A mold structure design method and system based on digital twinning technology

Abstract

The present application relates to the field of computer and auxiliary equipment repair, and particularly relates to a mold structure design method and system based on digital twin technology, which 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 the region division of the simulation sub-model in the initial digital twin simulation model, arranging a sensing module in the corresponding cavity region of the entity mold; respectively correcting the corresponding simulation sub-model based on the real-time state data corresponding to the simulation sub-model, and generating a target digital twin simulation model; using the target digital twin simulation model, simulating the thermal coupling of the cavity in the corresponding region based on the opening time sequence data of the cooling cavity flow valve in the corresponding region of each simulation sub-model in the current injection cycle; and generating the opening time sequence data of the flow valve in the corresponding region of each simulation sub-model in the next injection cycle according to the thermal coupling of the cavity in each region.

Description

Technical Field

[0001] This 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 Technology

[0002] Mold structure design is a core aspect of mass production of plastic products, directly affecting the geometric accuracy, mechanical properties, and production efficiency of the molded parts. Current multi-cavity injection mold designs largely rely on static finite element simulation or designer experience for thermo-mechanical coupling analysis, and arrange cooling water channels according to an average cooling strategy for the entire mold. However, traditional static simulation models typically use idealized material parameters and boundary conditions, making it difficult to fully reflect assembly errors, material nonlinearities, and injection molding machine process fluctuations in actual production. This leads to discrepancies between simulation results and the mold performance after mass production.

[0003] In typical multi-cavity injection mold applications (such as the production of high-precision, high-volume components like mobile phone back covers), this disconnect between simulation and reality is even more pronounced: First, due to geometric differences in the cooling water channels and gate locations, the cooling rates of different cavities vary. The cavity closest to the inlet cools rapidly and has a lower shrinkage rate, while the cavity furthest away cools slowly and has a higher shrinkage rate, leading to warping of the plastic parts and concentration of residual stress. Second, due to differences in cavity thickness and runner layout, the flow shear force distribution during the high-pressure melting stage varies in each cavity, making the mold prone to fatigue cracks or early wear. Third, in current production practices, process parameters and maintenance cycles are often only based on the average value of the overall mold temperature and pressure, making it impossible to implement differentiated adjustments for "excessively high temperature in a certain cavity" or "abnormal stress in a certain cavity," resulting in increased defect rates, higher rework costs, and longer production cycles. Summary of the Invention

[0004] This invention provides a mold structure design method based on digital twin technology, which aims to associate a multi-cavity injection mold entity with a virtual digital twin model with high fidelity. By collecting and calibrating the on-site working condition data of each cavity in real time, the method performs coupled simulation and intelligent optimization of the multi-cavity partitions to solve the problems of uneven cooling and stress concentration during mold production, thereby achieving 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] Obtain a three-dimensional geometric model of the target mold, wherein the three-dimensional geometric model includes at least two cavities, and each cavity is matched with at least one cooling cavity;

[0007] Based on the aforementioned three-dimensional geometric model, an initial digital twin simulation model is generated, which consists of at least two simulation sub-models.

[0008] Based on the region division of the simulation sub-model in the initial digital twin simulation model, sensing modules are deployed in the corresponding cavity regions of the solid mold. Each of the sensing modules is used to collect real-time status data of the corresponding cavity.

[0009] Based on the real-time state data corresponding to each simulation sub-model, the corresponding simulation sub-model is calibrated, and the target digital twin simulation model is generated.

[0010] Using the target digital twin simulation model, based on the opening timing data of the cooling cavity flow valve in the corresponding region of each simulation sub-model within the current injection cycle, the fluid motion, heat conduction, and structural stress of the cavity in the corresponding region are simulated.

[0011] Based on the fluid motion, heat conduction, and structural stress conditions within each region's cavity, the timing data of the valve opening in the corresponding region of each simulation sub-model in the next injection cycle is generated.

[0012] Based on the opening timing data of the next injection cycle, the flow valves of the cooling chambers in the corresponding areas of each simulation sub-model in the solid mold are adjusted during 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 region in the three-dimensional geometric model of the mold and a cooling cavity region that matches the cavity region.

[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 corrected through the following steps:

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

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

[0018] Based on 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 a 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 all calibrated boundary conditions.

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

[0021] Obtain the opening timing data of the cooling chamber flow valve in the corresponding area of ​​the solid mold, and map the opening timing data to the inlet flow boundary conditions of the corresponding simulation sub-model.

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

[0023] Obtain the timing data of the first opening degree of the cooling chamber flow valve during the current injection cycle;

[0024] Based on the inlet flow boundary conditions mapped from the first opening time-series data, the corrected simulation sub-model is used to simulate the fluid motion, heat conduction, and structural stress of the cavity within the corresponding region.

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

[0026] If the temperature distribution uniformity and stress distribution uniformity meet the preset performance targets, then the first opening timing data is set as the second opening timing data for the next injection cycle; otherwise...

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

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

[0029] Based on the fluid motion, heat conduction and structural stress in the cavity simulated by the corresponding simulation sub-model, simulation time series snapshots are extracted. The simulation time series snapshots include temperature simulation data and stress simulation data at different wall positions of the cavity at each simulation node.

[0030] By using temperature simulation data at different wall locations, the uniformity of temperature distribution in the cavity is obtained. The uniformity of temperature distribution is represented by the ratio of the root mean square error to the maximum variance of the temperature simulation data.

[0031] By using stress simulation data at different wall locations, the stress distribution uniformity of the cavity is obtained. The stress distribution uniformity is represented by the ratio of the root mean square error to the maximum variance of the stress simulation data.

[0032] Secondly, 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 storing a computer program, which, when called by a processor, causes the processor to execute the aforementioned mold structure design method based on digital twin technology.

[0033] Thirdly, 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 the memory stores a computer program, and when the computer program is executed by the processor, it implements the above-mentioned mold structure design method based on digital twin technology.

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

[0035] This invention significantly improves the accuracy of simulation prediction by mapping a multi-cavity injection mold entity to a high-fidelity digital twin simulation model and collecting temperature, pressure and flow data of each cavity in real time using on-site sensing modules, and calibrating the model boundary conditions online.

[0036] Furthermore, by introducing partitioned simulation and intelligent optimization, the present invention dynamically adjusts the opening sequence of the flow valves in each cooling chamber, which can achieve fine balance and uniform control of the temperature field and stress field according to the actual working conditions of each chamber, effectively eliminating the problems of uneven cooling and stress concentration.

[0037] Furthermore, the closed-loop iterative scheme of the present invention helps to reduce the number of physical prototype verifications in the traditional "trial-modification-test" process, shorten the product development and trial molding cycle, and support continuous preventive maintenance and mass production with high consistency through automatically generated differentiated valve opening strategies, thereby achieving comprehensive gains in simulation accuracy, production efficiency, product quality and maintenance costs. Attached Figure Description

[0038] Figure 1 This is a flowchart of the mold structure design method based on digital twin technology provided in an embodiment of the present invention;

[0039] Figure 2This is a flowchart of the simulation sub-model calibration process provided in an embodiment of the present invention. Detailed Implementation

[0040] In the following description, specific details such as particular systems, structures, and techniques are set forth for illustrative purposes rather than limiting, in order to provide a thorough understanding of the embodiments of this application.

[0041] Those skilled in the art will understand that this application can also be implemented in other embodiments without these specific details. Detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted in the description of this application to avoid unnecessary detail from obscuring the description; furthermore, terms such as "first" and "second" are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

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

[0043] Furthermore, in the aforementioned production scenarios, the cooling water channels for each cavity are typically simulated and arranged according to the overall average cooling strategy of the mold. This fails to take into account the differences in operating conditions between cavities, and thus cannot provide targeted temperature control and maintenance solutions for each cavity.

[0044] To address the aforementioned problems, this invention provides a mold structure design method based on digital twin technology. During the mold design process, a high-fidelity digital twin simulation model dynamically maps the working conditions of each cavity in the physical mold. Using the digital twin simulation model, based on the opening data of the flow valves in each cooling cavity during the current injection cycle, the fluid movement, heat conduction, and structural stress of the cavity walls within each cavity are simulated, thereby obtaining the temperature, flow rate, and stress distribution of each cavity. Furthermore, based on the simulation results, the timing data of the opening of the flow valves in each cooling cavity for the next injection cycle is automatically generated and implemented, achieving precise and differentiated temperature control and customized maintenance of the cooling water circuits in each cavity, thus effectively eliminating uneven cooling and stress concentration problems.

[0045] Please see Figure 1 , Figure 1 This is a flowchart of the mold structure design method based on digital twin technology provided in an embodiment of the present invention; as follows: 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 the target mold, wherein the three-dimensional geometric model includes at least two cavities, and each cavity is matched with 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 this digital twin system will design, optimize, and simulate. In a multi-cavity injection mold, the cavity is a spatial area used for molding plastic parts, and the cooling cavity refers to a channel or duct entity located inside the mold to carry the cooling medium (usually water or oil).

[0048] Furthermore, the target mold described in this invention can be a mold that has not yet been manufactured during 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 of the common industrial standard formats such as .step, .stp, .iges, .igs, .x_t, and .x_b, or it can be the native file format of other CAD systems.

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

[0051] S02. Based on the three-dimensional geometric model, an initial digital twin simulation model is generated, which consists of at least two simulation sub-models.

[0052] It is understood that the initial digital twin simulation model described in this 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 data calibration is connected to the field sensing module. It is usually a CAE project file.

[0053] Furthermore, based on the region 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 values, and evaluated separately, or it can be coupled in parallel or in series with other independent simulation sub-models for multiphysics coupling.

[0054] Furthermore, each of the aforementioned simulation sub-models is composed of a virtual geometric block and initial boundary conditions for the virtual geometric block. Each virtual geometric block corresponds to a cavity region in the three-dimensional geometric model of the mold and a cooling cavity region that matches the cavity region. The initial boundary conditions for each virtual geometric block include, but are not limited to, cooling medium inlet conditions (such as inlet flow rate curve Q).in (t), inlet pressure curve p in (t)), Cooling medium outlet conditions (such as outlet flow rate curve Q) out (t), inlet pressure curve p out (t)), melt injection conditions (such as the melt injection pressure curve at the mold cavity gate p) melt (t), Dissolution injection rate curve v melt (t), initial temperature of the melt T 0_melt ), convective heat transfer conditions (convective heat transfer coefficient h at 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 pillars, or fixing bolts) and fluid-structure interaction interface conditions (such as the load transfer coefficient of fluid pressure to solid structures).

[0055] Understandably, any simulation sub-model, based on the above 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 sensor module data.

[0056] S03. Based on the region division of the simulation sub-model in the initial digital twin simulation model, sensor modules are deployed in the corresponding cavity regions of the solid mold. Each 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 temperature sensors are respectively installed at the cooling cavity inlet, the key heated surface of the inner wall of the cavity, and the cooling cavity outlet. Specifically, they can be thermocouples or platinum resistance thermometers, used to measure the medium inlet temperature, the cavity wall temperature, and the medium outlet temperature.

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

[0060] Furthermore, several flow sensors are respectively installed at the inlet and outlet of each branch cooling pipe, and can be electromagnetic or ultrasonic, to measure the instantaneous flow data of each branch loop.

[0061] Furthermore, all of the aforementioned sensors are connected to the data bus of the digital twin system through data acquisition units deployed on the mold, and upload the collected status data such as temperature, pressure, and flow rate to the model calibration and optimization module in real time, providing accurate on-site operating condition data for subsequent online calibration and closed-loop optimization.

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

[0063] Please see Figure 2 , Figure 2 This is a flowchart of the simulation sub-model calibration process provided in an embodiment of the present invention; as shown below. Figure 2 As shown, for any simulation sub-model, the boundary conditions are corrected through the following steps:

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

[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 takes the value of 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 is understandable that the real-time status data acquisition time t) j With simulation time t m Since the data may not correspond, linear interpolation or spline interpolation is performed on the real-time state data to obtain a standard state data sequence that is aligned with the simulation nodes.

[0067] Specifically, based on the real-time state data {(t j ,y j The standard state data sequence {y(t)} is obtained by linear interpolation of )}. m In )}, any data t j ≤t m ≤t j+1 , where y(t) m ) represents the simulation node time t n The corresponding real-time status data.

[0068] In other embodiments, low-pass filtering or moving average is applied to the interpolated sequence to remove high-frequency noise, thereby generating a smooth standard state data sequence. This avoids "excessive oscillation" or "divergence" in subsequent boundary conditions and update processes, and enhances the convergence and robustness of boundary condition correction.

[0069] S042. Based on the first boundary condition, a set of first simulation state data sequences aligned with the step size of the standard state data sequence is generated using the simulation sub-model.

[0070] It is understood that the first boundary condition mentioned in step S042 can be the initial boundary condition or the boundary condition that has been updated after any iteration, so as to continuously optimize the simulation accuracy in continuous iterations.

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

[0072] S043. Based on 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 a 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 Further, based on the data error at each simulation node, the objective function is constructed and solved to obtain the changes in each parameter in the boundary conditions; further, based on the changes in each parameter, the adjusted boundary conditions are obtained.

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

[0075] Understandably, based on the adjusted boundary conditions, a new simulation state data sequence is further fitted. When the new simulation state data sequence falls within the preset error range of the standard state data sequence, that is... When the preset error threshold is reached, the adjusted boundary condition becomes 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 all calibrated boundary conditions.

[0077] S05. Using the target digital twin simulation 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 injection cycle, simulate the fluid motion, heat conduction and structural stress of the cavity in the corresponding area.

[0078] Furthermore, in order to simulate the fluid motion, heat conduction, and structural stress of the cavity in the corresponding region of each simulation sub-model in the current solid mold based on the opening time sequence data of the cooling cavity flow valve in the corresponding region, any simulation sub-model also includes the following steps: obtaining the opening time sequence data of the cooling cavity flow valve in the corresponding region of the solid mold, and mapping the opening time sequence data to the inlet flow boundary conditions of the corresponding simulation sub-model.

[0079] Furthermore, the inlet flow boundary condition obtained based on the opening time series data mapping is: Q i,in (t m )=α i ·θ i (t m )·Q i,max , where Q i,in (t m ) represents the i-th simulation sub-model at simulation node time t m The inbound traffic data below, α i θ represents the inlet flow correction coefficient for the i-th simulation sub-model. i (t m ) represents the cooling cavity within the region corresponding to the i-th simulation sub-model at simulation node time t. m The opening of Q i,max This represents the full valve flow rate of the cooling chamber within the region corresponding to the i-th simulation sub-model.

[0080] It is understandable that any set of opening time sequence data refers to the time sequence signal formed by the real-time change of the opening of a cooling chamber flow valve (or micro valve) within 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 units are percentage (0%–100%) or specific valve core displacement (unit: mm, °, etc.). Specifically, it can be the set value or feedback value output in real time by the PLC or valve driver during execution, or it can be the actual valve core position collected by the position sensor (such as potentiometer, rotary encoder) of the valve assembly 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 motion, heat conduction, and structural stress of the cavity in each region.

[0082] S06. Based on the fluid movement, heat conduction, and structural stress of the cavity in each region, generate the timing data of the opening degree of the flow valve in the corresponding region of each simulation sub-model in the next injection cycle.

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

[0084] S061. Obtain the timing data of the first opening degree of the cooling chamber flow valve during the current injection cycle.

[0085] S062. Based on the inlet flow boundary conditions mapped from the first opening time-series data, the corrected simulation sub-model is used to simulate the fluid motion, heat conduction, and structural stress of the cavity within the corresponding region.

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

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

[0088] S0631. Based on the fluid motion, heat conduction, and structural stress conditions inside the cavity simulated by the corresponding simulation sub-model, extract simulation time-series snapshots. The simulation time-series snapshots include temperature simulation data and stress simulation data at different wall positions of the cavity at each simulation node.

[0089] S0632. Using temperature simulation data at different wall locations, the uniformity of temperature distribution in the cavity is obtained, wherein the uniformity of temperature distribution is the ratio of the root mean square error to the maximum variance of the temperature simulation data.

[0090] S0633. Using stress simulation data at different wall locations, the stress distribution uniformity of the cavity is obtained, wherein the stress distribution uniformity is the ratio of the root mean square error to the maximum variance of the stress simulation data.

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

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

[0093] It is understood that the preset uniformity performance target mentioned in this invention refers to a quantitative threshold determined in advance by process engineers during the design or process stage based on the quality requirements of the molded part and the fatigue limit of the mold material, used to measure whether the temperature and stress distribution is sufficiently uniform; it is further understood that only when the temperature uniformity index and stress uniformity index obtained from the simulation simultaneously meet these two preset targets is the current valve opening considered sufficient to guarantee the molding quality; otherwise, the opening sequence needs to be adjusted until the requirements are met.

[0094] In this embodiment, the preset uniformity performance target specifically includes 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 time series data is adjusted so that the cavity simulated by the corrected simulation sub-model based on the inlet flow boundary conditions mapped by the adjusted opening time series data satisfies the preset uniformity performance target in terms of temperature distribution uniformity and stress distribution uniformity. This can be achieved according to the optimization methods of steps S041 to S043 above, which will not be elaborated 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 solid mold during the next injection cycle.

[0097] Furthermore, before the start of the next injection cycle, the timing data of the valve opening of each cooling chamber generated by S06 is sent to the field control unit, and is precisely executed and monitored in real time according to the predetermined time throughout the entire injection and cooling process.

[0098] 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 that, when invoked by a processor, causes the processor to execute 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, the memory storing a computer program that, when executed by the processor, implements the mold structure design method based on digital twin technology proposed in any of the above embodiments.

[0102] Furthermore, the memory can be a medium capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0103] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0104] It should be noted that the above embodiments can be freely combined as needed. The above are merely preferred embodiments of the present invention; it should be observed that those skilled in the art can make several improvements and modifications without departing from the principle of the present invention, and these 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, Includes the following steps: Obtain a three-dimensional geometric model of the target mold, wherein the three-dimensional geometric model includes at least two cavities, and each cavity is matched with at least one cooling cavity; Based on the aforementioned three-dimensional geometric model, an initial digital twin simulation model is generated, which consists of at least two simulation sub-models. Based on the region division of the simulation sub-model in the initial digital twin simulation model, sensing modules are deployed in the corresponding cavity regions of the solid mold. Each of the sensing modules is used to collect real-time status data of the corresponding cavity. Based on the real-time state data corresponding to each simulation sub-model, the corresponding simulation sub-model is calibrated, and the target digital twin simulation model is generated. Using the target digital twin simulation model, based on the opening timing data of the cooling cavity flow valve in the corresponding region of each simulation sub-model within the current injection cycle, the fluid motion, heat conduction, and structural stress of the cavity in the corresponding region are simulated. Based on the fluid motion, heat conduction, and structural stress conditions within each region's cavity, the timing data of the valve opening in the corresponding region of each simulation sub-model in the next injection cycle is generated. Based on the opening timing data of the next injection cycle, the flow valves of the cooling chambers in the corresponding areas of each simulation sub-model in the solid mold are adjusted during the next injection cycle.

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

3. The mold structure design method based on digital twin technology according to claim 1, characterized in that, Any of the aforementioned 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, characterized in that, Any simulation sub-model is corrected through the following steps: Preprocess real-time state data to obtain a standard state data sequence with a set of simulation time intervals; Based on the first boundary condition, a set of first simulation state data sequences aligned with the step size of the standard state data sequence is generated using the simulation sub-model; Based on 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 a 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, characterized in that, The boundary conditions of any simulation sub-model in the target digital twin simulation model are all calibrated boundary conditions.

6. The mold structure design method based on digital twin technology according to claim 1, characterized in that, Each simulation sub-model simulates the fluid motion, heat conduction, and structural stress within the corresponding cavity region, and also includes the following steps: Obtain the opening timing data of the cooling chamber flow valve in the corresponding area of ​​the solid mold, and map the opening timing data to the inlet flow boundary conditions of the corresponding simulation sub-model.

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

8. The mold structure design method based on digital twin technology according to claim 7, characterized in that, The uniformity of temperature and stress distribution in any cavity during the current injection cycle is obtained through the following steps: Based on the fluid motion, heat conduction and structural stress in the cavity simulated by the corresponding simulation sub-model, simulation time series snapshots are extracted. The simulation time series snapshots include temperature simulation data and stress simulation data at different wall positions of the cavity at each simulation node. By using temperature simulation data at different wall locations, the uniformity of temperature distribution in the cavity is obtained. The uniformity of temperature distribution is represented by the ratio of the root mean square error to the maximum variance of the temperature simulation data. By using stress simulation data at different wall locations, the stress distribution uniformity of the cavity is obtained. The stress distribution uniformity is represented by the ratio of the root mean square error 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, which, when invoked by a processor, causes the processor to execute the mold structure design method based on digital twin technology as described in any one of claims 1-8.

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

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