Graphically assisted mold design system and method

Abstract

The application discloses a graphic-assisted mold design system and method. The system comprises a graphic database, which stores standard graphic data and modified graphic data; a graphic recognition module, which receives mold design requirements, extracts semantic data and graphic recognition data in the mold design requirements, and determines design features based on the semantic data and the graphic recognition data; a graphic determination module, which receives the design features, selects a plurality of standard graphic data with a similarity greater than a preset threshold; a graphic combination module, which receives the plurality of standard graphic data selected by the graphic determination module, determines preset feature points of each standard graphic data, and binds the modified graphic data to the preset feature points to perform a modification operation to obtain design graphic data; and a process correction module, which receives the semantic data, determines process variables, and performs correction on the design graphic data according to the process variables to obtain mold graphic data. The application associates structure changes with process influences, and realizes dynamic adaptive design.

Description

Technical Field

[0001] This invention relates to the field of industrial design technology, and more specifically to a graphic-aided mold design system and method. Background Technology

[0002] As a key process equipment in manufacturing, the design quality of molds directly affects product precision, production efficiency, and cost control. Traditional mold design mainly relies on engineers' experience, using CAD software for geometric modeling and then making process adjustments based on empirical parameters. With increasing product complexity and customization demands, this experience-based design method is no longer sufficient to meet the efficiency and precision requirements of modern manufacturing. Especially in areas such as multi-cavity molds and precision injection molds, the contradiction between structural complexity and process requirements is becoming increasingly prominent, necessitating intelligent design assistance systems to improve design quality.

[0003] A common problem with existing mold design systems is the disconnect between structural design and process requirements. These systems typically match standard graphics based solely on geometric feature similarity, neglecting the impact of cavity topology changes on melt flow characteristics and the effect of assembly interaction states on stress distribution. When design requirements involve structural changes, existing systems cannot accurately predict the resulting process impacts, leading to high design rework rates and long process debugging cycles. Furthermore, traditional systems use fixed adjustment ranges for graphic modifications, failing to dynamically adjust design parameters based on the actual impact of structural changes, resulting in over-design or under-design, severely affecting mold performance and lifespan.

[0004] Therefore, there is an urgent need for a graphically assisted mold design system and method that can accurately correlate structural changes with process influences and achieve dynamic adaptive design. Summary of the Invention

[0005] To address the aforementioned problems, this invention provides a graphic-aided mold design system and method, adapting to the intelligent needs of modern design.

[0006] A first aspect of the present invention provides a graphic-aided mold design system, comprising: A graphic database is configured to store standard graphic data and modified graphic data, wherein the modified graphic data includes modification parameters for preset orientation and preset size, and at least one of the standard graphic data and at least one of the modified graphic data define mold graphic data. The image recognition module is configured to receive mold design requirements, extract semantic data and image recognition data from the mold design requirements, and determine design features based on the semantic data and the image recognition data. The graphic determination module is configured to receive the design features and select multiple standard graphic data with a similarity greater than a preset threshold. The graphic combination module is configured to receive multiple standard graphic data selected by the graphic determination module, determine preset feature points of each standard graphic data, and bind the modified graphic data to the preset feature points to perform a modification operation to obtain design graphic data. The process correction module is configured to receive the semantic data, determine the process variables, and perform corrections on the design graphic data based on the process variables to obtain the mold graphic data.

[0007] As a preferred embodiment, the image recognition module is further configured to perform the following steps: The topological changes of the mold cavity are analyzed from the semantic data and / or graphic recognition data, and the dynamic change of melt flow resistance caused by the increase or decrease of the number of cavities is determined as the first feature value, wherein the dynamic change of melt flow resistance is determined based on the flow channel topology, melt rheological properties and cavity distribution density. The assembly interaction state between the slider and the core is identified from the semantic data and / or the graphic recognition data, and the dynamic change in stress concentration caused by the fluctuation of the mating gap is determined as the second feature value, wherein the dynamic change in stress concentration is determined based on the curvature of the assembly contact surface, the yield strength of the material and the thermo-coupling effect. A feature vector is constructed based on the first and second feature values, and the similarity with standard graphic data is calculated by weighting. When calculating the similarity with standard image data, When the material shrinkage rate exceeds a preset process threshold, the weight of the first feature value is increased; When the cooling rate exceeds a preset process threshold, the weight of the second feature value is increased.

[0008] As a preferred embodiment, the graphics combination module is also configured to perform the following steps: Determine the preset feature points of each of the standard graphic data, wherein the preset feature points include at least the thermal stress concentration area and the dynamic contact failure risk area in the mold structure caused by cavity topology changes or assembly interactions. A first adjustment range is determined for the thermal stress concentration region. The first adjustment range represents the adjustable range of the wall thickness. Its upper limit is limited by the first characteristic value. When the dynamic change of the melt flow resistance increases, the upper limit increases. When the dynamic change of the melt flow resistance decreases, the upper limit decreases. A second adjustment range is determined for the dynamic contact failure risk area. The second adjustment range represents the adjustable range of the gap. Its lower limit is dynamically limited by the second characteristic value. When the dynamic change of stress concentration increases, the lower limit narrows, and when the dynamic change of stress concentration decreases, the lower limit expands. The modified graphic data is bound to the preset feature points. Within the first adjustment range, the wall thickness offset is nonlinearly adjusted based on the thermal relaxation characteristics of the cooling process, so that the wall thickness change trend is adapted to the evolution of the transient temperature field. Within the second adjustment range, a linear compensation strategy is adopted when the gap fluctuation amplitude is below the critical value, and an exponential compensation strategy is switched when the gap fluctuation amplitude exceeds the critical value, so that the gap adjustment is adapted to the contact stress distribution.

[0009] As a preferred embodiment, there is a non-linear feedback relationship between the first feature value and the first adjustment interval, specifically: The upper limit of the first adjustment range exhibits a non-linear trend as the first characteristic value increases. When the first feature value is in a low change range, the upper limit value increases approximately linearly; When the first feature value enters the high change range, the rate of increase of the upper limit value accelerates; When multiple sets of standard graphic data are generated, and the first feature values ​​of each set are different, For each set of data, a suitable nonlinear feedback curve is dynamically generated so that the upper limit of the first adjustment interval for all groups satisfies the thermal balance constraint.

[0010] As a preferred embodiment, the process correction module is specifically configured as follows: The material property coefficients are determined based on the first feature value, and the changes in process variables are obtained based on the semantic data, wherein the process variables include material shrinkage rate, molding pressure and cooling rate; Calculate the size compensation value, and correct the design graphic data according to the size compensation value; The material property coefficient is positively correlated with the first characteristic value, and the size compensation value is adjusted accordingly when the first adjustment range changes to maintain the safety of the mold allowance. When the size compensation value causes the margin adjustment value to be less than the preset safety threshold, the preset size of the modified graphic data is adjusted.

[0011] A second aspect of the present invention provides a graphic-aided mold design method, comprising the following steps: Receive mold design requirements, extract semantic data and graphic recognition data, and determine design features based on the semantic data and graphic recognition data; Based on the design features, select multiple standard graphic data with a similarity greater than a preset threshold; Determine the preset feature points of each of the standard graphic data, wherein each preset feature point corresponds to a weak link in the mold structure; The modified graphic data is bound to the preset feature points, and wall thickness adjustment and gap compensation operations are performed according to the adjustment range of the preset feature points to obtain the design graphic data. The design graphic data is corrected based on the process variables to obtain mold graphic data, wherein the process variables are determined based on the design features.

[0012] As a preferred approach, the step of determining the design features includes: The dynamic change in melt flow resistance caused by cavity topology change is determined from the semantic data as the first feature value; The dynamic change in stress concentration caused by the slider-core assembly interaction is determined as the second feature value from the graphic recognition data; A feature vector is constructed based on the first and second feature values, and the similarity with standard graphic data is calculated through a dynamically calibrated weighting mechanism. The preset threshold is adjusted in real time based on the ratio of the first feature value to the second feature value, and standard graphic data with similarity requirements are selected.

[0013] As a preferred embodiment, the steps of performing wall thickness adjustment and gap compensation operations include: For areas of concentrated thermal stress, the adjustable range of wall thickness is determined based on the first characteristic value, and the wall thickness is nonlinearly adjusted based on the thermal relaxation characteristics of the cooling process. For areas at risk of dynamic contact failure, the adjustable range of the gap is determined based on the second characteristic value, and a linear or exponential compensation strategy is selected based on the gap fluctuation amplitude. When the first characteristic value enters the high change range, the upper limit of the adjustable wall thickness is accelerated to compensate for the nonlinear cumulative effect of thermal deformation. When the wall thickness parameter exceeds the safe range, the parameter is remapped to the safe range.

[0014] As a preferred embodiment, the step of making corrections based on process variables includes: The material property coefficients are determined based on the first characteristic value, and the changes in process variables are obtained; Calculate the size compensation value, and correct the design graphic data according to the size compensation value; When the size compensation value causes the allowance adjustment value to be less than the preset safety threshold, the upper limit of the modification amount is automatically adjusted, and the mold graphic data is updated synchronously through the cloud platform. The material property coefficient is positively correlated with the first characteristic value, and the size compensation value changes with the adjustment of the first adjustment range.

[0015] Compared with the prior art, the present invention has the following advantages: This invention analyzes changes in the topology of mold cavities from semantic data and / or graphic recognition data to determine the dynamic change in melt flow resistance caused by the increase or decrease in the number of cavities as the first feature value. It also identifies the assembly interaction state between the slider and the core from semantic data and / or graphic recognition data to determine the dynamic change in stress concentration caused by fluctuations in the fit clearance as the second feature value. This invention establishes a quantitative correlation between mold structural changes and process influence for the first time. This technical feature effectively solves the technical deficiency of traditional mold design, which relies solely on geometric features for matching and ignores the impact of structural changes on the process.

[0016] Specifically, a feature vector is constructed based on the first and second feature values. The similarity to standard graphic data is calculated using weighted averages. Furthermore, the weight of the first feature value is increased when the material shrinkage rate exceeds a preset process threshold, and the weight of the second feature value is increased when the cooling rate exceeds a preset process threshold. This allows the system to dynamically adjust the matching strategy according to different process conditions, achieving process-condition-driven intelligent matching. This dynamic weighting mechanism ensures that the system can select standard graphic data that is highly compatible with process requirements under different material properties and process parameters, fundamentally avoiding the problem of "structural design being disconnected from process requirements" in traditional design methods.

[0017] This invention determines preset feature points in various standard graphic data. These preset feature points include, at a minimum, areas of thermal stress concentration and dynamic contact failure risk areas within the mold structure caused by changes in cavity topology or assembly interactions. This allows for precise positioning of these preset feature points at structurally weak points, rather than simply geometric feature points as in traditional technologies. This technical feature enables the system to specifically optimize the design of critical areas of the mold, effectively preventing potential structural failure risks.

[0018] More importantly, a first adjustment range for the thermal stress concentration region is determined. This first adjustment range represents the adjustable range of the wall thickness, with its upper limit defined by a first characteristic value. The upper limit expands as the dynamic change in melt flow resistance increases and narrows as the dynamic change in melt flow resistance decreases. A second adjustment range for the dynamic contact failure risk region is also determined. This second adjustment range represents the adjustable range of the gap, with its lower limit dynamically defined by a second characteristic value. The lower limit narrows as the dynamic change in stress concentration increases and expands as the dynamic change in stress concentration decreases. This establishes a dynamic correlation mechanism between the adjustment range and the characteristic value. This correlation enables the system to automatically adjust the design parameter range according to the actual impact of structural changes, avoiding the over-design or under-design problems caused by the "fixed adjustment range" in traditional design, and significantly improving the accuracy and adaptability of the design.

[0019] This invention utilizes a nonlinear feedback relationship between a first characteristic value and a first adjustment range, enabling the system to adapt to structural changes of varying scales. Specifically, when the first characteristic value is in a low-change range, the upper limit increases approximately linearly; when the first characteristic value enters a high-change range, the upper limit increases at a faster rate. This technical feature allows the system to effectively compensate for the nonlinear cumulative effect of thermal deformation, making it particularly suitable for large-scale structural change scenarios.

[0020] Furthermore, when multiple sets of standard graphical data are generated and the first characteristic values ​​of each set are different, a suitable nonlinear feedback curve is dynamically generated for each set of data. This ensures that the upper limit of the first adjustment interval for all sets satisfies the thermal balance constraint, enabling the system to simultaneously process multiple design schemes and perform intelligent optimization. This feature not only improves design efficiency but also ensures the reliability of different design schemes in terms of thermodynamic performance, achieving a technological breakthrough in parallel design and evaluation of multiple schemes.

[0021] This invention determines the material property coefficient based on a first feature value, obtains the change in process variables based on semantic data, and shows that the material property coefficient is positively correlated with the first feature value. Furthermore, when the first adjustment range changes, the size compensation value is adjusted accordingly to maintain the safety of the mold allowance, thus realizing a close connection between the process correction module and the front-end design features.

[0022] This integration transforms process modification from a standalone post-processing step into an intelligent process deeply embedded within the design workflow. When structural changes alter process conditions, the system automatically adjusts dimensional compensation strategies to ensure the final mold design meets both geometric requirements and process constraints. When the dimensional compensation value causes the allowance adjustment value to fall below a preset safety threshold, the system adjusts and modifies the preset dimensions of the graphic data, further strengthening the safety mechanism of the design process and effectively preventing design defects caused by over- or under-compensation. Attached Figure Description

[0023] The present invention will be further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present invention. For those skilled in the art, other drawings can be obtained based on the following drawings without creative effort.

[0024] Figure 1 This is a schematic diagram of the system provided in an embodiment of the present invention. Detailed Implementation

[0025] 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.

[0026] The graphic-aided mold design system of this disclosure embodiment is as follows: Figure 1 As shown, it includes a graphic database, a graphic recognition module, a graphic determination module, a graphic combination module, and a process correction module.

[0027] The graphic database is configured to store standard graphic data and modified graphic data, wherein the modified graphic data includes preset orientation and preset size modification parameters, and at least one of the standard graphic data and at least one of the modified graphic data define the mold graphic data. In specific implementation, the graphic database adopts a hierarchical storage structure, with the bottom layer storing basic geometric graphics, the middle layer storing component-level graphics, and the top layer storing complete mold graphics, which facilitates flexible retrieval by the system according to design requirements.

[0028] The graphic recognition module is configured to receive mold design requirements, extract semantic data and graphic recognition data from the mold design requirements, and determine design features based on the semantic data and graphic recognition data. In specific implementation, the graphic recognition module first parses key parameters from the semantic description input by the user; for example, in "dual-cavity hot runner mold," "dual-cavity" indicates the number of cavities, and "hot runner" indicates the runner type. Simultaneously, it extracts geometric features and assembly relationships from reference drawings using image recognition technology. Specifically, the graphic recognition module is also configured to perform the following steps: parse changes in the mold cavity topology from the semantic data and / or graphic recognition data, and determine the dynamic change in melt flow resistance caused by the increase or decrease in the number of cavities as a first feature value, wherein the dynamic change in melt flow resistance is determined based on the runner topology, melt rheological properties, and cavity distribution density.

[0029] In one embodiment, when the user's design requirement is "to change a single-cavity mold to a four-cavity mold," the graphic recognition module parses the change in the number of cavities from 1 to 4, calculates a 200% increase in the flow channel length based on the flow channel topology, and determines that the dynamic change in melt flow resistance (first characteristic value) increases by approximately 3.5 times, combining the melt rheological properties and cavity distribution density. Simultaneously, the graphic recognition module identifies the assembly interaction state between the slider and the core from the graphic recognition data, determining the dynamic change in stress concentration caused by fluctuations in the fit clearance as the second characteristic value. This dynamic change in stress concentration is dynamically determined based on the curvature of the assembly contact surface, the material yield strength, and the thermo-coupling effect. For example, when the increase in the number of cavities leads to a change in the cooling system layout, the curvature of the contact surface between the slider and the core changes, and the system calculates that the dynamic change in stress concentration (second characteristic value) increases by 1.8 times.

[0030] In one implementation, the image recognition module constructs a feature vector based on the first and second feature values, and calculates the similarity with standard image data using a weighted average. Specifically, the system dynamically adjusts the weights according to the current process conditions: when the material shrinkage rate exceeds a preset process threshold (e.g., 1.5%), the weight of the first feature value is increased to 0.7 to enhance the matching accuracy for the influence of flow resistance; when the cooling rate exceeds a preset process threshold (e.g., 50℃ / s), the weight of the second feature value is increased to 0.6 to enhance the matching accuracy for the influence of stress concentration. This dynamic weighting mechanism ensures that the system can select the most suitable standard image data under different process conditions.

[0031] In a preferred embodiment, the graphic combination module is configured to receive multiple standard graphic data selected by the graphic determination module, determine preset feature points for each of the standard graphic data, and bind the modified graphic data to the preset feature points to perform a modification operation. Specifically, the graphic combination module determines preset feature points for each of the standard graphic data, wherein the preset feature points include at least areas of thermal stress concentration and areas of dynamic contact failure risk in the mold structure caused by changes in cavity topology or assembly interactions. For example, in injection molds, areas of thermal stress concentration are typically located at corners of the cavity wall, while areas of dynamic contact failure risk are located at the mating surfaces of the slider and the core.

[0032] In one embodiment, the graphic combination module determines a first adjustment range for the thermal stress concentration region, where the first adjustment range represents the adjustable wall thickness range, and its upper limit is defined by the first feature value. When the system detects an increase in the dynamic change of melt flow resistance, such as a 3.5-fold increase due to changing from a single cavity to a four-cavity system, the upper limit automatically increases by 20% to compensate for the risk of thermal deformation; when the dynamic change of melt flow resistance decreases, the upper limit narrows accordingly to avoid material redundancy. Similarly, the graphic combination module determines a second adjustment range for the dynamic contact failure risk region, where the second adjustment range represents the adjustable gap range, and its lower limit is dynamically defined by the second feature value. When the dynamic change of stress concentration increases, such as the aforementioned 1.8-fold increase, the lower limit narrows by 15% to enhance contact stability; when the dynamic change of stress concentration decreases, the lower limit expands to reduce assembly difficulty.

[0033] In one implementation, after the graphic combination module binds the modified graphic data to the preset feature points, it nonlinearly adjusts the wall thickness offset based on the thermal relaxation characteristics of the cooling process within the first adjustment range, so that the wall thickness change trend is adapted to the evolution of the transient temperature field. Specifically, the system adjusts the wall thickness using an exponential decay model based on the material's thermal conductivity characteristics, ensuring that the wall thickness change is synchronized with the cooling process. Within the second adjustment range, the system intelligently selects a compensation strategy based on the gap fluctuation amplitude: a linear compensation strategy is used when the gap fluctuation amplitude is below a critical value; when the gap fluctuation amplitude exceeds a critical value, it switches to an exponential compensation strategy, enabling dynamic coordination between gap adjustment and contact stress distribution, effectively preventing flash caused by excessively large gaps or jamming caused by excessively small gaps.

[0034] In a preferred embodiment, there is a nonlinear feedback relationship between the first characteristic value and the first adjustment interval. Specifically, the upper limit of the first adjustment interval exhibits a nonlinear trend as the first characteristic value increases: when the first characteristic value is in a low-change range (e.g., 1-2 times), the upper limit increases approximately linearly; when the first characteristic value enters a high-change range (e.g., more than 2 times), the upper limit increases at a faster rate to compensate for the nonlinear cumulative effect of thermal deformation. When the system generates multiple sets of standard graphic data and the first characteristic values ​​of each set are different, a suitable nonlinear feedback curve is dynamically generated for each set of data to ensure that the upper limit of the first adjustment interval for all sets satisfies the thermal balance constraint, thereby maintaining structural integrity in the transient temperature field. For example, when the first characteristic value is 2.5 times, the system uses a nonlinear curve with a moderate growth rate; when the first characteristic value reaches 4 times, the system automatically switches to a nonlinear curve with a high growth rate to ensure that the wall thickness adjustment can fully compensate for thermal deformation.

[0035] In one implementation, the process correction module is configured to receive the semantic data, determine process variables, and perform corrections to the design graphic data based on the process variables. Specifically, the process correction module determines a material property coefficient based on the first feature value and obtains the change in process variables based on the semantic data, wherein the process variables include material shrinkage rate, molding pressure, and cooling rate. The process correction module calculates a dimensional compensation value and performs corrections to the design graphic data based on the dimensional compensation value. In particular, the material property coefficient is positively correlated with the first feature value, and when the first adjustment range changes, the dimensional compensation value is adjusted accordingly to maintain mold allowance safety. When the dimensional compensation value causes the allowance adjustment value to be less than a preset safety threshold, the system automatically adjusts the preset size of the modified graphic data, sets the upper limit of the modification amount to 120% of the current value, and synchronously updates the mold graphic data through the cloud platform to ensure the safety and consistency of the design process.

[0036] This disclosure also provides a graphic-aided mold design method, comprising the following steps: receiving mold design requirements, extracting semantic data and graphic recognition data, and determining design features based on the semantic data and the graphic recognition data; selecting multiple standard graphic data with a similarity greater than a preset threshold based on the design features; determining preset feature points for each of the standard graphic data, wherein each preset feature point corresponds to a weak link in the mold structure; binding modified graphic data to the preset feature points, and performing wall thickness adjustment and gap compensation operations according to the adjustment range of the preset feature points to obtain design graphic data; and correcting the design graphic data according to process variables to obtain mold graphic data, wherein the process variables are dynamically determined based on the design features.

[0037] In one embodiment, the step of determining the design features includes: determining the dynamic change in melt flow resistance caused by cavity topology changes from the semantic data as a first feature value; determining the dynamic change in stress concentration caused by slider-core assembly interaction from the graphic recognition data as a second feature value; constructing a feature vector based on the first and second feature values, and calculating the similarity with standard graphic data through a dynamically calibrated weighting mechanism; adjusting a preset threshold in real time according to the ratio of the first feature value to the second feature value, and selecting standard graphic data with similarity requirements. This method enables the system to intelligently match standard graphics based on the degree of influence of structural changes on the process, rather than matching solely based on geometric similarity.

[0038] As a preferred embodiment, the steps of performing wall thickness adjustment and gap compensation operations include: for areas of concentrated thermal stress, dynamically determining the adjustable range of the wall thickness based on the first characteristic value, and nonlinearly adjusting the wall thickness based on the thermal relaxation characteristics of the cooling process; for areas with a risk of dynamic contact failure, dynamically determining the adjustable range of the gap based on the second characteristic value, and selecting a linear or exponential compensation strategy based on the gap fluctuation amplitude; when the first characteristic value enters a high-variance range, accelerating the expansion of the upper limit of the adjustable range of the wall thickness to compensate for the nonlinear cumulative effect of thermal deformation; when the wall thickness parameter exceeds the safe range, remapping the parameter to the safe range. This implementation ensures the dynamic coordination between structural changes and process requirements during mold design, effectively avoiding the process inaccuracies common in traditional design methods.

[0039] As one implementation, the step of correcting based on process variables includes: determining a material property coefficient based on the first characteristic value and obtaining the change in process variables; calculating a dimensional compensation value and correcting the design graphic data based on the dimensional compensation value; when the dimensional compensation value causes the allowance adjustment value to be less than a preset safety threshold, automatically adjusting the upper limit of the modification amount and synchronously updating the mold graphic data through a cloud platform; wherein, the material property coefficient is positively correlated with the first characteristic value, and the dimensional compensation value changes with the adjustment of the first adjustment range. This process correction mechanism, with its dynamic correlation with the aforementioned characteristic value and adjustment range, significantly improves the accuracy and reliability of mold design.

[0040] The foregoing description and accompanying drawings fully illustrate embodiments of this disclosure to enable those skilled in the art to practice them. Other embodiments may include structural, logical, electrical, procedural, and other changes. The embodiments represent only possible variations. Individual components and functions are optional unless explicitly required, and the order of operations may vary. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. Moreover, the terminology used in this application is for describing embodiments only and is not intended to limit the claims. As used in the description of embodiments and claims, the singular forms “a,” “an,” and “the” are intended to equally include the plural forms unless the context clearly indicates otherwise. Similarly, the term “and / or” as used in this application means including one or more of the associated listed items and all possible combinations thereof. Additionally, when used in this application, the term "comprise" and its variations "comprises" and / or "comprising" refer to the presence of stated features, integrals, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof. Without further limitations, an element defined by the phrase "comprises a..." does not exclude the presence of other identical elements in the process, method, or apparatus that includes said element. In this document, each embodiment may focus on the differences from other embodiments, and similar or identical parts between embodiments can be referred to mutually. For methods, products, etc., disclosed in the embodiments, if they correspond to the method section disclosed in the embodiments, the relevant parts can be referred to the description of the method section.

[0041] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented using electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods for each specific application to achieve the described functions, but such implementation should not be considered beyond the scope of the embodiments of this disclosure. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the described devices, apparatuses, and units can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0042] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, function, and operation of possible implementations of apparatus, methods, and computer program products according to embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than those disclosed in the description; sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. Each block in a block diagram and / or flowchart, and combinations of blocks in a block diagram and / or flowchart, can be implemented using a dedicated hardware-based device that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

Claims

1. A graphic-aided mold design system, characterized in that, include: A graphic database is configured to store standard graphic data and modified graphic data, wherein the modified graphic data includes modification parameters for preset orientation and preset size, and at least one of the standard graphic data and at least one of the modified graphic data define mold graphic data. The image recognition module is configured to receive mold design requirements, extract semantic data and image recognition data from the mold design requirements, and determine design features based on the semantic data and the image recognition data. The image recognition module is also configured to perform the following steps: The topological changes of the mold cavity are analyzed from the semantic data and / or graphic recognition data, and the dynamic change of melt flow resistance caused by the increase or decrease of the number of cavities is determined as the first feature value, wherein the dynamic change of melt flow resistance is determined based on the flow channel topology, melt rheological properties and cavity distribution density. The assembly interaction state between the slider and the core is identified from the semantic data and / or the graphic recognition data, and the dynamic change in stress concentration caused by the fluctuation of the mating gap is determined as the second feature value, wherein the dynamic change in stress concentration is determined based on the curvature of the assembly contact surface, the yield strength of the material and the thermo-coupling effect. A feature vector is constructed based on the first and second feature values, and the similarity with standard graphic data is calculated by weighting. When calculating the similarity with standard image data, When the material shrinkage rate exceeds a preset process threshold, the weight of the first feature value is increased; When the cooling rate exceeds a preset process threshold, the weight of the second feature value is increased; The graphic determination module is configured to receive the design features and select multiple standard graphic data with a similarity greater than a preset threshold. The graphic combination module is configured to receive multiple standard graphic data selected by the graphic determination module, determine preset feature points of each standard graphic data, and bind the modified graphic data to the preset feature points to perform a modification operation to obtain design graphic data. The graphics combination module is also configured to perform the following steps: Determine the preset feature points of each of the standard graphic data, wherein the preset feature points include at least the thermal stress concentration area and the dynamic contact failure risk area in the mold structure caused by cavity topology changes or assembly interactions. A first adjustment range is determined for the thermal stress concentration region. The first adjustment range represents the adjustable range of the wall thickness. Its upper limit is limited by the first characteristic value. When the dynamic change of the melt flow resistance increases, the upper limit increases. When the dynamic change of the melt flow resistance decreases, the upper limit decreases. A second adjustment range is determined for the dynamic contact failure risk area. The second adjustment range represents the adjustable range of the gap. Its lower limit is dynamically limited by the second characteristic value. When the dynamic change of stress concentration increases, the lower limit narrows, and when the dynamic change of stress concentration decreases, the lower limit expands. The modified graphic data is bound to the preset feature points. Within the first adjustment range, the wall thickness offset is nonlinearly adjusted based on the thermal relaxation characteristics of the cooling process, so that the wall thickness change trend is adapted to the evolution of the transient temperature field. Within the second adjustment range, when the gap fluctuation amplitude is below the critical value, a linear compensation strategy is adopted, and when the gap fluctuation amplitude exceeds the critical value, an exponential compensation strategy is switched to make the gap adjustment match the contact stress distribution. There is a non-linear feedback relationship between the first feature value and the first adjustment interval, specifically: The upper limit of the first adjustment range exhibits a non-linear trend as the first characteristic value increases. When the first feature value is in a low change range, the upper limit value increases approximately linearly; When the first feature value enters the high change range, the rate of increase of the upper limit value accelerates; When multiple sets of standard graphic data are generated, and the first feature values ​​of each set are different, For each set of data, a suitable nonlinear feedback curve is dynamically generated so that the upper limit of the first adjustment interval for all groups satisfies the thermal balance constraint. The process correction module is configured to receive the semantic data, determine the process variables, and perform corrections on the design graphic data based on the process variables to obtain the mold graphic data. The process correction module is specifically configured as follows: The material property coefficients are determined based on the first feature value, and the changes in process variables are obtained based on the semantic data, wherein the process variables include material shrinkage rate, molding pressure and cooling rate; Calculate the size compensation value, and correct the design graphic data according to the size compensation value; The material property coefficient is positively correlated with the first characteristic value, and the size compensation value is adjusted accordingly when the first adjustment range changes to maintain the safety of the mold allowance. When the size compensation value causes the margin adjustment value to be less than the preset safety threshold, the preset size of the modified graphic data is adjusted.

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