Moldflow injection molding simulation process parameter dynamic conversion and calibration method and system

Abstract

The application discloses a kind of Moldflow injection molding simulation process parameter dynamic conversion and calibration method and system, method includes: the machine table characteristic parameter of target injection molding machine is collected, and is stored to dynamic UDB parameter library;Moldflow injection molding simulation analysis result is obtained, and simulation process parameter is parsed;Parameter conversion model is established based on machine table characteristic parameter, and simulation process parameter is carried out physical quantity conversion, curve discrete mapping and execution precision compensation, generates target machine parameter;In the process based on target machine parameter, test mould, the actual operation data of target injection molding machine is collected, and deviation analysis is carried out with target machine parameter;Machine table characteristic parameter of target injection molding machine is updated based on deviation analysis result, and parameter conversion model is adjusted according to the machine table characteristic parameter after updating and re-generates machine parameter.The application can improve the accuracy of parameter conversion, machine table adaptability and multi-model process transplantation capacity.

Description

Technical Field

[0001] This invention relates to the field of injection molding process optimization and digital manufacturing technology, specifically to a method and system for dynamic conversion and calibration of Moldflow injection molding simulation process parameters. Background Technology

[0002] Injection molding is one of the most widely used molding methods in the production of plastic products. With the increasing complexity of plastic product structures and the growing demand for dimensional accuracy, the setting of process parameters in injection molding has a significant impact on product quality, production efficiency, and molding stability. During product development and mold verification, injection molding simulation software such as Moldflow is typically used to analyze processes such as filling, holding pressure, cooling, and warpage to obtain optimal theoretical process parameters, such as injection speed curves, holding pressure curves, V / P switching points, filling time, cooling time, volume shrinkage rate, and clamping force.

[0003] However, the process parameters output by Moldflow are primarily theoretical parameters derived from the simulation environment, material database, and ideal boundary conditions. Their parameter formats, unit systems, and curve representations are not entirely consistent with the tuning parameters required by the actual injection molding machine control system. For example, parameters such as filler volume percentage, volumetric flow rate, recommended screw speed curve, and holding pressure curve in Moldflow typically need to be converted into machine control parameters that the actual injection molding machine can recognize and execute, such as screw position, screw speed, holding pressure segments, holding time, and V / P switching position. Therefore, in actual production, experienced tuning personnel still need to manually convert and adjust the simulation results using trial molding. This process is not only inefficient but also easily affected by the experience level of the tuning personnel, leading to instability in the conversion results.

[0004] Furthermore, different brands and models of injection molding machines (such as ENGEL, Arburg, Changfeiya, Nissei, Sodick, Haitian, and Chen Hsong) differ in parameter definition methods, control logic, speed and pressure setting methods, communication interfaces, and data formats. Even injection molding machines of the same brand and model may exhibit differences in dynamic characteristics such as injection stroke, injection rate, pressure response, speed following capability, and V / P switching response due to factors such as manufacturing tolerances, service life, screw wear, changes in sealing performance, response delay, and back pressure effects. Simply relying on the injection molding machine manual or fixed conversion rules for static parameter conversion is insufficient to accurately adapt to the actual operating conditions of the target injection molding machine.

[0005] In existing technologies, some tools or systems have attempted to achieve data interaction between simulation data and injection molding machines. For example, ENGEL SimLink can achieve bidirectional data exchange between Moldflow and ENGEL brand injection molding machines, but it is mainly applicable to injection molding machines of specific brands and control units, making it difficult to be compatible with injection molding machines of multiple brands and models, and its applicability to traditional machines or machines without corresponding control interfaces is limited. Another example is that some bridge software (SPARKLE) can convert Moldflow process parameters into injection molding machine parameter language that can be understood by machine operators, but such solutions usually focus on static parameter format conversion, without fully considering the dynamic response characteristics of the target injection molding machine, the dynamic density changes of the material, and the impact of actual trial molding deviations on the parameter conversion results. It also lacks a closed-loop mechanism for continuously correcting the parameter conversion model based on actual operating data.

[0006] Furthermore, existing co-simulation solutions between Moldflow and software such as Abaqus primarily transfer injection molding analysis results to structural or mechanical property analysis software. Their focus remains on data transfer between simulation software, without addressing the issue of how to convert Moldflow theoretical process parameters into executable adjustment parameters for actual injection molding machines. Some online adaptive control systems emphasize online optimization of injection molding process parameters based on response surface methodology, statistical models, or optimization algorithms. Their focus is on parameter optimization or quality control during the molding process, and they haven't established a unified and dynamically updated conversion mechanism for the relationship between Moldflow simulation parameters and control parameters of different brands and models of injection molding machines.

[0007] Therefore, there is an urgent need to provide a method that can dynamically convert Moldflow injection molding process parameters based on machine characteristic parameters for different brands and models of injection molding machines, and can dynamically calibrate machine characteristic parameters and parameter conversion models by combining trial mold deviations, so as to improve the adaptability and stability of the conversion of simulation process parameters to actual injection molding machine adjustment parameters. Summary of the Invention

[0008] The technical problem to be solved by the present invention is to provide a method and system for dynamic conversion and calibration of Moldflow injection molding simulation process parameters, in order to improve the accuracy of parameter conversion, machine adaptability and multi-machine process transfer capability.

[0009] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A method for dynamic conversion and calibration of process parameters in Moldflow injection molding simulation includes: S1. Collect the machine characteristic parameters of the target injection molding machine and store the machine characteristic parameters in a dynamic UDB parameter library according to a unified data structure. The dynamic UDB parameter library is used to store the characteristic parameters of injection molding machines of different brands and models. S2, obtain the Moldflow injection molding simulation analysis results, and parse the simulation process parameters used to characterize the filling stage, holding pressure stage, cooling stage and key states of the injection molding process from the Moldflow injection molding simulation analysis results; S3. Based on the machine characteristic parameters in the dynamic UDB parameter library, a parameter conversion model is established. The simulation process parameters are then converted into physical quantities, discretized and mapped on curves, and the execution accuracy is compensated through the parameter conversion model to generate target machine adjustment parameters that match the target injection molding machine. S4. During the trial molding process based on the target machine adjustment parameters, the actual operating data of the target injection molding machine is collected, and the deviation analysis results are obtained by performing a deviation analysis between the actual operating data and the target machine adjustment parameters. S5. Based on the deviation analysis results, update the machine characteristic parameters of the target injection molding machine in the dynamic UDB parameter library, adjust the parameter conversion model according to the updated machine characteristic parameters, and regenerate the machine adjustment parameters adapted to the target injection molding machine. Send the regenerated machine adjustment parameters to the control system of the target injection molding machine to execute subsequent injection molding control.

[0010] Further, in step S1, the machine characteristic parameters include static structural parameters and dynamic response parameters. The static structural parameters include one or more of the following: injection molding machine brand, model, tonnage, screw diameter, maximum injection stroke, maximum injection pressure, screw length-to-diameter ratio, injection rate, and control system type. The dynamic response parameters include one or more of the following: pressure response delay time, speed following error, holding pressure decay characteristics, V / P switching response time, control command execution delay, and screw position feedback error.

[0011] Further, in step S2, the simulation process parameters include one or more of the following: filling time, volumetric flow rate, recommended screw speed, filling volume ratio, and V / P switching position during the filling stage; one or more of the following of the following during the holding pressure stage: holding pressure curve, holding pressure segment value, and duration of each holding pressure segment; one or more of the following during the cooling stage: cooling time, mold temperature, and molding cycle; and one or more of the following at the injection position corresponding to the key states of the injection molding process: maximum pressure value, clamping force, injection time, freezing time, volume shrinkage rate, and total volume of product and runner.

[0012] Furthermore, in step S3, the physical quantity conversion includes the conversion from filling volume percentage to screw position and the conversion from flow rate to screw speed. The conversion from fill volume percentage to screw position includes: determining the corresponding cumulative injection volume based on the fill volume percentage in the Moldflow injection molding simulation analysis results; calculating the screw cross-sectional area based on the screw diameter of the target injection molding machine; determining the screw advance distance based on the correspondence between the cumulative injection volume and the screw cross-sectional area; correcting the screw advance distance using the difference between the solid density and melt density of the material; and finally determining the corresponding screw position based on the injection stroke reference value of the target injection molding machine. The conversion of flow rate to screw speed includes: determining the screw cross-sectional area based on the screw diameter of the target injection molding machine; matching and converting the volumetric flow rate in the Moldflow injection molding simulation analysis results with the screw cross-sectional area; and obtaining the screw advance speed required by the target injection molding machine in the corresponding filling stage based on the ratio of the volumetric flow rate to be pushed out per unit time to the screw cross-sectional area.

[0013] Furthermore, in step S3, the discrete mapping of the curves includes discrete mapping of the velocity curve and discrete mapping of the pressure curve; The discrete mapping of the speed curve includes: obtaining the speed curve from the Moldflow injection molding simulation analysis results, which characterizes the relationship between the injection volume ratio and the recommended screw speed; dividing the speed curve into multiple speed control intervals based on the number of speed segments supported by the target injection molding machine, and combining one or more parameters such as product runner length, gating system and gate contact position, product structural weight, filling progress, product end-of-life air trapping state, and special structural position; wherein, the first speed control interval corresponds to the gating system to the gate contact position, the second speed control interval corresponds to the gate contact position to the product body filling interval, the third speed control interval corresponds to the product near-full filling interval, and determining whether to set an end-of-life speed control interval based on the product end-of-life air trapping state and / or special structural position; determining the screw speed setpoint and corresponding injection time for each speed control interval based on the injection volume ratio corresponding to each speed control interval, the Moldflow recommended screw speed, and the maximum injection speed of the target injection molding machine, so as to convert the speed curve output by Moldflow into a segmented stepped speed curve that can be set by the target injection molding machine; The discrete mapping of the pressure curve includes: acquiring one or more of the following from the Moldflow injection molding simulation analysis results: pressure change curve during the holding pressure stage, volume shrinkage rate at ejection, frozen layer factor, and total part weight change data; determining the changing trend, magnitude, and segmentation nodes of the holding pressure based on the pressure change curve, and determining the holding pressure setpoint corresponding to each holding pressure segment in conjunction with the volume shrinkage rate at ejection; determining the duration of the holding pressure stage and the time allocation of each holding pressure segment based on the frozen layer factor and total part weight change data; and converting the holding pressure setpoint and corresponding holding time into segmented holding pressure parameters that conform to the control logic of the target injection molding machine.

[0014] Furthermore, in step S3, the execution accuracy compensation includes target curve pre-compensation, material density compensation, and screw speed compensation; The target curve pre-compensation includes: performing machine dynamic response pre-compensation on the target curve obtained from the simulation process parameters based on one or more parameters of the target injection molding machine, such as injection stroke, maximum injection rate, screw diameter, maximum injection pressure, reinforcement ratio, and response time; the target curve includes one or more of the target screw speed curve, target pressure curve, and target screw position curve. The material density compensation includes: performing a preliminary conversion and evaluation of the density of the injection molding material used in the target injection molded product based on the material density parameters recorded in the material database, the changes from the screw start point to the V / P switching position in the Moldflow analysis log, and the product volume shrinkage rate; determining the curve range of the material's solid density and melt density under different pressure, volumetric volume, and temperature conditions based on the PVT curves in the material database, and determining the compensation coefficient used to correct the material density difference by combining the state change trends of the material in the filling, holding, and cooling stages; and using the compensation coefficient to correct the screw position, injection speed, and V / P switching position obtained from the simulation process parameters. The screw speed compensation includes: for a target injection molding machine where the speed following error meets preset conditions, correcting the screw speed setting value based on the deviation between the target speed curve and the actual speed curve, the material PVT curve, and the viscosity curve.

[0015] Furthermore, in step S3, the parameter conversion model also uses historical trial molding data for error reverse compensation. The historical trial molding data includes one or more of the following: simulation process parameters, target machine adjustment parameters, and actual operating data for different product types, different plastic materials, different injection molding machine brands, and different injection molding machine models. The compensation amount is determined by comparing the difference between the theoretical material quantity or theoretical molding weight and the actual material quantity or actual molding weight, and the compensation amount is superimposed on the corresponding target machine adjustment parameters. The compensation amount includes one or more of the following: screw stroke, injection speed, injection pressure, and V / P switching position.

[0016] Further, in step S4, the actual operating data includes one or more of the following: actual pressure curve, actual speed curve, actual screw position curve, actual V / P switching position, actual filling time, actual holding time, and actual molding cycle; the deviation analysis includes comparing the actual operating data with the target pressure curve, target speed curve, target screw position curve, target V / P switching position, target filling time, target holding time, and target molding cycle corresponding to the target adjustment parameters to obtain one or more of the following: pressure response deviation, speed following deviation, screw position deviation, switching point deviation, and cycle deviation.

[0017] Further, in step S5, updating the machine characteristic parameters of the target injection molding machine in the dynamic UDB parameter library based on the deviation analysis results includes: Based on one or more parameters collected during actual mold trials or actual production of the target injection molding machine, including actual pressure response, actual injection rate, actual screw stroke, actual material quantity, actual molding weight, actual V / P switching position, and actual release position, a comparison is made with one or more parameters obtained from Moldflow analysis, including theoretical pressure response, theoretical injection rate, theoretical screw stroke, theoretical material quantity, theoretical molding weight, and theoretical V / P switching position, to obtain the percentage deviation between the analysis results and the actual results. The theoretical material quantity or theoretical molding weight is determined based on screw size, injection stroke, and material density, while the actual material quantity or actual molding weight is corrected based on screw wear. Based on the aforementioned deviation percentage, the dynamic UDB parameter library is updated in reverse with one or more of the following parameters for the target injection molding machine: pressure response time, injection rate, screw stroke, reinforcement ratio, maximum response time, screw diameter, and screw wear. Based on the updated machine characteristic parameters, the execution accuracy compensation in the parameter conversion model is adaptively adjusted. The adaptive adjustment includes correcting one or more machine adjustment parameters, such as screw stroke compensation, injection pressure compensation, and V / P switching position compensation, based on one or more parameters, including the execution accuracy of the injection molding machine, changes in sealing performance, material leakage and return, differences in the release position of the injection molding machine, the influence of the injection molding machine back pressure on material density, and changes in volumetric specific volume during the injection process. The corrected commissioning parameters are input back into Moldflow for reverse verification. One or more of the following parameters are obtained: filling time, V / P switching position pressure, freezing time, volume shrinkage rate, and analysis log information. When the verification results meet the preset quality requirements, the corresponding theoretical and actual deviations, compensation amounts, and verification results are written into the process parameter knowledge base for subsequent parameter conversion and commissioning parameter output calls in new projects.

[0018] A dynamic conversion and calibration system for Moldflow injection molding simulation process parameters includes an interconnected microprocessor and a memory, wherein the microprocessor is programmed or configured to execute the steps of the Moldflow injection molding simulation process parameter dynamic conversion and calibration method.

[0019] Compared with the prior art, the advantages of the present invention are as follows: This invention constructs a dynamic UDB parameter library, which manages the machine characteristic parameters of injection molding machines of different brands and models according to a unified data structure. This allows Moldflow simulation process parameters to be converted to match the specific machine characteristics of the target injection molding machine. Through a parameter conversion model, physical quantity conversion, curve discretization mapping, and execution accuracy compensation are performed on the simulation process parameters. This converts filling, holding pressure, cooling, and key state parameters into machine adjustment parameters that can be recognized and executed by the target injection molding machine, improving the adaptability of simulation results to actual machine applications. By collecting actual operating data during mold trials and performing deviation analysis with the target machine adjustment parameters, actual execution deviations of the target injection molding machine in pressure response, speed following, screw position, and V / P switching can be identified. Based on the deviation analysis results, the machine characteristic parameters in the dynamic UDB parameter library are updated, and the parameter conversion model is further adjusted, ensuring that subsequently regenerated machine adjustment parameters better match the actual operating state of the target injection molding machine. This invention reduces reliance on manual machine adjustment, improves the stability of process parameter conversion and transfer between different machines, and enhances the adaptability of parameter conversion results to dynamic machine changes. Attached Figure Description

[0020] Figure 1 This is a flowchart of the dynamic conversion and calibration method for Moldflow injection molding simulation process parameters according to an embodiment of the present invention.

[0021] Figure 2 This is a flowchart of the dynamic conversion and calibration method for Moldflow injection molding simulation process parameters in a specific application embodiment.

[0022] Figure 3 This is an XY curve plot showing the relationship between recommended screw speed and injection volume percentage in the Moldflow analysis results of a specific application example. The horizontal axis represents the injection volume percentage, and the vertical axis represents the recommended screw speed percentage.

[0023] Figure 4 This is a PVT curve of the injection molding material under different pressure conditions as a function of temperature in a specific application example. The horizontal axis represents temperature, the vertical axis represents the material's volumetric volume, and different colored curves correspond to different pressure conditions.

[0024] Figure 5These are the preferred compensation coefficient values ​​for injection pressure corresponding to different brands and models of injection molding machines in specific application examples.

[0025] Figure 6 This is an exemplary Moldlfow process conversion parameter condition table for a specific application embodiment.

[0026] Figure 7 This is one of the comparison diagrams (initial filling stage) between the mold flow analysis results of component T0 trial molding of a TWS Bluetooth earphone project in a specific application embodiment and the actual product.

[0027] Figure 8 This is the second comparison chart (main body filling stage) between the mold flow analysis results of component T0 trial molding of a TWS Bluetooth earphone project in a specific application embodiment and the actual product.

[0028] Figure 9 The third figure (near full charge stage) shows the mold flow analysis results of component T0 trial molding of a TWS Bluetooth earphone project in a specific application embodiment, compared with the actual product.

[0029] Figure 10 Figure 4 (Final Molding Stage) shows the mold flow analysis results of the T0 trial mold of a TWS Bluetooth earphone project component in a specific application embodiment, compared with the actual product. Detailed Implementation

[0030] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0031] like Figure 1 As shown in this embodiment, the dynamic conversion and calibration method for Moldflow injection molding simulation process parameters includes: S1. Collect the machine characteristic parameters of the target injection molding machine and store the machine characteristic parameters in a unified data structure to the dynamic UDB parameter library. The dynamic UDB parameter library is used to store the characteristic parameters of injection molding machines of different brands and models. S2, obtain the Moldflow injection molding simulation analysis results, and parse the simulation process parameters used to characterize the filling stage, holding pressure stage, cooling stage and key states of the injection molding process from the Moldflow injection molding simulation analysis results; S3, based on the machine characteristic parameters in the dynamic UDB parameter library, establishes a parameter conversion model, and through the parameter conversion model, performs physical quantity conversion, curve discretization mapping and execution accuracy compensation on the simulation process parameters to generate target machine adjustment parameters that match the target injection molding machine; S4. During the trial molding process based on the target machine adjustment parameters, the actual operating data of the target injection molding machine is collected, and the deviation analysis results are obtained by performing a deviation analysis between the actual operating data and the target machine adjustment parameters. S5. Based on the deviation analysis results, update the machine characteristic parameters of the target injection molding machine in the dynamic UDB parameter library, adjust the parameter conversion model according to the updated machine characteristic parameters, and regenerate the machine adjustment parameters adapted to the target injection molding machine. Send the regenerated machine adjustment parameters to the control system of the target injection molding machine to execute subsequent injection molding control.

[0032] In a specific application embodiment, to implement the method of this embodiment, the system hardware configuration may include a data processing server and a user terminal. The data processing server runs the parameter conversion core engine and performs calculations such as Moldflow simulation process parameter analysis, machine characteristic parameter calling, parameter conversion, curve discretization mapping, accuracy compensation, and dynamic calibration. In specific applications, the data processing server can be a computer device with a CPU i9 or higher, at least 128GB of memory, and at least 1TB of hard disk space. The user terminal includes an engineer workstation running Moldflow software and a machine adjustment terminal set up in the workshop. The machine adjustment terminal can be a tablet computer, industrial computer, or other terminal device with parameter display, process sheet viewing, and data interaction functions, used by engineers or machine adjustment personnel to view the converted machine adjustment parameters and record and provide feedback on trial molding data.

[0033] In this embodiment, in step S1, the machine characteristic parameters include static structural parameters and dynamic response parameters. The static structural parameters include one or more of the following: injection molding machine brand, model, tonnage, screw diameter, maximum injection stroke, maximum injection pressure, screw length-to-diameter ratio, injection rate, and control system type. The dynamic response parameters include one or more of the following: pressure response delay time, speed following error, holding pressure attenuation characteristics, V / P switching response time, control command execution delay, and screw position feedback error.

[0034] In specific application embodiments, such as Figure 2 As shown, step S1 (constructing a multi-source machine characteristic parameter library) specifically includes: Characteristic parameters of injection molding machines from different brands and models are collected and standardized to form a dynamic UDB database with a unified structure. These characteristic parameters include: Static structural parameters: screw diameter, maximum injection stroke, maximum injection pressure, screw length-to-diameter ratio, injection rate, etc. Dynamic response characteristic parameters: pressure response delay time (characterizing the time delay required for the actual pressure to reach the set pressure), speed following error curve, holding pressure decay characteristics, V / P switching response time, etc.; dynamic response characteristic parameters can be calibrated for the dynamic response characteristics of each injection molding machine through actual testing or historical trial molding data. The methods for acquiring characteristic parameters include: acquiring injection molding machine operating data through a real-time acquisition device, and / or importing corresponding parameters from technical documents or UDB format files provided by the machine manufacturer.

[0035] In this embodiment, in step S2, the simulation process parameters include one or more of the following: filling time, volumetric flow rate, recommended screw speed, filling volume ratio, and V / P switching position during the filling stage; one or more of the following of the following during the holding pressure stage: holding pressure curve, holding pressure segment value, and duration of each holding pressure segment; one or more of the following during the cooling stage: cooling time, mold temperature, and molding cycle; and one or more of the following at the injection position corresponding to the key states of the injection molding process: maximum pressure value, clamping force, injection time, freezing time, volume shrinkage rate, and total volume of product and runner.

[0036] In specific application embodiments, such as Figure 2 As shown, step S2 (extracting and analyzing key process parameters from the Moldflow injection molding simulation analysis results) specifically includes: Filling stage parameters: Moldflow's recommended screw speed, injection volume or fill volume ratio (represented by ratio), and the corresponding position parameters when switching from V / P (represented by product fill volume ratio). Pressure holding stage parameters: pressure holding segment curve (including pressure value and duration of each segment); Cooling stage parameters: cooling time, molding cycle time, and mold temperature setting; Key process parameters: Maximum pressure at the injection point, clamping force, injection time, etc., are used to characterize the key states of the injection molding process.

[0037] In this embodiment, step S3 includes the conversion of physical quantities from filling volume ratio to screw position and the conversion of flow rate to screw speed. The conversion from fill volume percentage to screw position includes: determining the corresponding cumulative injection volume based on the fill volume percentage in the Moldflow injection molding simulation analysis results; calculating the screw cross-sectional area based on the screw diameter of the target injection molding machine; determining the screw advance distance based on the correspondence between the cumulative injection volume and the screw cross-sectional area; correcting the screw advance distance using the difference between the solid density and melt density of the material; and finally determining the corresponding screw position based on the injection stroke reference value of the target injection molding machine. The conversion from flow rate to screw speed includes: determining the screw cross-sectional area based on the screw diameter of the target injection molding machine; matching and converting the volumetric flow rate in the Moldflow injection molding simulation analysis results with the screw cross-sectional area; and obtaining the screw advance speed required by the target injection molding machine in the corresponding filling stage based on the ratio of the volumetric flow rate to be delivered per unit time to the screw cross-sectional area.

[0038] In specific application embodiments, such as Figure 2 As shown, step S3 specifically establishes a parameter conversion model for transforming the parameters from Moldflow co-simulation analysis to the parameters of the target injection molding machine. The parameter conversion model mainly includes processing steps such as unit conversion, curve discretization mapping, and execution accuracy compensation. Among them, unit conversion is used to convert the theoretical process parameters output by Moldflow into machine physical quantities that can be recognized and executed by the target injection molding machine.

[0039] Unit conversion is mainly used to convert simulation parameters such as product fill percentage and flow rate into machine parameters such as screw position, injection stroke percentage, and screw speed. Specifically, it includes: Fill volume percentage (%) converted to screw position (mm): For the fill volume percentage in the Moldflow injection molding simulation analysis results, it can be converted to the corresponding screw position of the target injection molding machine based on the screw diameter and injection stroke. The conversion formula is as follows:

[0040] In the above formula, X is the converted screw position, L is the injection stroke reference value of the target injection molding machine or the initial screw position, and V is the cumulative injection volume (i.e., the sum of the product volume and the runner volume; the hot runner volume can be ignored; the unit is cm). 3 D is the screw diameter of the target injection molding machine (usually taken as the actual screw diameter of the machine). The solid density of the material (g / cm³) 3 ), The melt density of the material (unit: g / cm³) 3 (This can be obtained from the PVT information in the materials database), where k is the unit conversion factor (take 1 when the units are the same).

[0041] Flow rate (cm) 3 Converting flow rate ( / s) to screw speed (mm / s): For the flow rate in the Moldflow injection molding simulation analysis results, it can be converted into the corresponding screw speed based on the screw cross-sectional area of ​​the target injection molding machine. Specifically, based on the correspondence between volumetric flow rate and screw cross-sectional area, the flow rate is divided by the screw cross-sectional area to obtain the required screw propulsion speed per unit time. The screw speed can be expressed as v = Q / A, where Q is the volumetric flow rate and A is the screw cross-sectional area determined by the screw diameter of the target injection molding machine.

[0042] In this embodiment, step S3 includes discrete mapping of velocity curves and discrete mapping of pressure curves. The discrete mapping of the speed curve includes: obtaining the speed curve from the Moldflow injection molding simulation analysis results, which characterizes the relationship between the injection volume ratio and the recommended screw speed; dividing the speed curve into multiple speed control intervals based on the number of speed segments supported by the target injection molding machine, and combining one or more parameters such as product runner length, gating system and gate contact position, product structural weight, filling progress, product end-of-life air trapping state, and special structural position; wherein, the first speed control interval corresponds to the gating system to the gate contact position, the second speed control interval corresponds to the gate contact position to the product body filling interval, the third speed control interval corresponds to the product near-full interval, and whether to set an end-of-life speed control interval is determined based on the product end-of-life air trapping state and / or special structural position; and determining the screw speed setpoint and corresponding injection time for each speed control interval based on the injection volume ratio corresponding to each speed control interval, the Moldflow recommended screw speed, and the maximum injection speed of the target injection molding machine, so as to convert the speed curve output by Moldflow into a segmented stepped speed curve that can be set by the target injection molding machine. The pressure curve discrete mapping includes: acquiring one or more of the following from the Moldflow injection molding simulation analysis results: pressure change curve during the holding phase, volume shrinkage rate at ejection, frozen layer factor, and total part weight change data; determining the trend, magnitude, and segmentation nodes of the holding pressure based on the pressure change curve, and determining the holding pressure setpoint for each holding segment in conjunction with the volume shrinkage rate at ejection; determining the duration of the holding phase and the time allocation for each holding segment based on the frozen layer factor and total part weight change data; and converting the holding pressure setpoint and corresponding holding time into segmented holding parameters that conform to the control logic of the target injection molding machine.

[0043] In a specific application embodiment, the curve discretization and mapping in step S3 is mainly used to convert the continuous ideal process curve output by Moldflow into a multi-segment stepped curve that can be recognized, set, and executed by the target injection molding machine, specifically including: Speed ​​curve discretization: For the screw speed curve recommended by Moldflow, based on the maximum number of speed segments supported by the target injection molding machine (usually 4-6 segments), the continuous speed curve or smooth speed change curve output by Moldflow is optimized and discretized into a segmented stepped speed curve that can be set by the target injection molding machine.

[0044] When segmenting the product, factors such as flow channel length, product structural weight, gating system and gate location, filling progress, and end-strument characteristics can be considered. For example, the first segment might correspond to the contact point between the gating system and the gate; the second segment might correspond to the point from the gate contact point to where most of the product body is filled (80%-90% of product volume); the third segment might correspond to the point where the product is nearly full (90%-98% of volume); and the fourth segment or subsequent segments can be determined based on whether there is a risk of trapped air or special structures at the end of the product, with the main purpose of improving venting and filling quality. The velocity values ​​for each segment can be determined based on the values ​​of the Moldflow recommended velocity curve at the corresponding locations.

[0045] like Figure 3 As shown, the relationship between injection volume and screw speed can usually be obtained from the Moldflow analysis results through a recommended screw speed XY plot. The horizontal axis represents the injection volume percentage (0%→100%) or filling progress, and the vertical axis represents the screw speed percentage, screw speed value, or volumetric flow rate. The calculation formula is:

[0046] In the above formula, v is the screw speed, v% (v / maximum injection speed of the injection molding machine) is the speed percentage of the corresponding segment in the Moldflow recommended speed curve, t is the injection time corresponding to the speed segment, T% is the injection volume percentage, filling volume percentage or screw stroke percentage corresponding to the speed segment, and T is the total injection volume, total filling volume or total screw stroke.

[0047] The relationship between injection volume and recommended screw speed is not constant linear, but rather varies non-linearly with the injection volume (fill progress). Moldflow typically recommends a multi-segment variable speed curve rather than a constant speed, usually outputting a smooth curve fitted to 4-6 discrete points, which can be directly imported into the injection molding machine for execution. Its purpose is to stabilize wavefront advance and control shear and pressure. The curve shape can be described as "slow start-acceleration-steady state-slow descent," with a relatively high speed in the middle filling stage, reaching its peak. The purpose of this variable speed control method is: to prevent defects such as spraying and scorching in the early filling stage using a lower speed; to increase the speed in the middle filling stage to achieve the target flow rate and maintain stable wavefront advance; and to appropriately reduce the speed at the end of the filling stage to stabilize pressure and avoid problems such as flash, overfilling, or trapped air.

[0048] Pressure curve mapping: For the pressure curve of the holding stage output by Moldflow, based on the pressure change characteristics of the holding stage, the continuous or theoretical pressure change curve is converted into holding segment parameters that conform to the control logic of the target injection molding machine.

[0049] Specifically, based on the pressure XY curve from the Moldflow injection molding simulation analysis results, the pressure magnitude, pressure change trend, and pressure segment nodes during the holding pressure stage are determined. The holding pressure setpoint is determined by combining the volume shrinkage rate at ejection. Specifically, when using two-layer analysis, the volume shrinkage rate at ejection can be used for judgment; when using 3D mesh analysis, the average volume shrinkage rate can be used. Simultaneously, based on the analysis results such as the freeze layer factor and the XY curve of the total part weight, the effective duration of the holding pressure stage and the duration of each holding pressure segment are determined.

[0050] In this embodiment, step S3 includes performing target curve pre-compensation, material density compensation, and screw speed compensation. The target curve pre-compensation includes: based on one or more parameters of the target injection molding machine, such as injection stroke, maximum injection rate, screw diameter, maximum injection pressure, reinforcement ratio, and response time, to perform machine dynamic response pre-compensation on the target curve obtained from the simulation process parameters; the target curve includes one or more of the target screw speed curve, target pressure curve, and target screw position curve. Material density compensation includes: performing preliminary conversion and evaluation of the density of the injection molding material used in the target injection molded product based on the material density parameters recorded in the material database, the changes from the screw start point to the V / P switching position in the Moldflow analysis log, and the product volume shrinkage rate; determining the curve ranges of the material's solid density and melt density under different pressure, volumetric volume, and temperature conditions based on the PVT curves in the material database, and determining the compensation coefficients used to correct for material density differences based on the material's state change trends during the filling, holding, and cooling stages; and using the compensation coefficients to correct the screw position, injection speed, and V / P switching position obtained from the simulation process parameters. Screw speed compensation includes: for a target injection molding machine where the speed following error meets preset conditions, correcting the screw speed setting value based on the deviation between the target speed curve and the actual speed curve, the material PVT curve, and the viscosity curve.

[0051] The parameter conversion model also uses historical trial molding data for error inverse compensation. The historical trial molding data includes one or more of the following: simulation process parameters, target machine adjustment parameters, and actual operating data for different product types, different plastic materials, different injection molding machine brands, and different injection molding machine models. The compensation amount is determined by comparing the difference between the theoretical material quantity or theoretical molding weight and the actual material quantity or actual molding weight, and the compensation amount is superimposed on the corresponding target machine adjustment parameters. The compensation amount includes one or more of the following: screw stroke, injection speed, injection pressure, and V / P switching position.

[0052] In a specific application embodiment, the execution accuracy compensation in step S3 is mainly used to further correct the target machine adjustment parameters obtained after unit conversion and curve discretization mapping, so as to reduce the impact of response deviation, material state deviation, and historical mold trial deviation during the actual execution of the target injection molding machine on the parameter conversion results. Execution accuracy compensation mainly includes: Pre-compensation based on machine dynamic response characteristics: Based on the dynamic response characteristics of the target injection molding machine, pre-compensation is performed on the target curve obtained from the Moldflow simulation process parameters. The target curve includes the target speed curve, target pressure curve, and / or target screw position curve. Specifically, the target curve can be corrected and compensated according to machine characteristic parameters such as injection stroke, maximum injection rate, screw diameter, maximum injection pressure, reinforcement ratio, and response time, so that the target injection molding machine is closer to the expected process control curve during actual execution.

[0053] Compensation is based on density changes during the transition from static to dynamic material states: The compensation coefficients during parameter conversion are corrected or compensated based on the density changes of the injection molding material as it transitions from a static to a dynamic injection state. Specifically, a preliminary analysis and result evaluation are first performed based on recommended material densities provided in the material database. Then, the screw information from the starting point to the V / P switching position in the Moldflow analysis log is used to determine whether the specified switching position has been reached. Simultaneously, the product volume shrinkage rate is considered to determine whether it is within the standard range.

[0054] Furthermore, based on the PVT curves in the materials database (such as...) Figure 4 As shown, the current solid density and melt density of the material are determined on the curve, and compensation is performed by combining the pressure, volumetric volume, and temperature change trends of the material at different stages (filling stage, holding stage, and cooling stage, etc.). Figure 4 As shown, for PC material, when the initial state corresponds to the state between the first and second curves in the PVT curve, i.e., the pressure range is 0 to 50 MPa, it can be adjusted to the density range between the second and third curves, and the compensation coefficient can be determined based on the difference between the two. Based on experience and data accumulation in the knowledge base, the compensation coefficient for PC material can be between 1.1 and 1.5, and for ABS material, it can be between 1.0 and 1.3. The compensated values ​​can be verified again using Moldflow analysis results. After the verification results meet the requirements, the final compensation coefficient value is determined.

[0055] Speed ​​correction based on speed following error: For target injection molding machines with large speed following errors, the actual speed of the target injection molding machine can be gradually brought closer to the target speed by correcting the set speed value. Specifically, the screw speed set value can be compensated and corrected by combining the material properties reflected by the material's PVT curve and viscosity curve, and by combining the actual operating conditions of the target injection molding machine. For example, in the application scenario of PC material and Nissei 110T injection molding machine, the corresponding speed compensation coefficient (preferably 1.242) can be determined based on the mold trial verification results, and the set speed can be corrected using this speed compensation coefficient to make the actual speed closer to the target speed.

[0056] Error Reverse Compensation Based on Historical Data: The compensation algorithm can also employ an error reverse compensation model based on historical data. Specifically, Moldflow software is used to analyze and verify multiple product types, various injection molding materials, different injection molding machine brands, and injection molding machines of different tonnages, establishing a technical knowledge base for recording simulation analysis results, conversion parameters, actual trial molding data, and their deviation relationships. Based on this technical knowledge base, empirical compensation values ​​can be obtained under different product, material, and machine conditions. In specific compensation, historical data from different tonnage machines can be statistically analyzed to compare the differences between the theoretical weight and runner weight, theoretical material quantity, and actual molding weight and actual material quantity. The compensation amount is determined based on the statistical average of these differences and used to correct machine adjustment parameters such as screw stroke, screw position, and / or V / P switching position. This allows subsequent new projects to utilize the compensation patterns accumulated in historical projects during parameter conversion, improving the consistency between conversion parameters and actual machine execution results.

[0057] In this embodiment, in step S4, the actual operating data includes one or more of the following: actual pressure curve, actual speed curve, actual screw position curve, actual V / P switching position, actual filling time, actual holding time, and actual molding cycle; the deviation analysis includes comparing the actual operating data with the target pressure curve, target speed curve, target screw position curve, target V / P switching position, target filling time, target holding time, and target molding cycle corresponding to the target adjustment parameters to obtain one or more of the following: pressure response deviation, speed following deviation, screw position deviation, switching point deviation, and cycle deviation.

[0058] In step S5, updating the machine characteristic parameters of the target injection molding machine in the dynamic UDB parameter library based on the deviation analysis results includes: Based on one or more parameters collected during actual mold trials or production of the target injection molding machine—actual pressure response, actual injection rate, actual screw stroke, actual material quantity, actual molding weight, actual V / P switching position, and actual release position—a comparison is made with one or more parameters obtained from Moldflow analysis—theoretical pressure response, theoretical injection rate, theoretical screw stroke, theoretical material quantity, theoretical molding weight, and theoretical V / P switching position—to obtain the percentage deviation between the analysis results and the actual results. The theoretical material quantity or theoretical molding weight is determined based on screw size, injection stroke, and material density, while the actual material quantity or actual molding weight is corrected for screw wear. Based on the percentage deviation, update one or more of the following parameters in the dynamic UDB parameter library for the target injection molding machine: pressure response time, injection rate, screw stroke, reinforcement ratio, maximum response time, screw diameter, and screw wear. Based on the updated machine characteristic parameters, the execution accuracy compensation in the parameter conversion model is adaptively adjusted. The adaptive adjustment includes correcting one or more of the following machine adjustment parameters based on one or more parameters: the execution accuracy of the injection molding machine, changes in sealing performance, material leakage and return, differences in the release position of the injection molding machine, the influence of the injection molding machine back pressure on material density, and changes in volumetric specific volume during the injection molding process. The adjustment includes correcting one or more machine adjustment parameters such as screw stroke compensation, injection pressure compensation, and V / P switching position compensation. The corrected commissioning parameters are input back into Moldflow for reverse verification. One or more of the following parameters are obtained: filling time, V / P switching position pressure, freezing time, volume shrinkage rate, and analysis log information. When the verification results meet the preset quality requirements, the corresponding theoretical and actual deviations, compensation amounts, and verification results are written into the process parameter knowledge base for subsequent parameter conversion and commissioning parameter output calls in new projects.

[0059] In specific application embodiments, steps S4 and S5 (dynamic UDB calibration and closed-loop optimization) are mainly used to achieve dynamic calibration and closed-loop optimization based on actual mold trial data. By feeding back the actual operating data during the mold trial process to the dynamic UDB parameter library and parameter conversion model, the machine characteristic parameters and compensation algorithm of the target injection molding machine can be continuously corrected, making the subsequently generated machine adjustment parameters more consistent with the actual operating state of the target injection molding machine. The main steps include: Real-time data acquisition: During trial molding based on target machine adjustment parameters, the actual operating data of the target injection molding machine is acquired in real time through IoT devices or machine data acquisition devices. The actual operating data includes parameters such as actual pressure curve, actual speed curve, actual screw position curve, actual V / P switching position, actual injection rate, actual injection stroke, and actual material quantity.

[0060] Deviation Analysis: The collected actual operating data is compared and analyzed with the target machine adjustment parameters obtained through step S3 to calculate the deviation between the actual execution results and the theoretical conversion results. Deviations include pressure response deviation, speed following deviation, switching point position deviation, screw stroke deviation, and actual material quantity deviation. Through the above deviation analysis, the actual deviations of the target injection molding machine in the pressure response, speed following, V / P switching, and injection execution processes can be determined.

[0061] Dynamically update machine characteristic parameters: Based on the deviation analysis results, update the dynamic response characteristic parameters of the target injection molding machine in the dynamic UDB parameter library in step S1. For example, when the actual pressure response delay is greater than the pressure response delay originally recorded in the dynamic UDB parameter library, update the pressure response time parameter corresponding to the target injection molding machine; when there are repeated differences between the analysis results and the actual operating results, update machine characteristic parameters such as injection rate, screw stroke, reinforcement ratio, and maximum response time according to the percentage of difference, and verify and fit the updated parameters through CAE back-calculation analysis.

[0062] For example, for a Nissei 110T injection molding machine, information such as screw diameter, glue volume, screw wear, and material density can be collected to obtain the ideal glue volume. Specifically, the theoretical glue volume can be calculated based on the screw radius and injection stroke, and then the theoretical weight can be calculated based on the material density; simultaneously, the actual screw material volume can be calculated by combining the screw wear coefficient. The calculation formula is as follows:

[0063] In the above formula, V0 represents the theoretical injection volume, r represents the screw radius, s represents the injection stroke, m0 represents the theoretical injection weight, ρ represents the material density, and m a This represents the actual effective injection weight after considering screw wear. η represents the screw wear coefficient or effective injection coefficient. For example, when η is 0.8, it means that the actual effective injection amount is 80% of the theoretical injection amount.

[0064] After the calculations are complete, parameters such as screw diameter, screw stroke, and screw wear can be updated in the corresponding UDB file for the injection molding machine, so that they can be used in subsequent new project analysis and injection molding machine adjustments. When screw wear is high, or when the screw is replaced, repaired, or modified, real-time testing needs to be performed again for confirmation, and the UDB file needs to be updated again based on the test results.

[0065] Adaptive adjustment of the parameter conversion model: Based on the updated machine characteristic parameters, the parameter conversion model and compensation algorithm in step S3 are adaptively adjusted. Adaptive adjustment may include compensating and correcting target machine adjustment parameters such as screw stroke, injection pressure, and V / P switching position to address factors such as changes in injection molding machine execution accuracy, deterioration in sealing, material leakage and return, differences in loosening / retraction positions, the impact of back pressure on material density, and changes in volumetric specific volume during injection molding.

[0066] Specifically, the material quantity compensation value can be determined based on the percentage difference between the product and runner weight obtained from theoretical analysis and the actual product and runner weight obtained from molding. This compensation value is then used to correct the screw stroke or screw position parameters. For pressure compensation, the compensation coefficient for injection pressure or holding pressure can be determined based on the percentage difference between the theoretical pressure value and the actual pressure value. For switching point compensation, the compensation distance or compensation ratio for the V / P switching position can be determined based on the pre-retractable position of different injection molding machines and the actual retractable position measured during the actual injection molding process, thereby correcting the switching position parameters.

[0067] After completing the above compensation, the parameters obtained from the initial conversion can be re-input into Moldflow for verification and analysis. Based on the filling time, V / P switching pressure, freezing time, volume shrinkage rate, and analysis log information in the verification results, it can be determined whether the product quality-related indicators meet the requirements. Based on the original conversion parameters, the compensation values ​​calculated by the compensation algorithm are used for system verification. If multiple analysis results of the product meet the standard values, it can be determined that the current compensated machine adjustment parameters meet the requirements, and the corresponding compensation values, deviation analysis results, and verification results are recorded in the dynamic UDB parameter library or process parameter knowledge base.

[0068] The compensation coefficient for injection pressure can be determined based on the machine parameters, actual pressure response capability, and trial molding deviation results of different injection molding machines. Since different injection molding machines have different machine structures, control logic, and dynamic response capabilities, their corresponding pressure compensation coefficients can also vary accordingly. An example injection pressure compensation coefficient value is shown below. Figure 5 As shown.

[0069] It is understood that the dynamic calibration mechanism in this embodiment can achieve continuous optimization of the parameter conversion model and the dynamic UDB parameter library. Specifically, after each trial molding, the actual operating data of the target injection molding machine can be automatically collected through the IoT device. Subsequently, the actual operating curve is compared with the set curve to calculate indicators such as response delay, speed following error, pressure deviation, and switching point deviation. Based on the above deviation calculation results, the dynamic characteristic parameters of the corresponding machine in the dynamic UDB parameter library can be automatically updated, such as pressure response delay time, speed following error, and V / P switching response time. When the same type of deviation persists, the compensation algorithm coefficients in the parameter conversion model can be further adjusted to make the parameter conversion results more accurate under the conditions of similar products, materials, or machine tools.

[0070] In a specific application embodiment, step S5 is followed by multi-format machine adjustment parameter output. Based on the control interface type or port configuration of different machine models, the machine adjustment parameters obtained after parameter conversion and dynamic calibration are converted into an output format matching the corresponding machine type to generate machine adjustment parameters executable or recognizable by the target injection molding machine. The specific steps are as follows: For injection molding machines that support intelligent interfaces, the machine adjustment parameters can be directly sent to the machine control system of the target injection molding machine through a two-way data interface, enabling the machine control system to perform subsequent trial molding or injection molding control based on the sent adjustment parameters.

[0071] For traditional machines that do not support intelligent interfaces, parameter forms can be generated for operators to read and input. The parameter forms include screw position segment tables (mm), injection speed segment tables (mm / s), holding pressure segments (MPa / bar / Psi), holding time (s), cooling time (s), V / P switching mode and position, etc.

[0072] Based on the target injection molding machine's model information, trial molding product information, material information, and converted machine adjustment parameters, generate and output an Excel-format trial molding process sheet for use in trial molding, machine adjustment, process recording, and subsequent process traceability.

[0073] Specifically, after the process parameter conversion is completed, the Moldflow software can output the process parameters, and the converted actual process parameter condition table can be derived by combining the Moldflow analysis and verification results. An example actual process parameter condition table is shown below. Figure 6 As shown, the actual process parameter condition table may include: screw position segment table, screw position segment table (4-6 segments in total); injection speed segment table; holding pressure segment table; V / P switching method and V / P switching position; process parameters such as injection time, cooling time, and mold temperature; Moldflow process parameter filling position diagram (similar to the short-shot flow chart in scientific injection molding), etc.

[0074] The machine operator can set the machine parameters of the target injection molding machine according to the process parameter condition table and conduct short-shot tests for verification. By comparing the actual short-shot filling position with the theoretical analysis results of Moldflow, the accuracy of the parameter conversion results can be verified.

[0075] The advantages of the present invention will be illustrated by a specific application case below.

[0076] like Figures 7-10 The image shows a comparison between the mold flow analysis results of the T0 trial mold and the actual product of a TWS Bluetooth earphone project. The target product is the lower shell assembly of a TWS Bluetooth earphone charging case. This product adopts a one-mold, two-cavity structure, with a cavity volume of approximately 8.65 cm³. 3 After converting the process parameters obtained from Moldflow injection molding simulation analysis using the method of this invention, the converted machine adjustment parameters are applied to the target injection molding machine, and T0 trial molding verification is carried out.

[0077] In the Moldflow analysis results, when the fill volume percentage is 18%, the corresponding screw position after parameter conversion is 21.5mm, and the short-shot fill position in the actual mold trial is basically consistent with the theoretical analysis result; when the fill volume percentage in the Moldflow analysis is converted to a screw starting position of 21.5mm, the actual short-shot position in the mold trial is close to the theoretical analysis result; when the fill volume percentage in the Moldflow analysis is 14%, the corresponding screw position after conversion is 19mm, and the actual short-shot position in the mold trial is close to the theoretical analysis result; when the fill volume percentage in the Moldflow analysis is 93%, the corresponding screw position after conversion is 4.5mm, and the actual short-shot position in the mold trial is close to the theoretical analysis result.

[0078] in Figure 7 , Figure 8 , Figure 9 , Figure 10 The comparison between Moldflow analysis results and actual trial-molded products is presented for the first stage of filling (initial filling stage), the second stage of filling (main body filling stage), the third stage of filling (near full filling stage), and the final molded state. The comparison results show that the actual filling positions at each stage are basically consistent with the Moldflow predicted filling positions, indicating that the parameter conversion and dynamic calibration method of this invention can effectively improve the accuracy of converting simulation process parameters to actual machine tool adjustment parameters.

[0079] As can be seen from the above trial molding results, the method of the present invention can accurately convert the filling volume ratio in the Moldflow into the screw position parameters of the target injection molding machine, so that the actual short shot filling position is consistent with the simulation analysis results, thereby verifying the effectiveness of converting the simulation process parameters into machine adjustment parameters.

[0080] The method of this invention can generate economic benefits in the following aspects: First, it reduces trial molding costs. Traditional machine adjustment methods typically require multiple trial molding adjustments, while using this invention, each trial molding can reduce adjustments by approximately 3-5 times. If the cost of a single trial molding is calculated at 5,000 yuan, then a single project can save approximately 15,000 to 25,000 yuan in trial molding costs.

[0081] Second, shorten the mass production preparation cycle. By quickly converting Moldflow simulation analysis parameters into machine adjustment parameters that can be executed by the target injection molding machine, and combining them with short-shot verification and dynamic calibration mechanisms, the time spent on repeated manual calculations and trial mold adjustments can be reduced, shortening the mass production preparation time for new products by more than 40%.

[0082] Third, it improves equipment utilization. When switching production of the same product between injection molding machines of different brands, models, or tonnages, this invention can convert and calibrate process parameters according to the machine characteristic parameters of the target machine, realizing rapid transfer of process parameters between machines, thereby improving equipment scheduling flexibility and equipment utilization.

[0083] Compared with the prior art, the present invention has the following beneficial effects: Improve debugging efficiency. In traditional methods, debugging personnel usually need to manually convert and adjust parameters based on Moldflow simulation results, which typically takes 30-60 minutes. However, with this invention, the complex manual calculation and judgment process can be transformed into a standardized parameter conversion process. In practical applications, the parameter conversion and debugging parameter generation process can be shortened to about 1-2 minutes, thereby significantly improving debugging efficiency.

[0084] Improve the success rate of trial molding. Traditional methods typically require 3-5 trial molding adjustments to gradually approach reasonable process parameters; this invention can generate initial target machine adjustment parameters based on Moldflow simulation parameters, target injection molding machine characteristic parameters, and compensation algorithms, so that the filling position in the T0 trial molding is basically consistent with the simulation analysis results, thereby reducing the number of repeated trial molding adjustments and improving the success rate of trial molding.

[0085] Improve parameter conversion accuracy. Traditional manual trial molding and adjustment methods are easily affected by the experience differences of the machine operators, and the parameter trial deviation may exceed 15%. This invention, through physical quantity conversion, curve discrete mapping, execution accuracy compensation, and trial molding deviation feedback calibration, enables the short shot position to maintain a high degree of consistency with the actual filling position, thereby improving the accuracy of the conversion of simulation parameters to actual machine adjustment parameters.

[0086] Improve the ability to switch between multiple injection molding machines. In the traditional way, when switching the production of the same product between injection molding machines of different brands, models or tonnages, the original process parameters usually cannot be directly copied and used, and need to be readjusted. This invention records and updates the machine characteristic parameters of different machines through a dynamic UDB parameter library, and converts and calibrates the process parameters according to the actual characteristics of the target injection molding machine. This enables the scientific conversion of process parameters between different machines, which helps to ensure the consistency of product quality when producing across machines.

[0087] The present invention further provides a dynamic conversion and calibration system for Moldflow injection molding simulation process parameters, including a microprocessor and a memory interconnected thereto, wherein the microprocessor is programmed or configured to execute the steps of the Moldflow injection molding simulation process parameter dynamic conversion and calibration method.

[0088] The system of the present invention corresponds to the method described above and has the same advantages as the method described above.

[0089] The present invention can implement all or part of the processes in the methods of the above embodiments, or it can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of the above method embodiments. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. Computer-readable media include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. The memory is used to store computer programs and / or modules. The processor implements various functions by running or executing the computer programs and / or modules stored in the memory, and by calling data stored in the memory. The memory may include high-speed random access memory, as well as non-volatile memory, such as hard disks, RAM, plug-in hard disks, smart media cards (SMC), secure digital (SD) cards, flash cards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

[0090] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A method for dynamic conversion and calibration of process parameters in Moldflow injection molding simulation, characterized in that, include: S1. Collect the machine characteristic parameters of the target injection molding machine and store the machine characteristic parameters in a dynamic UDB parameter library according to a unified data structure. The dynamic UDB parameter library is used to store the characteristic parameters of injection molding machines of different brands and models. S2, obtain the Moldflow injection molding simulation analysis results, and parse the simulation process parameters used to characterize the filling stage, holding pressure stage, cooling stage and key states of the injection molding process from the Moldflow injection molding simulation analysis results; S3. Based on the machine characteristic parameters in the dynamic UDB parameter library, a parameter conversion model is established. The simulation process parameters are then converted into physical quantities, discretized and mapped on curves, and the execution accuracy is compensated through the parameter conversion model to generate target machine adjustment parameters that match the target injection molding machine. S4. During the trial molding process based on the target machine adjustment parameters, the actual operating data of the target injection molding machine is collected, and the deviation analysis results are obtained by performing a deviation analysis between the actual operating data and the target machine adjustment parameters. S5. Based on the deviation analysis results, update the machine characteristic parameters of the target injection molding machine in the dynamic UDB parameter library, adjust the parameter conversion model according to the updated machine characteristic parameters, and regenerate the machine adjustment parameters adapted to the target injection molding machine. Send the regenerated machine adjustment parameters to the control system of the target injection molding machine to execute subsequent injection molding control.

2. The method for dynamic conversion and calibration of Moldflow injection molding simulation process parameters according to claim 1, characterized in that, In step S1, the machine characteristic parameters include static structural parameters and dynamic response parameters. The static structural parameters include one or more of the following: injection molding machine brand, model, tonnage, screw diameter, maximum injection stroke, maximum injection pressure, screw length-to-diameter ratio, injection rate, and control system type. The dynamic response parameters include one or more of the following: pressure response delay time, speed following error, holding pressure decay characteristics, V / P switching response time, control command execution delay, and screw position feedback error.

3. The method for dynamic conversion and calibration of Moldflow injection molding simulation process parameters according to claim 1, characterized in that, In step S2, the simulation process parameters include one or more of the following: filling time, volumetric flow rate, recommended screw speed, filling volume ratio, and V / P switching position during the filling stage; one or more of the following of the following during the holding pressure stage: holding pressure curve, holding pressure segment value, and duration of each holding pressure segment; one or more of the following during the cooling stage: cooling time, mold temperature, and molding cycle; and one or more of the following at the injection position corresponding to the key states of the injection molding process: maximum pressure value, clamping force, injection time, freezing time, volume shrinkage rate, and total volume of product and runner.

4. The method for dynamic conversion and calibration of Moldflow injection molding simulation process parameters according to claim 1, characterized in that, In step S3, the physical quantity conversion includes the conversion from filling volume ratio to screw position and the conversion from flow rate to screw speed. The conversion from fill volume percentage to screw position includes: determining the corresponding cumulative injection volume based on the fill volume percentage in the Moldflow injection molding simulation analysis results; calculating the screw cross-sectional area based on the screw diameter of the target injection molding machine; determining the screw advance distance based on the correspondence between the cumulative injection volume and the screw cross-sectional area; correcting the screw advance distance using the difference between the solid density and melt density of the material; and finally determining the corresponding screw position based on the injection stroke reference value of the target injection molding machine. The conversion of flow rate to screw speed includes: determining the screw cross-sectional area based on the screw diameter of the target injection molding machine; matching and converting the volumetric flow rate in the Moldflow injection molding simulation analysis results with the screw cross-sectional area; and obtaining the screw advance speed required by the target injection molding machine in the corresponding filling stage based on the ratio of the volumetric flow rate to be pushed out per unit time to the screw cross-sectional area.

5. The method for dynamic conversion and calibration of Moldflow injection molding simulation process parameters according to claim 1, characterized in that, In step S3, the discrete mapping of curves includes discrete mapping of velocity curves and discrete mapping of pressure curves; The discrete mapping of the speed curve includes: obtaining the speed curve from the Moldflow injection molding simulation analysis results, which characterizes the relationship between the injection volume ratio and the recommended screw speed; dividing the speed curve into multiple speed control intervals based on the number of speed segments supported by the target injection molding machine, and combining one or more parameters such as product runner length, gating system and gate contact position, product structural weight, filling progress, product end-of-life air trapping state, and special structural position; wherein, the first speed control interval corresponds to the gating system to the gate contact position, the second speed control interval corresponds to the gate contact position to the product body filling interval, the third speed control interval corresponds to the product near-full filling interval, and determining whether to set an end-of-life speed control interval based on the product end-of-life air trapping state and / or special structural position; determining the screw speed setpoint and corresponding injection time for each speed control interval based on the injection volume ratio corresponding to each speed control interval, the Moldflow recommended screw speed, and the maximum injection speed of the target injection molding machine, so as to convert the speed curve output by Moldflow into a segmented stepped speed curve that can be set by the target injection molding machine; The discrete mapping of the pressure curve includes: acquiring one or more of the following from the Moldflow injection molding simulation analysis results: pressure change curve during the holding pressure stage, volume shrinkage rate at ejection, frozen layer factor, and total part weight change data; determining the changing trend, magnitude, and segmentation nodes of the holding pressure based on the pressure change curve, and determining the holding pressure setpoint corresponding to each holding pressure segment in conjunction with the volume shrinkage rate at ejection; determining the duration of the holding pressure stage and the time allocation of each holding pressure segment based on the frozen layer factor and total part weight change data; and converting the holding pressure setpoint and corresponding holding time into segmented holding pressure parameters that conform to the control logic of the target injection molding machine.

6. The method for dynamic conversion and calibration of Moldflow injection molding simulation process parameters according to claim 1, characterized in that, In step S3, the execution accuracy compensation includes target curve pre-compensation, material density compensation, and screw speed compensation; The target curve pre-compensation includes: performing machine dynamic response pre-compensation on the target curve obtained from the simulation process parameters based on one or more parameters of the target injection molding machine, such as injection stroke, maximum injection rate, screw diameter, maximum injection pressure, reinforcement ratio, and response time; the target curve includes one or more of the target screw speed curve, target pressure curve, and target screw position curve. The material density compensation includes: performing a preliminary conversion and evaluation of the density of the injection molding material used in the target injection molded product based on the material density parameters recorded in the material database, the changes from the screw start point to the V / P switching position in the Moldflow analysis log, and the product volume shrinkage rate; determining the curve range of the material's solid density and melt density under different pressure, volumetric volume, and temperature conditions based on the PVT curves in the material database, and determining the compensation coefficient used to correct the material density difference by combining the state change trends of the material in the filling, holding, and cooling stages; and using the compensation coefficient to correct the screw position, injection speed, and V / P switching position obtained from the simulation process parameters. The screw speed compensation includes: for a target injection molding machine where the speed following error meets preset conditions, correcting the screw speed setting value based on the deviation between the target speed curve and the actual speed curve, the material PVT curve, and the viscosity curve.

7. The method for dynamic conversion and calibration of Moldflow injection molding simulation process parameters according to claim 1, characterized in that, In step S3, the parameter conversion model also uses historical trial molding data for error reverse compensation. The historical trial molding data includes one or more of the following: simulation process parameters, target machine adjustment parameters, and actual operating data for different product types, different plastic materials, different injection molding machine brands, and different injection molding machine models. The compensation amount is determined by comparing the difference between the theoretical material quantity or theoretical molding weight and the actual material quantity or actual molding weight, and the compensation amount is superimposed on the corresponding target machine adjustment parameters. The compensation amount includes one or more of the following: screw stroke, injection speed, injection pressure, and V / P switching position.

8. The method for dynamic conversion and calibration of Moldflow injection molding simulation process parameters according to claim 1, characterized in that, In step S4, the actual operating data includes one or more of the following: actual pressure curve, actual speed curve, actual screw position curve, actual V / P switching position, actual filling time, actual holding time, and actual molding cycle. The deviation analysis includes comparing the actual operating data with the target pressure curve, target speed curve, target screw position curve, target V / P switching position, target filling time, target holding time, and target molding cycle corresponding to the target adjustment parameters to obtain one or more of the following: pressure response deviation, speed following deviation, screw position deviation, switching point deviation, and cycle deviation.

9. The method for dynamic conversion and calibration of Moldflow injection molding simulation process parameters according to claim 1, characterized in that, In step S5, updating the machine characteristic parameters of the target injection molding machine in the dynamic UDB parameter library based on the deviation analysis results includes: Based on one or more parameters collected during actual mold trials or actual production of the target injection molding machine, including actual pressure response, actual injection rate, actual screw stroke, actual material quantity, actual molding weight, actual V / P switching position, and actual release position, a comparison is made with one or more parameters obtained from Moldflow analysis, including theoretical pressure response, theoretical injection rate, theoretical screw stroke, theoretical material quantity, theoretical molding weight, and theoretical V / P switching position, to obtain the percentage deviation between the analysis results and the actual results. The theoretical material quantity or theoretical molding weight is determined based on screw size, injection stroke, and material density, while the actual material quantity or actual molding weight is corrected based on screw wear. Based on the aforementioned deviation percentage, the dynamic UDB parameter library is updated in reverse with one or more of the following parameters for the target injection molding machine: pressure response time, injection rate, screw stroke, reinforcement ratio, maximum response time, screw diameter, and screw wear. Based on the updated machine characteristic parameters, the execution accuracy compensation in the parameter conversion model is adaptively adjusted. The adaptive adjustment includes correcting one or more machine adjustment parameters, such as screw stroke compensation, injection pressure compensation, and V / P switching position compensation, based on one or more parameters, including the execution accuracy of the injection molding machine, changes in sealing performance, material leakage and return, differences in the release position of the injection molding machine, the influence of the injection molding machine back pressure on material density, and changes in volumetric specific volume during the injection process. The corrected commissioning parameters are input back into Moldflow for reverse verification. One or more of the following parameters are obtained: filling time, V / P switching position pressure, freezing time, volume shrinkage rate, and analysis log information. When the verification results meet the preset quality requirements, the corresponding theoretical and actual deviations, compensation amounts, and verification results are written into the process parameter knowledge base for subsequent parameter conversion and commissioning parameter output calls in new projects.

10. A dynamic conversion and calibration system for Moldflow injection molding simulation process parameters, comprising a microprocessor and a memory interconnected, characterized in that, The microprocessor is programmed or configured to perform the steps of the Moldflow injection molding simulation process parameter dynamic conversion and calibration method according to any one of claims 1 to 9.

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