Filling control method and system based on micro-channel pressure fluctuation balance

Abstract

The application discloses a filling control method and system based on micro-channel pressure fluctuation balance, and relates to the technical field of injection molding process control, wherein the method comprises the following steps: arranging a distributed pressure sensor in a micro-channel mold, collecting real-time pressure data of each key node, and capturing pressure fluctuation through a high sampling rate; extracting pressure energy features by using a sliding time window, and aggregating pressure data of the same branch according to a micro-channel topology structure, so as to obtain pressure wave energy features of each micro-channel branch; calculating a global pressure wave energy balance index based on the pressure wave energy features of each micro-channel branch, and representing the overall pressure balance state of the micro-channel system; and dynamically adjusting an injection rate and a micro-valve opening degree according to a deviation amplitude through an adaptive control algorithm, so that pressure fluctuations of each micro-channel branch tend to be balanced. Therefore, the filling uniformity and molding precision are improved in the micro-channel injection molding process, and the molding quality of a micro-structure part is improved.

Description

Technical Field

[0001] This application relates to the field of injection molding process control technology, and in particular to a filling control method and system based on microchannel pressure fluctuation balance. Background Technology

[0002] Injection molding is a process that uses a screw or plunger to inject molten plastic into a mold cavity and then cools and solidifies it. It has advantages such as short cycle time, suitability for mass production, and good dimensional stability, and is widely used in automotive, electronics, medical, and microfluidic chip industries. With the increasing demand for micro-devices and complex functions, injection molding is developing towards the microscale, such as microfluidic chips, microlens arrays, and microneedle arrays. Micro-components require dense microscale channels or functional structures within the part, typically ranging in size from a few micrometers to hundreds of micrometers. The cross-sections are often irregularly shaped and arranged in an array, placing higher demands on processing accuracy and filling uniformity.

[0003] In macroscopic injection molding, typical process parameters include injection speed during the filling stage, pressure and holding time during the holding stage, and melt and mold temperatures. Existing control strategies largely rely on pre-set curves or feedback control based on cavity pressure. Taking cavity pressure as an example, research shows that the pressure curve within the cavity is closely related to part quality, and peak cavity pressure and the area under the pressure curve can be used as indicators to evaluate molding quality. By adjusting parameters such as the V / P (speed / pressure) switching point, holding pressure, and injection speed, part weight and dimensional accuracy can be indirectly improved. However, this type of monitoring and control mainly targets the overall cavity pressure and is difficult to reflect local pressure fluctuations and transient wave propagation within the microchannels. When the microchannel layout is complex or the cross-sectional dimensions vary significantly, local pressure fluctuations can lead to uneven filling, replication distortion, or hysteresis in different channels. Summary of the Invention

[0004] This application provides a filling control method, system, storage medium, computer program product, and electronic device based on microchannel pressure fluctuation balance, which can at least solve the problems of uneven local pressure fluctuation and uneven filling in the microchannel injection molding process in the current related technologies.

[0005] In a first aspect, embodiments of this application provide a filling control method based on microchannel pressure fluctuation balance, comprising: synchronously acquiring instantaneous pressure data of each node at a sampling rate higher than the characteristic frequency of the injection pressure wave, based on pressure sensors distributed in a microchannel key node within a microchannel mold; the microchannel mold is used for injection molding microstructure components through each microchannel branch, the microchannel key node including at least one of the following: microchannel branch inlet, microchannel branch end, and microchannel corner; preprocessing the acquired pressure data to extract the pressure energy characteristics of each pressure sensor within a sliding time window, and based on the... The microchannel topology aggregates the pressure energy characteristics belonging to the same microchannel branch, calculating the pressure wave energy characteristics of each microchannel branch. Based on the pressure wave energy characteristics of each microchannel branch, a global pressure wave energy balance index is determined to characterize the overall pressure fluctuation equilibrium state of the microchannel system. The deviation between the global pressure wave energy balance index and a preset target value is calculated, and microchannel control commands are generated using an adaptive control algorithm based on the deviation. The microchannel control commands are used to drive and adjust the injection rate and microvalve opening of the injection machine for each microchannel branch, so that the pressure energy of each microchannel branch tends to be balanced.

[0006] Secondly, embodiments of this application provide a filling control system based on microchannel pressure fluctuation balance, comprising: a pressure sensor array, distributedly arranged in key microchannel nodes within a microchannel mold, for synchronously acquiring instantaneous pressure data of each node at a sampling rate higher than the characteristic frequency of the injection pressure wave; the microchannel mold is used for injection molding microstructure components through each microchannel branch, the key microchannel nodes including at least one of the following: microchannel branch inlet, microchannel branch end, and microchannel corner; a processor, for generating microchannel control commands by executing the filling control method based on microchannel pressure fluctuation balance as described above; an injection molding machine control unit, for adjusting the injection rate of the injection molding machine according to the microchannel control commands; and a microvalve array drive unit, for adjusting the microvalve opening of each microchannel branch according to the microchannel control commands.

[0007] Thirdly, an electronic device is provided, comprising: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the steps of the filling control method based on microchannel pressure fluctuation balance according to any embodiment of the present application.

[0008] Fourthly, embodiments of this application provide a storage medium storing a computer program thereon, characterized in that, when the program is executed by a processor, it implements the steps of the filling control method based on microchannel pressure fluctuation balance according to any embodiment of this application.

[0009] Fifthly, embodiments of this application provide a computer program product, including a computer program / instructions, which, when executed by a processor, implement the steps of the filling control method based on microchannel pressure fluctuation balance according to any embodiment of this application.

[0010] The filling control method and system based on microchannel pressure fluctuation balance provided in this application can achieve at least the following technical effects:

[0011] (1) By arranging distributed pressure sensors in the microchannel mold, each sensor collects data synchronously at a sampling rate higher than the characteristic frequency of the injection pressure wave, which can capture the instantaneous pressure change in the microchannel. The distributed pressure acquisition method can capture the local pressure fluctuation inside the microchannel, breaking through the limitation of the traditional injection control strategy that only relies on the overall pressure monitoring of the mold cavity.

[0012] (2) After real-time acquisition of pressure data, pressure energy characteristics of each microchannel node are extracted through preprocessing and sliding time window analysis. Based on the microchannel topology, the pressure characteristics of the same branch are aggregated, thereby accurately assessing the pressure wave energy state of each microchannel branch. Furthermore, by calculating the global pressure wave energy balance index, the pressure fluctuations of all microchannel branches are integrated into an index reflecting the overall pressure fluctuation balance of the system. Thus, by introducing the global pressure wave energy balance index, the pressure fluctuation of the entire microchannel system is accurately quantified, avoiding filling imbalance or product quality problems caused by local pressure unevenness.

[0013] (3) By introducing an adaptive control algorithm, the injection rate and microvalve opening are dynamically adjusted according to the deviation between the global pressure wave energy balance index and the preset target value. Through real-time optimization control strategy, the pressure energy state of the microchannel branches can be adaptively adjusted according to different production batches and real-time conditions, making the filling process of each microchannel more uniform, thereby improving the stability and consistency of the injection molding process.

[0014] This technical solution, based on precise monitoring of local pressure fluctuation data and balanced control of global pressure wave energy, effectively avoids problems such as uneven filling, distortion, or lag in complex microchannel layouts. It can also significantly improve filling uniformity and molding accuracy during microchannel injection molding, thereby greatly improving the molding quality of microstructure components (such as microfluidic chips, microlens arrays, etc.). Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 A flowchart illustrating an example of a filling control method based on microchannel pressure fluctuation balance according to an embodiment of this application is shown;

[0017] Figure 2 It shows according to Figure 1 An example operation flowchart of step S130 in the process;

[0018] Figure 3 It shows according to Figure 1 An example operation flowchart of step S140 in the process;

[0019] Figure 4 A schematic diagram showing the simulation effect of the average pressure in the microchannel changing over time is presented.

[0020] Figure 5 A schematic diagram showing the simulation results comparing the scatter plot distribution relationship between the mean pressure variance of the microchannel and the standard deviation of the injection rate is presented.

[0021] Figure 6 A structural block diagram of an example of a filling control system based on microchannel pressure fluctuation balance according to an embodiment of this application is shown. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0023] It should be noted that, in the current related technologies, some experts and scholars have proposed some novel directions for microchannel control.

[0024] In recent years, researchers have proposed solutions to improve the replication accuracy of microfluidic chips in injection molding by increasing holding pressure, extending holding time, and increasing mold temperature. Specifically, increasing holding pressure can significantly improve the replication accuracy of longitudinal microchannels; when the holding pressure reaches 80 MPa, melt feeding is more complete, and the size of the longitudinal channel is closer to the design value. However, too low a holding pressure cannot effectively eliminate the hysteresis effect, resulting in obvious non-circular deviations in the microchannel cross-section. Further extending the holding time can also improve replication quality; when the holding time is extended from 1 s to 3 s, the root mean square error at multiple measuring points decreases by about 1.6 μm. However, excessively long holding times reduce production efficiency and increase energy consumption. In addition, measures such as mold heating and increasing injection speed can also improve microstructure filling, but these measures can only compensate for shrinkage marks macroscopically and have limited effect on balancing transient pressure fluctuations within the microchannel.

[0025] To reduce the number of trial moldings and improve quality stability, some researchers have proposed predicting part weight and geometric accuracy through real-time monitoring of cavity pressure or tie rod elongation. Cavity pressure curves can help determine key time points such as the filling endpoint, peak holding pressure, and cooling stage. Experiments show that peak cavity pressure and the area under the curve are highly correlated with part weight, with the area under the curve having a higher coefficient of determination. Another study used the elongation of the four tie rods of the injection molding machine as a monitoring indicator, suggesting that tie rod elongation reflects changes in clamping force and cavity pressure, and these changes are highly correlated with cap weight and geometric dimensions. This method achieves stable control of part weight by adjusting parameters such as residual material position and metering endpoint. While the above monitoring and prediction methods improve the consistency of macroscopic quality, they still cannot solve the problem of uneven filling caused by local pressure fluctuations in the microchannels.

[0026] It should be noted that the injection pressure is provided by the hydraulic system of the injection molding machine to overcome the melt viscosity and flow resistance, ensuring the plastic fills the mold cavity. The system pressure acts on the injection cylinder and is transmitted as injection pressure through the screw. Injection pressure is typically adjusted rapidly by a PID control system, but the system pressure response is relatively slow, requiring the hydraulic system to store and boost it. The formula for calculating injection pressure is as follows: ,in For injection pressure, Instantaneous flow rate The projected area of ​​the part. The coefficients are related to materials and equipment, indicating that injection pressure is related to flow rate and material properties. Increasing injection pressure can improve melt flowability, but excessive pressure can lead to uneven mold stress and increased equipment load. However, existing control methods mainly focus on the relationship between injection speed and pressure, without addressing local flow regulation within the microchannels or measures to suppress pressure wave propagation.

[0027] It should be understood that the above description of the relevant technologies is intended only to help the public better understand the inventive spirit and motivation of this application, and is not intended to limit this application. Furthermore, the technical solutions described in the above-mentioned relevant technologies are not prior art, and may also be undisclosed technical solutions, such as those under research or in the laboratory stage.

[0028] The technical solutions in this application, including the collection, storage, use, processing, transmission, provision, and disclosure of users' personal information, comply with relevant laws and regulations and do not violate public order and good morals.

[0029] Figure 1 A flowchart illustrating an example of a filling control method based on microchannel pressure fluctuation balance according to an embodiment of this application is shown.

[0030] Regarding the execution subject of the method in the embodiments of this application, it can be any controller or processor with computing or processing capabilities, such as a microchannel injection molding controller. By meticulously capturing and adjusting the local pressure fluctuations of different microchannels in the microchannel mold, the pressure fluctuations of the microchannels are balanced, thereby ensuring uniform filling and high-precision replication during the microchannel molding process.

[0031] In some examples, it can be integrated into electronic devices or terminals through software, hardware, or a combination of both, and the types of terminals or electronic devices can be diverse, such as various optical line terminals, etc.

[0032] like Figure 1 As shown, in step S110, pressure sensors distributed in the microchannel key nodes within the microchannel mold are used to synchronously collect instantaneous pressure data of each node at a sampling rate higher than the characteristic frequency of the injection pressure wave.

[0033] Microchannel molds are used to injection mold microstructured components through various microchannel branches, each of which may experience different pressure fluctuations during fluid flow. Accurate monitoring of these pressure fluctuations in each microchannel branch is crucial for controlling process uniformity and precision during microchannel injection molding. Multiple high-precision pressure sensors are placed at key nodes in the microchannel mold. These key nodes include at least one of the following: microchannel branch inlet, microchannel branch end, and microchannel corner.

[0034] The inlet of a microchannel branch is typically the starting point of a pressure wave. When the injection molding material enters the microchannel from the injection molding machine, it is affected by pressure and flow rate. The end of a microchannel branch is typically the termination point of the pressure wave propagation. Fluid encounters resistance changes as it flows through the microchannel, and the performance of the pressure wave at this point often reflects the final filling state. At microchannel corners, the direction of fluid flow changes, easily leading to localized flow instability or pressure fluctuations, which are also important reference points for assessing pressure fluctuations in the microchannel.

[0035] More specifically, the arrangement and calibration of pressure sensors can be targeted at the characteristics of different microchannel molds, and the geometry and flow characteristics of the microchannels need to be fully considered. The optimal distribution of sensors is usually determined by simulation and optimization algorithms to ensure that they can cover all key locations of the microchannels.

[0036] In addition, to avoid the influence of injection pressure waves on fluid pressure monitoring results, a sampling rate higher than the characteristic frequency of injection pressure waves is required to synchronously collect instantaneous pressure data of each node, ensuring real-time and accurate monitoring of pressure fluctuations within the microfluidic system.

[0037] In some examples of embodiments of this application, the pressure sensor is a MEMS (Micro-Electro-Mechanical Systems) piezoresistive sensor, used to accurately capture transient pressure fluctuations within a microchannel. MEMS piezoresistive sensors have high sensitivity and small size, enabling them to accurately capture transient changes in pressure waves within a microchannel.

[0038] More specifically, the operating parameters of the injection machine are obtained, and the characteristic frequency of the injection pressure wave is calculated based on the operating parameters of the injection machine.

[0039] Equation (1)

[0040] In the formula, The characteristic frequency of the injection pressure wave represents the dominant frequency of the pressure wave during the injection process. This is a proportionality coefficient, representing the relationship between injection speed and pressure wave propagation speed; Injection speed represents the speed at which the melt flows during the injection process. The length of the microchannel represents the effective path length for the pressure wave to propagate within the microchannel.

[0041] By modeling the injection speed and pressure wave propagation speed using a linear relationship, and combining the working parameters of the injection machine and the physical characteristics of the microchannel to calculate the characteristic frequency of the pressure wave, a suitable sampling frequency can be accurately derived, avoiding redundant data acquisition and optimizing the data acquisition process.

[0042] The sampling frequency needs to be higher than the characteristic frequency of the injection pressure wave to capture sufficiently fine pressure data. For example, according to the Nyquist theorem, the sampling frequency of the pressure sensor is set to be more than twice the calculated characteristic frequency of the injection pressure wave to ensure that sufficiently fine pressure data is acquired.

[0043] To ensure accurate capture of transient fluctuations in the pressure wave, the sampling frequency can be set to 3-5 times the characteristic frequency. For example, if the characteristic frequency of the injected pressure wave is 10 Hz, the sampling frequency should be at least 30 Hz, or even higher, to ensure that the pressure wave is covered by a sufficient number of data points in each cycle.

[0044] Furthermore, the sampling times of each pressure sensor are synchronized using network clock synchronization technology to ensure data real-time performance and consistency. For example, the IEEE 1588 Precision Time Protocol (PTP) or embedded synchronization technology can be used to enable all sensors to acquire data at the same time.

[0045] Therefore, based on the complexity and transient characteristics of pressure fluctuations during microchannel injection molding, local pressure fluctuations within the microchannel are accurately captured by deploying an array of pressure sensors and combining them with the operating parameters of the injection molding machine.

[0046] In step S120, the collected pressure data is preprocessed to extract the pressure energy characteristics of each pressure sensor within the sliding time window, and the pressure energy characteristics belonging to the same microchannel branch are aggregated based on the microchannel topology to calculate the pressure wave energy characteristics of each microchannel branch.

[0047] Specifically, since instantaneous pressure data may contain high-frequency noise, the raw data needs to be filtered to remove unnecessary noise, such as using a low-pass filter or Kalman filter, to effectively remove high-frequency noise from the signal and retain low-frequency pressure fluctuation characteristics. Furthermore, since pressure data is continuous on the time axis, to analyze the periodic changes in pressure fluctuations, the data can be divided into multiple sliding time windows. The data within each window is used to calculate pressure energy characteristics. The length of the time window can be set or adjusted according to business needs (such as the duration of the injection molding process), typically ranging from a few milliseconds to a few seconds. The characteristic form of the pressure energy feature can be diverse, for example, expressing the pressure energy feature through the root mean square (RMS) value or power spectral density of the pressure signal within the sliding time window; this should not be restricted here.

[0048] After preprocessing, pressure sensor data belonging to the same branch are merged according to the topology of the microchannel to generate pressure wave energy characteristics of each microchannel branch. This not only reflects the pressure situation of fluid propagation in the microchannel, but also reveals the equilibrium and stability of local fluctuations.

[0049] In step S130, based on the pressure wave energy characteristics of each microchannel branch, a global pressure wave energy balance index is determined to characterize the overall pressure fluctuation balance state of the microchannel system.

[0050] The global pressure wave energy balance index is an important indicator for measuring the pressure fluctuation balance of the entire microfluidic system. It is used to assess whether the pressure fluctuations of each branch in the microfluidic system are balanced, and whether there are local pressure fluctuations that are too high or too low.

[0051] In some implementations, a global pressure wave energy balance index for the entire microfluidic system can be obtained by weighted averaging or other mathematical operations on the pressure wave energy characteristics of all microfluidic branches, thus reflecting the overall pressure fluctuation balance within the microfluidic system. Ideally, the pressure fluctuations of all microfluidic branches should be relatively balanced, and excessively large or small fluctuations in any one branch should be avoided.

[0052] In step S140, the deviation between the global pressure wave energy balance index and the preset target value is calculated, and microchannel control commands are generated based on the deviation amplitude using an adaptive control algorithm.

[0053] Here, based on the deviation between the calculated global pressure wave energy balance index and the preset target value, the corresponding microchannel control command is generated by calling the adaptive control algorithm, thereby driving the injection machine to dynamically adjust the injection rate and microvalve opening of each microchannel branch, ensuring that the pressure energy of each microchannel branch tends to be balanced.

[0054] Specifically, the deviation amplitude is calculated based on the difference between the global pressure wave energy balance index and the preset target value. If the deviation is large, it indicates that the pressure fluctuations in some microchannel branches are too large or too small, requiring adjustment of the injection rate and microvalve opening. The adaptive control algorithm can determine the adjustment amount based on the deviation amplitude and adjust the operating parameters of the injection machine in real time.

[0055] Here, the type of adaptive control algorithm can be diverse and should not be limited for the time being. For example, by using adaptive control algorithms such as PID control, fuzzy control, or reinforcement learning, control parameters such as injection rate and microvalve opening are dynamically adjusted based on the deviation amplitude in real-time feedback, adapting to changes in the microchannel layout through a real-time response. Thus, precise microchannel control commands are generated based on the deviation amplitude, driving the injection machine to adjust the injection rate and microvalve opening, optimizing the uniform filling of each microchannel branch.

[0056] Through the embodiments of this application, pressure fluctuations within the microchannel are accurately monitored, and the injection rate and microvalve opening of the microchannel are optimized by combining adaptive control algorithms to ensure pressure balance in each branch during the microchannel molding process. This effectively addresses the problem of uneven pressure in the microchannel and significantly improves molding quality, precision, and production efficiency.

[0057] Regarding the implementation details of step S120, in some examples of embodiments of this application, wavelet denoising is used to denoise the original pressure signal of each pressure sensor, and Kalman filtering algorithm is further used to compensate for temperature drift, thereby preprocessing the collected pressure data.

[0058] It should be noted that during microchannel injection molding, pressure signals are often affected by noise and temperature drift. Traditional noise removal methods may not be able to handle complex transient signal changes. A combination of wavelet denoising and Kalman filtering can effectively remove high-frequency noise and temperature drift while preserving the main characteristics of the signal. This method can significantly improve the accuracy and stability of the data.

[0059] The preprocessed pressure data is time-series integrated using a sliding time window to calculate the pressure energy characteristics.

[0060] Here, by using a sliding window, the pressure energy characteristics within each time period can be dynamically calculated from the constantly changing pressure signal. Specifically, the energy of the pressure wave is calculated by squaring and integrating the pressure signal within each window, thus reflecting the intensity and trend of pressure fluctuations in real time.

[0061] Equation (2)

[0062] In the formula, For the first Each sensor in the time window The pre-processed pressure signal inside, The window length is the sliding time window. For the virtual time variable of integration, For the first Each sensor at time The energy of the pressure signal.

[0063] Equation (2) allows us to extract the intensity of real-time pressure fluctuations within a given time window.

[0064] The pressure energy characteristics of various pressure sensors belonging to the same microchannel branch are weighted and aggregated.

[0065] Since multiple sensors may be arranged in each microchannel branch, the data from multiple sensors in the same microchannel branch are aggregated to obtain the overall pressure wave energy characteristics of that branch. Thus, the overall pressure wave energy of that branch can be accurately reflected through data aggregation.

[0066] Equation (3)

[0067] In the formula, For the first Each microchannel branch at time Pressure wave energy characteristics For the first The number of sensors within each microchannel branch For the pre-calibrated first The branch influence weight of each sensor.

[0068] It should be noted that, This can be set up during the sensor deployment phase through calibration testing, for example, by designing or adjusting based on the distance between the sensor and the branch inlet, as well as the sensor's accuracy. For sensor data within the same microchannel branch, the total energy of that branch is calculated using a weighted average. Thus, weighted data aggregation based on the microchannel topology, combined with sensor accuracy and location, enables more accurate monitoring of the microchannel's pressure status.

[0069] Figure 2 It shows according to Figure 1 An example operation flowchart for step S130.

[0070] like Figure 2 As shown, in step S210, the instantaneous variance of pressure wave energy between microchannel branches is calculated based on the pressure wave energy characteristics of each microchannel branch.

[0071] Specifically, a quantified pressure wave energy variance is obtained by calculating the difference between the pressure wave energy characteristics of each microchannel branch and its mean.

[0072] Equation (4)

[0073] In the formula, Indicates the microchannel mold at time... The instantaneous variance of pressure wave energy. This indicates that all microchannel branches are at time [time]. The average energy of the pressure wave. This indicates the total number of branches in the microchannel mold.

[0074] In step S220, the instantaneous variance of the pressure wave energy is integrated over a sliding time window to calculate the global pressure wave energy equilibrium index.

[0075] Here, to maintain real-time performance and accuracy in a dynamically changing production environment, a sliding time window method is used to calculate the global pressure wave energy balance index. By using a sliding time window, the change in pressure wave energy over time can be dynamically tracked, and the global pressure wave energy balance index can be calculated within a given time range.

[0076] Equation (5)

[0077] In the formula, Indicates at time The global pressure wave energy balance index, Indicates the microchannel mold at time... Instantaneous variance of pressure wave energy.

[0078] Through the embodiments of this application, the pressure wave energy variance of each microchannel branch is calculated, and these variances are aggregated and time-series integrated by a sliding time window to obtain the global pressure wave energy balance index, thereby tracking the distribution of pressure waves in the microchannel system in real time.

[0079] Figure 3 It shows according to Figure 1 An example operation flowchart for step S140.

[0080] In step S310, the deviation between the global pressure wave energy balance index and the preset target value is calculated.

[0081] Here, the global pressure wave energy balance index This reflects the current equilibrium state of the pressure wave in the microfluidic system. To ensure a uniform distribution of pressure wave energy, the real-time calculated global pressure wave energy equilibrium index can be compared with a preset target value, which represents the desired ideal equilibrium state.

[0082] Equation (6)

[0083] In the formula, Indicates at time The deviation range, The preset target value represents the ideal pressure wave energy equilibrium state; This is the trend sensitivity coefficient, used to adjust the influence of the rate of change of pressure wave energy on the deviation amplitude. Its value can be set between 0.2 and 0.5. The rate of change of the global pressure wave energy balance index, i.e. the rate of change of the global pressure wave energy balance index over time, can be estimated by Kalman filtering to smooth system fluctuations.

[0084] Specifically, in addition to the static difference term between the current pressure wave energy and the target value, Equation (6) also introduces a dynamic change term, which reflects the influence of the pressure wave energy change rate. This is intended to adjust the control system's response to rapid fluctuations and ensure that the system can quickly respond to sudden pressure fluctuations (such as flow instability).

[0085] For example, when This indicates that the pressure wave energy in the microfluidic system deteriorates rapidly, and the deviation amplitude... Automatic amplification triggers a more aggressive control response. On the other hand, when When the deviation amplitude reaches a certain value, it indicates that the system is approaching stability. Gradually revert to static differential control, make gentle flow control adjustments, or do not intervene in the current injection machine or microvalve control process.

[0086] In step S320, if the deviation amplitude is detected to exceed the warning threshold, the injection rate is updated in real time according to the deviation amplitude using the fuzzy proportional-resonant control algorithm (Fuzzy-PR).

[0087] Here, adjustments are made based on proportional gain and integral gain, as well as hyperparameters learned dynamically.

[0088] Equation (7)

[0089] Equation (8)

[0090] Equation (9)

[0091] In the formula, and They represent the times respectively. and Injection rate, For proportional gain, For integral gain; These are learnable hyperparameters; , and These represent the minimum and maximum injection rates of the injection molding machine, respectively, to ensure the effectiveness of the control commands.

[0092] Specifically, when When it is large, the proportional gain This will enhance the effect, thereby rapidly adjusting the injection rate and suppressing pressure fluctuations. When When it is large, the integral gain Attenuation will occur to prevent overshooting of the system. When When the value is small, enhance the integral term. This eliminates steady-state errors and ensures stable system operation.

[0093] Therefore, by adopting a fuzzy proportional-resonant control algorithm and dynamically adjusting the injection rate based on the deviation amplitude, and by modeling the nonlinear response characteristics and dynamically adjusting the gain coefficient and control gain term, it can accurately adapt to the strong time-varying characteristics in the injection molding process, quickly respond to pressure fluctuations based on real-time feedback, effectively regulate sudden fluctuations and persistent errors, and ensure that pressure fluctuations in the injection molding process are kept within the ideal range.

[0094] In step S330, the micro-valve opening is generated based on the deviation amplitude using a distributed collaborative control algorithm that couples the flow channel topology.

[0095] Here, in terms of deviation magnitude When the preset threshold is exceeded, the micro-valve opening is adjusted in real time by controlling the pressure wave energy and global deviation of each microchannel branch to regulate the injection pressure and maintain the balance of pressure fluctuation.

[0096] Equation (10)

[0097] Equation (11)

[0098] In the formula, and They represent the first Each microchannel branch at time and The control increment of the micro-valve opening, Indicates the first Each microchannel branch at time Pressure wave energy characteristics; As a branch-related adjustment factor, it can be updated through an online parameter self-learning mechanism. Its update strategy is optimized based on the sensitivity of historical control effects and current energy deviation to microvalve opening. Let be the compensation function, representing the result obtained through the th... The flow channel geometry of each microchannel branch is adaptively adjusted to compensate for the intensity of the flow compensation. and The first The branch length and cross-sectional area of ​​each microchannel branch. and These represent the maximum length and minimum cross-sectional area among all microchannel branches, respectively. , Minimum valve opening to prevent clogging.

[0099] Here, by adjusting and It can flexibly adjust the response of each microchannel branch to pressure wave energy, ensuring that pressure fluctuations in each branch are compensated evenly. When When, decrease the first Opening of each branch entrance To suppress the flow in this branch and reduce its pressure fluctuation intensity; when At that time, increase the number of times. Opening of each branch entrance This is to increase the flow rate of this branch and enhance the intensity of its pressure fluctuations.

[0100] In addition, through The function can adaptively adjust the compensation intensity based on the length and cross-sectional area of ​​the microchannel, achieving adaptive compensation of the channel geometry. For example, long channels require greater compensation intensity to overcome flow resistance, while narrow cross-section channels require increased compensation intensity to reduce high shear fluctuations. Furthermore, by incorporating global bias... It can make micro-adjustments to the microchannel under uneven macro pressure fluctuations, ensuring that the pressure fluctuations of each microchannel branch are balanced.

[0101] Through the embodiments of this application, by employing an adaptive compensation function and a branch-related adjustment factor, the compensation intensity can be automatically adjusted according to the geometric characteristics of the microchannel, thereby maintaining pressure balance under different flow channel conditions and enhancing the adaptive matching to changes in the microchannel mold.

[0102] In some examples of embodiments of this application, if continuous If, after the adaptive control adjustment, the deviation is still detected to be greater than the warning threshold, a mold blockage warning is triggered. It can be set or adjusted according to actual business needs or testing results.

[0103] Here, when continuous If, after the adaptive control adjustment, the energy deviation amplitude still fails to converge to the target value even when the valve opening is too small, it indicates that there may be blockage or poor flow inside the microfluidic system, triggering a mold blockage warning. This helps to detect abnormalities in the production process in a timely manner, such as issuing an alarm immediately to remind operators to check whether the mold is blocked, thus avoiding continued production and effectively reducing the generation of defective products.

[0104] To verify the effectiveness of the proposed method, a numerical simulation was performed on an injection molding system with four microchannel branches. A baseline injection rate was set, and a sine wave and random noise were superimposed to simulate the fluctuation of the injection rate under conventional control. Four equidistant sensors were selected to measure the pressure waveform, and the pressure dynamics were simulated using a first-order linear model. The differences in pressure equalization index and injection rate stability between the baseline no-control scheme and the proposed method were compared. The baseline scheme refers to driving injection solely with a preset injection curve without local feedback control; while the proposed scheme adjusts the injection rate and valve opening in real time according to the aforementioned adaptive algorithm.

[0105] Figure 4 A schematic diagram showing the simulation effect of the average pressure in the microchannel changing over time is presented.

[0106] like Figure 4 As shown, the baseline scheme represents a traditional control method without local feedback control, resulting in significant pressure fluctuations. Furthermore, over time, the pressure distribution becomes noticeably uneven, with large fluctuation amplitudes and poor injection rate stability. In contrast, the scheme proposed in this application effectively reduces pressure fluctuations by adjusting the injection rate and microvalve opening in real time using an adaptive control algorithm, maintaining the pressure within a relatively stable range. This control method significantly reduces pressure fluctuations, achieves a more uniform pressure distribution, and significantly suppresses injection rate fluctuations, verifying the advantages of the proposed method in improving pressure uniformity and injection rate stability.

[0107] Figure 5 A schematic diagram showing the simulation results comparing the scatter plot distribution relationship between the average pressure variance of the microchannel and the standard deviation of the injection rate is presented.

[0108] Here, to examine the stability of the control strategy under different random conditions, 30 Monte Carlo experiments were conducted, comparing the scatter distributions of pressure variance and injection rate standard deviation. Figure 5 As shown, the scatter points in the baseline scheme are mostly concentrated in the region with large pressure variance and large injection fluctuation, indicating that the traditional control strategy cannot effectively suppress pressure fluctuation and injection rate instability when faced with random disturbances. In contrast, the scatter points of the control scheme proposed in this application are concentrated in the region with small pressure variance and extremely low injection fluctuation, indicating that the method can effectively reduce pressure fluctuation and maintain injection rate stability, verifying the pressure fluctuation suppression effect of the adaptive control algorithm under different random conditions.

[0109] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of combined actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Secondly, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application. In the above embodiments, the descriptions of each embodiment have their own emphasis; for parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0110] Figure 6 A structural block diagram of an example of a filling control system based on microchannel pressure fluctuation balance according to an embodiment of this application is shown.

[0111] like Figure 6 As shown, the filling control system 600 based on microchannel pressure fluctuation balance includes a pressure sensor array 610, a processor 620, an injection machine control unit 630, and a microvalve array drive unit 640.

[0112] The pressure sensor array 610 is distributed in the microchannel key nodes within the microchannel mold to synchronously acquire instantaneous pressure data of each node at a sampling rate higher than the characteristic frequency of the injection pressure wave; the microchannel mold is used to injection mold microstructure components through each microchannel branch, and the microchannel key nodes include at least one of the following: microchannel branch inlet, microchannel branch end, and microchannel corner.

[0113] The processor 620 is used to generate microchannel control instructions by executing the filling control method based on microchannel pressure fluctuation balance as described in any of the above embodiments of this application.

[0114] The injection machine control unit 630 is used to adjust the injection rate of the injection machine according to the microfluidic control command.

[0115] The microvalve array drive unit 640 is used to adjust the opening degree of the microvalve of each microchannel branch according to the microchannel control command.

[0116] In some embodiments, this application provides a non-volatile computer-readable storage medium storing one or more programs including execution instructions, which can be read and executed by electronic devices (including but not limited to computers, servers, or network devices) to perform the steps of any of the filling control methods based on microchannel pressure fluctuation balance described above.

[0117] In some embodiments, this application also provides a computer program product, the computer program product including a computer program stored on a non-volatile computer-readable storage medium, the computer program including program instructions, which, when executed by a computer, cause the computer to perform the steps of any of the above-described filling control methods based on microchannel pressure fluctuation balance.

[0118] In some embodiments, this application also provides an electronic device comprising: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform steps of a filling control method based on microchannel pressure fluctuation balancing.

[0119] The above-described product can perform the methods provided in the embodiments of this application, and has the corresponding functional modules and beneficial effects for performing the methods. Technical details not described in detail in this embodiment can be found in the methods provided in the embodiments of this application.

[0120] The electronic devices in this application can exist in various forms, including but not limited to: mobile communication devices, ultra-mobile personal computer devices, portable entertainment devices, or other airborne electronic devices with data interaction functions.

[0121] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0122] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented using software plus a general-purpose hardware platform, or of course, using hardware. Based on this understanding, the above technical solutions, in essence or the parts that contribute to the related technology, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0123] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A filling control method based on microchannel pressure fluctuation balance, characterized in that, The method includes: Based on pressure sensors distributed at key nodes of the microchannel within the microchannel mold, instantaneous pressure data of each node are synchronously acquired at a sampling rate higher than the characteristic frequency of the injection pressure wave; the microchannel mold is used to injection mold microstructure components through each microchannel branch, and the key nodes of the microchannel include at least one of the following: microchannel branch inlet, microchannel branch end, and microchannel corner; The collected pressure data is preprocessed to extract the pressure energy characteristics of each pressure sensor within the sliding time window, and the pressure energy characteristics belonging to the same microchannel branch are aggregated based on the topology of the microchannel to calculate the pressure wave energy characteristics of each microchannel branch. Based on the pressure wave energy characteristics of each microchannel branch, a global pressure wave energy balance index is determined to characterize the overall pressure fluctuation balance state of the microchannel system. The deviation between the global pressure wave energy balance index and the preset target value is calculated, and a microchannel control command is generated based on the deviation amplitude using an adaptive control algorithm. The microchannel control command is used to drive and adjust the injection rate and microvalve opening of the injection machine for each microchannel branch, so that the pressure energy of each microchannel branch tends to be balanced. The determination of the global pressure wave energy balance index, based on the pressure wave energy characteristics of each microchannel branch, to characterize the overall pressure fluctuation balance state of the microchannel system includes: Based on the pressure wave energy characteristics of each microchannel branch, the instantaneous variance of pressure wave energy between microchannel branches is calculated: ， In the formula, Indicates the microchannel mold at time... The instantaneous variance of pressure wave energy. This indicates that all microchannel branches are at time [time]. The average energy of the pressure wave. This indicates the total number of branches in the microchannel within the microchannel mold; The global pressure wave energy equilibrium index is calculated by integrating the instantaneous variance of the pressure wave energy over a sliding time window. ， In the formula, Indicates at time The global pressure wave energy balance index, Indicates the microchannel mold at time... The instantaneous variance of pressure wave energy; The calculation of the deviation between the global pressure wave energy balance index and the preset target value includes: ， In the formula, Indicates at time The deviation range, The preset target value represents the ideal pressure wave energy equilibrium state; This is a trend sensitivity coefficient used to adjust the effect of the pressure wave energy change rate on the deviation amplitude; This indicates the rate of change of the global pressure wave energy equilibrium index; The step of generating microchannel control commands based on the deviation amplitude using an adaptive control algorithm includes: If the deviation exceeds the warning threshold, the injection rate is updated in real time based on the deviation using a fuzzy proportional-resonant control algorithm, the control law of which is: ， ， ， In the formula, and They represent the times at time 1 and 2 respectively. and Injection rate, For proportional gain, This is the integral gain; These are learnable hyperparameters; , and These represent the minimum and maximum injection rates of the injection molding machine, respectively. The step of generating microchannel control commands based on the deviation amplitude using an adaptive control algorithm includes: If the deviation exceeds the warning threshold, the micro-valve opening is generated based on the deviation using a distributed collaborative control algorithm coupled with the flow channel topology. The control law is as follows: ， ， In the formula, and They represent the first Each microchannel branch at time and The control increment of the micro-valve opening, Indicates the first Each microchannel branch at time Pressure wave energy characteristics Branch-related modulatory factors; Let be the compensation function, representing the result obtained through the th... The flow channel geometry of each microchannel branch is adaptively adjusted to compensate for the intensity of the flow compensation. and The first The branch length and cross-sectional area of ​​each microchannel branch. and These represent the maximum length and minimum cross-sectional area among all microchannel branches, respectively. , Minimum valve opening to prevent clogging.

2. The method according to claim 1, characterized in that, The pressure sensor is a MEMS piezoresistive sensor, used to accurately capture transient pressure fluctuations within the microchannel. The sampling times of each pressure sensor are synchronized using network clock synchronization technology to ensure data real-time performance and consistency. The method also includes: Obtain the operating parameters of the injection molding machine, and calculate the characteristic frequency of the injection pressure wave based on the operating parameters: ， In the formula, The characteristic frequency of the injection pressure wave represents the dominant frequency of the pressure wave during the injection process. This is a proportionality coefficient, representing the relationship between injection speed and pressure wave propagation speed; Injection speed represents the speed at which the melt flows during the injection process. The length of the microchannel represents the effective path length for the pressure wave to propagate within the microchannel. According to the Nyquist theorem, the sampling frequency of the pressure sensor is set to be more than twice the calculated characteristic frequency of the injection pressure wave.

3. The method according to claim 1, characterized in that, The preprocessing of the collected pressure data extracts the pressure energy characteristics of each pressure sensor within a sliding time window. Based on the microchannel topology, the pressure energy characteristics belonging to the same microchannel branch are aggregated to calculate the pressure wave energy characteristics of each microchannel branch, including: The original pressure signal of each pressure sensor is denoised by wavelet denoising, and the temperature drift is further compensated by Kalman filtering algorithm, thereby preprocessing the collected pressure data. The preprocessed pressure data is integrated over time using a sliding time window to calculate the pressure energy characteristics: ， In the formula, For the first Each sensor in the time window The pre-processed pressure signal inside, The window length is the sliding time window. For the virtual time variable of integration, For the first Each sensor at time The energy of the pressure signal; The pressure energy characteristics of various pressure sensors belonging to the same microchannel branch are weighted and aggregated: ， In the formula, For the first Each microchannel branch at time Pressure wave energy characteristics For the first The number of sensors within each microchannel branch For the pre-calibrated first The branch influence weight of each sensor.

4. The method according to claim 1, characterized in that, After generating microchannel control commands using an adaptive control algorithm based on the deviation magnitude, the method further includes: If continuous If, after the adaptive control adjustment, the deviation is still detected to be greater than the warning threshold, a mold blockage warning is triggered. .

5. A filling control system based on microchannel pressure fluctuation balance, characterized in that, include: A pressure sensor array is distributed within key microchannel nodes of a microchannel mold to synchronously acquire instantaneous pressure data of each node at a sampling rate higher than the characteristic frequency of the injection pressure wave. The microchannel mold is used to injection mold microstructure components through each microchannel branch. The key microchannel nodes include at least one of the following: microchannel branch inlet, microchannel branch end, and microchannel corner. A processor is configured to generate microchannel control instructions by executing the filling control method based on microchannel pressure fluctuation balance as described in any one of claims 1 to 4; The injection machine control unit is used to adjust the injection rate of the injection machine according to the microchannel control command; The microvalve array drive unit is used to adjust the opening degree of the microvalve in each microchannel branch according to the microchannel control command.

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