Die structure design method and system based on digital twinning technology

Abstract

The invention relates to the field of computer and auxiliary equipment repair, in particular to a mold structure design method and system based on the digital twin technology, and the method comprises the following steps: obtaining a three-dimensional geometric model of a target mold; generating an initial digital twin simulation model based on the three-dimensional geometric model; according to region division of simulation sub-models in the initial digital twin simulation model, sensing modules are arranged in corresponding cavity regions in the entity mold; correcting the corresponding simulation sub-models based on the real-time state data corresponding to the simulation sub-models, and generating a target digital twin simulation model; utilizing the target digital twin simulation model to simulate the thermal-mechanical coupling condition of the cavity in the corresponding area based on the opening time sequence data of the cooling cavity flow valve in the corresponding area of each simulation sub-model in the current injection period; and respectively generating opening time sequence data of the flow valve in the corresponding area of each simulation sub-model in the next injection period according to the thermal-mechanical coupling condition of the cavity in each area.

Description

Technical Field

[0001] The present invention relates to the field of computer and auxiliary equipment repair, and in particular to a mold structure design method and system based on digital twin technology. Background Art

[0002] Mold structure design is a core step in the mass production of plastic products, directly impacting the geometric accuracy, mechanical properties, and production efficiency of molded parts. Existing multi-cavity injection mold designs often rely on static finite element simulation or designer experience to conduct thermal-mechanical coupling analysis, with cooling water paths arranged according to an average cooling strategy for the entire mold. However, traditional static simulation models typically employ idealized material parameters and boundary conditions, making it difficult to fully reflect assembly errors, material nonlinearities, and injection molding machine process fluctuations encountered in actual production. This can lead to discrepancies between simulation results and post-production mold performance.

[0003] In typical multi-cavity injection mold application scenarios (such as the production of high-precision, large-scale components such as mobile phone back shells), this problem of disconnection between simulation and reality is more prominent: First, due to the geometric differences in the cooling water channels and the gate positions, the cooling rates of different cavities are different. The cavity closest to the water inlet cools quickly and has a low shrinkage rate, while the cavity farthest away cools slowly and has a high shrinkage rate, which causes warping of the plastic part and residual stress concentration; Second, due to the different cavity thickness and runner layout, the flow shear force distribution of each cavity during the high-pressure melting stage is different, and fatigue cracks or early wear are prone to localized mold parts; Third, in existing production practices, process parameters and maintenance cycles can often only be formulated based on the average value of the temperature and pressure of the entire mold. It is impossible to implement differentiated adjustments for "excessive temperature in a certain cavity" or "abnormal stress in a certain cavity", resulting in an increase in the defective rate, increased rework costs and extended production cycle. Summary of the Invention

[0004] The present invention provides a mold structure design method based on digital twin technology, which aims to associate the multi-cavity injection mold entity with the virtual digital twin model with high fidelity. By real-time acquisition and calibration of the on-site working condition data of each cavity, coupled simulation and intelligent optimization of the multi-cavity partitions are performed to solve the problems of uneven cooling and stress concentration during the mold production process, thereby realizing refined management of mold structure design and production.

[0005] In a first aspect, the present invention provides a mold structure design method based on digital twin technology, comprising the following steps:

[0006] Acquire a three-dimensional geometric model of a target mold, wherein the three-dimensional geometric model includes at least two cavities, and any of the cavities matches at least one cooling cavity;

[0007] Based on the three-dimensional geometric model, generating an initial digital twin simulation model, wherein the initial digital twin simulation model is composed of at least two simulation sub-models;

[0008] According to the regional division of the simulation sub-model in the initial digital twin simulation model, sensor modules are respectively arranged in the corresponding cavity areas in the physical mold, and any of the sensor modules is used to collect real-time status data of the corresponding cavity;

[0009] Based on the real-time status data corresponding to each simulation sub-model, the corresponding simulation sub-model is corrected and the target digital twin simulation model is generated;

[0010] Using the target digital twin simulation model, based on the opening time series data of the cooling cavity flow valve in the corresponding area of ​​each simulation sub-model during the current injection cycle, the fluid movement, heat conduction and structural stress of the cavity in the corresponding area are simulated;

[0011] Generate the time series data of the flow valve opening in the corresponding area of ​​each simulation sub-model during the next injection cycle based on the fluid movement, heat conduction and structural stress conditions in the cavity of each area;

[0012] Based on the opening timing data of the next injection cycle, the flow valve of the cooling cavity in the corresponding area of ​​each simulation sub-model in the physical mold is adjusted in the next injection cycle.

[0013] In some embodiments, any of the simulation sub-models is composed of a virtual geometric block and the boundary conditions of the virtual geometric block, and any of the virtual geometric blocks corresponds to a cavity area in the three-dimensional geometric model of the mold and a cooling cavity area matching the cavity area.

[0014] In some embodiments, any of the sensing modules is used to collect real-time temperature data, real-time pressure data, and real-time flow data of the corresponding cavity.

[0015] In some embodiments, any simulation sub-model is calibrated by the following steps:

[0016] Preprocessing the real-time state data to obtain a set of standard state data sequences consistent with the simulation period step size;

[0017] Based on the first boundary condition, using the simulation sub-model, a set of first simulation state data sequences aligned with the step size of the standard state data sequence is generated;

[0018] According to the difference between the first simulation state data and the standard state data sequence, the first boundary condition of the simulation sub-model is adjusted until the second boundary condition is generated, so that the second simulation state data sequence predicted by the simulation sub-model based on the second boundary condition is within the preset error range of the standard state data sequence.

[0019] In some embodiments, the boundary conditions of any simulation sub-model in the target digital twin simulation model are calibrated boundary conditions.

[0020] In some embodiments, any simulation sub-model realizes the simulation of fluid movement, heat conduction and structural stress in the cavity in the corresponding area, and further includes the following steps:

[0021] The opening time series data of the cooling cavity flow valve in the corresponding area of ​​the physical mold is obtained, and the opening time series data is mapped to the inlet flow boundary condition of the corresponding simulation sub-model.

[0022] In some embodiments, the opening time series data of the cooling chamber flow valve in the next injection cycle is generated by the following steps:

[0023] Obtain the first opening time series data of the cooling chamber flow valve in the current injection cycle;

[0024] Based on the inlet flow boundary conditions mapped by the first opening time series data, the corrected simulation sub-model is used to simulate the fluid movement, heat conduction and structural stress of the cavity in the corresponding area;

[0025] Based on the fluid movement, heat conduction and structural stress conditions in the cavity within the corresponding area, the temperature distribution uniformity and stress distribution uniformity of the cavity during the current injection cycle are evaluated:

[0026] If the temperature distribution uniformity performance and the stress distribution uniformity performance meet the preset performance target, the first opening time series data is set as the second opening time series data of the next injection cycle; otherwise

[0027] If the temperature distribution uniformity performance or the stress distribution uniformity performance does not meet the preset uniformity performance target, the first opening timing data is adjusted so that the corrected simulation sub-model, based on the cavity simulated by the inlet flow boundary conditions mapped by the adjusted opening timing data, meets the preset uniformity performance target in terms of temperature distribution uniformity performance and stress distribution uniformity performance.

[0028] In some embodiments, the temperature distribution uniformity and stress distribution uniformity of any cavity in the current injection cycle are obtained by the following steps:

[0029] Extract simulation time series snapshots based on the fluid movement, heat conduction, and structural stress conditions in the cavity simulated by the corresponding simulation sub-model. The simulation time series snapshots include temperature simulation data and stress simulation data at different wall locations of the cavity at each simulation node moment.

[0030] Using the temperature simulation data at different wall positions, the temperature distribution uniformity of the cavity is obtained, wherein the temperature distribution uniformity is represented by the ratio of the mean square deviation to the maximum square deviation of the temperature simulation data;

[0031] The stress distribution uniformity of the cavity is obtained by using the stress simulation data at different wall positions. The stress distribution uniformity is expressed as the ratio of the mean square deviation to the maximum variance of the stress simulation data.

[0032] In the second aspect, based on the mold structure design method based on digital twin technology provided in the first aspect, the present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is called by the processor, the processor executes the above-mentioned mold structure design method based on digital twin technology.

[0033] In the third aspect, based on the mold structure design method based on digital twin technology provided in the first aspect, the present invention also provides a computer system, including a processor and a memory, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the above-mentioned mold structure design method based on digital twin technology is implemented.

[0034] The mold structure design method based on digital twin technology provided by the present invention has the following benefits:

[0035] The present invention significantly improves the accuracy of simulation prediction by matching the multi-cavity injection mold entity with a high-fidelity digital twin simulation model, relying on an on-site sensing module to collect temperature, pressure, and flow data of each cavity in real time, and calibrating the model boundary conditions online.

[0036] Furthermore, the present invention introduces partitioned simulation and intelligent optimization to dynamically adjust the opening timing of the flow valve of each cooling cavity. This can achieve refined balance and uniform control of the temperature and stress fields based on the actual working conditions of each cavity, effectively eliminating the problems of uneven cooling and stress concentration.

[0037] Furthermore, the closed-loop iterative solution of the present invention helps to reduce the number of traditional "try-modify-test" physical prototype verifications, shorten the product development and trial mold cycle, and support continuous preventive maintenance and batch high-consistency production through automatically generated differentiated valve opening strategies, thereby achieving comprehensive gains in simulation accuracy, production efficiency, product quality and maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 A flow chart of the mold structure design method based on digital twin technology provided in an embodiment of the present invention;

[0039] Figure 2This is a simulation sub-model correction flow chart provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0040] In the following description, for the purpose of explanation rather than limitation, specific details such as particular systems, structures, and technologies are set forth to provide a thorough understanding of the embodiments of the present application.

[0041] Those skilled in the art will appreciate that the present application can also be implemented in other embodiments without these specific details. In the description of this application, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of this application with unnecessary details. In addition, the terms "first," "second," etc. are used only to distinguish descriptions and should not be understood to indicate or imply relative importance.

[0042] In the mass production scenario of multi-cavity injection molds, multiple cavities of the same or similar shapes are usually arranged in a mold, and the temperature of each cavity is controlled simultaneously through a shared cooling water circuit and its branch pipe network to achieve batch control of mold production.

[0043] Furthermore, in the above-mentioned production scenarios, the cooling water channels of each cavity are usually simulated and arranged according to the average cooling strategy of the entire mold. This fails to take into account the differences in working conditions between the cavities, and thus fails to provide targeted temperature control and maintenance solutions for each cavity.

[0044] Based on the above problems, the present invention provides a mold structure design method based on digital twin technology, which dynamically maps the working conditions of each cavity of the physical mold through a high-fidelity digital twin simulation model during the mold design process, and uses the digital twin simulation model to simulate the fluid movement, heat conduction and structural stress of the cavity wall in each cavity based on the opening data of each cooling cavity flow valve in the current injection cycle, thereby obtaining the temperature, flow and stress distribution of each cavity; further, based on the simulation results, the opening timing data of each cooling cavity flow valve in the next injection cycle is automatically generated and implemented to achieve precise differentiated temperature control and customized maintenance of the cooling water circuit of each cavity, thereby effectively eliminating the problems of uneven cooling and stress concentration.

[0045] See Figure 1 , Figure 1 Flowchart of the mold structure design method based on digital twin technology provided in an embodiment of the present invention; Figure 1 As shown, the mold structure design method based on digital twin technology includes the following steps:

[0046] S01. Obtain a three-dimensional geometric model of a target mold, wherein the three-dimensional geometric model includes at least two cavities, and any of the cavities matches at least one cooling cavity.

[0047] It is understood that the target mold described in this invention refers to the multi-cavity injection mold entity that the digital twin system is optimizing and simulating. In a multi-cavity injection mold, the cavity is the spatial area used to form the plastic part, and the cooling cavity refers to the channel or channel located inside the mold that carries the cooling medium (usually water or oil).

[0048] Furthermore, the target mold described in the present invention can be a mold that has not yet been manufactured in the new product development stage, or it can be a mold that has been put into production and needs to be improved or monitored online.

[0049] Furthermore, the three-dimensional geometric model of the target mold is a CAD file, and its format can be any common industrial standard format among .step, .stp, .iges, .igs, .x_t, and .x_b, or it can be a native file format of other CAD systems.

[0050] Furthermore, in order to ensure the accurate simulation of the temperature distribution, heat conduction and stress distribution of each cavity in the future, in this embodiment, the three-dimensional geometric model of the target model is divided into regions; specifically, the region division is performed based on the cavity distribution in the three-dimensional geometric model, so that the entire three-dimensional geometric model Ω is divided into N sub-regions, and any sub-region R i (i is a positive integer less than N, where N is the total number of cavities in the target mold) contains a cavity.

[0051] S02. Generate an initial digital twin simulation model based on the three-dimensional geometric model, where the initial digital twin simulation model consists of at least two simulation sub-models.

[0052] It can be understood that the initial digital twin simulation model described in the present invention refers to a high-fidelity virtual model constructed based on the three-dimensional geometric model and design / process parameters of the target mold before the on-site sensing module data is calibrated, which is usually a CAE engineering file.

[0053] Furthermore, based on the regional division of the three-dimensional geometric model in step S01, the initial digital twin simulation model generated by the present invention is also divided into at least two independent simulation sub-models; each independent simulation sub-model can be meshed, assigned and evaluated independently, and can also be coupled with other independent simulation sub-models in parallel or in series for multi-physical fields.

[0054] Furthermore, any of the simulation sub-models is composed of a virtual geometric block and the initial boundary conditions of the virtual geometric block. Any of the virtual geometric blocks corresponds to a cavity area in the three-dimensional geometric model of the mold and a cooling cavity area matching the cavity area. The initial boundary conditions of any virtual geometric block include but are not limited to: cooling medium inlet conditions (such as inlet flow curve Qin (t), inlet pressure curve p in (t)), cooling medium outlet conditions (such as outlet flow curve Q out (t), inlet pressure curve p out (t)), melt injection conditions (such as the melt injection pressure curve p at the cavity gate melt (t), melt injection speed curve v melt (t), initial melt temperature T 0_melt ), convection heat transfer conditions (convection heat transfer coefficient h of the contact surface between the cavity and the cooling cavity conv 、Ambient temperature T env ), structural constraints (such as displacement constraints at locating pins, guide columns or fixing bolts), and fluid-solid coupling interface conditions (such as the load transfer coefficient of fluid pressure to solid structure).

[0055] It can be understood that any simulation sub-model, based on the setting of the above-mentioned initial boundary conditions, can simulate the fluid field distribution, thermal field distribution and structural stress field distribution in the virtual geometric block under the corresponding conditions, and output the corresponding numerical results (such as temperature field, velocity field, pressure field, stress field, etc.), providing reliable basic data for subsequent model calibration and closed-loop optimization based on field sensing module data.

[0056] S03. According to the regional division of the simulation sub-model in the initial digital twin simulation model, sensor modules are respectively arranged in the corresponding cavity areas in the physical mold, and any of the sensor modules is used to collect real-time status data of the corresponding cavity.

[0057] In this embodiment, any of the sensing modules is used to collect real-time temperature data, real-time pressure data and real-time flow data of the corresponding cavity; further, any of the sensing modules includes a plurality of temperature sensors, a plurality of pressure sensors and a plurality of flow sensors.

[0058] Furthermore, several of the temperature sensors are respectively arranged at the cooling cavity inlet corresponding to the cavity, the key heating surface of the cavity inner wall and the cooling cavity outlet. They can specifically be thermocouples or platinum resistors, which are used to measure the medium inlet temperature, the cavity wall temperature and the medium outlet temperature.

[0059] Furthermore, several of the pressure sensors are respectively arranged at the cavity gate and on the matching cooling cavity inlet and outlet pipes. They can be piezoelectric or strain gauge type, and are used to measure the injection melt pressure and the cooling medium flow path pressure.

[0060] Furthermore, a plurality of flow sensors are respectively arranged at the inlet and outlet of each branch cooling pipeline, which may be electromagnetic or ultrasonic, and are used to measure the instantaneous flow data of each branch circuit.

[0061] Furthermore, each of the above sensors is connected to the data bus of the digital twin system through a data acquisition unit installed on the mold, and the collected temperature, pressure, flow and other status data are uploaded to the model calibration and optimization module in real time, providing accurate on-site working conditions for subsequent online calibration and closed-loop optimization.

[0062] S04. Based on the real-time status data corresponding to each simulation sub-model, calibrate the corresponding simulation sub-model and generate a target digital twin simulation model.

[0063] See Figure 2 , Figure 2 The simulation sub-model correction flow chart provided in the embodiment of the present invention; Figure 2 As shown, for any simulation sub-model, the boundary conditions are corrected by the following steps:

[0064] S041. Pre-process the real-time state data to obtain a set of standard state data sequences consistent with the simulation period step.

[0065] In this embodiment, the simulation uses a fixed time step Δt; further, the simulation node time is t m =t0+m·Δt, where m is zero or a positive integer, and t0 is the initial simulation time.

[0066] Furthermore, the real-time status data is {(t j ,y j (}, it can be understood that the acquisition time of real-time status data is t j and simulation time t m It is not necessarily corresponding, so linear interpolation or spline interpolation is performed on the real-time state data to obtain a standard state data sequence aligned with the simulation node.

[0067] Specifically, based on the real-time status data {(t j ,y j )}, the obtained standard state data sequence {y(t m )}, any data t j ≤t m ≤t j+1 , where y(t m ) represents the simulation node time t n Corresponding real-time status data.

[0068] In some other embodiments, low-pass filtering or sliding mean is applied to the interpolated sequence to remove high-frequency noise to generate a smooth standard state data sequence, thereby avoiding "excessive oscillation" or "divergence" in subsequent boundary conditions and update processes, and enhancing the convergence and robustness of boundary condition correction.

[0069] S042. Based on the first boundary condition, use the simulation sub-model to generate a set of first simulation state data sequences that are aligned with the step size of the standard state data sequence.

[0070] It is understandable that the first boundary condition described in step S042 may be an initial boundary condition, or may be an updated boundary condition after any iteration, so as to continuously optimize the simulation accuracy in successive iterations.

[0071] Furthermore, the first boundary condition B of the current simulation sub-model is (1) As input, call the simulation operator of the simulation sub-model Calculate the state data y of any simulation node at any moment sim (t m ); specifically, m=0,1,...,M, where M is the simulation node time number.

[0072] S043. According to the difference between the first simulation state data and the standard state data sequence, adjust the first boundary condition of the simulation sub-model until the second boundary condition is generated, so that the second simulation state data sequence predicted by the simulation sub-model based on the second boundary condition is within the preset error range of the standard state data sequence.

[0073] In this embodiment, the data error at any simulation node is e m =y(t m )-ysimtim; further, based on the data error at each simulation node, the objective function is constructed and solved to obtain the change of each parameter in the boundary condition; further, based on the change of each parameter, the adjusted boundary condition is obtained.

[0074] Specifically, the objective function constructed in this embodiment is Wherein, em=ytm-ysimtm, ΔP=Δpk, Δpk represents the change of the kth boundary parameter in the boundary condition, K represents the number of boundary parameters in the boundary condition, m is the simulation node time number, and M is the maximum simulation node time number.

[0075] It can be understood that, based on the adjusted boundary conditions, a new simulation state data sequence is further fitted. When the new simulation state data sequence is within the preset error range of the standard state data sequence, that is, (preset error threshold), the adjusted boundary condition is the second boundary condition, otherwise it remains the first boundary condition, and the iteration continues until convergence.

[0076] Furthermore, the generated target digital twin simulation model is also composed of several simulation sub-models, and the boundary conditions of any simulation sub-model in the target digital twin simulation model are calibrated boundary conditions.

[0077] S05. Using the target digital twin simulation model, based on the opening time series data of the cooling cavity flow valve in the corresponding area of ​​each simulation sub-model during the current injection cycle, simulate the fluid movement, heat conduction and structural stress of the cavity in the corresponding area.

[0078] Furthermore, in order to simulate the fluid movement, heat conduction and structural stress conditions of the cavity in the corresponding area of ​​any simulation sub-model based on the opening timing data of the cooling cavity flow valve in the corresponding area of ​​each simulation sub-model in the current physical mold, the following steps are also included: obtaining the opening timing data of the cooling cavity flow valve in the corresponding area in the physical mold, and mapping the opening timing data to the inlet flow boundary conditions of the corresponding simulation sub-model.

[0079] Furthermore, the inlet flow boundary condition is obtained based on the mapping of the opening time series data: Q i,in (t m )=α i ·θ i (t m )·Q i,max , where Q i,in (t m ) represents the simulation sub-model i at the simulation node time t m Inlet flow data under α i represents the inlet flow correction coefficient of the i-th simulation sub-model, θ i (t m ) represents the cooling cavity in the corresponding area of ​​the ith simulation sub-model at the simulation node time t m The opening under Q i,max Represents the full valve flow of the cooling cavity in the area corresponding to the i-th simulation sub-model.

[0080] It can be understood that any set of opening timing data refers to the time series signal formed by the real-time change of the opening of a cooling chamber flow valve (or microvalve) during an injection molding cycle; further, the opening (θ) represents the relative percentage or absolute angle / displacement of the opening of the cooling chamber flow valve. Common dimensions include percentage (0%-100%) or specific valve core displacement (unit: mm, °, etc.), which can be the set value or feedback value output in real time by the PLC or valve driver during the execution process, or the actual valve core position can be collected by the position sensor (such as potentiometer, rotary encoder) installed on the valve and then converted into an opening value.

[0081] Furthermore, based on the updated inlet flow boundary conditions of each simulation sub-model, the updated target digital twin simulation model is further used to simulate the fluid movement, heat conduction and structural stress conditions of the cavity in each area.

[0082] S06. Generate time series data of the opening degree of the flow valve in the corresponding area of ​​each simulation sub-model in the next injection cycle according to the fluid movement, heat conduction and structural stress of the cavity in each area.

[0083] In this embodiment, the opening time series data of the cooling chamber flow valve in the next injection cycle is generated by the following steps:

[0084] S061. Obtain first opening time series data of the cooling chamber flow valve in the current injection cycle.

[0085] S062. Based on the inlet flow boundary conditions mapped by the first opening time series data, use the corrected simulation sub-model to simulate the fluid movement, heat conduction and structural stress of the cavity in the corresponding area.

[0086] S063. Evaluate the temperature distribution uniformity and stress distribution uniformity of the cavity during the current injection cycle based on the fluid movement, heat conduction, and structural stress conditions in the cavity within the corresponding area.

[0087] Furthermore, the temperature distribution uniformity and stress distribution uniformity of any cavity in the current injection cycle are obtained through the following steps:

[0088] S0631. Extract simulation timing snapshots based on the fluid movement, heat conduction, and structural stress conditions in the cavity simulated by the corresponding simulation sub-model. The simulation timing library snapshots include temperature simulation data and stress simulation data at different wall positions of the cavity at each simulation node moment.

[0089] S0632. Utilize temperature simulation data at different wall positions to obtain a temperature distribution uniformity performance of the cavity, wherein the temperature distribution uniformity performance is a ratio of the mean square deviation to the maximum variance of the temperature simulation data.

[0090] S0633. Using stress simulation data at different wall positions, obtain a stress distribution uniformity performance of the cavity, wherein the stress distribution uniformity performance is a ratio of the mean square deviation to the maximum variance of the stress simulation data.

[0091] S064: If the temperature distribution uniformity performance and the stress distribution uniformity performance meet the preset performance targets, the first opening time series data is set as the second opening time series data for the next injection cycle; otherwise,

[0092] If the temperature distribution uniformity performance or the stress distribution uniformity performance does not meet the preset uniformity performance target, the first opening timing data is adjusted so that the corrected simulation sub-model, based on the cavity simulated by the inlet flow boundary conditions mapped by the adjusted opening timing data, meets the preset uniformity performance target in terms of temperature distribution uniformity performance and stress distribution uniformity performance.

[0093] It can be understood that the preset uniformity performance target described in the present invention refers to a quantitative threshold value predetermined by the process engineer during the design or process stage based on the quality requirements of the molded part and the fatigue limit of the mold material, which is used to measure whether the temperature and stress distribution are sufficiently uniform; it can be further understood that only when the temperature uniformity index and stress uniformity index obtained by simulation meet these two preset targets at the same time, the current valve opening is considered sufficient to ensure the molding quality, otherwise the opening timing needs to be further adjusted until the requirements are met.

[0094] In this embodiment, the preset uniformity performance targets specifically include a temperature distribution uniformity performance target and a stress distribution uniformity performance target; further, the temperature distribution uniformity performance target is less than 20%, and the stress distribution uniformity performance target is less than 25%, so as to achieve a balance between ensuring molding quality and taking into account simulation convergence and execution feasibility.

[0095] Furthermore, in step S064, the first opening timing data is adjusted so that the corrected simulation sub-model, based on the cavity simulated by the inlet flow boundary conditions mapped by the adjusted opening timing data, meets the preset uniformity performance targets in terms of temperature distribution uniformity and stress distribution uniformity. This can be achieved according to the optimization method of the above steps S041 to S043, which will not be repeated here.

[0096] S07. Based on the opening timing data of the next injection cycle, adjust the flow valve of the cooling cavity in the corresponding area of ​​each simulation sub-model in the physical mold in the next injection cycle.

[0097] Furthermore, before the next injection cycle begins, the timing data of the valve opening of each cooling chamber generated by S06 is sent to the on-site control unit, and is accurately executed and monitored in real time according to the predetermined time during the entire injection and cooling process.

[0098] In order to efficiently implement the above-mentioned mold structure design method based on digital twin technology, in some embodiments, the present invention also provides a computer-readable storage medium.

[0099] In these embodiments, the computer-readable storage medium stores a computer program, and when the computer program is called by the processor, the processor executes the mold structure design method based on digital twin technology proposed in any of the above embodiments.

[0100] Similarly, in order to efficiently implement the above-mentioned mold structure design method based on digital twin technology, in some embodiments, the present invention also provides a computer system.

[0101] In these embodiments, the computer system includes a processor and a memory, and a computer program is stored in the memory. When the computer program is executed by the processor, the mold structure design method based on digital twin technology proposed in any of the above embodiments is implemented.

[0102] Furthermore, the memory may be a medium that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0103] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0104] It should be noted that the above embodiments can be freely combined as needed. The above are only preferred embodiments of the present invention; it should be noted that those skilled in the art can make several improvements and modifications without departing from the principles of the present invention, and such improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A mold structure design method based on digital twin technology, characterized in that: The steps include: Acquire a three-dimensional geometric model of a target mold, wherein the three-dimensional geometric model includes at least two cavities, and any of the cavities matches at least one cooling cavity; Based on the three-dimensional geometric model, generating an initial digital twin simulation model, wherein the initial digital twin simulation model is composed of at least two simulation sub-models; According to the regional division of the simulation sub-model in the initial digital twin simulation model, sensor modules are respectively arranged in the corresponding cavity areas in the physical mold, and any of the sensor modules is used to collect real-time status data of the corresponding cavity; Based on the real-time status data corresponding to each simulation sub-model, the corresponding simulation sub-model is corrected and the target digital twin simulation model is generated; Using the target digital twin simulation model, based on the opening time series data of the cooling cavity flow valve in the corresponding area of ​​each simulation sub-model during the current injection cycle, the fluid movement, heat conduction and structural stress of the cavity in the corresponding area are simulated; Generate the time series data of the flow valve opening in the corresponding area of ​​each simulation sub-model during the next injection cycle based on the fluid movement, heat conduction and structural stress conditions in the cavity of each area; Based on the opening timing data of the next injection cycle, the flow valve of the cooling cavity in the corresponding area of ​​each simulation sub-model in the physical mold is adjusted in the next injection cycle.

2. The mold structure design method based on digital twin technology according to claim 1 is characterized in that: Any of the simulation sub-models is composed of a virtual geometric block and boundary conditions of the virtual geometric block. Any of the virtual geometric blocks corresponds to a cavity area in the three-dimensional geometric model of the mold and a cooling cavity area matching the cavity area.

3. The mold structure design method based on digital twin technology according to claim 1 is characterized in that: Any of the sensing modules is used to collect real-time temperature data, real-time pressure data and real-time flow data of the corresponding cavity.

4. The mold structure design method based on digital twin technology according to claim 1 is characterized in that: Any simulation sub-model is calibrated by the following steps: Preprocessing the real-time state data to obtain a set of standard state data sequences consistent with the simulation period step size; Based on the first boundary condition, using the simulation sub-model, a set of first simulation state data sequences aligned with the step size of the standard state data sequence is generated; According to the difference between the first simulation state data and the standard state data sequence, the first boundary condition of the simulation sub-model is adjusted until the second boundary condition is generated, so that the second simulation state data sequence predicted by the simulation sub-model based on the second boundary condition is within the preset error range of the standard state data sequence.

5. The mold structure design method based on digital twin technology according to claim 4 is characterized in that: The boundary conditions of any simulation sub-model in the target digital twin simulation model are calibrated boundary conditions.

6. The mold structure design method based on digital twin technology according to claim 1 is characterized in that: Any simulation sub-model can simulate the fluid movement, heat conduction and structural stress of the cavity in the corresponding area, and also includes the following steps: The opening time series data of the cooling cavity flow valve in the corresponding area of ​​the physical mold is obtained, and the opening time series data is mapped to the inlet flow boundary condition of the corresponding simulation sub-model.

7. The mold structure design method based on digital twin technology according to claim 6 is characterized in that: The opening time series data of the cooling chamber flow valve in the next injection cycle is generated through the following steps: Obtain the first opening time series data of the cooling chamber flow valve in the current injection cycle; Based on the inlet flow boundary conditions mapped by the first opening time series data, the corrected simulation sub-model is used to simulate the fluid movement, heat conduction and structural stress of the cavity in the corresponding area; Based on the fluid movement, heat conduction and structural stress conditions in the cavity within the corresponding area, the temperature distribution uniformity and stress distribution uniformity of the cavity during the current injection cycle are evaluated: If the temperature distribution uniformity performance and the stress distribution uniformity performance meet the preset performance target, the first opening time series data is set as the second opening time series data of the next injection cycle; otherwise If the temperature distribution uniformity performance or the stress distribution uniformity performance does not meet the preset uniformity performance target, the first opening timing data is adjusted so that the corrected simulation sub-model, based on the cavity simulated by the inlet flow boundary conditions mapped by the adjusted opening timing data, meets the preset uniformity performance target in terms of temperature distribution uniformity performance and stress distribution uniformity performance.

8. The mold structure design method based on digital twin technology according to claim 7 is characterized in that: The temperature distribution uniformity and stress distribution uniformity of any cavity during the current injection cycle are obtained through the following steps: Extract simulation time series snapshots based on the fluid movement, heat conduction, and structural stress conditions in the cavity simulated by the corresponding simulation sub-model. The simulation time series snapshots include temperature simulation data and stress simulation data at different wall locations of the cavity at each simulation node moment. Using the temperature simulation data at different wall positions, the temperature distribution uniformity of the cavity is obtained, wherein the temperature distribution uniformity is represented by the ratio of the mean square deviation to the maximum square deviation of the temperature simulation data; The stress distribution uniformity of the cavity is obtained by using the stress simulation data at different wall positions. The stress distribution uniformity is expressed as the ratio of the mean square deviation to the maximum variance of the stress simulation data.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is called by the processor, the processor executes the mold structure design method based on digital twin technology described in any one of claims 1-8.

10. A computer system, characterized in that: It includes a processor and a memory, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the mold structure design method based on digital twin technology as described in any one of claims 1 to 8 is implemented.

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