total texture volume, and perform a product operation on the depth ratio and the volume ratio to obtain the filling quality evaluation value.

[0033] In this example, based on the phase volume fraction field distribution data already solved by the VOF method, at three moments, namely, the end of the fast charging stage t1 = 0.8 s, the end of the slow charging stage t2 = 2.5 s, and the end of the pressure holding stage t3 = 8 s, the interface units of the entire adaptive refined grid model are identified respectively. The identification method is to traverse all grid units and select the units with the phase volume fraction F value between 0 and 1, that is, the units satisfying 0 < F < 1 are interface units. Extract the geometric centroid coordinates of these units in the micro-texture area, especially the vertical coordinate position along the z-axis, and record the difference between the z coordinate of the interface unit and the bottom coordinate of the groove with the bottom surface of the groove as the reference as the local filling depth of the corresponding texture groove. Perform the interface extraction operation for the center line area of each texture groove to obtain the instantaneous filling depth of the groove, and then statistically average the filling depths of all 200 grooves to obtain the average filling depth values corresponding to the three moments t1, t2, and t3, denoted as the first filling depth value d1, the second filling depth value d2, and the third filling depth value d3, which are used to evaluate the phased progress of the material front advancing into the microstructure. At the same time, to quantify the impact of bubble defects on the final quality, the bubble identification and screening conditions are executed for all units in the simulation grid, that is, to find those units with a phase volume fraction F lower than a set threshold (such as 0.95) and a local pressure P lower than the atmospheric pressure (such as P < 0.1 MPa). These units are considered as possible areas where bubbles exist, and the volume values of these units are accumulated and summed to obtain the bubble defect volume; combined with the mold cavity design parameters, such as the volume of each of the 200 texture grooves can be calculated from the groove depth, groove width, and groove length to obtain the theoretical total texture volume, and divide the actual final filling depth d3 by the texture design depth to obtain the filling depth ratio, and then divide the bubble defect volume by the total texture volume to obtain the bubble volume fraction. Combine the two to construct a unified filling quality evaluation index Q = filling depth ratio × (1 The Q value (volume fraction of air bubbles) is used to make the evaluation value between 0 and 1. The closer the Q value is to 1, the more fully the material is filled and the fewer air bubbles there are, which means the higher the molding quality.

[0034] In one example, the simulation-based molding process for anti-slip mirror holders also includes: The pressure rise gradient during the slow charging phase and the pressure value during the holding phase are fixed at a fixed time point and pressure value. The pressure rise gradient during the fast charging phase is changed, and multiple first value levels are set within the first gradient range for simulation calculation. The filling quality evaluation value corresponding to each first value level is extracted. The pressure rise gradient during the fast charging phase and the pressure value during the holding phase are fixed at a fixed time point and pressure value. The pressure rise gradient during the slow charging phase is changed, and multiple second value levels are set within the second gradient range for simulation calculation. The filling quality evaluation value corresponding to each second value level is extracted. The pressure rise gradient at each first value level during the fast charging phase is fitted with the corresponding filling quality evaluation value to obtain the first mapping function. The pressure rise gradient at each second value level during the slow charging phase is fitted with the corresponding filling quality evaluation value to obtain the second mapping function. Based on the first and second mapping functions, the quantitative mapping relationship between the injection pressure curve and the microtexture filling quality is obtained.

[0035] In this example, based on a multiphysics coupled simulation model and an adaptive fine mesh structure, the parameters of the slow charging and holding pressure stages in the injection pressure curve are kept constant. Specifically, the pressure gradient k2 in the slow charging stage and the constant pressure in the holding pressure stage are fixed. For example, k2 is set to 0.88 MPa / s, and the constant pressure is 7 MPa. Within a set time interval in the fast charging stage, a reasonable range of first gradient variation is selected, such as multiple discrete gradients between 4.0 MPa / s and 8.0 MPa / s as the first value level, such as [4.0, 5.0, 6.25, 7.5, 8.0] MPa / s. The injection pressure curve is regenerated for each value level and loaded into the simulation model for a complete flow-heat-interface coupled solution. After the simulation, the corresponding final filling quality evaluation value Q1(k1) is extracted. Then, in another set of analyses, the pressure gradient k1 in the fast charging stage and the holding pressure are kept constant. With constant stage pressure, the pressure gradient k2 during the slow charging stage is set to multiple levels within the second gradient range of 0.5 MPa / s to 1.5 MPa / s, such as [0.5, 0.7, 0.88, 1.1, 1.3, 1.5] MPa / s. The simulation is repeated and the corresponding evaluation value Q2(k2) under each k2 value is extracted. After completing the two sets of parameter scans, the first mapping function Q1=f1(k1) is constructed using the first set (k1,Q1) as the data basis, and the second mapping function Q2=f2(k2) is constructed using the second set (k2,Q2). This characterizes the influence of pressure gradient changes in the fast and slow charging stages on the final molding quality. Furthermore, by jointly expressing the two mapping functions as the filling quality isosurface Q=F(k1,k2) in the two-dimensional process parameter space, a continuous quantitative mapping relationship between the injection pressure curve and the microtexture filling quality can be obtained.

[0036] In one example, a first mapping function is obtained by fitting the pressure rise gradient at each first value level during the fast charging phase to the corresponding filling quality evaluation value; a second mapping function is obtained by fitting the pressure rise gradient at each second value level during the slow charging phase to the corresponding filling quality evaluation value. Based on the first and second mapping functions, a quantitative mapping relationship between the injection pressure curve and the microtexture filling quality is obtained, including: The pressure rise gradient at each first value level during the fast charging phase is used as the independent variable and the corresponding filling quality evaluation value is used as the dependent variable to perform a quadratic function fitting to obtain the first mapping function. The pressure rise gradient at each second value level during the slow charging stage is used as the independent variable, and the corresponding filling quality evaluation value and bubble defect volume value are used as the dependent variables to perform quadratic function fitting to obtain the second mapping function. The optimal value of the first pressure gradient during the fast charging stage is obtained by solving the first mapping function when the filling quality evaluation value is maximized, and the optimal value of the second pressure gradient during the slow charging stage is obtained by solving the second mapping function when the bubble defect volume value is minimized. The optimal values ​​of the first and second pressure gradients are combined to obtain the quantitative mapping relationship between the injection pressure curve and the microtexture filling quality.

[0037] In this example, based on parameter scanning experiments, the values ​​of the pressure rise gradient k1 during multiple fast charging stages and their corresponding filling quality evaluation values ​​Q1 are obtained. By performing least squares fitting calculations on the (k1, Q1) data pairs, a quadratic function form Q1 = a1·k1 is constructed with k1 as the independent variable and Q1 as the dependent variable. 2 + b1·k1+ c1, and verify the coefficient of determination R of the fitted curve. 2 To ensure regression accuracy, a value of at least 0.95 is used. After fitting, this value is used as the first mapping function to describe the influence trend of the pressurization rate on the filling quality during the fast charging stage. For the pressure rise gradient k2 during the slow charging stage, two dependent variable data are extracted based on multiple discrete value levels: the filling quality evaluation value Q2 and the bubble defect volume V_bubble. Based on this, two independent quadratic fitting functions are constructed for (k2, Q2) and (k2, V_bubble), respectively, denoted as Q2 = a2·k2. 2 +b2·k2+ c2 and V_bubble = a3·k2 2 + b3·k2+ c3, the former representing the quality dimension in the second mapping function, and the latter representing the defect dimension. Together, they are used to evaluate the dual effect of slow charging pressurization on molding quality and defect control. After constructing the mapping function, the first derivative of the first mapping function is set to zero, i.e., dQ1 / dk1 = 2a1·k1 + b1 = 0, yielding k1_opt = b1 / (2a1) yields the optimal pressure gradient for fast charging that maximizes Q1; similarly, by differentiating the fitting function of the second mapping function with respect to the bubble volume and setting its derivative to zero, i.e., dV_bubble / dk2 = 2a3·k2 + b3 = 0, we obtain k2_opt = b3 / (2a3) yields the optimal slow-charge pressure gradient that minimizes V_bubble. k1_opt and k2_opt are embedded as the optimal combination of process parameters into the segmented injection pressure curve construction function. This retains the original time nodes and constant pressure values ​​during the holding phase, while setting the pressure to rise at the optimal slope during both the fast and slow charging phases. This achieves injection pressure curve reconstruction based on mathematical modeling results. The reconstruction results achieve optimal values ​​for filling depth and defect volume. Furthermore, the first and second mapping functions are combined to form a quantitative mapping model from the multi-stage injection parameter space to the micro-texture molding quality space.

[0038] Reference Figure 2 This embodiment provides a simulation-based anti-slip mirror holder molding process device, including: Input module 1 is used to input the zero shear viscosity, relaxation time, shear thinning index, reference temperature and temperature sensitivity coefficient of silicone material into the Polyflow material database, and to apply the time nodes and pressure values ​​of the fast charging stage, slow charging stage and holding pressure stage to the injection port of the anti-slip mirror mold cavity. Solver Module 2 is used to divide the three-dimensional geometry of the microtextured cavity into the texture root region, texture sidewall region and flat bottom region and set the mesh size to obtain an adaptive fine mesh model, and solve the phase volume fraction field distribution data of each mesh element in the adaptive fine mesh model. The calculation module 3 is used to extract the first filling depth value at the end of the fast charging stage, the second filling depth value at the end of the slow charging stage, and the third filling depth value at the end of the pressure holding stage from the phase volume fraction field distribution data, and to calculate the filling quality evaluation value.

[0039] In this embodiment, the specific implementation of each unit in the above device embodiment is described in the above method embodiment, and will not be repeated here.

[0040] 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, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, apparatus, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0041] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method of die-molding an antiskid mirror support based on simulation, characterized by, The method comprises the following steps: The zero shear viscosity, relaxation time, shear thinning index, reference temperature and temperature sensitivity coefficient of the silica gel material are input into the Polyflow material database, and the time nodes and pressure values of the fast filling stage, slow filling stage and pressure maintaining stage are applied to the injection port of the anti-slip mirror holder mold cavity; The micro-texture mold cavity three-dimensional geometry is divided into a texture root area, a texture side wall area and a flat bottom area, and a grid size is set to obtain an adaptive encryption grid model, and the phase volume fraction field distribution data of each grid cell in the adaptive encryption grid model is solved; The first filling depth value at the end of the fast filling stage, the second filling depth value at the end of the slow filling stage, and the third filling depth value at the end of the pressure maintaining stage are extracted from the phase volume fraction field distribution data, and the filling quality evaluation value is calculated.

2. The simulated anti-slip mirror bracket press molding process method according to claim 1, characterized in that, The method comprises the following steps: Rheological tests are performed on the silica gel material at multiple temperature points and multiple shear rate points to obtain test data; The test data is fitted to a Cross-WLF viscoelastic constitutive equation to calculate the zero shear viscosity, relaxation time, shear thinning index, reference temperature and temperature sensitivity coefficient; The zero shear viscosity, relaxation time, shear thinning index, reference temperature and temperature sensitivity coefficient are input into the Polyflow material database as custom material properties; The time nodes and pressure values of the fast filling stage, slow filling stage and pressure maintaining stage are constructed, and the time nodes and pressure values are applied to the injection port of the anti-slip mirror holder mold cavity.

3. The simulated anti-slip mirror bracket press forming process method according to claim 2, characterized in that, The method comprises the following steps: The fast filling stage is set to rise from zero pressure to a first pressure value at a first pressure gradient in a first time interval, the slow filling stage is set to rise to a second pressure value at a second pressure gradient in a second time interval, and the pressure maintaining stage is set to a constant third pressure value in a third time interval, to obtain the time nodes and pressure values of the fast filling stage, slow filling stage and pressure maintaining stage, respectively; The time nodes and pressure values are applied to the injection port of the anti-slip mirror holder mold cavity as time-dependent boundary conditions in the Polyflow boundary condition module.

4. The simulated based anti-slip mirror bracket compression molding process method according to claim 1, characterized in that, The method comprises the following steps: The micro-texture mold cavity three-dimensional geometry imported into the Polyflow geometry module is divided into a texture root area, a texture side wall area and a flat bottom area, and the texture root area is set to a first grid size, the texture side wall area is set to a second grid size, and the flat bottom area is set to a third grid size; Adaptive mesh generation algorithm is adopted to generate meshes in the texture root area, the texture side wall area and the flat bottom area and set gradient transition parameters at the sub-domain boundary to make the mesh size transition smoothly, so as to obtain an adaptive refined mesh model; The adaptive refined mesh model is iteratively solved according to a preset time step, so as to obtain pressure field distribution data, velocity field distribution data, temperature field distribution data and phase volume fraction field distribution data of each mesh unit.

5. The simulated slip plane mold press forming process method according to claim 4, wherein, The adaptive refined mesh model is iteratively solved according to a preset time step, so as to obtain pressure field distribution data, velocity field distribution data, temperature field distribution data and phase volume fraction field distribution data of each mesh unit. The viscosity field distribution data of each mesh unit of the adaptive refined mesh model is calculated based on a Cross-WLF viscoelastic constitutive equation, and the viscosity field distribution data is substituted into a momentum conservation equation and an energy conservation equation; The momentum conservation equation and the energy conservation equation are iteratively calculated according to a pressure-velocity coupling method and a preset time step, and the pressure field distribution data, the velocity field distribution data and the temperature field distribution data of each mesh unit are updated until a convergence criterion is met; The volume fraction method is adopted to track the free surface position of the silica gel material and air and to calculate the pressure field distribution data, the velocity field distribution data, the temperature field distribution data and the phase volume fraction field distribution data of each mesh unit.

6. The simulated slip plane mold press forming process method of claim 5, wherein, The volume fraction method is adopted to track the free surface position of the silica gel material and air and to calculate the pressure field distribution data, the velocity field distribution data, the temperature field distribution data and the phase volume fraction field distribution data of each mesh unit. The phase volume fraction function of each mesh unit is defined, and the mesh units with a phase volume fraction of 1 are marked as silica gel material, the mesh units with a phase volume fraction of 0 are marked as air, and the mesh units with a phase volume fraction between 0 and 1 are marked as interface units; The phase volume fraction transport equation is solved according to the velocity field distribution data, and the phase volume fraction function value of each mesh unit is updated to track the free surface position of the silica gel material and air; The spatial position coordinates of the interface units in the micro-texture area are identified, and the pressure field distribution data, the velocity field distribution data, the temperature field distribution data and the phase volume fraction field distribution data are outputted.

7. The simulated anti-slip mirror bracket compression molding process method according to claim 1, wherein, The first filling depth value at the end of the fast filling stage, the second filling depth value at the end of the slow filling stage and the third filling depth value at the end of the pressure maintaining stage are extracted from the phase volume fraction field distribution data, and a filling quality evaluation value is calculated, including: The spatial positions of the interface units at the end of the fast filling stage, the end of the slow filling stage and the end of the pressure maintaining stage are identified from the phase volume fraction field distribution data, and the first filling depth value, the second filling depth value and the third filling depth value are extracted, respectively; Target units with a phase volume fraction less than a preset threshold value and a pressure lower than atmospheric pressure are identified by traversing each mesh unit, and the total volume of the target units is counted to obtain a bubble defect volume value; The depth ratio of the third filling depth value to the texture design depth is calculated, the volume ratio of the bubble defect volume value to the total volume of the texture is calculated, and the depth ratio and the volume ratio are multiplied to obtain the filling quality evaluation value.

8. The simulated anti-slip mirror bracket compression molding process method according to claim 1, wherein, The simulation-based anti-slip mirror support molding process also includes: The pressure rise gradient during the slow charging phase and the pressure value during the holding phase are fixed at a fixed time point and pressure value. The pressure rise gradient during the fast charging phase is changed, and multiple first value levels are set within the first gradient range for simulation calculation. The filling quality evaluation value corresponding to each first value level is extracted. The pressure rise gradient during the fast charging phase and the pressure value during the holding phase are fixed at a fixed time point and pressure value. The pressure rise gradient during the slow charging phase is changed, and multiple second value levels are set within the second gradient range for simulation calculation. The filling quality evaluation value corresponding to each second value level is extracted. The pressure rise gradient at each first value level during the fast charging phase is fitted with the corresponding filling quality evaluation value to obtain a first mapping function. The pressure rise gradient at each second value level during the slow charging phase is fitted with the corresponding filling quality evaluation value to obtain a second mapping function. Based on the first and second mapping functions, a quantitative mapping relationship between the injection pressure curve and the microtexture filling quality is obtained.

9. The simulated anti-slip mirror bracket compression molding process method according to claim 8, characterized in that, The process of fitting a first mapping function to the pressure rise gradient at each first value level during the fast charging phase and the corresponding filling quality evaluation value, and fitting a second mapping function to the pressure rise gradient at each second value level during the slow charging phase and the corresponding filling quality evaluation value, and obtaining a quantitative mapping relationship between the injection pressure curve and the microtexture filling quality based on the first and second mapping functions, includes: The pressure rise gradient at each first value level during the fast charging phase is used as the independent variable and the corresponding filling quality evaluation value is used as the dependent variable to perform a quadratic function fitting to obtain the first mapping function. The pressure rise gradient at each second value level during the slow charging stage is used as the independent variable, and the corresponding filling quality evaluation value and bubble defect volume value are used as the dependent variables to perform quadratic function fitting to obtain the second mapping function. Based on the first mapping function, the optimal value of the first pressure gradient in the fast charging stage when the filling quality evaluation value is maximized is obtained. Based on the second mapping function, the optimal value of the second pressure gradient in the slow charging stage when the bubble defect volume value is minimized is obtained. The optimal values ​​of the first and second pressure gradients are combined to obtain the quantitative mapping relationship between the injection pressure curve and the microtexture filling quality.

10. An emulated anti-slip mirror mounting press-molding process apparatus characterized by, The steps for implementing the simulation-based anti-slip mirror holder molding process method according to any one of claims 1 to 9 include: The input module is used to input the zero shear viscosity, relaxation time, shear thinning index, reference temperature and temperature sensitivity coefficient of the silicone material into the Polyflow material database, and to apply the time nodes and pressure values ​​of the fast charging stage, slow charging stage and holding pressure stage to the injection port of the anti-slip mirror mold cavity. The solver module is used to divide the three-dimensional geometry of the microtextured cavity into the texture root region, the texture sidewall region and the flat bottom region, and set the mesh size to obtain an adaptive densified mesh model, and solve the phase volume fraction field distribution data of each mesh element in the adaptive densified mesh model. The calculation module is used to extract the first filling depth value at the end of the fast charging stage, the second filling depth value at the end of the slow charging stage, and the third filling depth value at the end of the pressure holding stage from the phase volume fraction field distribution data, and to calculate the filling quality evaluation value.

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