A control method and system for improving the punching accuracy of an injection mold

Abstract

The application provides a kind of control method and system for improving the punching precision of injection mold, relating to injection mold processing technical field.The method comprises: real-time acquisition of temperature, pressure, displacement multi-source data in the process of mold punching;Based on data calculation dynamic deformation compensation coefficient and punching stress balance factor, respectively representing material nonlinear deformation trend and blade stress distribution difference;According to the calculation result, dynamically adjust the punching speed, pressure gradient and mold temperature field parameters;Through closed-loop feedback mechanism, combined with workpiece size error iterative optimization control model, another mold wear compensation step is provided, and the stress balance factor is dynamically corrected based on the cumulative punching number and blade roughness.The system includes multi-source data acquisition, dynamic parameter calculation, adaptive control and closed-loop feedback module, to realize whole-process collaborative control.The application solves the problems of insufficient deformation compensation and stress imbalance control in traditional methods through multi-physical field data coupling analysis, intelligent parameter optimization and closed-loop feedback mechanism.

Description

Technical Field

[0001] This invention relates to the field of injection mold processing technology, and in particular to a control method and system for improving the punching accuracy of injection molds. Background Technology

[0002] In injection molding die blanking, blanking accuracy directly determines the dimensional consistency and surface quality of the workpiece. Its control effect is influenced by the complex interaction of multiple physical field parameters, including die temperature, material deformation, and stress distribution. The core challenge of current technology lies in the difficulty of traditional control methods to capture the nonlinear deformation characteristics of the material during the blanking process in real time. Uneven die surface temperature leads to changes in the material's thermal expansion characteristics, while dynamic fluctuations in blanking pressure cause stress concentration. Under the combined effect of these two factors, the actual material displacement often deviates from the theoretical design trajectory. However, existing solutions rely solely on static mechanical models to estimate deformation trends, lacking real-time synchronous acquisition of die temperature distribution and the deviation between actual and theoretical displacement. This makes it impossible to quantify the nonlinear deformation law under temperature-stress coupling, resulting in dimensional deviations in the blanked workpiece due to insufficient deformation compensation.

[0003] Controlling the stress balance in the contact area between the blanking cutting edge and the material is another key challenge. Material positioning deviations and differences in die structural stiffness can lead to uneven pressure distribution at the cutting edge, resulting in localized stress concentrations or abnormal shear stresses, directly affecting the accuracy of the blanking section. While existing technologies can monitor the average blanking pressure, they cannot integrate multi-dimensional data such as positioning offset, die stiffness coefficient, and the difference between actual and theoretical shear stress values, making it difficult to establish an accurate quantitative model of stress distribution. This causes the blanking pressure gradient adjustment strategy to become disconnected from the actual stress state, making it impossible to eliminate stress differences in the contact area through dynamic parameter adjustments, ultimately leading to defects such as burrs and out-of-tolerance dimensions in the workpiece.

[0004] The lack of real-time performance in multi-parameter collaborative optimization during the blanking process further restricts the improvement of accuracy. Blanking speed, die temperature field, and pressure gradient need to be dynamically matched according to material deformation and stress state, while traditional methods generally employ experience-based fixed parameters or segmented adjustment strategies. For example, when a local temperature increase in the die leads to enhanced material plasticity, it is impossible to adjust the blanking speed in time to suppress excessive deformation; when stress concentrates at the cutting edge, it is also difficult to achieve stress balance through local temperature field control. This lagging parameter adjustment mode results in poor stability of the machining process under the coupling effect of multiple physical fields, and the accuracy control effect depends on the operator's experience, making it difficult to meet the requirements of high-precision machining.

[0005] The lack of a closed-loop feedback mechanism makes the control model unable to adapt to the dynamic changes in actual working conditions. Most existing blanking systems are open-loop control systems, lacking accurate measurement and error analysis of the three-dimensional contour of the blanked workpiece. Even with the introduction of offline detection in some systems, it is impossible to effectively back-calculate dimensional errors into the dynamic deformation compensation and stress equalization calculation models. This results in control parameters relying heavily on initial settings and failing to be iteratively optimized using actual processing data. As the number of processing cycles increases, factors such as material property fluctuations and changes in ambient temperature gradually amplify the deviation between the theoretical model and actual working conditions, causing a decrease in the stability of blanking accuracy, especially for complex structural workpieces where processing accuracy is difficult to guarantee.

[0006] Furthermore, the wear of the cutting edge after long-term use of the die further exacerbates the difficulty of precision control. With the increase in the cumulative number of punching operations, the rising roughness of the cutting edge alters the stress transmission path in the contact area, leading to abnormal punching pressure distribution. However, existing technologies lack a quantitative model based on the degree of wear and cannot dynamically correct the stress balance factor according to changes in cutting edge roughness, making it impossible for the punching pressure parameters to adaptively adjust with the die condition. When wear accumulates to a certain level, traditional methods can only maintain accuracy through manual inspection or die replacement, which not only increases downtime costs but also makes it difficult to guarantee consistent processing quality throughout the die's lifespan. These problems are intertwined, forming a multi-layered technical bottleneck from real-time monitoring and parameter control to model optimization. An innovative solution that integrates real-time analysis of multi-source data, dynamic parameter compensation, and closed-loop feedback optimization is urgently needed to overcome the limitations of existing technologies in punching precision control. Summary of the Invention

[0007] To address the technical problems in existing technologies, such as the difficulty in real-time quantification of the nonlinear deformation trend of materials under temperature-stress coupling, insufficient control of stress distribution uniformity in the blanking cutting edge contact area, lag of multi-parameter collaborative optimization behind actual working condition changes, lack of closed-loop feedback mechanism leading to the inability of control model to iteratively optimize, and the impact of mold wear on the long-term blanking accuracy stability, this invention provides a control method and system for improving the blanking accuracy of injection molds.

[0008] The technical solution provided by this invention is as follows:

[0009] First aspect:

[0010] This invention provides a method for controlling the punching accuracy of injection molds, comprising:

[0011] S1. Real-time acquisition of multi-source data during the die punching process, including die temperature distribution data, punching pressure data, and material displacement trajectory data;

[0012] S2. Based on the multi-source data, calculate the dynamic deformation compensation coefficient and the punching stress equalization factor, wherein the dynamic deformation compensation coefficient is used to characterize the nonlinear deformation trend of the material during the punching process, and the punching stress equalization factor is used to quantify the stress distribution difference between the punching cutting edge and the material contact area.

[0013] S3. Based on the real-time calculation results of the dynamic deformation compensation coefficient and the blanking stress balance factor, dynamically adjust the blanking speed curve, blanking pressure gradient and die temperature field distribution parameters.

[0014] S4. Through a closed-loop feedback mechanism, the adjusted parameters are input to the mold control system, and the calculation models of the dynamic deformation compensation coefficient and the blanking stress balance factor are iteratively optimized by combining the workpiece size measurement data after blanking.

[0015] The second aspect:

[0016] This invention provides a control system for improving the blanking accuracy of injection molds, comprising:

[0017] The multi-source data acquisition module integrates an infrared temperature sensor, a piezoelectric pressure sensor, and a laser displacement sensor. It is configured to acquire pressure data at a frequency of ≥1kHz during the blanking contact stage, acquire displacement data at a frequency of 500Hz~1kHz during the material separation stage, and synchronously acquire mold temperature distribution data at a fixed frequency of 100Hz.

[0018] The dynamic parameter calculation module is configured to perform the calculation of dynamic deformation compensation coefficient and punching stress balance factor, and has built-in wear coefficient correction logic.

[0019] The adaptive control module is configured to generate the punching speed curve based on the particle swarm optimization algorithm, generate the temperature field adjustment command based on the thermal stress coupling model, and correct the punching pressure gradient parameters according to the wear coefficient.

[0020] The closed-loop feedback module integrates a laser scanner and a data analysis unit. It is configured to acquire the workpiece size data after punching and feed back the size error to the dynamic parameter calculation module to iteratively optimize the control parameters.

[0021] The beneficial effects of the technical solution provided by this invention include at least the following:

[0022] (1) In this invention, by collecting multi-source data on pressure, displacement, and temperature in real time during the punching process, and combining the characteristics of the die material and the material physical parameters, a dynamic deformation compensation coefficient calculation model is constructed to accurately quantify the nonlinear deformation trend under the temperature-stress coupling effect. This method enables the system to dynamically adjust the punching parameters according to the actual deformation of the material, effectively solving the deformation compensation lag problem in traditional methods and significantly improving the workpiece size control accuracy.

[0023] (2) In this invention, a blanking stress balance factor is calculated by integrating multi-dimensional data such as cutting edge pressure, positioning offset, and die stiffness. Based on this factor, the blanking speed curve, pressure gradient, and temperature field distribution parameters are dynamically calibrated to achieve real-time balance of stress distribution in the contact area. This technology breaks through the limitations of traditional fixed parameter adjustment, effectively suppresses defects such as burrs and dimensional deviations caused by stress concentration, and improves the blanking cross-section quality and processing stability.

[0024] (3) In this invention, a closed-loop mechanism of "data acquisition - parameter calculation - dynamic control - error feedback" is constructed. The dynamic deformation compensation and stress balance calculation model is corrected by reverse correction of workpiece size error, and the stress distribution parameter is calibrated in real time by introducing the die wear coefficient. This method solves the problem that traditional open-loop control cannot adapt to changes in working conditions, enabling the system to dynamically evolve with the die state and material properties, ensuring the continuous stability and consistency of blanking accuracy throughout the entire life cycle. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 A flowchart illustrating a method for improving the blanking accuracy of injection molds, provided in an embodiment of the present invention;

[0027] Figure 2 This is a schematic diagram of the multi-source data acquisition process for a control method to improve the blanking accuracy of injection molds, provided by an embodiment of the present invention.

[0028] Figure 3 This is a schematic diagram of a closed-loop feedback mechanism for a control method to improve the blanking accuracy of injection molds, provided in an embodiment of the present invention. Detailed Implementation

[0029] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0030] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0031] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, their intended meanings are consistent. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, their intended meanings are consistent.

[0032] In this embodiment of the invention, sometimes a subscript such as W1 may be mistakenly written as a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0033] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0034] Reference manual attached Figure 1 The diagram shows a flowchart of a control method for improving the punching accuracy of injection molds provided by an embodiment of the present invention.

[0035] This invention provides a control method for improving the blanking accuracy of injection molds. This method can be implemented by a control device for improving the blanking accuracy of injection molds, which can be a terminal or a server. The processing flow of this control method for improving the blanking accuracy of injection molds may include the following steps:

[0036] S1. Real-time acquisition of multi-source data during the die punching process, including die temperature distribution data, punching pressure data, and material displacement trajectory data.

[0037] It should be noted that the above steps involve real-time acquisition of multi-source data during the die-cutting process, including die temperature distribution data, cutting pressure data, and material displacement trajectory data. This aims to provide comprehensive foundational information for subsequent precise control. Die temperature affects the physical properties and deformation behavior of the material, cutting pressure reflects the interaction strength between the cutting edge and the material, and the material displacement trajectory directly reflects the actual deformation process during cutting. These three factors together constitute the core physical parameters of the cutting process. By acquiring this data in real time, the system can perceive the dynamic changes in the cutting state, providing the necessary input conditions for accurately calculating compensation coefficients and equilibrium factors, ensuring that the formulation of subsequent control strategies is targeted and real-time.

[0038] S2. Based on multi-source data, calculate the dynamic deformation compensation coefficient and the punching stress balance factor. The dynamic deformation compensation coefficient is used to characterize the nonlinear deformation trend of the material during the punching process, and the punching stress balance factor is used to quantify the stress distribution difference between the punching cutting edge and the material contact area.

[0039] It should be noted that the above steps, based on the collected multi-source data, calculate the dynamic deformation compensation coefficient and the punching stress equalization factor, which are crucial steps in transforming physical phenomena into quantifiable controllable indicators. The dynamic deformation compensation coefficient is used to characterize the nonlinear deformation trend of the material during the punching process. This is because the actual deformation of the material under temperature changes and stress often deviates from the theoretical model, and this coefficient is needed to quantify the nonlinear characteristics. The punching stress equalization factor is used to quantify the stress distribution difference in the contact area between the punching cutting edge and the material, reflecting the uniformity of the force on the cutting edge. Both extract key features from the two dimensions of deformation characteristics and stress distribution, respectively, transforming the complex physical process into controllable parameters. This provides a direct basis for subsequent adjustments to the punching parameters, enabling the system to precisely control the core influencing factors in the actual punching process.

[0040] S3. Based on the real-time calculation results of the dynamic deformation compensation coefficient and the blanking stress balance factor, dynamically adjust the blanking speed curve, blanking pressure gradient, and die temperature field distribution parameters.

[0041] It should be noted that the above steps dynamically adjust the blanking speed curve, blanking pressure gradient, and die temperature field distribution parameters based on the real-time calculation results of the dynamic deformation compensation coefficient and the blanking stress equalization factor. Essentially, this achieves closed-loop control of the blanking process through real-time feedback. The dynamic deformation compensation coefficient reflects the current deformation trend of the material; adjusting the speed curve and pressure gradient accordingly can compensate for the nonlinear deformation of the material and avoid dimensional deviations caused by uneven deformation. The blanking stress equalization factor reflects the stress distribution in the cutting edge contact area; combined with the adjustment of temperature field parameters, it can balance local stress differences and reduce blanking defects caused by stress concentration. Through the coordinated adjustment of these three key parameters, the system can correct deviations in the blanking process in real time, keeping the blanking process in an optimized state, thereby improving blanking accuracy.

[0042] S4. Through a closed-loop feedback mechanism, the adjusted parameters are input to the mold control system, and the calculation models of the dynamic deformation compensation coefficient and the blanking stress balance factor are iteratively optimized by combining the workpiece size measurement data after blanking.

[0043] It should be noted that the above steps input the adjusted parameters into the die control system through a closed-loop feedback mechanism. Combined with the workpiece dimension measurement data after blanking, the calculation models for the dynamic deformation compensation coefficient and blanking stress balance factor are iteratively optimized, aiming to form a cyclical optimization system of "measurement-adjustment-optimization." In actual blanking processes, fluctuations in material properties and changes in environmental factors may lead to deviations between theoretical calculations and actual results. By collecting workpiece dimension data and mapping it into the calculation model, the system can identify the shortcomings of the current control strategy and generate parameter corrections to update subsequent control parameters. This iterative optimization process allows the system to continuously learn from actual blanking experience, gradually improving the accuracy of the calculation model and the adaptability of the control strategy, ensuring a continuous and stable improvement in blanking accuracy over long-term operation, forming a self-optimizing closed-loop control logic.

[0044] Specifically, such as Figure 2 As shown, in step S1, the acquisition of multi-source data includes the following sub-steps:

[0045] S101. During the punching contact stage, pressure data is collected at a frequency greater than or equal to 1 kHz.

[0046] S102. During the material separation stage, displacement data is collected at a frequency of 500 Hz to 1 kHz.

[0047] S103. The temperature distribution data of the mold is synchronously collected at a fixed frequency of 100 Hz by an infrared temperature sensor.

[0048] It should be noted that pressure data is acquired through a piezoelectric pressure sensor, displacement data through a laser displacement sensor, and temperature data through an infrared temperature sensor. All sensor data are synchronously transmitted to the central processing unit through the industrial control module and stored in the embedded database.

[0049] It should be noted that, considering the differences in physical characteristics at different stages of the blanking process, the acquisition frequency and deployment method of various types of sensors have been specifically designed: During the blanking contact stage, the instantaneous pressure interaction between the material and the cutting edge is intense. Pressure data is acquired using piezoelectric pressure sensors at frequencies greater than or equal to 1 kHz, accurately capturing the sudden load changes at the moment of contact and avoiding the loss of critical signals. During the material separation stage, the displacement trajectory is a core parameter for judging the blanking completion and deformation state. This is acquired using laser displacement sensors at frequencies from 500 Hz to 1 kHz, satisfying both the detailed reproduction of the dynamic process and balancing the data processing load. The die temperature field distribution, as a slowly varying parameter, is synchronously acquired using infrared temperature sensors at a fixed frequency of 100 Hz, sufficient to cover the dynamic response cycle of die heat conduction. The high-frequency response characteristics of the piezoelectric pressure sensors, the sub-micron-level measurement accuracy of the laser displacement sensors, and the non-contact, full-range temperature measurement capabilities of the infrared temperature sensors are technically compatible with the data acquisition requirements at each stage, ensuring the spatiotemporal consistency of multi-source data. All sensor data is synchronously transmitted to the central processing unit through the industrial control module and stored in the embedded database. This design enables real-time synchronization of sensor signals, anti-interference processing, and historical data tracing, providing a reliable data foundation for subsequent dynamic parameter calculation and control strategy optimization.

[0050] Specifically, in step S2, the calculation of the dynamic deformation compensation coefficient includes the following sub-steps:

[0051] S201. Obtain the maximum temperature difference ΔT on the mold surface using an infrared temperature sensor. max The actual measured displacement δ is obtained through a laser displacement sensor. real With theoretical design displacement δ nominal ;

[0052] S202. According to the following formula:

[0053]

[0054] The basic value DDCC is used to calculate the dynamic deformation compensation coefficient, where E is the elastic modulus of the mold material, and ΔT is the elastic modulus. max σ represents the maximum temperature difference on the mold surface. y δ represents the yield strength of the punched material. real For actual displacement measurement, δ nominal To design displacements theoretically;

[0055] S203, based on the material's thermal diffusivity α and the punching pressure holding time t hold By correction factor The base values ​​are corrected to obtain the final dynamic deformation compensation coefficient.

[0056] It should be noted that the calculation process of the dynamic deformation compensation coefficient closely integrates the material's physical properties with real-time data from the punching process, achieving precise quantification of nonlinear deformation through multi-dimensional parameter coupling. First, an infrared temperature sensor is used to obtain the maximum temperature difference on the die surface. This parameter reflects the potential impact of uneven thermal field on material deformation. Simultaneously, a laser displacement sensor compares the actual measured displacement with the theoretically designed displacement to capture the actual deformation deviation of the material during punching. Based on these two types of data, combined with the elastic modulus of the die material and the yield strength of the punched material, a basic value of the dynamic deformation compensation coefficient is calculated using a specific formula. This basic value couples the thermal stress effect caused by temperature with the mechanical deformation effect caused by displacement deviation, forming a preliminary quantification of the material's nonlinear deformation trend. Considering the continuous influence of the material's thermal diffusion characteristics and the punching pressure holding time on the deformation process, a correction factor incorporating the thermal diffusion coefficient and pressure holding time is further introduced to dynamically calibrate the basic value. The resulting dynamic deformation compensation coefficient more accurately reflects the actual deformation characteristics of the material during punching due to the combined effects of temperature changes, stress state, and time accumulation, providing a crucial basis for the subsequent adaptive adjustment of punching parameters.

[0057] The calculation of the punching stress equilibrium factor includes the following steps:

[0058] S211. The average pressure P of the blanking cutting edge is obtained by using a piezoelectric pressure sensor. avg The positioning offset d between the material and the mold is obtained through a vision positioning system. offset ;

[0059] S212. Based on the die structure stiffness coefficient K and the thickness h of the punched material. material Calculate the initial stress distribution value;

[0060] S213. According to the following formula:

[0061]

[0062] By correcting the initial stress distribution value, the punching stress equilibrium factor is obtained, where τ max To actually measure the shear stress, τ nominal To theoretically design shear stress.

[0063] It should be noted that the calculation of the blanking stress equalization factor aims to quantify the uniformity of stress distribution in the contact area between the blanking cutting edge and the material. This process integrates pressure monitoring, position positioning, and structural mechanical parameters. First, the average pressure on the blanking cutting edge is acquired using a piezoelectric pressure sensor. This parameter directly reflects the magnitude of the contact load between the cutting edge and the material. Simultaneously, a vision positioning system captures the positioning offset between the material and the die. These two factors together constitute the initial input conditions for stress distribution analysis. Combining the die structure stiffness coefficient K and the thickness of the blanked material, the initial stress distribution value is calculated. The stiffness coefficient reflects the die structure's ability to resist deformation, while the material thickness affects the characteristics of the stress transmission path. Based on this, the ratio of the actual measured shear stress to the theoretically designed shear stress is introduced. A specific formula is used to correct the initial stress distribution value, forming the blanking stress equalization factor. This factor couples multiple physical quantities such as cutting edge pressure, positioning deviation, structural stiffness, and shear stress differences, and can accurately characterize the stress concentration and distribution uniformity in the contact area. It provides key quantitative indicators of stress state for subsequent blanking pressure gradient adjustment and temperature field optimization, ensuring uniform force on the cutting edge during blanking and reducing processing defects caused by uneven stress.

[0064] Specifically, the dynamic adjustment of the punching speed curve in step S3 includes the following sub-steps:

[0065] S301. Construct a fitness function with cutting efficiency and smoothness as optimization objectives:

[0066]

[0067] λ1 and λ2 are preset weighting coefficients, and SSEF is the punching stress equalization factor. This represents the rate of change of the dynamic deformation compensation coefficient over time.

[0068] S302. The velocity curve is iteratively optimized using a particle swarm optimization algorithm.

[0069] S303. Send the optimized speed curve to the punching actuator.

[0070] It should be noted that the process of dynamically adjusting the blanking speed curve is achieved by combining a multi-objective optimization model with intelligent algorithms to precisely control the dynamic characteristics of the blanking process. First, a fitness function is constructed with blanking efficiency and smoothness as the core objectives. The rate of change of the blanking stress equilibrium factor SSEF and the dynamic deformation compensation coefficient over time is then considered. As key optimization variables, the influence of SSEF and λ2 in the objective function is balanced by preset weighting coefficients λ1 and λ2. SSEF reflects the uniformity of stress distribution, while the rate of change index characterizes the dynamic fluctuation of deformation trend. Together, they constitute the core constraint for velocity curve optimization. Based on this fitness function, the particle swarm optimization algorithm (PSO) is used to iteratively optimize the velocity curve. This algorithm simulates bird flock foraging behavior, efficiently searching for the optimal solution in a multi-dimensional parameter space, and can quickly converge to a velocity curve shape that balances efficiency and stability. Finally, the optimized velocity curve is sent to the punching actuator. By adjusting the punching speed in real time, the problems of stress abrupt changes and uneven deformation caused by speed fluctuations are effectively reduced, achieving smooth and efficient operation of the punching process and ensuring improved punching accuracy from a dynamic perspective.

[0071] In step S3, adjusting the mold temperature field distribution parameters includes the following sub-steps:

[0072] S311. Based on the mold temperature distribution data, calculate the ratio of the equivalent radii of the cooling zone to the heating zone. Generate temperature regulation command, where r cool r is the equivalent radius of the cooling region. heat The equivalent radius of the heating area;

[0073] S312. According to the following formula:

[0074]

[0075] Generate a temperature adjustment command, where β is the coefficient of thermal expansion of the material, E is the elastic modulus of the mold material, and ΔT is the real-time temperature difference. The equivalent radius ratio calculated in S311;

[0076] S313. Synchronously adjust the power output of the mold heater and cooling system according to the temperature adjustment command.

[0077] It should be noted that the adjustment process of the mold temperature field distribution parameters is based on the thermo-structural coupling effect, achieving precise temperature control by quantifying the relationship between temperature distribution characteristics and thermal stress. First, based on the mold temperature distribution data collected by an infrared temperature sensor, the ratio of the equivalent radius of the cooling area to the equivalent radius of the heating area is calculated. This ratio reflects the spatial distribution characteristics of the hot and cold areas on the mold surface and is a key parameter for evaluating the uniformity of the temperature field. Then, combining the material's thermal expansion coefficient, the mold material's elastic modulus, and the real-time temperature difference, a specific formula is used to transform the temperature distribution difference into a quantitative index of thermal stress, generating a temperature adjustment command. This command comprehensively considers the material's thermophysical properties and the mold's structural parameters, accurately characterizing the thermal deformation trend caused by uneven temperature. Finally, based on the temperature adjustment command, the power output of the heater and cooling system is adjusted synchronously. By dynamically balancing the heat distribution in each area of ​​the mold, nonlinear material deformation caused by temperature gradients is suppressed, providing a stable thermal environment for the punching process and reducing the impact of deformation errors on accuracy from a thermodynamic perspective.

[0078] like Figure 3 As shown, in step S4, the closed-loop feedback mechanism includes the following sub-steps:

[0079] S401. Obtain the three-dimensional contour data of the blanked workpiece using a laser scanner;

[0080] S402. Map the workpiece dimensional error to the calculation model of the dynamic deformation compensation coefficient and the punching stress balance factor;

[0081] S403. Generate parameter correction amounts based on the error distribution and update the control parameters for the next punching cycle.

[0082] It should be noted that the closed-loop feedback mechanism integrates workpiece dimensional measurement data with control model parameters to form an adaptive loop system of "detection-correction-optimization". First, a laser scanner acquires the three-dimensional contour data of the blanked workpiece, accurately capturing the difference between the actual and designed dimensions, providing intuitive quantitative evidence for error analysis. Then, the workpiece dimensional error is mapped to the calculation models of the dynamic deformation compensation coefficient and the blanking stress balance factor, establishing a mapping relationship between actual processing deviations and theoretical control parameters, enabling the system to identify deficiencies in the current model calculations or the influence of external factors. Finally, based on the error distribution characteristics, corresponding parameter correction amounts are generated, and the control parameters for the next blanking cycle are updated in real time, thus transforming the result of a single blanking cycle into optimized inputs for subsequent control strategies. This closed-loop mechanism breaks the limitations of traditional open-loop control, continuously iterating the dynamic deformation compensation and stress balance calculation models to approximate actual blanking conditions, ensuring that control parameters dynamically evolve with the processing, ultimately achieving continuous improvement and stable maintenance of blanking accuracy.

[0083] In one possible implementation, the present invention further includes a mold wear compensation step:

[0084] S5. Based on the cumulative punching times N stamping With respect to the maximum lifespan N of the mold design max The ratio of the two values ​​is used to calculate the wear coefficient η.

[0085]

[0086] Where, N stamping N represents the current cumulative number of punching attempts. max For the maximum lifespan of the mold design, μ abrasion μ represents the current cutting edge roughness. initial As the initial cutting edge roughness, the wear coefficient η is introduced as a multiplier into the calculation formula of the blanking stress balance factor, and the blanking pressure gradient parameter is recalculated based on the modified blanking stress balance factor.

[0087] It should be noted that die wear is inevitable during long-term punching processes, and this die wear compensation step is crucial for maintaining punching accuracy. With increasing punching cycles, the die cutting edge gradually wears down, increasing its roughness, which in turn affects stress distribution and force transmission during punching. By calculating the ratio of cumulative punching cycles to the die's maximum designed lifespan, and combining this with the current and initial cutting edge roughness, a wear coefficient can be derived, effectively quantifying the degree of die wear. This wear coefficient is then used as a multiplier in the calculation formula for the punching stress balance factor, fully considering the impact of die wear on the stress distribution in the contact area between the cutting edge and the material. Based on the corrected punching stress balance factor, the punching pressure gradient parameters are recalculated, enabling the system to adjust the punching pressure in real time according to the actual die wear state, compensating for stress changes caused by die wear. This maintains the stability and accuracy of the punching process as much as possible throughout the die's entire service life, extending the effective service life of the die and reducing production costs.

[0088] Furthermore, the modified punching stress equalization factor specifically includes:

[0089] S501. Calculate the correction value SSEF based on the original punching stress balance factor SSEF and wear coefficient η. 修正 =η·SSEF;

[0090] S502, Verify the revised SSEF 修正 Does it meet the preset stress equilibrium threshold?

[0091] S503. If the threshold is not met, the wear coefficient η is recalculated and iteratively corrected.

[0092] It should be noted that this correction process ensures the accuracy of stress balance control throughout the entire life cycle of the die by quantifying the impact of wear and establishing a closed-loop verification mechanism. First, the original blanking stress balance factor is multiplied by the wear coefficient to dynamically calibrate the stress distribution model. The blanking number ratio in the wear coefficient reflects the macroscopic wear stage of the die, while the cutting edge roughness ratio quantifies the impact of microscopic surface morphology changes on contact stress. Then, the system verifies whether the corrected stress balance factor is within a preset threshold range. This threshold is a stress uniformity index set based on material properties and processing requirements, directly related to blanking dimensional accuracy. If the threshold is not met, the system automatically triggers iterative correction: re-collecting cutting edge roughness data, updating the wear coefficient based on the cumulative blanking number, and recalibrating the stress balance factor until the stress distribution state meets the standard. This iterative mechanism forms a closed-loop control of "monitoring-correction-verification," which can adaptively compensate for stress concentration problems caused by die wear, ensuring that even in the later stages of the die's life, the blanking pressure gradient parameter can still dynamically match changes in the cutting edge state, effectively extending the die's service life and maintaining the stability of processing accuracy.

[0093] This invention also provides a control system for improving the blanking accuracy of injection molds, applied to the aforementioned control method for improving the blanking accuracy of injection molds, comprising:

[0094] The multi-source data acquisition module integrates an infrared temperature sensor, a piezoelectric pressure sensor, and a laser displacement sensor, and configures acquisition strategies differently for different stages of the punching process: During the punching contact stage, the piezoelectric pressure sensor captures the cutting edge contact pressure in real time at a frequency of ≥1 kHz, accurately identifying sudden load changes; during the material separation stage, the laser displacement sensor acquires the material displacement trajectory at a frequency of 500 Hz to 1 kHz, obtaining deviation data between the actual displacement and the theoretically designed displacement; the infrared temperature sensor scans the mold surface at a fixed frequency of 100 Hz, simultaneously acquiring global temperature distribution data. All sensor signals are filtered for interference resistance and synchronized with the clock by the industrial control module before being transmitted to the central processing unit via a high-speed data bus and stored in an embedded database, ensuring the spatiotemporal consistency of the multi-source data.

[0095] The dynamic parameter calculation module, based on collected multi-source data, performs real-time calculations of the dynamic deformation compensation coefficient and the blanking stress balance factor through a dedicated calculation unit. First, the module utilizes the maximum temperature difference data on the mold surface from an infrared temperature sensor and the displacement deviation from a laser displacement sensor, combined with mold material characteristic parameters, to calculate the dynamic deformation compensation coefficient, reflecting the nonlinear deformation trend of the material. Simultaneously, it quantifies the stress distribution differences in the blanking cutting edge contact area using the average cutting edge pressure from a piezoelectric pressure sensor, the material positioning offset from a vision positioning system, and mold structural parameters, generating the blanking stress balance factor. The module incorporates wear coefficient correction logic, dynamically calculating the wear coefficient based on the cumulative number of blanking operations, mold design life, and changes in cutting edge roughness, and introducing this into the calculation model of the stress balance factor to achieve real-time compensation for mold wear.

[0096] The adaptive control module receives compensation coefficients and balancing factors from the dynamic parameter calculation module, and adjusts the blanking speed, die temperature field, and pressure gradient in real time. In blanking speed control, the module constructs a speed curve with efficiency and smoothness as objectives using a particle swarm optimization algorithm, driving the servo motor to adjust the motion trajectory of the blanking actuator. For the die temperature field, it analyzes temperature distribution data based on a thermal stress coupling model, generating temperature adjustment commands to synchronously control the power output of the die heater and cooling system, balancing the temperature gradient on the die surface. Combined with a blanking stress balancing factor corrected for wear coefficients, the module dynamically calibrates the blanking pressure gradient through a proportional servo valve, ensuring uniform stress distribution in the cutting edge contact area.

[0097] The closed-loop feedback module acquires the 3D contour data of the blanked workpiece through an integrated laser scanner. After processing by the data analysis unit, key dimensional features are extracted and compared with the design model for deviation. The module maps the workpiece dimensional error to the calculation model of the dynamic deformation compensation coefficient and the blanking stress balance factor. Through error distribution analysis, it generates parameter correction values ​​and feeds them back to the dynamic parameter calculation module in real time. The corrected control parameters are compiled by the embedded controller and updated to the adaptive control module for the next blanking cycle, forming a fully closed-loop control link of "data acquisition - parameter calculation - dynamic control - error feedback - model optimization," realizing iterative optimization of control parameters and continuous calibration of blanking accuracy.

[0098] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0099] (1) In this invention, by collecting multi-source data on pressure, displacement, and temperature in real time during the punching process, and combining the characteristics of the die material and the material physical parameters, a dynamic deformation compensation coefficient calculation model is constructed to accurately quantify the nonlinear deformation trend under the temperature-stress coupling effect. This method enables the system to dynamically adjust the punching parameters according to the actual deformation of the material, effectively solving the deformation compensation lag problem in traditional methods and significantly improving the workpiece size control accuracy.

[0100] (2) In this invention, a blanking stress balance factor is calculated by integrating multi-dimensional data such as cutting edge pressure, positioning offset, and die stiffness. Based on this factor, the blanking speed curve, pressure gradient, and temperature field distribution parameters are dynamically calibrated to achieve real-time balance of stress distribution in the contact area. This technology breaks through the limitations of traditional fixed parameter adjustment, effectively suppresses defects such as burrs and dimensional deviations caused by stress concentration, and improves the blanking cross-section quality and processing stability.

[0101] (3) In this invention, a closed-loop mechanism of "data acquisition - parameter calculation - dynamic control - error feedback" is constructed. The dynamic deformation compensation and stress balance calculation model is corrected by reverse correction of workpiece size error, and the stress distribution parameter is calibrated in real time by introducing the die wear coefficient. This method solves the problem that traditional open-loop control cannot adapt to changes in working conditions, enabling the system to dynamically evolve with the die state and material properties, ensuring the continuous stability and consistency of blanking accuracy throughout the entire life cycle.

[0102] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

[0103] The following points need to be explained:

[0104] (1) The accompanying drawings of the embodiments of the present invention only involve the structures involved in the embodiments of the present invention. Other structures can refer to the general design.

[0105] (2) For clarity, the thickness of layers or regions is enlarged or reduced in the drawings used to describe embodiments of the present invention; that is, these drawings are not drawn to actual scale. It is understood that when an element such as a layer, film, region, or substrate is referred to as being “above” or “below” another element, the element may be “directly” located “above” or “below” the other element, or there may be intermediate elements.

[0106] (3) Where there is no conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other to obtain new embodiments.

[0107] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. The scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for controlling the blanking accuracy of injection molds, characterized in that... ,include: S1. Real-time acquisition of multi-source data during the die punching process, including die temperature distribution data, punching pressure data, and material displacement trajectory data; S2. Based on the multi-source data, calculate the dynamic deformation compensation coefficient and the punching stress balance factor, wherein: The calculation of the dynamic deformation compensation coefficient includes: according to the formula ； The basic value for calculating the dynamic deformation compensation coefficient ,in The elastic modulus of the mold material, For the maximum temperature difference on the mold surface, The yield strength of the punched material, For actual displacement measurement, The displacement was designed theoretically; and the thermal diffusivity of the material was used as a reference. and punching pressure holding time By correction factor The base values ​​are corrected to obtain the final dynamic deformation compensation coefficient; The calculation of the punching stress balance factor includes: according to the formula ； Calculate the punching stress equilibrium factor SSEF, where To provide the average pressure on the cutting edge, Where K is the positioning offset between the material and the mold, and K is the mold structural stiffness coefficient. For the thickness of the material being punched, To actually measure shear stress, To theoretically design shear stress; S3. Dynamically adjust the punching parameters, where: The dynamic adjustment of the blanking speed curve is based on the rate of change of the blanking stress balance factor and the dynamic deformation compensation coefficient; Dynamic adjustment of mold temperature field distribution parameters is performed as follows: based on the mold temperature distribution data, the ratio of the equivalent radii of the cooling zone to the heating zone is calculated. , and according to the formula ； The temperature distribution differences are converted into a quantitative index of thermal stress, which is used to generate temperature field regulation commands. Where E is the coefficient of thermal expansion of the material, and E is the elastic modulus of the mold material. For real-time temperature difference; The dynamic adjustment of the blanking pressure gradient is based on a blanking stress equalization factor corrected for the wear coefficient. S4. Through a closed-loop feedback mechanism, the adjusted parameters are input to the mold control system, and the calculation models of the dynamic deformation compensation coefficient and the blanking stress balance factor are iteratively optimized by combining the workpiece size measurement data after blanking.

2. The control method for improving the blanking accuracy of injection molds according to claim 1, characterized in that... S3 further includes: The dynamic adjustment of the punching speed curve includes the following sub-steps: S301. Construct a fitness function with cutting efficiency and smoothness as optimization objectives: ； and For the preset weighting coefficients, For punching stress equalization factor, The rate of change of the dynamic deformation compensation coefficient over time; S302. The velocity curve is iteratively optimized using a particle swarm optimization algorithm. S303. Send the optimized speed curve to the punching actuator.

3. The control method for improving the blanking accuracy of injection molds according to claim 2, characterized in that... The dynamic adjustment of the mold temperature field distribution parameters in S3 further includes: The power output of the mold heater and cooling system is adjusted synchronously according to the temperature field adjustment command.

4. The control method for improving the blanking accuracy of injection molds according to claim 1, characterized in that... The acquisition of multi-source data in S1 specifically includes: S101. During the punching contact stage, pressure data is collected at a frequency greater than or equal to 1 kHz; S102. During the material separation stage, displacement data is collected at a frequency of 500 Hz to 1 kHz; S103. The temperature distribution data of the mold is synchronously collected at a fixed frequency of 100 Hz by an infrared temperature sensor.

5. The control method for improving the blanking accuracy of injection molds according to claim 4, characterized in that... The closed-loop feedback mechanism in S4 specifically includes: S401. Obtain the three-dimensional contour data of the blanked workpiece using a laser scanner; S402. Map the workpiece dimensional error to the calculation model of the dynamic deformation compensation coefficient and the punching stress equalization factor; S403. Generate parameter correction amounts based on the error distribution and update the control parameters for the next punching cycle.

6. The control method for improving the blanking accuracy of injection molds according to claim 5, characterized in that... It also includes mold wear compensation: S5. Based on the cumulative number of punching operations Maximum lifespan of the mold design The ratio of the two values ​​is used to calculate the wear coefficient. : ； in, This represents the current cumulative number of arbitration attempts. The maximum lifespan of the mold is designed for. The current cutting edge roughness, As the initial edge roughness, the wear coefficient is... The blanking pressure gradient parameters are recalculated based on the modified blanking stress balance factor in the calculation formula, which is introduced as a multiplier in the blanking stress balance factor calculation formula.

7. The control method for improving the blanking accuracy of injection molds according to claim 6, characterized in that... The modified punching stress equalization factor specifically includes: S501, Based on the original punching stress balance factor and wear coefficient Calculate the correction value ; S502, Verify the revised version Does it meet the preset stress equilibrium threshold? S503. If the threshold is not met, the wear coefficient is recalculated. And iteratively correct it.

8. A control system for improving the blanking accuracy of injection molds, characterized in that... ,include: The multi-source data acquisition module integrates an infrared temperature sensor, a piezoelectric pressure sensor, and a laser displacement sensor. It is configured to acquire pressure data at a frequency of ≥1 kHz during the blanking contact stage, acquire displacement data at a frequency of 500 Hz to 1 kHz during the material separation stage, and synchronously acquire mold temperature distribution data at a fixed frequency of 100 Hz. The dynamic parameter calculation module, based on the multi-source data collected by the multi-source data acquisition module, is configured as follows: According to the formula ； The basic value DDCC for calculating the dynamic deformation compensation coefficient is given by: The elastic modulus of the mold material, For the maximum temperature difference on the mold surface, The yield strength of the punched material, For actual displacement measurement, The displacement was designed theoretically; and the thermal diffusivity of the material was used as a reference. and punching pressure holding time And through correction factors After making corrections, the final dynamic deformation compensation coefficient is obtained; According to the formula ； Calculate the punching stress equilibrium factor SSEF, where To provide the average pressure on the cutting edge, Where K is the positioning offset between the material and the mold, and K is the mold structural stiffness coefficient. For the thickness of the material being punched, To actually measure shear stress, To theoretically design shear stress; It also incorporates wear coefficient correction logic; The adaptive control module is configured to: based on the rate of change of the punching stress equalization factor and the dynamic deformation compensation coefficient, and through a fitness function... ； The punching speed curve is generated using a particle swarm optimization algorithm. and For the preset weighting coefficients, For punching stress equalization factor, The rate of change of the dynamic deformation compensation coefficient over time; Based on the mold temperature distribution data, a thermal stress coupling model is used. ； The system generates temperature field regulation commands, converting temperature distribution differences into quantitative indicators of thermal stress. Where E is the coefficient of thermal expansion of the material, and E is the elastic modulus of the mold material. The real-time temperature difference is used; the blanking pressure gradient parameters are corrected based on the blanking stress equalization factor after the wear coefficient is corrected. The closed-loop feedback module integrates a laser scanner and a data analysis unit. It is configured to acquire the workpiece size data after punching and feed back the size error to the dynamic parameter calculation module to iteratively optimize the control parameters.

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