Flow channel structure design method and system of variable cross-section spiral flow channel system

Abstract

The invention belongs to the technical field of computer aided design, and discloses a flow channel structure design method and system of a variable cross-section spiral flow channel system. The method comprises the steps that a topological path of a runner system is determined based on a three-dimensional digital model and a mold cavity layout scheme, and a parameterized variable cross-section spiral runner model distributed along the topological path is constructed according to the topological path; the collaborative design input parameters and the parameterized variable cross-section spiral flow channel model are combined, and coupling numerical simulation of fluid dynamics and heat transfer science is carried out in a simulation environment; key geometric parameters are automatically adjusted through an optimization algorithm, and iterative correction is conducted on the parameterized variable cross-section spiral flow channel model; and when the quantitative simulation result meets preset balance and uniformity threshold values, outputting a final variable cross-section spiral runner three-dimensional model cooperating with the mold cavity layout scheme and the target injection molding process parameters. Through the collaborative design mode of the runner, the mold and the process, self-balance of melt pressure and precise regulation and control of a temperature field are achieved.

Description

Technical Field

[0001] This invention relates to the field of computer-aided design technology, specifically to a method and system for designing the flow channel structure of a variable cross-section helical flow channel system. Background Technology

[0002] Injection molding is one of the core processes in plastic product processing. Hot runner systems, as a key technology, can effectively reduce waste generated by cold runners by continuously maintaining the temperature of the molten plastic in the runner, thereby improving production efficiency and product quality, and are therefore widely used in industry.

[0003] However, the traditional hot runner systems widely used today mostly employ simple circular or trapezoidal straight-section runners, or symmetrically distributed runner layouts. When faced with the molding requirements of complex mold cavities (such as multi-cavity, asymmetrical cavity, or large thin-walled products), this conventional design exposes many technical defects: First, due to the simple runner structure, the melt is prone to uneven shear heating and large differences in thermal history during the flow process, resulting in uneven melt temperature at the gates of different cavities, affecting the stability of product quality; Second, for mold layouts with significant differences in flow path length, simple runner designs cannot achieve natural balance of melt pressure, causing asynchronous filling of each cavity, resulting in defects such as flash and short shots, and leading to uneven internal stress distribution in the product.

[0004] To overcome the above-mentioned shortcomings, some technologies have proposed improved hot runners, such as setting mixing elements in the runner or using more complex heater layouts. However, these solutions are often expensive to manufacture, have complex structures, and fail to fundamentally control the flow and heat transfer behavior of the melt through the geometric design of the runner itself.

[0005] Therefore, a design scheme is needed that can design a flow channel system that is highly compatible with complex mold topology and process parameters to ensure the uniformity of melt pressure and temperature field. Summary of the Invention

[0006] The purpose of this invention is to provide a flow channel structure design method and system for a variable cross-section helical flow channel system to solve the problems mentioned in the background art.

[0007] This invention provides a method for designing the flow channel structure of a variable cross-section helical flow channel system, comprising the following steps: Step S10: Obtain collaborative design input parameters, including the three-dimensional digital model of the target plastic product, the preset mold cavity layout scheme, and the target injection molding process parameters.

[0008] Step S20: Determine the topological path of the flow channel system based on the three-dimensional digital model and the mold cavity layout scheme, and construct a parameterized variable cross-section spiral flow channel model distributed along the topological path accordingly; wherein, the key geometric parameters of the parameterized variable cross-section spiral flow channel model include the flow channel cross-section diameter, cross-section variation function, helix angle and pitch.

[0009] Step S30: Combine the collaborative design input parameters with the parameterized variable cross-section helical flow channel model, and perform coupled numerical simulation of fluid dynamics and heat transfer in the simulation environment to evaluate the melt pressure balance and temperature field uniformity of the flow channel system.

[0010] Step S40: Based on the quantitative simulation results output by the coupled numerical simulation, with the optimization goal of improving the melt pressure balance and the temperature field uniformity, the key geometric parameters are automatically adjusted by the optimization algorithm and the parameterized variable cross-section spiral flow channel model is iteratively corrected; wherein, for the branch flow channels flowing to different mold cavities, the parameters are adjusted asymmetrically according to the difference in their flow resistance.

[0011] Step S50: When the quantitative simulation results meet the preset balance and uniformity thresholds, output the final variable cross-section spiral flow channel three-dimensional model that is coordinated with the mold cavity layout scheme and the target injection molding process parameters.

[0012] The present invention also provides a flow channel structure design system for a variable cross-section helical flow channel system, the system comprising an acquisition sub-unit, a construction sub-unit, a simulation sub-unit, an optimization sub-unit, and an output sub-unit.

[0013] The acquisition subunit is used to: acquire collaborative design input parameters, including the three-dimensional digital model of the target plastic product, the preset mold cavity layout scheme, and the target injection molding process parameters.

[0014] The construction subunit is used to: determine the topological path of the flow channel system based on the three-dimensional digital model and the mold cavity layout scheme, and construct a parameterized variable cross-section spiral flow channel model distributed along the topological path accordingly; wherein, the key geometric parameters of the parameterized variable cross-section spiral flow channel model include the flow channel cross-section diameter, cross-section variation function, helix angle and pitch.

[0015] The simulation subunit is used to: combine the collaborative design input parameters with the parameterized variable cross-section helical flow channel model to perform coupled numerical simulation of fluid dynamics and heat transfer in a simulation environment, so as to evaluate the melt pressure balance and temperature field uniformity of the flow channel system.

[0016] The optimization subunit is used to: based on the quantitative simulation results output by the coupled numerical simulation, with the optimization goal of improving the melt pressure balance and the temperature field uniformity, automatically adjust the key geometric parameters and iteratively correct the parameterized variable cross-section spiral flow channel model through an optimization algorithm; wherein, for the branch flow channels flowing to different mold cavities, asymmetric parameter adjustments are made according to the differences in their flow resistance.

[0017] The output subunit is used to: when the quantitative simulation results meet the preset balance and uniformity thresholds, output the final variable cross-section spiral flow channel three-dimensional model that is coordinated with the mold cavity layout scheme and the target injection molding process parameters.

[0018] The present invention also provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the electronic device performs the method as described in any of the preceding claims.

[0019] This invention establishes a collaborative design approach for runners, molds, and processes by systematically and deeply integrating a parametric variable cross-section spiral runner structure with mold cavity layout and injection molding process parameters. Specifically, it constructs a parametric variable cross-section spiral runner model and deeply integrates it with mold cavity layout and injection molding process parameters. Then, it utilizes coupled numerical simulation based on non-Newtonian fluid constitutive modeling and active thermal cycling boundary conditions to drive a multi-objective optimization algorithm to perform asymmetric iterative correction of key geometric parameters. This invention overcomes the limitations of traditional isolated runner structure design, achieving self-balancing of melt pressure and precise control of temperature field at the structural design stage, significantly improving the filling balance and molding quality stability of complex molds. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating a flow channel structure design method for a variable cross-section spiral flow channel system disclosed in an embodiment of the present invention.

[0021] Figure 2 This is a schematic diagram of the flow channel structure design system of a variable cross-section spiral flow channel system disclosed in an embodiment of the present invention.

[0022] Figure 3 This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of the present invention. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] Please see Figure 1 The present invention provides a flow channel structure design method 100 for a variable cross-section spiral flow channel system, including the following steps: Step S10, obtaining collaborative design input parameters, including a three-dimensional digital model of the target plastic product, a preset mold cavity layout scheme, and target injection molding process parameters.

[0025] This step involves collecting the following comprehensive input information required for flow channel design: specifically, obtaining a three-dimensional digital model of the target plastic product (such as an STL or STEP format file). It is understood that this three-dimensional digital model needs to fully represent the plastic product's external shape, wall thickness distribution, critical dimensional tolerances, and assembly interfaces.

[0026] Obtain the preset mold cavity layout scheme, including the number of cavities (single cavity / multiple cavities), the spatial distribution of cavities (symmetrical distribution / asymmetrical distribution), the cavity spacing, the preset position of the gate, and the preliminary planning of the main runner and branch runners, as the topological constraints of the runner system.

[0027] Obtain the target injection molding process parameters, including key process parameters such as melt temperature, injection pressure, injection speed, holding pressure, holding time, and mold temperature. It is understandable that these key process parameters directly affect the flow characteristics and heat transfer behavior of the melt within the runner, serving as the core boundary conditions for subsequent simulation analysis.

[0028] Step S20: Determine the topological path of the flow channel system based on the three-dimensional digital model and the mold cavity layout scheme, and construct a parameterized variable cross-section spiral flow channel model distributed along the topological path accordingly; wherein, the key geometric parameters of the parameterized variable cross-section spiral flow channel model include the flow channel cross-section diameter, cross-section variation function, helix angle and pitch.

[0029] In this step, based on the three-dimensional digital model of the plastic product and the mold cavity layout scheme obtained in step S10, a path planning algorithm (such as Dijkstra's algorithm or A* algorithm) is used to plan the optimal flow path of the melt from the injection molding machine nozzle through the main runner and branch runners to the gates of each cavity, i.e., the topological path of the runner system. It should be noted that this topological path must meet the following principles: the flow distance is adapted to the cavity layout, and the runners should avoid interfering with the mold structure.

[0030] Following the aforementioned topological path, a parametric variable cross-section helical flow channel model is constructed using 3D modeling software (such as UG, SolidWorks) or a parametric modeling platform (such as Python + OpenSCAD). The key geometric parameters of this parametric variable cross-section helical flow channel model are all set as adjustable variables, specifically including: Flow channel cross-section diameter: This refers to the reference diameter of the flow channel cross-section, providing the basic dimension for subsequent cross-sectional changes.

[0031] Cross-section variation function: Defines the variation law of the cross-section diameter along the length of the flow channel (such as linear increase / decrease, exponential change, piecewise function change, etc.) to adapt to the flow resistance requirements of different sections.

[0032] Helix angle: refers to the angle between the helix of the spiral flow channel and the axis of the channel. The value range is usually 5°-30°, and it is used to control the spiral flow intensity of the melt.

[0033] Pitch: refers to the axial distance between two adjacent spiral channels. The value needs to be designed in conjunction with the spiral angle to ensure a smooth transition of the inner wall of the channel and avoid melt stagnation.

[0034] Step S30: Combine the collaborative design input parameters with the parameterized variable cross-section helical flow channel model, and perform coupled numerical simulation of fluid dynamics and heat transfer in the simulation environment to evaluate the melt pressure balance and temperature field uniformity of the flow channel system.

[0035] In this step, the target injection molding process parameters (such as melt temperature, injection pressure, etc.) obtained in step S10 are used as boundary conditions and associated with the parameterized variable cross-section spiral flow channel model constructed in step S20, and then imported into fluid dynamics and heat transfer coupled simulation software (such as ANSYS Fluent, Moldflow).

[0036] Based on the constitutive equations of polymer melts (such as the Cross model and Power-Law model), simulation parameters are set, including mesh generation accuracy (with a finer mesh on the flow channel walls), time step, and convergence criteria. Coupled numerical simulation is then initiated to obtain results such as pressure distribution contour maps, temperature field distribution contour maps, and melt velocity vector maps within the flow channel system. The core quantitative evaluation indicators are melt pressure balance and temperature field uniformity.

[0037] Among them, the melt pressure balance is quantified by the melt pressure difference at the gate of each branch runner; the temperature field uniformity is quantified by the melt temperature difference at the gate of each branch runner and the maximum temperature fluctuation of the melt in the runner.

[0038] Step S40: Based on the quantitative simulation results output by the coupled numerical simulation, with the optimization goal of improving the melt pressure balance and the temperature field uniformity, the key geometric parameters are automatically adjusted by the optimization algorithm and the parameterized variable cross-section spiral flow channel model is iteratively corrected; wherein, for the branch flow channels flowing to different mold cavities, the parameters are adjusted asymmetrically according to the difference in their flow resistance.

[0039] This step addresses the pressure imbalance and temperature unevenness issues in complex cavity layouts by specifically adjusting parameters. Specifically: An optimization objective function is set: minimizing both the pressure difference and temperature difference at each branch runner gate is constructed as dual objectives. An optimization algorithm (such as a genetic algorithm or particle swarm optimization algorithm) is selected, using the runner cross-section diameter, cross-section variation function, helix angle, and pitch from step S20 as optimization variables, and the quantized simulation results output in step S30 as feedback signals.

[0040] Initiating the iterative optimization process: The optimization algorithm automatically adjusts the key geometric parameters based on the simulation results, and corrects the parameterized variable cross-section spiral flow channel model. Specifically, for branch flow channels flowing to different mold cavities, asymmetric parameter adjustments are made based on the differences in flow resistance caused by the flow path length and cavity position. For example, for branch flow channels with high resistance, the cross-sectional diameter is appropriately increased and the spiral angle is adjusted to reduce flow resistance; for branch flow channels with low resistance, the local diameter is appropriately reduced through the cross-sectional change function to balance the overall pressure.

[0041] Repeat the coupled numerical simulation in step S30 to obtain the performance index of the corrected model and continue iterative optimization.

[0042] Step S50: When the quantitative simulation results meet the preset balance and uniformity thresholds, output the final variable cross-section spiral flow channel three-dimensional model that is coordinated with the mold cavity layout scheme and the target injection molding process parameters.

[0043] In this step, the iterative process of steps S30-S40 is continuously executed until the quantitative simulation results output by the coupled numerical simulation meet the preset thresholds. For example, the melt pressure difference at each branch runner gate is ≤5MPa, the temperature difference is ≤3℃, and there are no obvious problems such as melt stagnation or excessive shear heat generation in the runner. When the above preset conditions are met, the iterative optimization is terminated, and the parameterized variable cross-section spiral runner model at this time is exported. This is the final three-dimensional model of the variable cross-section spiral runner that is highly coordinated with the mold cavity layout scheme and the target injection molding process parameters. This model can be directly used for subsequent mold processing and manufacturing.

[0044] This invention establishes a collaborative design approach for runners, molds, and processes by systematically and deeply integrating a parametric variable cross-section spiral runner structure with mold cavity layout and injection molding process parameters. Specifically, it constructs a parametric variable cross-section spiral runner model and deeply integrates it with mold cavity layout and injection molding process parameters. Then, it utilizes coupled numerical simulation based on non-Newtonian fluid constitutive modeling and active thermal cycling boundary conditions to drive a multi-objective optimization algorithm to perform asymmetric iterative correction of key geometric parameters. This invention overcomes the limitations of traditional isolated runner structure design, achieving self-balancing of melt pressure and precise control of temperature field at the structural design stage, significantly improving the filling balance and molding quality stability of complex molds.

[0045] As an example, the step of determining the topological path of the runner system based on the three-dimensional digital model and the mold cavity layout scheme, and constructing a parameterized variable cross-section spiral runner model distributed along the topological path, includes: step S21, based on the geometric center position of each cavity in the mold cavity layout scheme, calculating the runner topological path with the shortest total length from the injection molding machine nozzle to the gate of each cavity through a spatial optimization algorithm.

[0046] In this step, the mold cavity layout scheme is read and parsed, and the geometric center coordinates of each cavity are located. Based on these spatial coordinates, they are abstracted as key nodes in a path planning network. A built-in spatial optimization algorithm (such as a minimum spanning tree algorithm, like Prim's algorithm or Kruskal's algorithm) is invoked to perform automated path search and calculation, using the injection molding machine nozzle position as the path start point and the gate position of each cavity as the path end point. The core optimization objective of this spatial optimization algorithm is to calculate the shortest total length runner topology path connecting all start and end points.

[0047] It should be noted that this runner topology is the skeleton of the runner system, which determines the basic flow architecture of the melt from the injection molding machine to each cavity. The principle of minimizing its total length helps to minimize the overall runner volume, thereby reducing material consumption and melt residence time.

[0048] Step S22: Using the flow channel topology path as the central axis, a three-dimensional flow channel entity is generated through parameterization, thus obtaining a parameterized variable cross-section spiral flow channel model; wherein, the cross-sectional diameter of the three-dimensional flow channel entity is configured as a function that continuously varies along the central axis, and the three-dimensional flow channel entity is configured to extend around the central axis with varying spiral angles and pitches; and, for branch flow channels flowing to different mold cavities, the values ​​of their spiral angles and pitches are asymmetrically set based on the estimated flow resistance of each branch flow channel.

[0049] In this step, the calculated flow channel topology path is used as an invisible central axis. Using a parametrically driven approach (e.g., using scanning or pipe features in CAD software and controlling the cross-section and trajectory through equations), a three-dimensional flow channel entity with physical dimensions is generated based on this axis, which is the geometric expression of the parametric variable cross-section helical flow channel model.

[0050] In this design, the cross-sectional diameter of the three-dimensional flow channel is not constant but is configured as a continuous function of the length (l) along the central axis, i.e., D=f(l). This function can be a linear function, a polynomial function, or a specific curve set according to the pressure drop optimization objective. In this way, the flow channel diameter can adapt to the pressure requirements of different sections of the flow channel, for example, appropriately increasing the diameter in the downstream or high-pressure region to reduce flow resistance.

[0051] The three-dimensional flow channel entity is further configured as a spiral structure extending around its central axis, with its spiral angle and pitch defined as key parameters that can be controlled independently and allowed to vary along the axis to achieve different mixing and heat transfer effects.

[0052] Crucially, the helix angle and pitch of the branch channels flowing to different mold cavities are asymmetrically set based on the estimated flow resistance of each branch channel. Specifically, the flow resistance of each branch channel is estimated (e.g., based on its flow length, number of bends, and other geometric characteristics), and a more pumpable combination of helix parameters (e.g., smaller helix angle, tighter pitch to enhance shear and promote flow) is assigned to branches with higher flow resistance, while relatively gentler helix parameters are assigned to branches with lower resistance. Understandably, this asymmetrical setting allows for a natural pressure balance in each branch channel, ensuring that the melt can simultaneously reach and fill cavities at different geometric locations.

[0053] As an example, the co-design input parameters are combined with the parameterized variable cross-section helical flow channel model to perform coupled numerical simulation of fluid dynamics and heat transfer in a simulation environment, including: step S31, configuring the physical properties of the plastic melt as a non-Newtonian fluid model dependent on local shear rate and temperature based on the target injection molding process parameters, and assigning it to the fluid domain defined by the parameterized variable cross-section helical flow channel model, thereby completing the configuration of the physical properties of the fluid domain.

[0054] In traditional injection molding simulations, to simplify calculations, the viscosity of the plastic melt is often treated as a constant or only varies with temperature, approximating it as a Newtonian fluid. However, the plastic melts used in actual engineering are typical non-Newtonian fluids, whose apparent viscosity depends strongly on both the local shear rate and the local temperature.

[0055] To address this, this invention selects or creates a specific non-Newtonian fluid constitutive model (e.g., Cross-WLF or Power-Law model) for the material database in the simulation software, based on information such as the melt base temperature provided in the target injection molding process parameters. This model can accurately describe the rheological behavior of the melt viscosity decreasing with increasing shear rate (shear thinning characteristic) and decreasing with increasing temperature. Subsequently, this accurate physical property model is assigned to the internal space enclosed by the parameterized variable cross-section helical flow channel model, i.e., the fluid domain.

[0056] It should be noted that in a variable cross-section helical flow channel, the shear rate experienced by the melt changes drastically as it flows through different cross-sections and helical curvatures. Simultaneously, due to shear heat generation and heat exchange with the mold, the melt temperature varies in different regions within the flow channel. By employing this non-Newtonian fluid model that relies on local shear and thermal history, the flow resistance, velocity distribution, and temperature rise caused by viscous dissipation of the melt within the flow channel can be simulated more realistically. Traditional methods neglect these characteristics, leading to significant deviations in pressure drop and flow front predictions.

[0057] Step S32: Based on the mold thermal management scheme corresponding to the mold cavity layout scheme, configure active thermal circulation boundary conditions that change periodically with time for the parameterized variable cross-section spiral flow channel model and the wall of the mold cavity, thereby completing the thermal boundary condition setting of the simulation model.

[0058] In this step, in actual injection molding production, the mold thermal management scheme (i.e., the design of the cooling water circuit and the temperature control strategy of the cooling medium) causes the wall temperature of the mold cavity and runner to change periodically with the injection cycle (injection-holding pressure-cooling-mold opening and closing). The traditional steady-state thermal boundary assumption cannot reflect this dynamic process.

[0059] To address this, this invention, based on the mold cavity layout scheme and corresponding mold thermal management scheme (e.g., cooling water channel arrangement, inlet water temperature, flow rate, etc.), no longer sets simple constant temperature or convective heat transfer boundaries for the outer wall of the parameterized variable cross-section spiral flow channel model and the mold cavity wall in the simulation software. Instead, it configures active thermal circulation boundary conditions that change periodically with time. These boundary conditions, for example, define the variation law of wall temperature or heat flux density as a time function to simulate the actual effect of active temperature control in the cooling system.

[0060] Step S33: In the simulation solver, the fluid domain with physical property configuration completed in step S31 and the simulation model with thermal boundary condition setting completed in step S32 are called to synchronously couple and solve the fluid dynamics control equation and the transient heat conduction control equation.

[0061] In this step, the simulation solver calls the fluid domain, whose accurate physical properties have been configured in step S31, and the complete simulation model (including flow channel and cavity geometry), whose dynamic thermal boundary conditions have been set in step S32, and starts the coupled solution calculation.

[0062] In this context, coupled solution refers to the simultaneous solution of the fluid dynamics governing equations (such as the continuity equation and momentum equation) and the transient heat conduction governing equations, rather than calculating the flow field and temperature field in isolation. Furthermore, the calculated results of the flow field (such as flow velocity and shear rate) instantly affect the calculated temperature field (through viscous heat generation and convective heat transfer terms), while the calculated results of the temperature field (temperature distribution) in turn affect the calculated flow field (by changing the melt viscosity). This bidirectional, real-time data exchange coupled calculation accurately captures the interaction between flow and heat transfer, which is crucial for obtaining high-fidelity simulation results.

[0063] Step S34: Extract the quantitative simulation results from the coupled solution, including at least the set of melt pressure values ​​at the end of each branch channel in the flow channel system, and the melt temperature distribution cloud map of the entire flow channel system.

[0064] In this step, after the coupled solution calculation converges, quantitative simulation results for directly evaluating the performance of the runner system are extracted from the calculation result database. These results include at least: a set of melt pressure values ​​at the ends of each branch runner in the runner system: this set contains the melt pressure values ​​at the outlet (i.e., gate) of each branch runner flowing to different cavities. By comparing the differences in the pressure values ​​in this set, the melt pressure balance of the entire runner system can be quantitatively evaluated.

[0065] Melt temperature distribution contour map of the entire runner system: This contour map displays the melt temperature field from the runner inlet to the ends of all branches in a visually distributed color format. By observing the color uniformity of the contour map, the temperature field uniformity of the runner system can be assessed intuitively and quantitatively, and the existence of local overheated or undercooled areas can be identified.

[0066] This invention, by configuring the plastic melt as a non-Newtonian fluid model dependent on local shear and temperature, and setting active thermal cycling boundary conditions that change periodically over time, achieves accurate characterization of melt rheological behavior and dynamic thermal management of the mold. This enables coupled numerical simulation to simultaneously and accurately capture the interaction between shear thinning effect, viscous heat generation, and periodic cooling, thereby obtaining high-fidelity melt pressure and temperature field data, providing precise physical basis for flow channel structure optimization that cannot be achieved by traditional methods.

[0067] As an example, configuring active thermal circulation boundary conditions that change periodically with time for the parameterized variable cross-section spiral flow channel model and the wall of the mold cavity includes: step S321, analyzing the wall thickness distribution of the target plastic product at different positions in the mold cavity based on the three-dimensional digital model, extracting the thermal history data of each branch flow channel from the parameterized variable cross-section spiral flow channel model, and obtaining the cooling water channel layout geometry and the preset flow rate of the cooling medium in the mold cavity layout scheme.

[0068] In this step, based on the three-dimensional digital model of the target plastic product, geometric feature recognition and cross-sectional analysis algorithms are used to analyze and calculate the wall thickness distribution of the plastic product at different locations in the mold cavity. For example, for plastic products with features such as reinforcing ribs or bosses, it is necessary to accurately identify the wall thickness variations in these local areas. Understandably, wall thickness is a key factor determining the cooling rate; thicker areas dissipate heat slowly and require stronger cooling, while thinner areas dissipate heat quickly and require gentler cooling.

[0069] Furthermore, from the parameterized variable cross-section spiral flow channel model with completed physical property configuration, thermal history data of the melt flowing through each branch flow channel is extracted. This includes, but is not limited to, the temperature change history of the melt at the inlet, middle section, and outlet of the flow channel, as well as the temperature rise caused by shear heat generation. It is understandable that due to differences in length and direction, the thermal history of the melt inside different branch flow channels varies, resulting in different melt temperatures when reaching the gate.

[0070] In addition, the system reads the corresponding cooling water channel layout geometry information (such as water channel diameter, distance from the cavity / flow channel, water channel spacing, etc.) and the preset flow rate of the cooling medium from the mold cavity layout scheme. It is understandable that these parameters directly determine the local cooling capacity of the mold.

[0071] Step S322: Based on the wall thickness distribution, the thermal history data, the cooling water channel layout geometry, and the preset flow rate of the cooling medium, a differentiated temperature cycle curve for different wall areas of the flow channel and cavity is dynamically calculated through a heat transfer model, which is synchronized with the injection molding cycle.

[0072] In this step, the previously acquired wall thickness distribution, thermal history data, cooling water channel layout geometry, and preset cooling medium flow rate are used as input parameters to establish a complete three-dimensional transient heat transfer model including the mold cavity, flow channel system, and cooling water channels. The mathematical description of the three-dimensional transient heat transfer model is as follows: Governing equations: .

[0073] Initial conditions: .

[0074] Boundary conditions: 1. Cooling water channel boundary (convective heat transfer boundary): .

[0075] 2. Cavity / flow channel wall (time-varying heat flux boundary): .

[0076] 3. Mold outer surface (insulation / natural convection boundary): .

[0077] in, Density of mold material [kg / m³] 3 ]; The specific heat capacity of the mold material [J / (kg⋅K)]; t represents the temperature field [K]; t represents time [s]; k represents the thermal conductivity coefficient of the mold material [W / (m⋅K)]; Internal heat source term [W / m 3 ]; The convective heat transfer coefficient of cooling water [W / (m 2 ⋅K)]; The temperature of the cooling medium is [K]. The heat flux density at the melt-mold interface [W / m] 2 )]; The ambient convective heat transfer coefficient [W / (m²]] 2 ⋅K)]; The ambient temperature [K]; These represent the cooling water channels, cavity / flow channels, and outer surface boundaries, respectively.

[0078] It should be noted that the input wall thickness distribution is used to determine the heat flux density at the melt-mold interface. Thermal history data is used to determine the initial temperature field. Cooling water channel geometry is used to determine the convective heat transfer boundary. The flow rate of the cooling medium is used to determine the convective heat transfer coefficient. .

[0079] This model is based on the law of conservation of energy and solves the unsteady-state heat conduction equations using the finite element method or finite volume method. The solution process is roughly as follows: Initial conditions are set: Based on the thermal history data extracted from the parametric variable cross-section spiral flow channel model, the temperature field of the flow channel region and the adjacent mold region is initialized to accurately reflect the residual temperature distribution at the end of the previous injection molding cycle.

[0080] Boundary condition configuration: On the cooling water channel wall, based on the cooling water channel layout geometry and the preset flow velocity of the cooling medium, calculate the convective heat transfer coefficient of each cooling channel and set the corresponding heat dissipation boundary conditions.

[0081] Thermal load application: Time-dependent heat flux density loads are dynamically applied to the cavity and runner walls according to the various stages of the injection molding cycle (injection, holding pressure, cooling, and mold opening). Among them, the wall thickness distribution directly affects the intensity and duration of local thermal load application. Specifically, the cavity surface corresponding to the thick-walled area of ​​the product needs to withstand a longer holding pressure and cooling time, while the thin-walled area experiences more rapid heat exchange.

[0082] Solving the above model using the time-stepping method allows for the dynamic simulation of the temperature evolution of various parts of the mold over time during a complete injection molding cycle. Finally, for different wall regions of the runner and cavity, unique temperature cycle curves are output. For example, the cavity surface temperature curve corresponding to the thick-walled area of ​​the plastic product exhibits a slow heating and cooling characteristic; the runner region near the gate maintains a high temperature level due to continuous contact with the high-temperature melt; while the temperature curve adjacent to the efficient cooling water channel region shows a rapid cooling characteristic.

[0083] Step S323: Assign the differentiated temperature cycle curves to the corresponding wall regions of the parameterized variable cross-section spiral flow channel model and the mold cavity, thereby completing the setting of the active thermal cycle boundary conditions.

[0084] In this step, the calculated differential temperature cycle curves (e.g., in the form of time-temperature functions or data tables) are used as boundary conditions and assigned to the outer wall of the parameterized variable cross-section spiral flow channel model and the corresponding regions of the mold cavity. Subsequently, in the coupled numerical simulation, different parts of the flow channel and cavity will exchange heat with the melt according to their pre-calculated dynamic temperature curves that conform to actual physical laws.

[0085] This implementation method integrates product wall thickness distribution, runner thermal history, and mold cooling parameters. Based on a heat transfer model, it dynamically generates differentiated temperature cycle curves that are strictly synchronized with the injection molding cycle and precisely configures them to each wall region of the runner and cavity. This achieves precise control of the heat exchange process in different parts of the mold. This proactive thermal management strategy can provide customized temperature control for the heat dissipation characteristics of different regions such as thick-walled areas, thin-walled areas, and runner areas. This allows for accurate reproduction of dynamic thermal boundary conditions in actual production during simulation, thereby solving the problem of temperature field fluctuations caused by uneven cooling, significantly improving temperature field uniformity, and facilitating the acquisition of high-precision prediction results for melt flow and solidification behavior.

[0086] As an example, based on the quantitative simulation results output by the coupled numerical simulation, with the optimization goal of improving the melt pressure balance and the temperature field uniformity, the key geometric parameters are automatically adjusted by the optimization algorithm and the parameterized variable cross-section spiral flow channel model is iteratively corrected. This includes: step S41, defining the key geometric parameters as optimization variables, using the quantitative indices of the melt pressure balance and the temperature field uniformity to form a multi-objective optimization function, and setting the manufacturing process constraints of the parameterized variable cross-section spiral flow channel model as boundary conditions.

[0087] In this step, key geometric parameters are defined as optimization variables for this optimization process. These parameters will be converted into mathematical variables that the optimization algorithm can recognize and process, and their value range is pre-set based on engineering experience and manufacturing feasibility.

[0088] Next, a multi-objective optimization function is constructed. Specifically, the quantitative index of melt pressure balance (defined as the standard deviation of the pressure values ​​at the ends of each branch channel) and the quantitative index of temperature field uniformity (defined as the standard deviation of the melt temperature distribution within the channel system) jointly constitute the optimization objective function. It should be noted that these two indices are usually mutually restrictive and require a multi-objective optimization method for coordinated optimization.

[0089] Meanwhile, the manufacturing process constraints of the parameterized variable cross-section helical flow channel model are set as boundary conditions for the optimization problem, including but not limited to minimum flow channel diameter limit (to ensure machinability), maximum helix angle limit (to avoid excessive flow resistance), minimum radius of curvature limit (to prevent stress concentration), etc.

[0090] Step S42: The quantization simulation results are used as input to the multi-objective optimization function. A new combination of optimization variables is automatically generated through the multi-objective optimization algorithm, and the parameterized variable cross-section spiral flow channel model is automatically updated. In this step, the quantized simulation results obtained in step S34 are used as input to the multi-objective optimization function to calculate the pressure balance and temperature uniformity index values ​​corresponding to the current flow channel structure. The multi-objective optimization algorithm then automatically generates new combinations of optimization variables based on the current optimization function value and its built-in search strategy. This process can be understood as updating and optimizing the solution through operations such as selection, crossover, and mutation. Finally, the newly generated combination of optimization variables is passed to the parametric variable cross-section helical flow channel model, driving it to automatically update its geometry. This process is implemented through a parametric modeling platform, ensuring that the geometric model can be reconstructed in real time based on the input parameters.

[0091] Repeat step S42 until the calculation result of the multi-objective optimization function meets the preset convergence tolerance or reaches the maximum number of iterations, at which point the result is output.

[0092] Repeat step S42, checking after each iteration whether the calculation results of the multi-objective optimization function meet the preset convergence tolerance. Convergence criteria include the rate of change of the Pareto front being less than a threshold and the improvement of the objective function being less than a set value.

[0093] The optimization process terminates when either of the following conditions is met: 1) the preset convergence tolerance is reached; or 2) the maximum number of iterations is reached. Upon termination, the current optimization results are output, including: a set of Pareto optimal solutions, the corresponding combination of key geometric parameters, and the final performance index value.

[0094] Please see Figure 2 The present invention also provides a flow channel structure design system 200 for a variable cross-section spiral flow channel system, the system including an acquisition sub-unit 2001, a construction sub-unit 2002, a simulation sub-unit 2003, an optimization sub-unit 2004, and an output sub-unit 2005; The acquisition subunit 2001 is used to: acquire collaborative design input parameters, including the three-dimensional digital model of the target plastic product, the preset mold cavity layout scheme, and the target injection molding process parameters.

[0095] The construction subunit 2002 is used to: determine the topological path of the flow channel system based on the three-dimensional digital model and the mold cavity layout scheme, and construct a parameterized variable cross-section spiral flow channel model distributed along the topological path accordingly; wherein, the key geometric parameters of the parameterized variable cross-section spiral flow channel model include the flow channel cross-section diameter, cross-section variation function, helix angle and pitch.

[0096] The simulation subunit 2003 is used to: combine the collaborative design input parameters with the parameterized variable cross-section helical flow channel model to perform coupled numerical simulation of fluid dynamics and heat transfer in a simulation environment, so as to evaluate the melt pressure balance and temperature field uniformity of the flow channel system.

[0097] The optimization subunit 2004 is used to: based on the quantitative simulation results output by the coupled numerical simulation, with the optimization goal of improving the melt pressure balance and the temperature field uniformity, automatically adjust the key geometric parameters and iteratively correct the parameterized variable cross-section spiral flow channel model through an optimization algorithm; wherein, for the branch flow channels flowing to different mold cavities, asymmetric parameter adjustments are made according to the differences in their flow resistance.

[0098] The output subunit 2005 is used to: when the quantitative simulation results meet the preset balance and uniformity thresholds, output the final variable cross-section spiral flow channel three-dimensional model that is coordinated with the mold cavity layout scheme and the target injection molding process parameters.

[0099] As an example, the construction subunit 2002 is specifically used to: calculate the shortest runner topology path from the injection molding machine nozzle to the gate of each cavity based on the geometric center position of each cavity in the mold cavity layout scheme through a spatial optimization algorithm.

[0100] Using the flow channel topology path as the central axis, a three-dimensional flow channel entity is generated through parametric driving, thus obtaining a parametric variable cross-section helical flow channel model. The cross-sectional diameter of the three-dimensional flow channel entity is configured as a function that continuously varies along the central axis, and the three-dimensional flow channel entity is configured to extend around the central axis with varying helical angles and pitches. Furthermore, for branch flow channels flowing to different mold cavities, the values ​​of their helical angles and pitches are asymmetrically set based on the estimated flow resistance of each branch flow channel.

[0101] As an example, the simulation subunit 2003 is specifically used to: configure the physical properties of the plastic melt as a non-Newtonian fluid model that depends on local shear rate and temperature based on the target injection molding process parameters, and assign it to the fluid domain defined by the parameterized variable cross-section helical flow channel model, thereby completing the configuration of the physical properties of the fluid domain.

[0102] Based on the mold thermal management scheme corresponding to the mold cavity layout scheme, active thermal circulation boundary conditions that change periodically with time are configured for the parameterized variable cross-section spiral flow channel model and the wall of the mold cavity, thereby completing the thermal boundary condition setting of the simulation model.

[0103] In the simulation solver, the fluid domain with its physical properties configured and the simulation model with its thermal boundary conditions set are invoked to solve the fluid dynamics control equations and the transient heat conduction control equations simultaneously and in a coupled manner.

[0104] The quantitative simulation results extracted from the coupled solution include at least the set of melt pressure values ​​at the ends of each branch channel in the flow channel system, and the melt temperature distribution cloud map of the entire flow channel system.

[0105] As an example, the simulation subunit 2003 is specifically used to: analyze the wall thickness distribution of the target plastic product at different positions in the mold cavity based on the three-dimensional digital model, extract the thermal history data of each branch flow channel from the parameterized variable cross-section spiral flow channel model, and obtain the cooling water channel layout geometry and the preset flow rate of the cooling medium in the mold cavity layout scheme.

[0106] Based on the wall thickness distribution, the thermal history data, the cooling water channel layout geometry, and the preset flow rate of the cooling medium, a differentiated temperature cycle curve for different wall regions of the flow channel and cavity is dynamically calculated through a heat transfer model, which is synchronized with the injection molding cycle.

[0107] The differentiated temperature cycle curves are assigned to the corresponding wall regions of the parameterized variable cross-section spiral flow channel model and the mold cavity, thereby completing the setting of the active thermal cycle boundary conditions.

[0108] As an example, the optimization subunit 2004 is specifically used to: define the key geometric parameters as optimization variables, use the quantitative indicators of melt pressure balance and temperature field uniformity to form a multi-objective optimization function, and set the manufacturing process constraints of the parameterized variable cross-section spiral flow channel model as boundary conditions.

[0109] The quantization simulation results are used as input to the multi-objective optimization function. A new combination of optimization variables is automatically generated through the multi-objective optimization algorithm, which drives the parameterized variable cross-section spiral flow channel model to update automatically.

[0110] Repeat the above steps until the calculation result of the multi-objective optimization function meets the preset convergence tolerance or reaches the maximum number of iterations, at which point output is given.

[0111] Please see Figure 3 The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the electronic device performs the method as described in any of the foregoing embodiments.

[0112] In addition, the electronic device also includes at least I / O interfaces, power management modules, and other supporting modules, which will not be described in detail here.

[0113] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A method for designing the flow channel structure of a variable cross-section helical flow channel system, characterized in that, The methods and steps include the following: Step S10: Obtain collaborative design input parameters, including the three-dimensional digital model of the target plastic product, the preset mold cavity layout scheme, and the target injection molding process parameters; Step S20: Determine the topological path of the flow channel system based on the three-dimensional digital model and the mold cavity layout scheme, and construct a parameterized variable cross-section spiral flow channel model distributed along the topological path accordingly; wherein, the key geometric parameters of the parameterized variable cross-section spiral flow channel model include the flow channel cross-section diameter, cross-section variation function, helix angle and pitch; Step S30: Combine the collaborative design input parameters with the parameterized variable cross-section spiral flow channel model, and perform coupled numerical simulation of fluid dynamics and heat transfer in the simulation environment to evaluate the melt pressure balance and temperature field uniformity of the flow channel system. Step S40: Based on the quantitative simulation results output by the coupled numerical simulation, with the optimization goal of improving the melt pressure balance and the temperature field uniformity, the key geometric parameters are automatically adjusted by the optimization algorithm and the parameterized variable cross-section spiral flow channel model is iteratively corrected; wherein, for the branch flow channels flowing to different mold cavities, the parameters are adjusted asymmetrically according to the difference in their flow resistance. Step S50: When the quantitative simulation results meet the preset balance and uniformity thresholds, output the final variable cross-section spiral flow channel three-dimensional model that is coordinated with the mold cavity layout scheme and the target injection molding process parameters.

2. The flow channel structure design method for a variable cross-section helical flow channel system according to claim 1, characterized in that: Based on the three-dimensional digital model and the mold cavity layout scheme, the topological path of the flow channel system is determined, and a parameterized variable cross-section helical flow channel model distributed along the topological path is constructed accordingly, including: Step S21: Based on the geometric center position of each cavity in the mold cavity layout scheme, the shortest runner topology path from the injection molding machine nozzle to the gate of each cavity is calculated by the spatial optimization algorithm. Step S22: Using the flow channel topology path as the central axis, a three-dimensional flow channel entity is generated through parameterization, thus obtaining a parameterized variable cross-section spiral flow channel model; wherein, the cross-sectional diameter of the three-dimensional flow channel entity is configured as a function that continuously varies along the central axis, and the three-dimensional flow channel entity is configured to extend around the central axis with varying spiral angles and pitches; and, for branch flow channels flowing to different mold cavities, the values ​​of their spiral angles and pitches are asymmetrically set based on the estimated flow resistance of each branch flow channel.

3. The flow channel structure design method for a variable cross-section helical flow channel system according to claim 1, characterized in that: The co-design input parameters are combined with the parameterized variable cross-section helical flow channel model to perform coupled numerical simulations of fluid dynamics and heat transfer in a simulation environment, including: Step S31: Based on the target injection molding process parameters, configure the physical properties of the plastic melt as a non-Newtonian fluid model that depends on local shear rate and temperature, and assign it to the fluid domain defined by the parameterized variable cross-section spiral flow channel model, thereby completing the configuration of the physical properties of the fluid domain. Step S32: Based on the mold thermal management scheme corresponding to the mold cavity layout scheme, configure active thermal circulation boundary conditions that change periodically with time for the parameterized variable cross-section spiral flow channel model and the wall of the mold cavity, thereby completing the thermal boundary condition setting of the simulation model. Step S33: In the simulation solver, the fluid domain with physical property configuration completed in step S31 and the simulation model with thermal boundary condition setting completed in step S32 are called to synchronously couple and solve the fluid dynamics control equation and the transient heat conduction control equation. Step S34: Extract the quantitative simulation results from the coupled solution, including at least the set of melt pressure values ​​at the end of each branch channel in the flow channel system, and the melt temperature distribution cloud map of the entire flow channel system.

4. The flow channel structure design method for a variable cross-section helical flow channel system according to claim 3, characterized in that: Configure active thermal cycling boundary conditions that vary periodically with time on the walls of the parameterized variable cross-section helical flow channel model and the mold cavity, including: Step S321: Based on the three-dimensional digital model, the wall thickness distribution of the target plastic product at different positions in the mold cavity is obtained. The thermal history data of each branch flow channel is extracted from the parameterized variable cross-section spiral flow channel model. The cooling water channel layout geometry and the preset flow rate of the cooling medium in the mold cavity layout scheme are obtained. Step S322: Based on the wall thickness distribution, the thermal history data, the cooling water channel layout geometry and the preset flow rate of the cooling medium, a differentiated temperature cycle curve for different wall areas of the flow channel and cavity is dynamically calculated through a heat transfer model, which is synchronized with the injection molding cycle. Step S323: Assign the differentiated temperature cycle curves to the corresponding wall regions of the parameterized variable cross-section spiral flow channel model and the mold cavity, thereby completing the setting of the active thermal cycle boundary conditions.

5. The flow channel structure design method for a variable cross-section helical flow channel system according to claim 1, characterized in that: Based on the quantitative simulation results output by the coupled numerical simulation, with the optimization objectives of improving the melt pressure balance and the temperature field uniformity, the key geometric parameters are automatically adjusted through an optimization algorithm, and the parameterized variable cross-section spiral flow channel model is iteratively corrected, including: Step S41: Define the key geometric parameters as optimization variables, use the quantitative indicators of melt pressure balance and temperature field uniformity to form a multi-objective optimization function, and set the manufacturing process constraints of the parameterized variable cross-section spiral flow channel model as boundary conditions. Step S42: The quantization simulation results are used as input to the multi-objective optimization function. A new combination of optimization variables is automatically generated through the multi-objective optimization algorithm, and the parameterized variable cross-section spiral flow channel model is automatically updated. Repeat step S42 until the calculation result of the multi-objective optimization function meets the preset convergence tolerance or reaches the maximum number of iterations, at which point the result is output.

6. A flow channel structure design system for a variable cross-section helical flow channel system, characterized in that: The system includes acquisition sub-units, construction sub-units, simulation sub-units, optimization sub-units, and output sub-units; The acquisition subunit is used to: acquire collaborative design input parameters, including the three-dimensional digital model of the target plastic product, the preset mold cavity layout scheme, and the target injection molding process parameters; The construction subunit is used to: determine the topological path of the flow channel system based on the three-dimensional digital model and the mold cavity layout scheme, and construct a parameterized variable cross-section spiral flow channel model distributed along the topological path accordingly; wherein, the key geometric parameters of the parameterized variable cross-section spiral flow channel model include the flow channel cross-section diameter, cross-section variation function, helix angle and pitch; The simulation subunit is used to: combine the collaborative design input parameters with the parameterized variable cross-section helical flow channel model to perform coupled numerical simulation of fluid dynamics and heat transfer in the simulation environment, so as to evaluate the melt pressure balance and temperature field uniformity of the flow channel system. The optimization subunit is used to: based on the quantitative simulation results output by the coupled numerical simulation, with the optimization goal of improving the melt pressure balance and the temperature field uniformity, automatically adjust the key geometric parameters and iteratively correct the parameterized variable cross-section spiral flow channel model through an optimization algorithm; wherein, for the branch flow channels flowing to different mold cavities, asymmetric parameter adjustments are made according to the differences in their flow resistance. The output subunit is used to: when the quantitative simulation results meet the preset balance and uniformity thresholds, output the final variable cross-section spiral flow channel three-dimensional model that is coordinated with the mold cavity layout scheme and the target injection molding process parameters.

7. The flow channel structure design system for a variable cross-section helical flow channel system according to claim 6, characterized in that: The construction subunit is specifically used for: Based on the geometric center position of each cavity in the mold cavity layout scheme, the shortest runner topology path from the injection molding machine nozzle to the gate of each cavity is calculated by the spatial optimization algorithm. Using the flow channel topology path as the central axis, a three-dimensional flow channel entity is generated through parametric driving, thus obtaining a parametric variable cross-section helical flow channel model. The cross-sectional diameter of the three-dimensional flow channel entity is configured as a function that continuously varies along the central axis, and the three-dimensional flow channel entity is configured to extend around the central axis with varying helical angles and pitches. Furthermore, for branch flow channels flowing to different mold cavities, the values ​​of their helical angles and pitches are asymmetrically set based on the estimated flow resistance of each branch flow channel.

8. The flow channel structure design system for a variable cross-section helical flow channel system according to claim 6, characterized in that: The simulation subunit is specifically used for: Based on the target injection molding process parameters, the physical properties of the plastic melt are configured as a non-Newtonian fluid model that depends on local shear rate and temperature, and then assigned to the fluid domain defined by the parameterized variable cross-section helical flow channel model, thereby completing the configuration of the physical properties of the fluid domain. Based on the mold thermal management scheme corresponding to the mold cavity layout scheme, active thermal circulation boundary conditions that change periodically with time are configured for the parameterized variable cross-section spiral flow channel model and the wall of the mold cavity, thereby completing the thermal boundary condition setting of the simulation model. In the simulation solver, the fluid domain with physical property configuration and the simulation model with thermal boundary condition settings are called to solve the fluid dynamics control equation and the transient heat conduction control equation simultaneously and coupled. The quantitative simulation results extracted from the coupled solution include at least the set of melt pressure values ​​at the ends of each branch channel in the flow channel system, and the melt temperature distribution cloud map of the entire flow channel system.

9. The flow channel structure design system for a variable cross-section helical flow channel system according to claim 8, characterized in that: The simulation subunit is specifically used for: Based on the analysis of the three-dimensional digital model, the wall thickness distribution of the target plastic product at different positions in the mold cavity is obtained. The thermal history data of each branch flow channel is extracted from the parameterized variable cross-section spiral flow channel model, and the cooling water channel layout geometry and the preset flow rate of the cooling medium in the mold cavity layout scheme are obtained. Based on the wall thickness distribution, the thermal history data, the cooling water channel layout geometry, and the preset flow rate of the cooling medium, a differentiated temperature cycle curve for different wall areas of the flow channel and cavity is dynamically calculated through a heat transfer model, which is synchronized with the injection molding cycle. The differentiated temperature cycle curves are assigned to the corresponding wall regions of the parameterized variable cross-section spiral flow channel model and the mold cavity, thereby completing the setting of the active thermal cycle boundary conditions.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, it causes the electronic device to implement the method as described in any one of claims 1-5.

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