Density uniformity control method and system based on injection molding gas dosage and pressure collaborative optimization

Abstract

The invention discloses a density uniformity control method and system based on injection molding gas dosage and pressure collaborative optimization, and relates to the technical field of injection molding control. The method comprises the following steps: acquiring process parameters in real time; the technological parameters are preprocessed, and a virtual cavity model is constructed based on the technological parameters; carrying out defect prediction based on the virtual cavity model to obtain a defect area; generating an optimization decision scheme by integrating the defect area and the corresponding process parameters; utilizing a pre-constructed multivariable coupling model to obtain correlation coefficients among the process parameters; based on the correlation coefficient between the process parameters and the optimization decision scheme, strategy calculation is carried out for different defect types, and an adjustment strategy of each process parameter is obtained; according to the invention, crazing and shrinkage defects can be effectively inhibited, the surface quality, dimensional precision and mechanical property stability of the product are improved, the rejection rate and reworking cost are reduced, and meanwhile, the injection molding production efficiency and the product quality consistency are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of injection molding control, more particularly, the present application relates to a method and system for uniform density control based on synergistic optimization of injection gas dosage and pressure. BACKGROUND

[0002] In the injection molding production of complex structural parts such as automobile parts, in order to achieve the balance between lightweight and structural strength, chemical or physical foaming process is often used to reduce the material density and optimize the forming performance by introducing bubbles into the molten plastic. In the prior art, the control of the injection foaming process mainly relies on preset process parameters and offline monitoring means. Its technical principle is to set parameters such as injection gas addition ratio, holding pressure, back pressure value and cooling water temperature based on historical production experience, collect local cavity data through a single pressure sensor or temperature sensor, and then make simple feedback adjustment according to fixed threshold.

[0003] However, the prior art has significant limitations. Since the bubble nucleation and bubble growth process is affected by multiple factors such as material properties, melt flow state and mold temperature field, the existing monitoring means cannot capture the dynamic changes of bubble density distribution and radial growth of bubbles in real time, resulting in a lag in parameter optimization. At the same time, the control of injection gas dosage, holding pressure and cooling gradient is independent of each other, and no synergistic mechanism is formed. When the melt viscosity fluctuates or the cavity is not filled uniformly, it is easy to cause imbalance of bubble distribution. The local bubble density is too high, which will cause the melt strength to decrease and form silver lines at the stress concentration; and the bubble growth rate is not synchronized with the cooling shrinkage, which will produce shrinkage depression in the thick wall area of the product. Such defects not only reduce the mechanical properties and structural stability of the product, but also lead to insufficient assembly precision of the parts, increase the waste rate and rework cost, and seriously restrict the production efficiency and product quality stability.

[0004] In view of this, the present application proposes a method and system for uniform density control based on synergistic optimization of injection gas dosage and pressure to solve the above problems. SUMMARY

[0005] In order to overcome the above-mentioned defects of the prior art and achieve the above-mentioned purposes, the present application provides the following technical scheme: a method for uniform density control based on synergistic optimization of injection gas dosage and pressure, comprising: real-time acquisition of process parameters; preprocessing the process parameters, and constructing a virtual cavity model based on the process parameters; carrying out defect prediction based on the virtual cavity model to obtain a defect area; generating an optimization decision scheme by integrating the defect area and the corresponding process parameters; obtaining the correlation coefficients between each process parameter by using a pre-constructed multivariate coupling model; Based on the correlation coefficient between each process parameter and the optimization decision scheme, the strategy calculation is carried out for different defect types, and the adjustment strategy of each process parameter is obtained.

[0006] Further, the process parameters include cavity pressure distribution, holding pressure, mold temperature field data, cooling water temperature, melt viscosity, bubble nucleation density distribution, bubble growth rate, injection gas actual injection amount and back pressure real-time value.

[0007] Further, the method for obtaining the bubble nucleation density distribution comprises: The original echo electric signal is collected by the ultrasonic sensor array, and the amplitude, phase and propagation time of the original echo electric signal are extracted by the wavelet transform algorithm; the propagation speed is obtained by the preset ratio of the cavity wall thickness to the propagation time; the real-time density value of the melt is obtained by substituting the propagation speed into the preset correlation formula; the bubble nucleation density of different regions of the cavity is analyzed by substituting the real-time density value of the melt into the preset corresponding relationship model, and the bubble nucleation density distribution is formed.

[0008] Further, the method for obtaining the bubble growth rate comprises: The amplitudes and phases of the original echo electric signals at each time are substituted into the preset bubble radius-echo characteristic parameter correlation model respectively, and the bubble radius value at the corresponding time is calculated; the difference between the bubble radius values of adjacent two times is calculated to obtain the bubble radius difference value; the bubble growth rate in the time interval between adjacent two times is obtained by dividing the bubble radius difference value by the time interval between adjacent two times.

[0009] Further, the method for obtaining the defect region comprises: The bubble nucleation density of each cavity region is obtained by counting the bubble nucleation density distribution. The deviation degree of the bubble nucleation density of each cavity region from the average value of the bubble nucleation density of all regions is calculated to obtain the density standard deviation; the variation coefficient is obtained by dividing the density standard deviation by the average value of the bubble nucleation density of all regions. The numerical difference between the variation coefficient of each cavity region and the preset variation coefficient threshold value is calculated; if the numerical difference is greater than zero, the corresponding cavity region is marked as a bubble nucleation density abnormal region. The cooling and solidification speed of each cavity region is calculated by the preset heat conduction model combined with the mold temperature field data; the synchronization parameter of each cavity region is obtained by difference operation between the cooling and solidification speed of each cavity region and the bubble growth rate of the corresponding region. If the bubble nucleation density of the bubble nucleation density abnormal region is higher than the average value of the bubble nucleation density of the surrounding region by a preset proportion coefficient, and the melt viscosity of the bubble nucleation density abnormal region is lower than a preset reference viscosity value, it is judged that the bubble nucleation density abnormal region is a silver line risk region. If the synchronism parameter of the bubble nucleus density abnormal area exceeds the preset difference threshold value, it is determined that the bubble nucleus density abnormal area is a shrinkage risk area.

[0010] Further, the method for generating the optimization decision scheme comprises: For the craze risk area, according to the size of the bubble nucleus density difference value of the corresponding area, a parameter adjustment mode is selected, when the corresponding bubble nucleus density difference value is less than a set first difference threshold value, a mode of reducing the injection gas dose is adopted as the optimization decision scheme; when the bubble nucleus density difference value of the corresponding area is greater than or equal to the first difference threshold value, a mode of applying cavity gas counter pressure is adopted as the optimization decision scheme; the bubble nucleus density difference value is the difference between the actual bubble nucleus density of the craze risk area and the preset ideal bubble nucleus density; For the shrinkage risk area, a mode of adjusting the holding pressure and mold temperature field data is adopted as the optimization decision scheme.

[0011] Further, the method for obtaining the adjustment strategy of each process parameter comprises: The prediction time domain is set as the sum of the remaining length of the current injection cycle and the next complete injection cycle; For the craze risk scenario, if the optimization decision scheme is to adopt the mode of reducing the injection gas dose, the reduction value of the injection gas dose and the final adjustment value of the back pressure are calculated based on the bubble nucleus density change prediction result in the prediction time domain obtained through the multivariate coupling model and the correlation coefficient between each process parameter; if the optimization decision scheme is to adopt the mode of applying cavity gas counter pressure, the pressure peak value of the gas counter pressure is determined based on the melt filling progress prediction result in the prediction time domain obtained through the multivariate coupling model and the correlation coefficient between each process parameter; For the shrinkage risk scenario, based on the synchronism parameter prediction result in the prediction time domain obtained through the multivariate coupling model, the holding pressure action time, the extension amount of the mold high temperature maintenance time in the mold temperature field data, and the adjustment gradient of the cooling water temperature are calculated.

[0012] Further, the method for obtaining the holding pressure action time, the extension amount of the mold high temperature maintenance time in the mold temperature field data, and the adjustment gradient of the cooling water temperature comprises: The correlation coefficient between the synchronism parameter and the holding pressure action time adjustment amount is denoted as a first correlation coefficient; The synchronism parameter is multiplied by the first correlation coefficient to obtain the shortening range of the holding pressure action time; The correlation coefficient between the temperature and the holding pressure is denoted as a second correlation coefficient; The shortening range of the holding pressure action time is multiplied by the second correlation coefficient to obtain the initial extension amount of the mold high temperature maintenance time; Based on the mold temperature field dynamic prediction data in the prediction time domain, a cooling speed deviation coefficient is obtained; The mold high-temperature maintenance time extension amount is the mold high-temperature maintenance time initial extension amount multiplied by a cooling speed deviation coefficient; According to the determined mold high-temperature maintenance time extension amount and the cooling solidification speed of the thick-walled area in the prediction time domain, the descending gradient of the cooling water channel temperature is calculated.

[0013] Further, the method for obtaining the cooling speed deviation coefficient comprises: Based on the mold temperature field dynamic prediction data in the prediction time domain, the cooling solidification speed of the thick-walled area at different time nodes in the prediction time domain is calculated through a preset heat conduction model, the cooling solidification speed corresponding to the time node with the highest shrinkage risk of the thick-walled area is selected as the calculation reference, and the time node with the highest shrinkage risk of the thick-walled area is the time node corresponding to the maximum value of the synchronicity parameter; The cooling solidification speed corresponding to the time node with the highest shrinkage risk of the thick-walled area is divided by the ideal cooling speed obtained in advance to obtain the cooling speed deviation coefficient.

[0014] Further, the method for obtaining the descending gradient of the cooling water channel temperature comprises: The correlation coefficient between the cooling water channel temperature adjustment gradient and the mold high-temperature maintenance time is denoted as a third correlation coefficient; The correlation coefficient between the cooling water channel temperature adjustment gradient and the cooling solidification speed is denoted as a fourth correlation coefficient; The first gradient adjustment component is obtained by multiplying the mold high-temperature maintenance time extension amount and the third correlation coefficient; The second gradient adjustment component is obtained by multiplying the cooling solidification speed and the fourth correlation coefficient; The first gradient adjustment component and the second gradient adjustment component are added to obtain the cooling water channel temperature descending gradient initial value; The pre-stored cooling water channel temperature descending gradient constraint range in the multivariable coupling model is retrieved, if the cooling water channel temperature descending gradient initial value is within the cooling water channel temperature descending gradient constraint range, the descending gradient initial value is the final cooling water channel temperature descending gradient, if the descending gradient initial value exceeds the cooling water channel temperature descending gradient constraint range, the boundary value of the cooling water channel temperature descending gradient constraint range is taken as the final cooling water channel temperature descending gradient.

[0015] The injection gas amount and pressure collaborative optimization density uniform control system comprises: The data acquisition module is used for acquiring the process parameters in real time; The scheme generation module is used for preprocessing the process parameters, and constructing a virtual cavity model based on the process parameters; carrying out defect prediction based on the virtual cavity model to obtain a defect area; and generating an optimization decision scheme by comprehensively considering the defect area and the corresponding process parameters; The correlation coefficient module is configured to obtain correlation coefficients between the process parameters by using a pre-constructed multivariate coupling model; The strategy generation module is configured to calculate strategies for different defect types based on the correlation coefficients between the process parameters and the optimization decision scheme, and obtain adjustment strategies for the process parameters.

[0016] Compared with the prior art, the technical effects and advantages of the density uniformity control method and system based on injection gas dosage and pressure collaborative optimization of the present application are as follows: The present application first collects process parameters such as real-time cavity pressure distribution, holding pressure, mold temperature field data, cooling water temperature, melt viscosity, bubble nucleation density distribution, bubble growth rate, injection gas actual injection amount, and back pressure real-time value, and then constructs a virtual cavity model based on these data after preprocessing the process parameters, carries out defect prediction through the virtual cavity model to obtain a defect area, and generates an optimization decision scheme by comprehensively considering the defect area and the deviation degree of the corresponding process parameters; then, the correlation coefficients between the process parameters are obtained by using a pre-constructed multivariate coupling model, and the adjustment strategies for the process parameters such as injection gas dosage reduction value, back pressure final adjustment value, holding pressure action time shortening range, mold high temperature maintenance time extension amount, and cooling water temperature drop gradient are obtained by calculating strategies for different defect types such as silver line risk scenario and shrinkage risk scenario based on the correlation coefficients and the optimization decision scheme, and finally the adjustment strategies are encoded into control instruction sets conforming to the communication protocol and are sent to each execution unit to complete the control.

[0017] The present application solves the problem that the existing injection foaming control relies on preset process parameters and offline detection, cannot capture the bubble nucleation density distribution and bubble growth dynamic change in real time, and the injection gas dosage, holding back pressure, and cooling gradient regulation are independent and not collaborative, which easily causes defects such as silver line and shrinkage, resulting in decreased mechanical properties of the product, insufficient assembly precision, high scrap rate, and low production efficiency. The present application has the advantages of being able to obtain key molding data in real time, intuitively presenting the working condition through the virtual cavity model and accurately predicting defects, and realizing collaborative regulation of process parameters by using the multivariate coupling model, thereby avoiding the limitations of single parameter adjustment; the beneficial effects are that the silver line and shrinkage defects are effectively inhibited, the surface quality, size precision, and mechanical property stability of the product are improved, the scrap rate and rework cost are reduced, and the injection molding production efficiency and product quality consistency are improved. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 The present application is a density uniformity control system based on injection gas dosage and pressure collaborative optimization, and the schematic diagram of the present application is shown in the figure; Figure 2 The present application is a density uniformity control method based on injection gas dosage and pressure collaborative optimization, and the flowchart of the present application is shown in the figure; Figure 3 The present application is a method for obtaining a defect area, and the flowchart of the present application is shown in the figure; Figure 4 The method flow chart for obtaining the adjustment strategy of each process parameter of the embodiment of the present application. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present application will be described in detail, clearly and completely below with reference to the drawings in the embodiments of the present application. It should be particularly noted that the specific embodiments described below are only used to better illustrate and describe the technical solutions of the present application, and are intended to enable those skilled in the art to better understand and implement the present application, and should not be understood as limiting the protection scope of the present application. Those skilled in the art can modify, adjust or equivalently replace the present application according to the content disclosed in the present application without departing from the spirit and essence of the present application, and these should be regarded as the protection scope of the present application.

[0020] Embodiment 1: Please refer to Figure 1 As shown in the figure, the embodiment discloses a density uniformity control system based on injection gas dosage and pressure collaborative optimization, which comprises a data acquisition module, a scheme generation module, a correlation coefficient module and a strategy generation module. Each module is connected through wired and / or wireless connection to realize data transmission.

[0021] The data acquisition module is used for real-time acquisition of process parameters.

[0022] The process parameters include cavity pressure distribution, holding pressure, mold temperature field data, cooling water temperature, melt viscosity, bubble nucleation density distribution, bubble growth rate, actual injection amount of injection gas and real-time value of back pressure.

[0023] The injection gas amount is monitored by a sensor / mass flow meter installed on the injection machine screw cylinder or supercritical fluid injection system to monitor the amount of chemical or physical injection gas added, and the actual weight or volume of injection gas added during each injection cycle is obtained. The holding pressure is provided by the holding pressure set value and real-time measured value provided by the injection machine control system. The cavity pressure curve during holding can be obtained by a pressure sensor or cavity sensor provided by the machine hydraulic cylinder, and the peak value and duration information are collected. The real-time value of the back pressure is the back pressure set and actual value during the screw plasticizing stage. The back pressure applied during screw plasticizing is recorded by a pressure sensor at the end of the injection machine cylinder, which represents the level of back pressure experienced by the melt during metering mixing. Temperature sensors are installed at the inlet and outlet of the mold cooling circuit to monitor the cooling medium temperature in real time. If necessary, each water circuit is equipped with an adjustable valve to realize dynamic control of the local cooling flow and temperature, and the actual cooling temperature change of each area is recorded. Cavity gas back pressure: introduce a controllable gas pressure gauge into the mold cavity to raise the internal gas pressure before injection. The cavity gas pressure value is monitored by a pressure sensor, and the inflation / pressure relief time is controlled by a solenoid valve to suppress premature foaming during melt filling and ensure dense surface formation. The pressure distribution in the cavity is obtained by arranging multiple embedded high-precision micro pressure sensors in the mold cavity to synchronously collect the pressure changes at each point during filling and holding. The pressure sensor provides the spatial distribution and pressure gradient information of the pressure in the cavity, which is used to determine the position of the melt flow front and the pressure condition of each region. The mold temperature field data is obtained by arranging multiple thermocouples or optical fiber temperature sensors on the mold cavity wall to measure the temperature change of each region in real time, and the temperature field distribution in the cavity is obtained. Non-contact monitoring can also be combined with a mold surface infrared thermal imager to capture the temperature gradient and ensure a comprehensive understanding of the dynamic mold temperature. The melt viscosity is estimated by monitoring the relationship between the screw advancing speed and the injection pressure during injection, and the real-time change of the melt viscosity is calculated; or the state of the melt is detected by an ultrasonic sensor installed at the cylinder / nozzle to indirectly infer the real-time viscosity of the melt, and the change of the melt viscosity can be calculated from the characteristics of the pressure-time curve, such as the increase of the viscosity leading to the increase of the filling pressure. The melt viscosity is used to reflect the fluctuation of material properties for dynamic adjustment of the control strategy.

[0024] First, based on the wall thickness distribution and cavity complexity of the automotive parts in the injection molding foam molding machine, and considering the equipment specifications and product structure, various sensors are deployed and initial process parameters are entered. Specific methods include: embedding high-precision miniature pressure sensors at key locations such as the mold cavity inlet, middle, and end using a flush mounting structure, ensuring the sensor probes are flush with the cavity wall to avoid disturbing melt flow; fixing temperature sensors at each independent water inlet and outlet of the mold cooling circuit; arranging multiple thermocouples and fiber optic temperature sensors in different areas of the mold cavity wall, such as thick-walled and thin-walled regions, to form a three-dimensional monitoring network for the mold temperature field; and installing sensors at the injection gas injection port or supercritical fluid injection system of the injection molding machine screw and barrel. Mass flow meters are installed in the pipeline to measure the injection volume of molding gas in real time. Ultrasonic sensor arrays are fixed in the thick-wall transition zone, corners, melt flow ends, and areas where abnormal bubble nucleus density or unbalanced bubble growth rate frequently occurs in historical production, corresponding to the outer wall of the mold cavity. These areas are prone to abnormal bubble evolution due to changes in melt flow state and temperature gradient differences during injection molding foaming, and are the core locations affecting the density uniformity and defects of the product. The probes of the ultrasonic sensors are tightly attached to the outer wall of the mold through coupling agent to ensure that the ultrasonic waves can effectively penetrate the mold steel and act on the melt in the cavity. Torque sensors and displacement sensors are installed in the pre-plasticizing section and injection section of the injection molding machine screw, respectively, for subsequent estimation of melt viscosity.

[0025] After sensor deployment, the preset injection gas dosage, peak holding pressure, back pressure reference value, and target cooling water temperature are recorded. Simultaneously, the data acquisition sequence and frequency are set; for example, the data acquisition frequency must be synchronized with the injection cycle to ensure data capture of at least 50ms / time at each stage of melt filling, holding pressure, and cooling. After starting the injection molding machine, the pressure sensor continuously captures pressure changes at various locations in the cavity and generates pressure curves. The temperature sensor synchronously records dynamic data of the cooling water temperature and mold wall temperature. The mass flow meter provides real-time feedback on the instantaneous and cumulative injection volume of the injection gas. The ultrasonic sensor array emits ultrasonic waves according to a preset sequence and receives echo signals reflected by the melt. The screw torque sensor and displacement sensor record torque changes during the pre-plasticizing stage and the screw forward speed during the injection stage.

[0026] The acquired raw signals are then processed in real time. These raw signals refer to the unprocessed initial electrical signals directly output by each sensor during data acquisition, including the voltage signal output by the high-precision miniature pressure sensor based on cavity pressure changes, the resistance signal output by the temperature sensor based on temperature changes, the raw echo electrical signal received by the ultrasonic sensor array, the pulse signal output by the mass flow meter based on injection gas flow changes, the current signal output by the screw torque sensor based on torque changes, and the position signal output by the screw displacement sensor based on displacement changes. For the pressure signal output by the high-precision miniature pressure sensor and the temperature signal output by the temperature sensor, a low-pass filter algorithm is used to filter out high-frequency noise generated by equipment vibration, obtaining cavity pressure distribution and mold temperature field data. For the raw echo electrical signal received by the ultrasonic sensor array, a wavelet transform algorithm is used to extract the amplitude, phase, and propagation time of the raw echo electrical signal, which is then combined with the correlation between the sound velocity and density of the melt. The formula derives the bubble nucleus density distribution. The specific methods include: obtaining the propagation speed using the ratio of a preset cavity wall thickness to propagation time; the mold cavity wall thickness data is a fixed parameter determined during the mold design phase; then, calling a pre-established correlation formula between melt sound velocity and density for the material used in the current injection molding process. This correlation formula was obtained through prior experiments, which involved testing the sound velocity of the corresponding melt at different bubble nucleus densities and fitting multiple sets of sound velocity and density data to obtain a functional relationship between sound velocity and density; substituting the calculated propagation speed of ultrasound in the melt into the correlation formula to obtain the real-time density value of the melt; calling a pre-established correspondence model between melt density and bubble nucleus density, which was also obtained through prior experiments, which involved testing the density of the corresponding melt at different bubble nucleus densities and establishing a mapping relationship between the two; and finally, substituting the obtained real-time melt density value into the correspondence model to deduce the bubble nucleus density in different regions of the cavity, thus forming the bubble nucleus density distribution.Simultaneously, the bubble growth rate is calculated based on the time-series change rate of the echo signal. The specific method includes: calling a pre-established parameter correlation model between the bubble radius and echo characteristics, specifically for the material used in the current injection molding. This parameter correlation model was obtained through prior experiments. The experiment involved observing the bubble growth process using a high-speed camera, simultaneously recording the bubble radius and corresponding ultrasonic echo amplitude and phase data at different times, fitting multiple sets of bubble radius and echo characteristic parameter data to obtain the functional relationship between the bubble radius and the echo characteristic parameters; and extracting the echo signal at each time point using a wavelet transform algorithm based on the original echo electrical signals acquired in real-time by the ultrasonic sensor array. The amplitude and phase characteristic parameters of the wave are obtained. The amplitude and phase characteristic parameters at each moment are substituted into the correlation model between the bubble radius and the echo characteristic parameters to calculate the bubble radius value at the corresponding moment. The difference between the bubble radii at two adjacent moments is calculated, and then the difference between the bubble radii is divided by the time interval between the two adjacent moments to obtain the bubble growth rate within the time interval between the two adjacent moments. For the torque data recorded by the screw torque sensor and the displacement data recorded by the displacement sensor, the power law fluid model is used in combination with the injection pressure data of the injection molding machine. The viscosity estimation formula based on the correlation equation between the screw shear rate and the torque is used to derive the melt viscosity in real time.

[0027] The scheme generation module is used to preprocess the process parameters and construct a virtual cavity model based on the process parameters; perform defect prediction based on the virtual cavity model to obtain the defect area; and generate an optimized decision scheme by combining the defect area and the corresponding process parameters.

[0028] The methods for preprocessing process parameters include: using a time-series alignment algorithm to match data from different sensors collected in the same period according to timestamps to eliminate time deviations caused by differences in sensor response speeds; further filtering out fluctuation noise in pressure and temperature data using data smoothing algorithms such as the moving average method; simultaneously performing spatial interpolation on the bubble nucleus density distribution data obtained from ultrasonic analysis to fill data gaps in local areas of the cavity caused by insufficient sensor density, forming a complete spatial distribution map of bubble nucleus density in the cavity; and performing outlier detection on the melt viscosity estimate. If any viscosity value exceeds the upper or lower limit of the viscosity preset based on the melt index of the plastic, linear interpolation correction is performed using viscosity data from adjacent times to ensure data validity.

[0029] Based on real-time acquired process parameters, a virtual cavity model consistent with the actual molding process is constructed to simulate the dynamic processes of melt flow, bubble evolution, and cooling solidification, intuitively presenting the working conditions inside the cavity. Specific methods include: acquiring the three-dimensional geometric parameters of the cavity determined during the mold design stage, including the overall cavity outline dimensions, wall thickness of each region, corner radius, and inlet position and size; establishing a three-dimensional geometric model of the cavity in the simulation platform based on these parameters; importing the real-time acquired process parameters into the three-dimensional geometric model of the cavity, mapping the cavity pressure distribution to the model's pressure boundary conditions, mapping the mold temperature field data to the model's temperature boundary conditions, using melt viscosity as the melt flow characteristic parameter input, bubble nucleus density distribution as the bubble initial state parameter input, the actual injection volume of injection gas as the material source parameter input for bubble generation, and the real-time back pressure value as the back pressure constraint parameter input for the melt plasticization stage; and building a coupled physical field model in the simulation platform, including a melt flow model. The system employs a bubble evolution model and a cooling and solidification model. The melt flow model uses continuum mechanics equations to describe the melt flow state within the mold cavity, with parameters determined by melt viscosity estimates and cavity pressure distribution data. The bubble evolution model uses bubble kinetics equations to describe the nucleation, growth, and merging of bubbles, with parameters determined by bubble nucleus density distribution data and actual injection gas volume data. The cooling and solidification model uses heat conduction equations to describe the temperature transfer process between the mold cavity and the melt, with parameters determined by mold temperature field data. Numerical calculation methods such as the finite element method are used to solve the coupled physical field model. During the solution process, the latest acquired process parameters are used in real-time to update the parameters of the virtual cavity model, ensuring that the calculation results of the virtual cavity model are consistent with the dynamic changes of the actual molding process. The obtained melt flow trajectory, bubble size changes, and temperature distribution changes are visualized in the three-dimensional geometric model of the cavity, resulting in a complete virtual cavity model.

[0030] Please see Figure 3 As shown, defect prediction is performed based on the constructed virtual cavity model to obtain the defect region. The specific methods include: The standard deviation is obtained by statistically analyzing the deviation of the bubble nucleus density in different regions of the cavity from the average bubble nucleus density of all regions. The coefficient of variation is obtained by dividing the standard deviation by the average bubble nucleus density of all regions. The coefficient of variation of the bubble nucleus density in different regions of the cavity is compared with the coefficient of variation threshold obtained from testing similar qualified products. During the comparison, the difference between the current coefficient of variation and the coefficient of variation threshold is calculated first. If the difference is greater than zero, it indicates that the uniformity of the current bubble distribution does not meet the preset requirements, and the corresponding cavity region is marked as an abnormal bubble nucleus density region. If the difference is less than or equal to zero, it indicates that the uniformity of the current bubble distribution meets the preset requirements. By comparison, it can be clearly identified whether the bubble distribution is uniform and the specific areas of abnormal distribution. At the same time, combined with the mold temperature field data, the cooling and solidification rate of each region of the cavity is calculated using a heat conduction model. The difference between the cooling and solidification rate of each region of the cavity and the bubble growth rate of the corresponding region is calculated to obtain the synchronization parameter between the cooling and solidification rate of each region of the cavity and the bubble growth rate of the corresponding region.

[0031] If the bubble nucleus density in an abnormal bubble nucleus density area is higher than the average bubble nucleus density of the surrounding area by a preset proportional coefficient, and the melt viscosity in the abnormal bubble nucleus density area is lower than the reference viscosity value of the corresponding plastic material in the molten state, it indicates that the melt flow is too strong, and bubbles are more likely to aggregate in the abnormal bubble nucleus density area, further verifying the judgment of melt strength reduction, and thus predicting that the abnormal bubble nucleus density area is a risk area for silver streaks. The reference viscosity value is determined by consulting the material handbook and previous experimental calibration; the ideal range is the reasonable range of the coefficient of variation of bubble nucleus density obtained based on the test of similar qualified products, and the reasonable range of the coefficient of variation of bubble nucleus density is consistent with the coefficient of variation threshold used for comparison in the early stage. The occurrence of silver streaks is directly related to the degree of bubble aggregation inside the melt and the strength of the melt itself. When the coefficient of variation of bubble nucleus density exceeds the ideal range and the local bubble nucleus density is too high, it means that the bubbles in the abnormal bubble nucleus density area are excessively aggregated, which will destroy the integrity of the internal structure of the melt and reduce the tensile and shear resistance of the melt; while the melt viscosity is lower than the reference viscosity value, which will further enhance the melt flow and make bubbles more likely to aggregate in local areas. Under the dual effects, the melt strength decreases significantly. When the melt is constrained by the cavity wall or by the internal stress generated by subsequent cooling and shrinkage, it is easy to form silver streaks in the area of ​​abnormal bubble nucleus density. Therefore, the area of ​​abnormal bubble nucleus density can be predicted as the risk area of ​​silver streaks based on the above conditions.

[0032] If the difference between the bubble growth rate and the cooling and solidification rate in a region with abnormal bubble nucleus density exceeds a preset threshold, it is determined that the bubble expansion rate cannot match the material's cooling and contraction rate, and the region with abnormal bubble nucleus density is predicted to be a shrinkage risk area. The threshold is preset through a matching test between the material's cooling and contraction rate and the bubble expansion rate. For example, the difference should not exceed 10% of the cooling and solidification rate, and can be determined based on statistical data from production of similar products without shrinkage defects. Shrinkage defects arise from the failure to effectively compensate for the volume shrinkage during the material's cooling process. Only when the bubble growth rate is synchronized with the cooling and solidification rate can the volume shrinkage generated during the material's cooling process be compensated for by bubble expansion.

[0033] Finally, by combining the defective areas and their corresponding process parameters, an optimized decision-making scheme is generated. Specific methods include: For areas with silver streaks, parameter adjustment methods are selected based on the magnitude of the out-of-tolerance bubble nucleus density in the corresponding area. When the out-of-tolerance bubble nucleus density is less than the first difference threshold, the injection gas dosage is reduced. The dosage adjustment amount is calculated using the correlation curve between the out-of-tolerance bubble nucleus density and the injection gas dosage. The correlation curve is obtained by fitting multiple sets of bubble nucleus density test data under different injection gas dosages to ensure that the bubble nucleus density in this area returns to the ideal range after the dosage is reduced. When the out-of-tolerance bubble nucleus density in this specific area is greater than or equal to the first difference threshold, cavity gas back pressure is applied. The back pressure value is determined based on the difference between the real-time cavity pressure value and the ideal pressure value in this specific area. The ideal pressure value is the critical pressure to inhibit excessive bubble nucleation in this material. The critical pressure is obtained through material nucleation pressure tests. For example, after the back pressure is applied, the cavity pressure in this area should be maintained within the range of the ideal pressure value ±0.2 bar.

[0034] For areas at risk of shrinkage, the adjustment duration of the holding pressure, the extension of the corresponding high-temperature maintenance time of the mold based on mold temperature field data, and the adjustment gradient of the cooling water circuit temperature are calculated, and corresponding optimization decision schemes are generated. Specific methods include: shortening the holding pressure duration according to the calculated adjustment duration to reduce excessive melt shrinkage; extending the high-temperature maintenance time of the mold in the corresponding area according to the calculated extension to slow down the cooling rate in that area and provide sufficient time for bubble growth; the high-temperature maintenance time of the mold is a control dimension of the mold temperature field data, calculated from the temperature maintenance parameters of that area in the mold temperature field data; increasing the gradient of the cooling water circuit temperature from the high-temperature stage to the low-temperature stage according to the calculated adjustment gradient to ensure that the cooling rate adjustment promotes synchronous bubble expansion and material shrinkage; and adjusting the cooling water circuit temperature by adjusting the relevant control parameters of the cooling water circuit in the mold temperature field data. The aforementioned reduction in the holding pressure duration, extension of the mold high-temperature maintenance time, and increase in the cooling water temperature gradient are implemented synergistically to form a complete optimized decision-making scheme for shrinkage risk areas. Through multi-parameter linkage adjustment, the synchronization parameter between bubble growth rate and cooling solidification rate is brought back to a reasonable range, thereby suppressing the generation of shrinkage defects. Specifically, the holding pressure duration adjustment is calculated based on the magnitude of the synchronization parameter, which is the difference between the bubble growth rate and cooling solidification rate in the shrinkage risk area. Each time the synchronization parameter reaches a preset first proportional threshold, the holding pressure duration is shortened by a preset first duration unit. The first proportional threshold and the first duration unit are obtained through correlation tests between the holding pressure parameters and shrinkage defects of similar qualified products. The extension of the mold high-temperature maintenance time in the corresponding area is determined based on the difference between the cooling solidification rate and the ideal cooling rate in the corresponding area. The ideal cooling rate is the cooling rate that matches bubble growth and material shrinkage. The ideal cooling rate is obtained through cooling tests of similar qualified products. The temperature curve test is used to determine the temperature rate threshold. For every time the cooling solidification rate exceeds the ideal cooling rate and reaches the preset temperature rate threshold, the high temperature maintenance time of the mold is extended by a preset second duration unit. The temperature rate threshold and the second duration unit are obtained through a correlation test between the cooling parameters and shrinkage defects of similar qualified products. The cooling water temperature adjustment gradient is set according to the degree of deviation of the synchronization parameter. For every time the deviation of the synchronization parameter reaches the preset second proportional threshold, the temperature drop gradient of the cooling water from the high temperature stage to the low temperature stage is increased by a preset temperature gradient unit. The second proportional threshold and the temperature gradient unit are obtained through a correlation test between the cooling gradient and shrinkage defects of similar qualified products.

[0035] The correlation coefficient module is used to obtain the correlation coefficients between various process parameters using a pre-built multivariate coupling model.

[0036] The method for constructing a multivariate coupling model includes: the multivariate coupling model contains a multivariate coupling matrix, which is the core component of the multivariate coupling model for quantifying the correlation between process parameters. The multivariate coupling model quantifies the correlation between injection gas dosage, holding pressure, back pressure, and cooling water temperature based on historical data and real-time operating conditions. Specifically, the process involves: first, determining the mutual influence factors between each process parameter, including the direct impact strength and indirect impact path of a change in one process parameter on other process parameters; then, designing multiple orthogonal experiments, performing injection molding and foaming under different combinations of injection gas dosage, holding pressure, back pressure, and cooling water temperature, and collecting product quality data and dynamic response data of process parameters for each group of experiments to obtain experimental data; based on the experimental data, using multiple linear regression or partial least squares regression algorithms, calculating the correlation coefficients between each process parameter, where the correlation coefficient represents the amount of other process parameters that need to change synchronously corresponding to a unit change in one process parameter; finally, arranging all correlation coefficients according to the correspondence between injection gas dosage, holding pressure, back pressure, and cooling water temperature to form a multivariate coupling matrix. By using a multivariate coupling matrix, the correlation magnitude of other process parameters that need to be adjusted synchronously when one process parameter is adjusted is clearly defined, ensuring that the adjustments of each parameter form a synergistic effect rather than mutual interference. For example, it clarifies the back pressure adjustment magnitude required to maintain melt flowability when the injection gas dosage decreases by 1%; and determines the gradient value of the cooling water temperature that needs to be adjusted synchronously when the holding pressure peak changes by 0.5 bar, ensuring that the adjustments of each process parameter do not interfere with each other but instead form a synergistic effect.

[0037] Setting the prediction time domain of MPC to the sum of the remaining duration of the current injection molding cycle and the next complete injection molding cycle is crucial. Since defects in the injection molding foaming process, from the initial germination of process parameter deviations to their final appearance on the finished product, must go through the remaining melt flow, bubble evolution, and cooling stages of the current cycle, and may even extend into the molding process of the next cycle, setting the prediction time domain of MPC allows for the early capture of dynamic trends in process parameters such as injection gas dosage, holding pressure, back pressure, and cooling water temperature, as well as the defect development paths that these changes may trigger, based on historical molding data and real-time operating condition data within this time range. This provides time-dimensional data support covering the entire defect generation process for subsequent specific strategy calculations targeting defect scenarios, avoiding errors due to insufficient prediction time range. The incomplete tracking of defect evolution can lead to omissions of key control nodes in strategy calculations. The control time domain of the MPC is set according to the response speed of each process parameter adjustment. Due to differences in hardware structure and working principle, the execution units corresponding to different process parameters have different adjustment response speeds, such as injection gas metering devices, injection molding machine hydraulic / servo systems, and mold temperature controllers. The setting of the MPC's control time domain must be adapted to the response speed of each process parameter to ensure that the control commands generated for each parameter can be issued to the execution unit within the effective time corresponding to its response speed and complete the adjustment action. This avoids the situation where the control time domain is mismatched with the response speed of the process parameters, resulting in delayed or premature command issuance, which would prevent the process parameter adjustment from intervening in the defect development in a timely manner and thus affect the control effect. Setting the prediction and control time domains in MPC is a crucial prerequisite for subsequent strategy calculations based on multivariate coupled models for silver ripple and shrinkage risk scenarios. In subsequent strategy calculations, the determination of the adjustment amount and timing of each process parameter needs to be based on the prediction results of process parameter changes in the prediction time domain to determine the development node of the defect in the time dimension. At the same time, the control time domain serves as the time constraint for instruction execution, ensuring that the generated adjustment strategy can be applied to the execution unit in time before the defect worsens, thereby achieving precise suppression of the defect.

[0038] The strategy generation module calculates strategies for different defect types based on the correlation coefficients between various process parameters and the optimization decision scheme, and obtains the adjustment strategies for each process parameter.

[0039] Please see Figure 4 As shown, based on the correlation coefficients between various process parameters and the optimization decision scheme, the methods for calculating the adjustment strategies of each process parameter for different defect types include: For the silver streak risk scenario, if the optimized decision-making scheme is to reduce the injection gas dosage, firstly, based on the predicted bubble nucleus density change results in the prediction time domain, determine the peak node of the bubble nucleus density deviation in the silver streak risk area in the time dimension. Extract the bubble nucleus density deviation value of the silver streak risk area corresponding to this peak node from the optimized decision-making scheme. The bubble nucleus density deviation value is the difference between the actual bubble nucleus density and the ideal bubble nucleus density in the silver streak risk area. The ideal bubble nucleus density is obtained by testing the bubble nucleus density of similar qualified products. Specifically, bubble nucleus density data of the corresponding cavity area of ​​multiple similar qualified products are collected, and the average value of the bubble nucleus density data is calculated to obtain the ideal bubble nucleus density. Multiply the bubble nucleus density deviation value by the correlation coefficient between the bubble nucleus density deviation value and the injection gas dosage adjustment amount in the multivariate coupling matrix to obtain the reduction value of the injection gas dosage. At the same time, ensure that the adjustment action corresponding to the reduction value of the injection gas dosage can be completed within the control time domain so as to take effect before the bubble nucleus density deviation value reaches the peak. Since there is a coupled relationship between the injection gas dosage and the back pressure value, reducing the injection gas dosage will change the gas content of the melt, thereby affecting the viscosity and flow characteristics of the melt. If only the injection gas dosage is adjusted without adjusting the back pressure at the same time, the melt may experience excessive shearing when the back pressure is too high or insufficient fluidity when the back pressure is too low during the plasticizing stage. The former will damage the internal structure of the melt and exacerbate the risk of silver streaks, while the latter may lead to uneven distribution of bubble nuclei. Therefore, the back pressure adjustment value needs to be calculated simultaneously based on the coupling relationship between the two. The initial back pressure adjustment value is obtained by multiplying the reduction in injection gas dosage by the correlation between injection gas dosage and back pressure value in the multivariate coupling matrix. Combining this with the predicted melt viscosity change over the predicted time domain, the required synchronous reduction in back pressure is calculated. Specifically, the ratio of the predicted melt viscosity to the baseline viscosity of the corresponding plastic material in the molten state is calculated to obtain the melt viscosity deviation coefficient. If the predicted melt viscosity increases due to the reduction in injection gas dosage (i.e., the melt viscosity deviation coefficient is greater than 1), the final back pressure adjustment value is the initial back pressure adjustment value multiplied by (2 - melt viscosity deviation coefficient). This coefficient (2 - melt viscosity deviation coefficient) is determined through previous melt viscosity and back pressure matching experiments to ensure that the back pressure is appropriately reduced as viscosity increases. Adjustment range; if the melt viscosity is predicted to decrease due to the reduction in the injection gas dosage, i.e., the melt viscosity deviation coefficient is less than 1, then the final back pressure adjustment value is the initial back pressure adjustment value multiplied by (1 / melt viscosity deviation coefficient), ensuring that the back pressure adjustment range is appropriately increased as the viscosity decreases; through the above calculation, the value at which the back pressure needs to be reduced synchronously is finally determined, and the execution time of the back pressure adjustment action is constrained within the control time domain to avoid excessive melt shearing due to excessive back pressure; through the above calculation, the value at which the back pressure needs to be reduced synchronously is finally determined, and the execution time of the back pressure adjustment action is constrained within the control time domain, thereby reducing excessive nucleation of bubble nuclei to suppress silver streaks and ensuring melt flow and plasticization stability through the coordinated adjustment of injection gas dosage and back pressure.

[0040] If the optimized decision-making scheme adopts the method of applying cavity gas back pressure, then firstly, based on the melt filling progress prediction results in the predicted time domain, the key filling stage of excessive bubble nucleation in the crazing risk area is determined. Then, the melt filling progress of the current cycle is calculated using data from the cavity pressure sensor and displacement sensor. This melt filling progress is the ratio of the current melt filling volume to the total cavity volume. The correlation constraints between the pressure parameter and the melt filling progress and bubble nucleation density in the multivariate coupling matrix are invoked. These constraints limit the reasonable range of cavity gas back pressure under different melt filling progresses. Based on the bubble nucleation density control target in the crazing risk area, the correlation constraints are further refined. Within the constraints of the constraints, the peak pressure of the gas back pressure is determined. Simultaneously, based on the predicted melt flow velocity in the prediction time domain, the timing for applying gas back pressure is set when the melt filling progress reaches a preset threshold. This preset threshold is determined based on the correlation between melt flow and bubble nucleation in a multivariate coupling model, and the timing of application matches the control time domain to ensure that the back pressure application action can be completed within the critical filling stage, effectively suppressing excessive foaming in the silver streak risk area. Finally, the actual value of the cavity gas back pressure is collected in real time through a pressure closed-loop model and compared with the pressure peak value. The opening of the back pressure control valve is dynamically adjusted to ensure that the back pressure application process is smooth and shock-free.

[0041] For the shrinkage risk area, the correlation coefficient between the synchronization parameter and the adjustment amount of the holding pressure action time in the multivariate coupling matrix is ​​recorded as the first correlation coefficient. The synchronization parameter of the shrinkage risk area is multiplied by the first correlation coefficient to obtain the reduction of the holding pressure action time. The adjustment action corresponding to the shortened holding pressure action time is constrained within the control time domain to ensure that it takes effect before the synchronization parameter deteriorates, thereby reducing excessive melt shrinkage. The correlation coefficient between temperature and holding pressure in the multivariate coupling matrix is ​​denoted as the second correlation coefficient. The reduction in the holding pressure application time is multiplied by the second correlation coefficient to obtain the initial extension of the high-temperature maintenance time of the mold. Based on the dynamic prediction data of the mold temperature field in the prediction time domain, the cooling and solidification rate of the thick-walled region at different time nodes in the prediction time domain is calculated through a preset heat conduction model. The cooling and solidification rate corresponding to the time node with the highest shrinkage risk in the thick-walled region is selected as the calculation benchmark. The time node with the highest shrinkage risk is the time node corresponding to the maximum value of the synchronization parameter. The ideal cooling rate obtained in advance through the cooling curve test of similar qualified products is retrieved. The ideal cooling rate is the cooling and solidification rate of similar qualified products in the same time node and the same wall thickness region corresponding to the above calculation benchmark. The cooling and solidification rate corresponding to the selected time node with the highest shrinkage risk in the thick-walled region is divided by the corresponding ideal cooling rate to obtain the cooling rate deviation coefficient. The extension of the high-temperature maintenance time of the mold is the initial extension of the high-temperature maintenance time of the mold multiplied by the cooling rate deviation coefficient. Through the above calculations, the extension of the high-temperature maintenance time of the mold in the corresponding region is finally determined, and the execution time of the extension action is matched with the control time domain to ensure timely reduction of the cooling rate. The correlation coefficient between the cooling water channel temperature adjustment gradient and the mold high-temperature holding time in the multivariate coupling matrix is ​​denoted as the third correlation coefficient. The extension of the mold high-temperature holding time is multiplied by the third correlation coefficient to obtain the first gradient adjustment component. The correlation coefficient between the cooling water channel temperature adjustment gradient and the cooling solidification rate in the multivariate coupling matrix is ​​denoted as the fourth correlation coefficient. The cooling solidification rate is multiplied by the fourth correlation coefficient to obtain the second gradient adjustment component. The first gradient adjustment component and the second gradient adjustment component are added to obtain the initial value of the cooling water channel temperature decrease gradient. Finally, the pre-stored cooling water channel temperature decrease gradient constraint range in the multivariate coupling model is retrieved, and the cooling water channel temperature... The descent gradient constraint range is determined based on the maximum adjustment capacity of the injection molding equipment cooling system and the effective gradient value in the production of similar qualified products. If the initial value of the descent gradient is within the constraint range of the cooling water circuit temperature descent gradient, then the initial value of the descent gradient is the final cooling water circuit temperature descent gradient. If the initial value of the descent gradient exceeds the constraint range of the cooling water circuit temperature descent gradient, then the boundary value of the constraint range of the cooling water circuit temperature descent gradient is taken as the final cooling water circuit temperature descent gradient. This ensures that the calculated descent gradient conforms to the parameter correlation law and meets the equipment operation limitations in actual production, thereby ensuring that the cooling rate adjustment can promote the synchronous expansion of bubbles and contraction of materials.

[0042] After the adjustment strategy calculation is completed, the adjustment strategy of each process parameter is encoded into a control instruction set that conforms to the communication protocol between the injection molding machine and each execution unit. The execution time of the control instruction set is strictly matched with the control time domain to ensure that it accurately corresponds to the defect development node in the prediction time domain. Then, the control instruction set is sent to each execution unit to control each execution unit accordingly, so as to achieve early suppression and correction of defect development, and reduce scrap rate and rework cost.

[0043] Example 2: Please see Figure 2 As shown, this embodiment provides a density uniformity control method based on the synergistic optimization of injection gas dosage and pressure, including: Process parameters are acquired in real time; The process parameters are preprocessed, and a virtual cavity model is constructed based on the process parameters; defect prediction is carried out based on the virtual cavity model to obtain the defect area; and an optimized decision scheme is generated by combining the defect area and the corresponding process parameters. Using a pre-constructed multivariate coupling model, the correlation coefficients between various process parameters are obtained; Based on the correlation coefficients between various process parameters and the optimization decision scheme, strategies are calculated for different defect types to obtain adjustment strategies for each process parameter.

[0044] 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.

[0045] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for uniform density control based on injection gas dose and pressure synergy optimization, characterized in that, The method comprises the following steps: real-time acquisition of process parameters; the process parameters include cavity pressure distribution, holding pressure, mold temperature field data, cooling water temperature, melt viscosity, bubble nucleation density distribution, bubble growth rate, actual injection amount of injection gas, and real-time value of back pressure; preprocessing the process parameters and constructing a virtual cavity model based on the process parameters; based on the virtual cavity model, defects are predicted to obtain a defect area; comprehensive defect area and corresponding process parameters are generated to generate an optimization decision scheme; the method for obtaining the defect area comprises: by statistically analyzing the bubble nucleation density distribution, the bubble nucleation density of each cavity area is obtained; the deviation of the bubble nucleation density of each cavity area from the average value of the bubble nucleation density of all areas is calculated to obtain the density standard deviation; the density standard deviation is divided by the average value of the bubble nucleation density of all areas to obtain the coefficient of variation; the numerical difference between the coefficient of variation of each cavity area and the preset coefficient of variation threshold value is calculated; if the numerical difference is greater than zero, the corresponding cavity area is marked as a bubble nucleation density abnormal area; combined with the mold temperature field data, the cooling and solidification speed of each cavity area is calculated through a preset heat conduction model; the cooling and solidification speed of each cavity area is subtracted from the corresponding bubble growth rate to obtain a synchronization parameter of each cavity area; if the bubble nucleation density of the bubble nucleation density abnormal area is higher than the average value of the bubble nucleation density of the surrounding area by a preset proportion coefficient, and the melt viscosity of the bubble nucleation density abnormal area is lower than a preset reference viscosity value, the bubble nucleation density abnormal area is determined as a silver line risk area; if the synchronization parameter of the bubble nucleation density abnormal area exceeds a preset difference threshold value, the bubble nucleation density abnormal area is determined as a shrinkage risk area; a multivariate coupling model is pre-constructed to obtain the correlation coefficient between each process parameter; based on the correlation coefficient between each process parameter and the optimization decision scheme, strategy calculation is performed for different defect types to obtain the adjustment strategy of each process parameter.

2. The method of claim 1, wherein the method is based on the injection gas dose and pressure synergy optimization density uniform control method. The method for obtaining the bubble nucleation density distribution comprises: the original echo electric signal is collected by an ultrasonic sensor array, and the amplitude, phase and propagation time of the original echo electric signal are extracted by a wavelet transform algorithm; the propagation speed is obtained by a preset ratio of the cavity wall thickness to the propagation time; the propagation speed is substituted into a preset correlation formula to obtain the real-time density value of the melt; the real-time density value of the melt is substituted into a preset corresponding relationship model to analyze the bubble nucleation density of different areas of the cavity, thereby forming the bubble nucleation density distribution.

3. The method of claim 2, wherein the method is based on the injection gas dose and pressure synergy to optimize the density uniformity control. The method for obtaining the bubble growth rate comprises: the amplitudes and phases of the original echo electric signals at each time are substituted into a preset bubble radius and echo characteristic parameter correlation model to calculate the bubble radius value at the corresponding time; the difference between the bubble radius values at adjacent two times is calculated to obtain a bubble radius difference value; the bubble radius difference value is divided by the time interval between adjacent two times to obtain the bubble growth rate in the time interval between adjacent two times.

4. The method of claim 1, wherein the method is based on the injection gas dose and pressure synergy optimization density uniform control method, characterized by, The method for generating the optimization decision scheme comprises: For the silver risk area, according to the size of the corresponding area bubble density difference value, the parameter adjustment mode is selected, when the corresponding bubble density difference value is less than the set first difference threshold, the injection gas dose is reduced as the optimization decision scheme; when the bubble density difference value of the corresponding area is greater than or equal to the first difference threshold, the cavity gas back pressure is applied as the optimization decision scheme; the bubble density difference value is the difference between the actual bubble density of the silver risk area and the preset ideal bubble density; For the shrinkage risk area, the adjustment of the holding pressure and the mold temperature field data is used as the optimization decision scheme.

5. The method of claim 4, wherein the method is based on the injection gas dose and pressure synergy to optimize the density uniformity control. The method for obtaining the adjustment strategy of each process parameter comprises: The prediction time domain is set as the sum of the remaining time of the current injection cycle and the next complete injection cycle; For the silver risk scenario, if the optimization decision scheme is to reduce the injection gas dose, the reduction value of the injection gas dose and the final adjustment value of the back pressure are calculated based on the bubble density change prediction result in the prediction time domain obtained through the multivariate coupling model and the correlation coefficient between the process parameters; if the optimization decision scheme is to apply the cavity gas back pressure, the pressure peak value of the gas back pressure is determined based on the melt filling progress prediction result in the prediction time domain obtained through the multivariate coupling model and the correlation coefficient between the process parameters; For the shrinkage risk scenario, based on the synchronization parameter prediction result in the prediction time domain obtained through the multivariate coupling model, the holding pressure action time, the extension amount of the mold high temperature maintenance time in the mold temperature field data, and the adjustment gradient of the cooling water temperature are calculated.

6. The method of claim 5, wherein the method is based on the injection gas dose and pressure synergy to optimize the density uniformity control. The method for obtaining the holding pressure action time, the extension amount of the mold high temperature maintenance time in the mold temperature field data, and the adjustment gradient of the cooling water temperature comprises: The correlation coefficient between the synchronization parameter and the holding pressure action time adjustment amount is denoted as a first correlation coefficient; The synchronization parameter is multiplied by the first correlation coefficient to obtain the shortening range of the holding pressure action time; The correlation coefficient between the temperature and the holding pressure is denoted as a second correlation coefficient; The shortening range of the holding pressure action time is multiplied by the second correlation coefficient to obtain the initial extension amount of the mold high temperature maintenance time; Based on the mold temperature field dynamic prediction data in the prediction time domain, a cooling speed deviation coefficient is obtained; The mold high temperature maintenance time extension amount is the initial extension amount of the mold high temperature maintenance time multiplied by the cooling speed deviation coefficient; According to the determined mold high temperature maintenance time extension amount and the cooling solidification speed of the thick wall area in the prediction time domain, a cooling water temperature drop gradient is calculated.

7. The method of claim 6, wherein the method is based on the injection gas dose and pressure synergy to optimize the density uniformity control. The method for obtaining the cooling speed deviation coefficient comprises: Based on the mold temperature field dynamic prediction data in the prediction time domain, the cooling solidification speed of the thick wall area at different time nodes in the prediction time domain is calculated through a preset heat conduction model, the cooling solidification speed corresponding to the highest shrinkage risk time node of the thick wall area is selected as the calculation reference, and the highest shrinkage risk time node is the time node corresponding to the maximum synchronization parameter; The cooling solidification speed corresponding to the highest shrinkage risk time node of the thick wall area is divided by the pre-obtained ideal cooling speed to obtain the cooling speed deviation coefficient.

8. The method of claim 7, wherein the method is based on the injection gas dose and pressure synergy to optimize the density uniformity control. The method for obtaining the descending gradient of the cooling water path temperature comprises: a third correlation coefficient is used to record the correlation between the cooling water path temperature adjustment gradient and the mold high temperature maintenance time; a fourth correlation coefficient is used to record the correlation between the cooling water path temperature adjustment gradient and the cooling solidification speed; the first gradient adjustment component is obtained by multiplying the mold high temperature maintenance time extension amount and the third correlation coefficient; the second gradient adjustment component is obtained by multiplying the cooling solidification speed and the fourth correlation coefficient; the first gradient adjustment component and the second gradient adjustment component are added to obtain the initial value of the cooling water path temperature descending gradient; the pre-stored cooling water path temperature descending gradient constraint range in the multivariate coupling model is called, if the initial value of the cooling water path temperature descending gradient is within the cooling water path temperature descending gradient constraint range, the initial value of the cooling water path temperature descending gradient is the final cooling water path temperature descending gradient, if the initial value of the cooling water path temperature descending gradient exceeds the cooling water path temperature descending gradient constraint range, the boundary value of the cooling water path temperature descending gradient constraint range is taken as the final cooling water path temperature descending gradient.

9. A system for implementing the method of any one of claims 1-8, wherein the system is a system for injection molding gas dose and pressure collaborative optimization density uniformity control. comprise: a data acquisition module for acquiring process parameters in real time; a scheme generation module for preprocessing process parameters and constructing a virtual cavity model based on process parameters; carrying out defect prediction based on the virtual cavity model to obtain a defect area; generating an optimization decision scheme by integrating the defect area and corresponding process parameters; a correlation coefficient module for obtaining the correlation between process parameters by using a pre-constructed multivariate coupling model; a strategy generation module for calculating strategies for different defect types based on the correlation between process parameters and the optimization decision scheme to obtain adjustment strategies for process parameters.

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