A parameterized design method for plastic backplane mold runner features

Abstract

This invention discloses a parametric design method for the flow channel features of a plastic backplate mold, relating to the field of parametric design technology. The method includes acquiring first state data, second state data, and priority data; determining correction parameters for each branch flow channel based on the first state data; comparing the correction parameters with the second state data when the correction parameters for any branch flow channel are not within a threshold range to determine a target node, and generating damping parameters based on the target node; determining a target cavity when the second state data and priority data meet preset conditions, and tracing along the material supply channel of the target cavity to obtain a first compensation feature; adjusting the first compensation feature using the damping parameters to obtain a second compensation feature; generating structural parameters based on the second compensation feature, and updating the mold flow channel based on the structural parameters. This invention improves the stability of flow channel design.

Description

Technical Field

[0001] This invention relates to the field of parametric design technology, specifically a parametric design method for the flow channel features of a plastic backplate mold. Background Technology

[0002] Plastic backplates are widely used in automotive interiors, consumer electronics and other fields. Multi-gate sequential filling is the mainstream molding process. The runner design directly determines the molding quality and production efficiency of the product. In the runner design process of plastic backplate molds, the runner geometric parameters are usually determined based on the rheological properties of standard materials, and compensation is made by adjusting the local runner cross-sectional dimensions when filling imbalance occurs.

[0003] However, in actual production, batch differences in raw materials or the mixing of recycled materials can easily lead to sudden changes in material viscosity. A single local size adjustment can trigger a redistribution of pressure in the entire mold cavity, causing an imbalance in filling sequence, weld line drift or flash defects, and even a vicious cycle in which local compensation leads to global imbalance and global adjustment exacerbates local overload, resulting in a narrow mold process window and unstable production line yield. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a parametric design method for the flow channel features of plastic backplate molds.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: This invention provides a parametric design method for the flow channel features of a plastic backplate mold, comprising: Obtain the first state data, the second state data, and the priority data; Based on the first state data, determine the correction parameters for each branch flow channel; If the correction parameter of any of the branch channels is not within the threshold range, the correction parameter is compared with the second state data to determine the target node, and a damping parameter is generated based on the target node. When the second state data and the priority data meet the preset conditions, the target cavity is determined, and the material supply channel of the target cavity is traced to obtain the first compensation feature; The first compensation feature is adjusted using the damping parameters to obtain the second compensation feature; Structural parameters are generated based on the second compensation feature, and the mold flow channel is updated based on the structural parameters.

[0006] As a preferred embodiment of the present invention, determining the correction parameters for each branch flow channel based on the first state data includes: Feature extraction is performed on the first state data to obtain drift features; The drift characteristics are input into a preset flow channel resistance calculation model to obtain the correction parameters for each branch flow channel.

[0007] As a preferred embodiment of the present invention, the method further includes: When the correction parameters of all the branch channels are within the threshold range, each branch channel is updated based on the base parameters.

[0008] As a preferred embodiment of the present invention, the step of generating damping parameters based on the target node includes: Obtain the pressure fluctuation characteristics corresponding to the target node; Based on a preset negative correlation relationship, the pressure fluctuation characteristics are calculated to obtain the damping coefficient for the target node; The damping parameters are generated based on the damping coefficient.

[0009] As a preferred embodiment of the present invention, the step of determining the target cavity and tracing along the feeding channel of the target cavity to obtain the first compensation feature when the second state data and the priority data meet preset conditions includes: The second state data is compared with the priority data; If the comparison results indicate that there is a deviation or lag in the filling timing of the cavity to be tested, it is determined that the preset condition is met, and the cavity to be tested is identified as the target cavity; The pressure loss parameters of each node are extracted by tracing along the feeding channel of the target cavity to the main channel node. The pressure loss parameter is fused with the priority level identifier in the priority data to obtain the first compensation feature.

[0010] As a preferred embodiment of the present invention, adjusting the first compensation feature using the damping parameter to obtain the second compensation feature includes: Extract the damping weights corresponding to the target node from the damping parameters; The first compensation feature is multiplied using the damping weights; Specifically, when the pressure sensitivity characteristic of the branch channel is greater than the sensitivity threshold, a first damping weight is applied to reduce the value corresponding to the first compensation characteristic; when the pressure sensitivity characteristic of the branch channel is not greater than the sensitivity threshold, a second damping weight is applied to increase the value corresponding to the first compensation characteristic, thus obtaining the second compensation characteristic.

[0011] As a preferred embodiment of the present invention, the step of generating structural parameters based on the second compensation feature and updating the mold flow channel based on the structural parameters includes: The corresponding volume feature is obtained by calculation based on the second compensation feature; If the volume feature is not greater than a preset volume threshold, the structural parameters are output to update the mold flow channel; If the volume characteristic is greater than the preset volume threshold, a backup control process is triggered.

[0012] As a preferred embodiment of the present invention, the backup control process includes: Pause the adjustment of cross-sectional parameters for each branch flow channel of the current mold runner; Extract unmatched pressure compensation information; The pressure compensation information is converted into temperature control commands, and the fluid temperature of the target cooling circuit is reduced based on the temperature control commands.

[0013] As a preferred embodiment of the present invention, the first state data includes the melt index characteristics and viscosity characteristics of the target injection molding raw material; The second state data includes topological information on the pressure distribution of the flow channel network and the timing characteristics of the melt. The priority data is obtained by processing the three-dimensional model of the backplate and is used to indicate the filling priority level identifier corresponding to the volume weight characteristics, appearance limitation characteristics and structural stress characteristics of each cavity.

[0014] As a preferred embodiment of the present invention, after updating the mold flow channel based on the structural parameters, the method further includes: The structural parameters are converted into machining code information, and process control data matching the structural parameters is generated. The process control data is sent to the control equipment to execute the injection molding process.

[0015] The beneficial effects of this invention are: 1. In the case that the correction parameters of any branch flow channel are not within the threshold range, the present invention compares the correction parameters with the second state data to determine the target node and generates damping parameters. The damping parameters constrain the subsequent compensation features, thereby avoiding the redistribution of pressure in the entire mold cavity caused by a single local size adjustment. This solves the problem that local compensation in the prior art can easily lead to global pressure imbalance and significantly improves the stability of the flow channel design.

[0016] 2. This invention uses damping parameters to adjust the first compensation feature to obtain the second compensation feature. It actively reduces the compensation amount of the high-sensitivity branch and reasonably amplifies the compensation amount of the low-sensitivity branch. This ensures the filling requirements of the high-priority cavity while preventing flash or weld line drift defects caused by over-compensation. It solves the problem of difficulty in balancing filling priority and pressure stability in the prior art and effectively broadens the process window of the mold. Attached Figure Description

[0017] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0018] Figure 1 This is a schematic diagram illustrating the workflow of the parametric design method for the flow channel features of the plastic backplate mold of the present invention. Detailed Implementation

[0019] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0020] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0021] like Figure 1 As shown, this application provides a parametric design method for the flow channel features of a plastic backplate mold. The execution subject of this method can be a parametric auxiliary engineering system running on a computer device, which is equipped with a processor, local memory and multi-channel data interface.

[0022] In practical industrial applications, the mold design and actual mass production of complex plastic back panels, such as multi-gate sequential filling for extra-long automotive interior trim or consumer electronics back panels, often suffer from abrupt changes in material properties due to batch variations in modified plastics or the incorporation of recycled materials. To address this issue, this method employs system-level data interaction and instruction calculation to perform dynamic cross-validation and directional compensation control.

[0023] During the initial evaluation phase of system operation, the processor obtains first state data, second state data, and priority data from peripheral detection components and business database through the data interface. Specifically, the peripheral detection components mainly include an online melt indexer and a rotational viscometer deployed on the production line for real-time acquisition of first-state data; in addition, the data can also be obtained by directly importing batch quality inspection reports provided by raw material suppliers from the business database.

[0024] Among them, the first state data is mainly used to reflect the physical property fluctuation of the current batch of injection molding raw materials; The second state data represents the global physical environment and temporal distribution state of the mold flow channel network; Priority data is usually derived from the analysis of the three-dimensional model of the backplane, and is used to indicate the importance of each cavity in the system in terms of business or physical structure and the priority weight of the filling order.

[0025] After acquiring the above data, the processor evaluates the change in flow resistance caused by fluctuations in material properties based on the first state data, and determines the correction parameters of each branch flow channel at the logic operation level. These correction parameters essentially reflect the trend vector that the system needs to adjust its theoretical dimensions if only local single-dimensional resistance is considered.

[0026] In traditional control logic, directly applying the adjustment of local resistance to the physical structure can easily lead to a drastic redistribution of pressure across the entire mold cavity, resulting in system-level negative feedback oscillations.

[0027] To mitigate this risk, this application performs the following actions: The processor monitors the correction parameters of each branch channel one by one. If the correction parameter of any branch channel is not within the threshold range, it determines that the system is currently facing an extremely high risk of global imbalance. At this point, the system immediately pauses the regular basic geometry update link and cross-compares the out-of-bounds correction parameters with the second state data representing the global environment. Through comparative analysis, the system accurately identifies target nodes that are highly sensitive to pressure fluctuations in the hydraulic transmission topology and generates damping parameters based on the physical tolerance boundary of the target nodes. This damping parameter, as a core control trigger variable, is temporarily stored by the system for subsequent suppression of overcompensation actions.

[0028] At the same time, the processor continuously monitors the filling timing status. When the second status data and the priority data meet preset conditions (for example, determining that the filling timing of the high priority region has been severely reversed or delayed), the system quickly intervenes and determines the target cavity where filling blockage occurs.

[0029] Subsequently, the system performs reverse physical topology tracing along the feeding channel of the target cavity to the main channel node, extracts the attenuation state along the way and integrates the business priority requirements to calculate the first compensation feature, which belongs to the original weighted pressure compensation demand without global constraints.

[0030] At this point, the system executes refined feature constraint logic, refuses to directly issue the first compensation feature, the processor calls the damping parameter temporarily stored in the cache, uses the damping parameter to constrain and adjust the first compensation feature, and performs active pressure drop or dynamic matching at the engineering level to obtain the second compensation feature.

[0031] After completing the above feature modulation, the processor generates executable structural parameters based on the second compensation feature, and updates the mold flow channel based on the structural parameters.

[0032] During this process, the updated flow channel features are transformed into topology reconstruction instructions that can be recognized by the 3D modeling engine. This not only ensures that the filling priority of high-value appearance surfaces is still guaranteed under extreme material viscosity drift scenarios, but also effectively eliminates local overload, flash, or shrinkage defects caused by blindly increasing global injection pressure, significantly widening the process tolerance window of the actual production line.

[0033] Furthermore, after retrieving the first state data, the processor performs feature extraction on the first state data to obtain drift features.

[0034] Specifically, the processor will extract the difference or perform a ratio calculation between the measured physical properties (such as measured viscosity value and melt index) of the current batch of injection molding raw materials acquired in real time and the standard material baseline data stored in the system, and extract a numerical vector representing the degree of deviation from the standard ideal working condition. This numerical vector is the drift feature.

[0035] This data processing method can transform complex nonlinear raw material fluctuations into engineering input indicators that can be directly quantified and invoked by subsequent control logic.

[0036] After obtaining the drift characteristics, the processor inputs the drift characteristics into a preset flow channel resistance calculation model to obtain the correction parameters of each branch flow channel.

[0037] To ensure the direct reproducibility of the process, the flow channel resistance calculation model is not a closed calculation rule in the system, but rather a specific parameterized calculation rule component.

[0038] The model is built and run according to the following logic: The basic mapping data of this model is constructed based on the fluid dynamics simulation experimental dataset of multiple sets of historical standard plastic backplate molds and the actual injection molding condition records; The model is represented by a table of correspondences between input feature identifiers and basic impedance values, along with a computational execution script, where the input feature identifiers correspond to different dimensions of material drift quantization levels. The processor uses the drift characteristics as input parameters, performs interpolation matching in the corresponding relationship table, extracts the corresponding basic impedance value, and then performs calculations based on preset arithmetic rules to output the correction parameters for each branch channel.

[0039] In this operation execution logic, the system uses the following numerical calculation formula to determine the correction parameter: ; Among them, C n F1 represents the correction parameter for the nth branch channel; F2 represents the viscosity deviation sub-feature in the drift characteristics; W1 represents the melt index deviation sub-feature in the drift characteristics; W1 represents the first weighting coefficient; W2 represents the second weighting coefficient; R n D represents the inherent resistance constant of the nth branch flow channel; n ε1 represents the equivalent cross-sectional diameter of the nth branch channel; ε1 represents the minimum stability constant.

[0040] Inherent resistance constant R n The calculation process is as follows: First, multiply the friction coefficient (usually between 0.02 and 0.03) by the friction length of the branch channel, and then divide by the initial equivalent cross-sectional diameter of the branch channel to calculate the friction term; then, sum the friction term with the local resistance coefficients of each corner on the branch channel to obtain the inherent resistance constant. The local resistance coefficient is determined according to the specific corner geometry, for example, 0.8 for a 90° corner, 0.5 for a 120° corner, and 0.3 for a 135° corner.

[0041] In actual injection molding processes, the variation in local flow resistance is not only affected by the dynamic drift of the material's rheological properties (quantified by F1 and F2), but also constrained by the physical geometry of the specific branch flow channel itself (as determined by R). n and D nBy applying spatial constraints and combining linear weighting with inverse proportional terms, the resistance variation trend of fluid within a specific network branch can be quickly fitted with a low system computational load.

[0042] The first weighting coefficient W1 is set to a value range of 0.65 to 0.85, and the second weighting coefficient W2 is set to a value range of 0.15 to 0.35 (and ensures that W1+W2=1).

[0043] In real rheological dynamics scenarios, the impact of sudden changes in material viscosity on flow resistance in pipelines is highly instantaneous, while the fluctuation of melt index is relatively slow and tends to be more macroscopic and statistical. Therefore, F1 is given a higher computational weight to ensure that the system can keenly capture the core causes of flow channel imbalance.

[0044] The minimum stability constant ε1 is typically taken as 1 × 10⁻⁶. -6 As a basic exception protection mechanism, it can prevent the processor from triggering a division-by-zero exception, ensuring the stable convergence of the parametric design process.

[0045] Through the above-mentioned standardized numerical conversion, the system successfully and accurately transforms the abstract raw material fluctuations into basic control variables that can be used for subsequent cross-comparison.

[0046] Furthermore, after the processor calculates the data in the target network, it will perform a round of global parameter boundary verification.

[0047] When the correction parameters of all the branch channels are within the threshold range, it indicates that the rheological properties of the current batch of injection molding raw material fluctuate very little, and the resulting changes in the flow resistance inside the channel are completely within the physical tolerance of the self-balancing multi-gate hydraulic dynamic network.

[0048] The threshold range mentioned here is a safe fluctuation range preset by the system based on historical trial molding data. Specifically, the threshold range is set to be within ±10% of the standard value of the correction parameter. That is, when the calculated correction parameter is within 90% to 110% of the standard value, it is determined to be within the threshold range. The parameter disturbance within this range is insufficient to cause global pressure imbalance or cause excessive preemption of the cavity melt flow.

[0049] Based on the above judgment conditions, the system determines that the current operating condition is safe and does not trigger the subsequent complex cross-modal damping anti-oscillation logic, but directly switches to the basic operation mode.

[0050] The processor retrieves the corresponding basic parameters from local memory. These basic parameters are typically reflected in the channel cross-sectional dimensions, basic geometric curing indexes, or conventional scaling factors that are predetermined based on the ideal rheological properties of standard polymer materials.

[0051] Subsequently, the processor updates each branch channel based on the basic parameters and directly issues conventional size-driven instructions to complete the local geometry solidification and parameter reconstruction of the three-dimensional model.

[0052] This processing logic constructs a complete closed loop for handling operating conditions within the system.

[0053] When the system detects minor environmental disturbances, it can quickly complete the design and update of the flow channel network along the conventional link, effectively avoiding excessive intervention and compensation for the multi-gate system that is in a stable state.

[0054] This move not only significantly reduces the redundant computation time of the processor and improves the instruction flow efficiency of the parameterized auxiliary engineering system, but also prevents the risk of local internal pressure overload caused by oversensitive adjustment at the physical level.

[0055] Furthermore, after performing cross-feature cross-comparison and locking the target node, the system needs to quantify the potential destructive force of the node on the global hydraulic network in order to generate control variables to limit local overcompensation.

[0056] The processor first obtains the pressure fluctuation characteristics corresponding to the target node. The data source of this characteristic is the global pressure distribution topology of the flow channel network extracted above. In actual representation, it is reflected as the expected pressure amplitude range of the target node in the hydraulic transmission topology, that is, the difference between the maximum transient pressure and the minimum pressure of the target node in the whole filling process (the quantification unit is usually megapascals).

[0057] After acquiring the data features, the processor calculates the pressure fluctuation features based on a preset negative correlation relationship to obtain the damping coefficient for the target node.

[0058] In the actual computational logic layer, to avoid step oscillations caused by linear control, this negative correlation is specifically embodied in a basic nonlinear negative exponential calculation logic, which calls the following algorithm formula: ; Wherein, D1 represents the calculated damping coefficient; P1 represents the quantified value of the pressure fluctuation characteristic; V1 represents the dynamic adjustment coefficient; α represents the sensitivity attenuation constant; and V0 represents the limit attenuation threshold.

[0059] The multi-gate flow channel system is a highly coupled hydraulic dynamic network. The more intense the expected pressure oscillation at the target node (i.e., the larger the value of P1), the stronger the suppression force must be applied to its cross-sectional expansion action. By introducing a negative exponential structure with the natural base e, when P1 increases significantly, the dynamic term in the formula decays rapidly, and the value of D1 will converge nonlinearly and approach the limit decay threshold V0.

[0060] The value range of the dynamic adjustment coefficient V1 is preferably set to 0.80 to 0.95, the sensitivity attenuation constant α is preferably set to 0.15 to 0.35, and the value range of the limit attenuation threshold V0 is preferably limited to 0.05 to 0.10.

[0061] The reason for setting V0 here is to act as a system-level safety defense and stability constant. When external environmental interference causes extreme distortion of physical parameters, V0 can ensure that the damping coefficient is not zero under extreme conditions. This not only prevents the subsequent system from triggering the processor's basic division-to-zero exception when performing multiplication and division operations, but also ensures that the sensitive branch flow channel retains a minimum physical material supply channel, avoiding cavity air trapping defects caused by complete blockage.

[0062] After calculating the core values ​​mentioned above, the processor generates the damping parameters based on the damping coefficients. In the scenario of large-scale mold auxiliary design with numerous branch channels, the processor extracts multiple discrete damping coefficients calculated independently for different target nodes and encapsulates them into a one-dimensional parameter array. In this one-dimensional parameter array, the array index strictly corresponds to the unique identifier of each channel node, and the value of the array element is the damping coefficient of the corresponding node, thereby generating the globally unified damping parameters. These damping parameters are then directly stored in the processor's state cache information, serving as the highest-priority physical constraint variable in the subsequent reverse tracing and compensation process, thus severing the causal link from the data source that caused the entire network pressure imbalance due to the amplification of a single cross-section.

[0063] Furthermore, in the specific implementation process, regarding the execution flow of determining the target cavity and tracing along the material supply channel of the target cavity to obtain the first compensation feature when the second state data and the priority data meet the preset conditions, the system achieves refined business constraints through topological traversal and weighted operation of multidimensional data.

[0064] The processor continuously extracts the melt timing features from the second state data and performs a synchronous comparison operation with the priority data.

[0065] During this stage, the system monitors the deviation between the actual fluid arrival time and the theoretically set time for each cavity in the hydraulic network. If the comparison results indicate that there is a deviation or delay in the filling sequence of the cavity under test (for example, a cavity with high surface finish requirements and which should be filled first in the preset schedule shows a significant decrease in flow rate and melt hysteresis), the system immediately determines that the preset conditions are met and formally identifies the cavity under test that is experiencing obstruction as the target cavity.

[0066] After locking the target cavity, the processor initiates a reverse tracing mechanism to perform reverse physical tracing along the feeding channel of the target cavity to the main channel node in the topology of the hydraulic dynamic network.

[0067] Along the trace link, the processor reads and extracts the pressure loss parameters of each node, which represent the objective resistance pressure drop generated when the fluid passes through each branch section and corner.

[0068] Subsequently, the processor prohibits using the physical pressure drop alone as the basis for compensation. Instead, it performs multi-dimensional data fusion, fusing the pressure loss parameter with the priority level identifier in the priority data to obtain the first compensation feature.

[0069] To ensure the computability and logical rigor of this process, the processor internally calls the following numerical calculation formulas to perform the fusion calculation: ; Wherein, E1 represents the calculated first compensation feature; I1 represents the priority level identifier corresponding to the target cavity (usually mapped to a quantization weight value greater than 1); N represents the total number of nodes on the reverse tracing link; L m ε represents the pressure loss parameter at the m-th node; W3 represents the friction loss weighting coefficient; C1 represents the basic compensation constant; T1 represents the standard filling time of the target cavity; ε2 represents the minimum stability constant.

[0070] Priority level indicator I1 is calculated using a weighted average method: The specific logic is as follows: multiply the appearance constraint weight by 0.5, add the structural stress weight by 0.3, and then combine this with the volume weight by 0.2, finally summing them up to obtain the weights for each of the above sub-items: Appearance restrictions are weighted according to level (2.0 for level A surfaces, 1.5 for level B surfaces, and 1.0 for level C surfaces). The structural stress weight is determined based on the degree of stress concentration (1.8 for high stress areas, 1.3 for medium stress areas, and 1.0 for low stress areas). The volume weight is determined based on the proportion of the cavity volume to the total volume (if the volume proportion is greater than 20%, then take 1.5; otherwise, take 1.0).

[0071] In highly coupled multi-gate injection molding networks, relying solely on physical pressure drop for compensation can easily lead to global imbalances, necessitating mandatory intervention from a business perspective.

[0072] The main body of the formula adopts a product modulation structure, which directly links the cumulative pressure loss representing physical obstruction with the priority level identifier I1 representing the importance of the business. This means that if timing delay occurs in a target cavity with high priority (such as an A-level surface), the system will proportionally amplify its initial compensation quota, thereby giving it priority in the reallocation of resources in the hydraulic network.

[0073] The value range of the friction loss weighting coefficient W3 is set to 0.85 to 0.95. The reason for this setting is that when tracing back from the far end cavity to the main channel node, there is an objective endogenous energy dissipation in the hydraulic transmission process. This coefficient is needed to appropriately reduce the pressure loss of each node nonlinearly in order to fit the real fluid working conditions.

[0074] The basic compensation constant C1 is set according to the reference volume of the mold back plate, and its value is usually in the range of 1.5 to 2.5. It is used to provide the envelope basis for compensation calculation.

[0075] The standard filling time T1 is introduced in the appendix at the end of the formula to provide an inversely proportional numerical excitation for cavities that require rapid filling in the design. Meanwhile, to prevent T1 from being incorrectly marked as zero by the system under extreme communication interference or data loss conditions, thus causing an abnormal division operation triggered by the processor, a value of 1×10 is forcibly added to the denominator. -6 The minimum stability constant ε2 is used to ensure the stable convergence of the entire parameterized optimization process.

[0076] After the rigorous topology tracing and cross-weighted calculations described above, the first compensation feature output by the system is no longer the mechanical physical pressure drop value, but rather a set of original compensation parameters that is deeply integrated with the priority-oriented injection molding process. This lays a reliable data foundation for the subsequent access to cross-feature dynamic gating mechanisms and damping suppression.

[0077] Furthermore, in the specific implementation process, for the core step of "adjusting the first compensation feature using the damping parameters to obtain the second compensation feature", the system introduces condition judgment and numerical calculation to realize refined constraints on physical constraints and business compensation requirements.

[0078] The processor first extracts the damping weight corresponding to the target node from the damping parameters in the local or shared storage area. Then, the system reads the pressure-sensitive characteristics of the current branch channel to be processed in the hydraulic dynamic network topology. The pressure-sensitive characteristics are specifically quantified as: the corresponding rate of change of the average pressure of the surrounding cavities when the cross-sectional size of the branch channel changes by 10%. The magnitude of this characteristic essentially reflects the theoretical interference intensity of the local size change on the pressure holding pressure of the surrounding cavities.

[0079] The processor performs a product operation on the first compensation feature using the damping weights. To ensure the accurate reproducibility of this modulation process at the software code level, the system internally calls the following piecewise conditional control formula to perform the calculation: ; Wherein, E2 represents the calculated and output second compensation feature; E1 represents the first compensation feature extracted by reverse tracing in the preceding step; S p The pressure-sensitive characteristic of the branch flow channel; S th This represents the sensitivity threshold preset by the system; W d1 W represents the first damping weight; d2 This represents the second damping weight.

[0080] Optionally, the sensitivity threshold S th The specific value is determined based on the type of injection molding material: for polypropylene (PP) or polyethylene (PE) materials, the value is 0.15; for ABS or polystyrene (PS) materials, the value is 0.20; for polycarbonate (PC) or polyamide (PA) materials, the value is 0.25.

[0081] In highly coupled multi-gate injection molding networks, scaling up a single dimension can easily trigger a negative feedback oscillation cycle.

[0082] Specifically, when the pressure sensitivity characteristic of the branch channel is greater than the sensitivity threshold (i.e., S) p >S th This indicates that the branch is on a vulnerable node in the global pressure redistribution. If the original compensation amount is directly issued, it will inevitably cause remote lag or near-end internal pressure overload.

[0083] Therefore, the system actively applies the first damping weight to reduce the value corresponding to the first compensation feature. Preferably, the first damping weight W d1 The attenuation factor is strictly limited to a range of 0.15 to 0.55. It is used to implement active voltage reduction at the physical level for this highly sensitive branch, significantly reducing its cross-sectional compensation capability.

[0084] Conversely, if the pressure sensitivity characteristic of the branch flow channel is not greater than the sensitivity threshold (i.e., S...), p ≤S th This indicates that the load-bearing branch is far from the pressure-sensitive area and has ample space to accommodate elastic deformation.

[0085] At this time, the system applies the second damping weight to increase the value corresponding to the first compensation feature, and the second damping weight W d2 This is the amplification factor, and its value is usually set between 1.10 and 1.45.

[0086] Through this differentiated product modulation operation, the system successfully and safely shifts the compensation gap caused by the voltage drop of highly sensitive nodes to the less sensitive bearer branches for digestion and compensation, and finally calculates the second compensation feature.

[0087] Furthermore, in the execution stage of generating structural parameters based on the second compensation feature and updating the mold flow channel based on the structural parameters, the system needs to complete the dimensional mapping from abstract control features to specific physical geometric dimensions, and simultaneously perform strict physical boundary anti-interference verification.

[0088] The processor extracts the second compensation feature after damping modulation. Since this feature is still in the logical dimension of hydraulic dynamics, the system must convert it into the volumetric feature in the three-dimensional spatial dimension to evaluate the feasibility of the dimensional enlargement action in actual mold steel.

[0089] In this step, the system internally calls the following geometric mapping formula to perform feature conversion: ; Where V1 represents the calculated volume characteristic (i.e., the total expected increase in flow channel space volume); N represents the total number of branch flow channels currently undergoing parameterized reconfiguration; E 2j L represents the second compensation feature corresponding to the j-th branch flow channel; j C1 represents the physical path length of the j-th branch channel in the three-dimensional topology; C2 represents the geometric mapping constant.

[0090] Pressure drop compensation for fluid resistance within a flow channel is typically manifested as an expansion of the pipe's cross-sectional area at the physical execution level. By introducing the geometric mapping constant C2, the system can quickly convert the pressure compensation weight into a single-step cross-sectional area increment and correlate it with the physical length L. j By combining these methods, the expansion trend of the global volume can be quickly fitted through lightweight numerical accumulation (∑) without invoking computationally intensive Boolean operations on 3D entities.

[0091] In actual engineering parameter configuration, the geometric mapping constant C2 is the conversion coefficient from pressure compensation to the increase in the cross-sectional area of ​​the flow channel. The specific value of this coefficient needs to be determined based on the shape of the flow channel cross-section: for circular flow channels, the constant is set to a range of 0.10 to 0.15; for trapezoidal flow channels, the constant is set to a range of 0.05 to 0.10. This numerical range is based on the safe machining allowance and cutting experience values ​​of conventional mold steels (such as P20 or H13 steel) to ensure that theoretical calculations match the actual machining accuracy of the CNC machine tool.

[0092] After completing the above conversion, the system executes the boundary check logic. The system retrieves the preset volume threshold pre-analyzed based on the three-dimensional solid model of the mold frame. This threshold represents the maximum spatial limit that the mold can accommodate the geometric expansion of the flow channel. Exceeding this limit will cause physical interference between the flow channel and the cooling water channel or ejector pin hole. If the volume feature is not greater than the preset volume threshold, it is determined that the current compensation scheme is within the physical safety envelope. The processor then calculates and outputs the compensation scheme as specific structural parameters (such as the specific diameter of each pipe section and the chamfer radius of the corner), and issues an instruction to update and solidify the local dimensions of the mold flow channel.

[0093] If the volume feature is not greater than the preset volume threshold, the current compensation scheme is determined to be within the physical safety envelope. The processor then calculates and outputs the compensation scheme as specific structural parameters (such as the specific diameter and chamfer radius of each pipe segment, and the structural parameters also include the coordinate position information and length information of each branch channel in three-dimensional space, which are used to generate a complete three-dimensional model of the channel). The processor then issues an instruction to update and solidify the local dimensions of the mold channel.

[0094] Furthermore, for the backup control process triggered when the volume feature is greater than a preset volume threshold, this application designs a fallback degradation mechanism to solve the problem of stable convergence of the flow field under space-constrained conditions.

[0095] When the system determines that continuing to enlarge the flow channel size will inevitably lead to interference with the internal physical structure of the mold (such as breaking through the mold frame envelope or intruding into the cooling water channel, ejector pin hole, or other restricted areas), the processor immediately executes a pause operation in the system control flow, suspending the adjustment of the cross-sectional parameters of each branch flow channel of the current mold flow channel. This operation effectively freezes the geometric expansion action that is in a critical state, ensuring the safety rigidity and manufacturing feasibility of the high-value mold body at the physical structure level, and preventing mechanical interference failures caused by excessive pursuit of fluid dynamic balance.

[0096] After freezing the geometry update, the system did not give up on optimizing the local hysteresis cavity. Instead, it switched the processing dimension from the spatial geometry field to the thermodynamic dimension. At the logic operation level, the processor calculated the difference between the actual allocated effective geometric compensation amount and the target theoretical total compensation requirement, and extracted the remaining compensation gap that could not be digested by simply changing the cross-sectional size due to the spatial envelope. In other words, it extracted the unmatched pressure compensation information.

[0097] After acquiring the gap data, the processor performs a data type and control domain dimension conversion, transforming the pressure compensation information into temperature control instructions.

[0098] In actual injection molding, the local mold temperature can significantly affect the apparent viscosity and rheological dynamics of the polymer melt. The system will send the generated instructions to the corresponding temperature control hardware (such as a mold temperature controller or a multi-channel water distribution control valve for controlling the on / off state and flow of the cooling circuit), and reduce the fluid temperature of the target cooling circuit based on the temperature control instructions.

[0099] The specific quantitative calculation logic for the temperature adjustment range is as follows: Extract the percentage of the remaining compensation gap relative to the total compensation requirement, and multiply it by the temperature coefficient of the selected material to obtain the corresponding temperature reduction.

[0100] The material temperature coefficient is configured according to the characteristics of the raw materials: Polypropylene (PP) or polyethylene (PE) is set at a 2°C reduction for every 10% gap; ABS or polystyrene (PS) is set at a 3°C reduction for every 10% gap; and polycarbonate (PC) or polyamide (PA) is set at a 4°C reduction for every 10% gap.

[0101] Through this action, the system alters the local heat exchange boundary conditions around a specific flow channel branch at the physical level, compensating for the physical limit that the flow channel geometry can no longer expand by adjusting the thermodynamic rheological state.

[0102] Furthermore, to ensure that the cross-feature cross-validation and targeted compensation mechanism has a reliable data foundation, this application has made strict physical and business definitions of the core data dimensions involved in the logical operation. Specifically, it constructs a complete control constraint boundary by acquiring and parsing the first state data, the second state data, and the priority data.

[0103] The first state data acquired by the system is mainly used to represent the sudden changes in the basic physical properties of the injection molding medium caused by external environmental interference.

[0104] Specifically, the data includes the melt index and viscosity characteristics of the target injection molding raw material. In real injection molding production, batch differences in modified plastics or fluctuations in the proportion of recycled materials can easily cause nonlinear changes in the rheological properties of materials. By using melt index and viscosity characteristics as the basic data sensing objects, the degree of drift in fluid flow resistance and shear rate caused by objective fluctuations in raw materials can be accurately quantified. This provides the most direct and objective physical property input variables for subsequent calculation of correction parameters for each branch flow channel.

[0105] Simultaneously, the system extracts the second state data, which is used to characterize the global hydraulic environment and kinematic performance of the multi-gate runner system under the current working conditions. The second state data includes the runner network pressure distribution topology information and melt timing characteristics. The runner network pressure distribution topology information maps the highly coupled pressure transmission and distribution relationship between various branch runners inside the injection mold. This is the core topological basis for the system to subsequently determine whether local cross-sectional size adjustments will cause global pressure imbalance (i.e., negative feedback oscillation cycle). (This runner network pressure distribution topology information can be pre-calculated by fluid dynamics simulation software (such as Moldflow) or obtained by actual measurement through pressure sensors deployed at key nodes of the runner.) The melt timing characteristics record the actual time state of the fluid front reaching each mold cavity, which is used to compare with theoretical expectations to quickly locate the physical area where filling lag occurs.

[0106] While incorporating physical field data, this application breaks away from the limitations of traditional parametric design, which only focuses on fluid dynamics, and introduces a refined business constraint mechanism.

[0107] The priority data is obtained by processing the three-dimensional model of the back panel (i.e., the three-dimensional digital engineering model of the plastic back panel product). This data is used to indicate the volume weight characteristics of each cavity (reflecting the material requirements for space filling), appearance limitation characteristics (such as the requirement for a consumer electronics A-grade appearance surface without obvious defects or weld line drift), and structural stress characteristics (reflecting the mechanical limit requirements of the product assembly or load-bearing area).

[0108] The processor uses preset parsing rules to convert the above three types of engineering attributes into quantified filling priority level identifiers.

[0109] Furthermore, after confirming that the three-dimensional topology of the parameterized auxiliary engineering system has been partially reconstructed and solidified according to the optimized structural parameters, the processor calls the compilation component through the data interface to convert the structural parameters into processing code information.

[0110] In actual engineering implementation, this machining code information is specifically manifested as a machine tool machining file (such as G code) that can be directly read and run by the CNC machine tool. This information is used to guide the tool running trajectory and cutting feed of the machining machine tool, so as to accurately depict the target branch flow channel cross-sectional geometry on the actual mold steel after damping modulation and reverse compensation.

[0111] Since changes in the physical dimensions of the runner in a multi-gate injection molding system inevitably lead to a change in the overall hydraulic transmission state inside the fluid, simple geometric processing is insufficient to guarantee stable mass production.

[0112] Therefore, while outputting the machining file, the system simultaneously performs process data calculations to generate process control data that matches the structural parameters. This process control data is typically represented as a multi-gate timing control process card or a pressure holding timing switching configuration table. The system generates the above data based on specific process adjustment rules, specifically including:

[0113] First, for every 1 mm increase in the flow channel diameter, the corresponding valve needle opening time needs to be set to 50 to 100 milliseconds earlier; Second, the adjustment ratio of injection pressure should be inversely proportional to the change ratio of flow channel cross-sectional area. Third, the flow rate switching point needs to be adjusted according to the channel filling time recalculated after the structural dimensions are updated, and the synchronization time node needs to be adjusted accordingly.

[0114] The core function of the above-mentioned refined mapping rules is to ensure that the multi-gate sequential filling control logic can maintain a good dynamic match with the geometric capacity and flow resistance distribution of the reconstructed physical flow channel.

[0115] The system then sends the process control data to the control equipment via fieldbus or industrial communication network.

[0116] In this business scenario, the control device specifically corresponds to the electrical control unit of the injection molding machine on the physical production line. The control device receives and loads the aforementioned timing and pressure constraint boundaries, and then drives the relevant hydraulic components and servo motors to perform the injection molding process.

[0117] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A parametric design method for the flow channel features of a plastic backplate mold, characterized in that, include: Obtain the first state data, the second state data, and the priority data; Based on the first state data, determine the correction parameters for each branch flow channel; If the correction parameter of any of the branch channels is not within the threshold range, the correction parameter is compared with the second state data to determine the target node, and a damping parameter is generated based on the target node. When the second state data and the priority data meet the preset conditions, the target cavity is determined, and the material supply channel of the target cavity is traced to obtain the first compensation feature; The first compensation feature is adjusted using the damping parameters to obtain the second compensation feature; Structural parameters are generated based on the second compensation feature, and the mold flow channel is updated based on the structural parameters.

2. The parametric design method for the flow channel features of a plastic backplate mold according to claim 1, characterized in that, The step of determining the correction parameters for each branch flow channel based on the first state data includes: Feature extraction is performed on the first state data to obtain drift features; The drift characteristics are input into a preset flow channel resistance calculation model to obtain the correction parameters for each branch flow channel.

3. The parametric design method for the flow channel features of a plastic backplate mold according to claim 2, characterized in that, The method further includes: When the correction parameters of all the branch channels are within the threshold range, each branch channel is updated based on the base parameters.

4. The parametric design method for the flow channel features of a plastic backplate mold according to claim 2, characterized in that, The generation of damping parameters based on the target node includes: Obtain the pressure fluctuation characteristics corresponding to the target node; Based on a preset negative correlation relationship, the pressure fluctuation characteristics are calculated to obtain the damping coefficient for the target node; The damping parameters are generated based on the damping coefficient.

5. The parametric design method for the flow channel features of a plastic backplate mold according to claim 4, characterized in that, When the second state data and the priority data meet preset conditions, the target cavity is determined, and the material supply channel of the target cavity is traced to obtain the first compensation feature, including: The second state data is compared with the priority data; If the comparison results indicate that there is a deviation or lag in the filling timing of the cavity to be tested, it is determined that the preset condition is met, and the cavity to be tested is identified as the target cavity; The pressure loss parameters of each node are extracted by tracing along the feeding channel of the target cavity to the main channel node. The pressure loss parameter is fused with the priority level identifier in the priority data to obtain the first compensation feature.

6. The parametric design method for the flow channel features of a plastic backplate mold according to claim 5, characterized in that, The step of adjusting the first compensation feature using the damping parameter to obtain the second compensation feature includes: Extract the damping weights corresponding to the target node from the damping parameters; The first compensation feature is multiplied using the damping weights; Specifically, when the pressure sensitivity characteristic of the branch channel is greater than the sensitivity threshold, a first damping weight is applied to reduce the value corresponding to the first compensation characteristic; when the pressure sensitivity characteristic of the branch channel is not greater than the sensitivity threshold, a second damping weight is applied to increase the value corresponding to the first compensation characteristic, thus obtaining the second compensation characteristic.

7. The parametric design method for the flow channel features of a plastic backplate mold according to claim 6, characterized in that, The step of generating structural parameters based on the second compensation feature and updating the mold flow channel based on the structural parameters includes: The corresponding volume feature is obtained by calculation based on the second compensation feature; If the volume feature is not greater than a preset volume threshold, the structural parameters are output to update the mold flow channel; If the volume characteristic is greater than the preset volume threshold, a backup control process is triggered.

8. The parametric design method for the flow channel features of a plastic backplate mold according to claim 7, characterized in that, The backup control process includes: Pause the adjustment of cross-sectional parameters for each branch flow channel of the current mold runner; Extract unmatched pressure compensation information; The pressure compensation information is converted into temperature control commands, and the fluid temperature of the target cooling circuit is reduced based on the temperature control commands.

9. The parametric design method for the flow channel features of a plastic backplate mold according to claim 1, characterized in that, The first state data includes the melt index characteristics and viscosity characteristics of the target injection molding raw material; The second state data includes topological information on the pressure distribution of the flow channel network and the timing characteristics of the melt. The priority data is obtained by processing the three-dimensional model of the backplate and is used to indicate the filling priority level identifier corresponding to the volume weight characteristics, appearance limitation characteristics and structural stress characteristics of each cavity.

10. The parametric design method for the flow channel features of a plastic backplate mold according to claim 1, characterized in that, After updating the mold flow channel based on the structural parameters, the method further includes: The structural parameters are converted into machining code information, and process control data matching the structural parameters is generated. The process control data is sent to the control equipment to execute the injection molding process.

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