Intelligent processing method and system for process parameters of plastic mold

Abstract

The invention relates to the technical field of plastic production, in particular to an intelligent processing method and system for plastic mold process parameters. The method comprises the following steps: carrying out a mold cavity pressure test on plastic mold equipment to obtain mold cavity pressure distribution data; constructing a local flow field model of the exhaust groove according to the pressure distribution data of the mold cavity; performing pressure transfer characteristic analysis by using the exhaust duct local flow field model to generate pressure transfer increase characteristic data; predicting the actual exhaust capacity of the exhaust groove according to the pressure transmission increase characteristic data, and generating actual exhaust capacity prediction data; intelligent inflation and exhaust control is carried out through the actual exhaust capacity prediction data, and optimized exhaust control parameters are obtained; and performing exhaust groove cooperative work control according to the optimized exhaust control parameters, and generating intelligent exhaust control sequence data. By dynamically adapting to analysis of the pressure change of the mold cavity, accurate control over intelligent inflation and exhaust is achieved, the exhaust efficiency is optimized, product defects are reduced, and the quality of plastic products is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of plastic production, and in particular to an intelligent processing method and system for plastic mold process parameters. Background Art

[0002] In the plastic mold process, the effective discharge of gas inside the mold cavity is an important link to ensure product quality and surface accuracy. During the mold injection molding process, high temperature and high pressure conditions make it difficult for air and volatile gases in the mold cavity to be discharged, and they are easy to accumulate during the molding process. If the gas cannot be effectively discharged, bubbles, gas lines, or even more serious gas spots and defects will appear on the surface of the finished product, affecting the appearance quality of the product, and may weaken its physical properties, leading to potential quality risks. Traditional mold design generally sets exhaust grooves in specific areas of the mold cavity, and this exhaust groove design mostly relies on fixed geometric structures and static exhaust methods, which can only help to discharge the gas inside the mold cavity to a certain extent. Subject to this fixed exhaust structure, if the exhaust groove cannot adapt to the dynamic changes in the pressure in the mold cavity, it will be difficult to ensure that the surface of the finished product is defect-free, especially when injecting larger, thicker or complex products, product quality issues are more prominent. However, the exhaust groove control in traditional plastic mold process parameters usually relies on static structure and fixed pressure sensor parameters, which makes it difficult to effectively adapt to the dynamic changes of pressure in the mold cavity and unable to optimize the gas accumulation state in different parts of the mold cavity, resulting in low exhaust efficiency and even gas residue, affecting product quality. Summary of the invention

[0003] Based on this, the present invention provides a method and system for intelligently processing process parameters of a plastic mold to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for intelligently processing process parameters of a plastic mold comprises the following steps:

[0005] Step S1: collecting the initial state of the mold cavity of the plastic mold equipment to generate the initial state data of the single mold cavity; calibrating the pressure sensor parameters according to the initial state data of the single mold cavity to generate the sensor parameter calibration data; performing the mold cavity pressure test according to the sensor parameter calibration data to obtain the mold cavity pressure distribution data;

[0006] Step S2: constructing a local flow field model of the exhaust groove according to the cavity pressure distribution data; using the local flow field model of the exhaust groove to simulate the gas flow and generate gas accumulation situation data;

[0007] Step S3: acquiring real-time exhaust groove pressure data; performing cavity pressure gradient distribution analysis on the real-time exhaust groove pressure data to obtain real-time cavity pressure distribution data; performing pressure transmission characteristic analysis based on the real-time cavity pressure distribution data to generate pressure transmission increase characteristic data; predicting the actual exhaust capacity of the exhaust groove based on the pressure transmission increase characteristic data to generate actual exhaust capacity prediction data; performing intelligent charging and exhaust control on the actual exhaust capacity prediction data through gas accumulation situation data to obtain intelligent charging and exhaust timing control data;

[0008] Step S4: Optimize the control parameters of the intelligent charging and exhaust timing control data to obtain optimized exhaust control parameters; perform exhaust slot collaborative work control according to the optimized exhaust control parameters to generate intelligent exhaust control sequence data.

[0009] The present invention collects the initial state of the mold cavity accurately and calibrates the sensor parameters. This accurate data collection helps to grasp the actual situation inside the mold cavity more accurately. Based on the mold cavity pressure distribution data, the exhaust groove local flow field model is constructed and the gas flow simulation is performed, which realizes the accurate prediction of the gas accumulation situation inside the mold cavity. By analyzing the gas flow path and the accumulation area, the potential gas residual risk can be predicted, and guidance is provided for the coordinated work control of the exhaust groove. By analyzing the mold cavity pressure gradient distribution and pressure transmission characteristics of the real-time exhaust groove pressure data, the actual exhaust capacity of the exhaust groove can be dynamically evaluated, and the exhaust process can be intelligently adjusted in combination with the gas accumulation situation data. This dynamic adjustment mechanism can effectively respond to the changes in the pressure in the mold cavity, ensure that the exhaust groove is always in the best working state, and exhaust the gas inside the mold cavity to the maximum extent. By optimizing the control parameters of the intelligent filling and exhaust timing control data, the coordinated work control of the exhaust groove is realized. According to the optimized exhaust control parameters, the opening and closing time, exhaust pressure and other parameters of each exhaust groove can be accurately controlled to realize the refined management of the exhaust process. This collaborative working mechanism can give full play to the exhaust efficiency of each exhaust groove, avoid exhaust blind spots and gas residue, and further improve exhaust efficiency and product quality. Therefore, the intelligent processing method of a plastic mold process parameter of the present invention realizes the dynamic optimization of the plastic mold process parameters by accurately collecting the initial state of the mold cavity and real-time calibration of sensor parameters, combined with advanced flow field simulation technology and intelligent control algorithms, and can monitor the mold cavity pressure distribution in real time, predict the gas accumulation situation, and intelligently adjust the exhaust operation according to the melt flow characteristics to ensure the effective discharge of the gas inside the mold cavity, thereby significantly improving the molding quality and surface accuracy of the plastic product, and further optimizing the exhaust efficiency by collaboratively controlling multiple exhaust grooves.

[0010] Preferably, step S1 comprises the following steps:

[0011] Step S11: Identify the exhaust groove area of ​​the mold cavity to obtain exhaust groove area data;

[0012] Step S12: deploying micro pressure sensors around the exhaust slots according to the exhaust slot area data to obtain pressure sensor array data;

[0013] Step S13: After the mold is closed, the mold cavity temperature, mold cavity vacuum, mold clamping force and exhaust groove air tightness of the mold cavity are collected to generate the single cavity initial state data;

[0014] Step S14: performing parameter range verification on the single cavity initial state data by using a preset standard cavity operation threshold value to generate cavity state verification data;

[0015] Step S15: calibrating the pressure sensor parameters on the pressure sensor array data using the mold cavity state verification data to generate sensor parameter calibration data;

[0016] Step S16: Performing a mold cavity pressure test using a pressure source device based on preset pressure value test sequence data, and controlling a micro pressure sensor to perform mold cavity pressure array acquisition according to sensor parameter calibration data to obtain mold cavity pressure test data;

[0017] Step S17: performing time series pressure distribution interpolation processing according to the mold cavity pressure test data to obtain mold cavity pressure distribution data.

[0018] The present invention realizes the refined monitoring of the pressure changes around the exhaust groove by identifying the exhaust groove area and deploying sensors. This distributed pressure monitoring can more comprehensively reflect the pressure state inside the mold cavity and capture subtle pressure changes. By collecting the initial state data of the mold cavity and performing parameter range verification, the accuracy and reliability of the pressure measurement are ensured. Parameters such as mold cavity temperature, vacuum degree, and clamping force will affect the pressure distribution inside the mold cavity. Therefore, before performing pressure measurement, these parameters need to be calibrated to eliminate their influence on the pressure measurement results. By calibrating the sensor using a preset standard mold cavity operation threshold, the error of the sensor itself can be eliminated, ensuring that each sensor can output an accurate pressure value. Through the preset pressure value test sequence and time-series pressure distribution interpolation processing, the complete construction of the mold cavity pressure distribution is realized. By using a pressure source device to apply different pressures to the mold cavity and recording the pressure response of each sensor, the pressure distribution of the mold cavity under different pressure states can be obtained. Through interpolation processing, discrete pressure measurement data can be converted into continuous pressure distribution data, thereby more intuitively showing the pressure change trend inside the mold cavity.

[0019] Preferably, step S2 comprises the following steps:

[0020] Step S21: dividing the exhaust groove influence area according to the cavity pressure distribution data to obtain exhaust groove influence area data;

[0021] Step S22: performing finite element local flow field analysis based on the cavity pressure distribution data, and constructing a two-dimensional flow field model to obtain a local flow field model of the exhaust groove;

[0022] Step S23: performing gas flow simulation using the exhaust slot local flow field model according to the exhaust slot influence area data to obtain regional gas accumulation trend data;

[0023] Step S24: Calculate the critical pressure time according to the regional gas accumulation trend data to obtain the critical pressure time data;

[0024] Step S25: Perform gas accumulation curve analysis on the regional gas accumulation trend data and critical pressure time data, and perform gas accumulation risk assessment based on the exhaust slot impact area data to generate gas accumulation situation data.

[0025] The present invention realizes accurate prediction of gas accumulation situation by simulating and analyzing the gas flow in the mold cavity, and provides important guidance for subsequent intelligent exhaust control. First, by analyzing the mold cavity pressure distribution data and dividing the exhaust groove influence area, the subsequent simulation and analysis are more targeted, unnecessary calculations are avoided, and efficiency is improved. A finite element local flow field model is constructed based on the mold cavity pressure distribution data, and a two-dimensional flow field model is constructed, which is more sophisticated than the traditional simplified model and can more accurately simulate the flow state of gas in the mold cavity. By using this model to simulate gas flow, more accurate regional gas accumulation trend data can be obtained. By calculating the critical pressure time, the time when gas accumulation reaches the critical state can be predicted, which is crucial for taking exhaust measures in time. Combining the regional gas accumulation trend data and the exhaust groove influence area data for gas accumulation risk assessment can provide a more comprehensive understanding of the potential risks of gas accumulation and generate gas accumulation situation data.

[0026] Preferably, step S3 comprises the following steps:

[0027] Step S31: real-time monitoring of the melt injection speed of the injection molding machine to generate melt injection speed data;

[0028] Step S32: monitoring the exhaust slot pressure in real time according to the pressure sensor array data to obtain real-time exhaust slot pressure data;

[0029] Step S33: performing cavity pressure time series aggregation on the real-time exhaust groove pressure data, and performing pressure gradient distribution analysis to obtain real-time cavity pressure distribution data;

[0030] Step S34: extracting the near-gate and far-gate venting groove pressure data from the real-time venting groove pressure data, and obtaining near-gate venting groove pressure data and far-gate venting groove pressure data respectively;

[0031] Step S35: Calculate the time when the melt reaches the venting groove according to the near-gate venting groove pressure data and the melt injection speed data to obtain the melt-venting groove time node data; perform pressure transmission characteristic analysis on the real-time cavity pressure distribution data through the melt-venting groove time node data to generate pressure transmission increase characteristic data; predict the actual venting capacity of the venting groove on the far-gate venting groove pressure data through the pressure transmission increase characteristic data to generate actual venting capacity prediction data;

[0032] Step S36: Based on the melt-exhaust slot time node data, the actual exhaust capacity prediction data is intelligently controlled through the gas accumulation situation data to obtain intelligent exhaust timing control data.

[0033] The present invention obtains real-time cavity pressure distribution data by real-time monitoring of melt injection speed and exhaust groove pressure, and performing time series aggregation and pressure gradient distribution analysis on pressure data. Compared with traditional static pressure measurement, this real-time monitoring and analysis can more accurately reflect the dynamic pressure changes inside the cavity. By extracting the pressure data of the exhaust grooves near the gate and far gate, and calculating the time when the melt reaches the exhaust groove in combination with the melt injection speed, accurate synchronization of the melt flow and gas discharge process is achieved. This enables the control system to start the exhaust operation at the best time and maximize the exhaust efficiency. Pressure transmission feature analysis and exhaust groove actual exhaust capacity prediction are introduced. By analyzing the pressure transmission increase feature, the exhaust capacity of the far gate exhaust groove can be more accurately predicted, thereby avoiding defects caused by insufficient or excessive exhaust. Intelligent filling and exhaust control is performed on the actual exhaust capacity prediction data, and intelligent filling and exhaust timing control data is generated. This intelligent control strategy can dynamically adjust the exhaust operation according to the real-time pressure changes in the cavity, realize accurate control of the exhaust process, and maximize product quality and production efficiency.

[0034] Preferably, step S35 includes the following steps:

[0035] Step S351: Calculate the pressure change rate of the venting groove pressure data near the gate to generate pressure change rate data near the gate;

[0036] Step S352: performing melt advancement speed analysis according to the near-gate pressure change rate data and the melt injection speed data to obtain melt advancement speed data;

[0037] Step S353: Calculate the time it takes for the melt to reach the exhaust groove using the melt advancement speed data to obtain melt-exhaust groove time node data;

[0038] Step S354: determining the melt filling state according to the melt-venting groove time node data, and generating the melt filling state data near the gate;

[0039] Step S355: performing a near-gate exhaust efficiency attenuation evaluation on the near-gate melt filling state data to obtain near-gate exhaust efficiency attenuation data;

[0040] Step S356: performing pressure transmission characteristic analysis on the near-gate exhaust efficiency attenuation data through the melt-exhaust groove time node data and the real-time cavity pressure distribution data to generate pressure transmission increase characteristic data;

[0041] Step S357: extract the pressure fluctuation spectrum characteristics of the far gate vent groove pressure data to obtain the far gate pressure fluctuation characteristic data; predict the actual exhaust capacity of the far gate pressure fluctuation characteristic data based on the pressure transfer increase characteristic data using the vent groove local flow field model to generate actual exhaust capacity prediction data.

[0042] The present invention can more accurately predict the time when the melt reaches the exhaust groove, that is, the melt-exhaust groove time node, by calculating the pressure change rate near the gate and analyzing the melt advancement speed in combination with the melt injection speed. By judging the melt filling state through the melt-exhaust groove time node data and evaluating the exhaust efficiency attenuation near the gate, the actual working state of the exhaust groove near the gate can be more accurately grasped. This is more reliable than relying solely on pressure data for judgment, because the change of the melt filling state will directly affect the exhaust efficiency. By analyzing the exhaust efficiency attenuation data near the gate and the real-time cavity pressure distribution data, more accurate pressure transmission increase characteristic data can be obtained. By extracting the far gate pressure fluctuation spectrum characteristics, and combining the pressure transmission increase characteristic data and the exhaust groove local flow field model, the actual exhaust capacity of the far gate exhaust groove is predicted. This prediction method that combines multi-faceted data and models is more accurate than the traditional single method, and can more accurately reflect the actual discharge capacity of the far gate exhaust groove. The actual exhaust capacity prediction data finally generated provides a more reliable reference for intelligent filling and exhaust control, thereby further improving the accuracy and efficiency of exhaust control, and ultimately improving product quality.

[0043] Preferably, step S356 includes the following steps:

[0044] Perform time difference calculation on melt-exhaust slot time node data to obtain exhaust slot time difference data;

[0045] Perform exhaust groove pressure difference analysis on real-time cavity pressure distribution data through exhaust groove time difference data to generate exhaust groove pressure difference data;

[0046] Calculate the pressure transfer rate according to the exhaust slot time difference data and the exhaust slot pressure difference data to obtain the pressure transfer rate data;

[0047] Extract the exhaust slot spacing according to the exhaust slot area data to obtain the exhaust slot spacing data;

[0048] Based on the exhaust groove spacing data and the pressure transfer rate data, the pressure transfer increase characteristic coefficient is calculated for the near-gate exhaust efficiency attenuation data to obtain the pressure transfer increase characteristic data.

[0049] The present invention obtains more detailed pressure change information by calculating the exhaust groove time difference and performing exhaust groove pressure difference analysis, which can better reflect the dynamic process of pressure transmission than a simple pressure value. By calculating the pressure transfer rate and combining the exhaust groove spacing, the pressure transfer increase characteristic coefficient is calculated for the near-gate exhaust efficiency decay data, and more comprehensive pressure transfer increase characteristic data is obtained. This comprehensive analysis method that takes into account time, pressure and space factors can more accurately describe the pressure transfer process in the mold cavity. Pressure transfer characteristic analysis is introduced. By analyzing the near-gate exhaust efficiency decay data and the real-time mold cavity pressure distribution data, more accurate pressure transfer increase characteristic data can be obtained. The actual exhaust capacity of the far-gate exhaust groove is predicted. This prediction method that combines multi-faceted data and models is more accurate than the traditional single method, and can more accurately reflect the actual exhaust capacity of the far-gate exhaust groove, and finally generate actual exhaust capacity prediction data.

[0050] Preferably, step S357 includes the following steps:

[0051] The pressure transfer increase feature data is used to analyze the gas pressure transfer of the melt-exhaust groove time node data, and the first pressure change of the exhaust groove is identified to generate the first pressure change time data of the exhaust groove;

[0052] Extract the pressure fluctuation spectrum characteristics of the remote gate exhaust groove pressure data to obtain the remote gate pressure fluctuation characteristic data;

[0053] Perform gas residual risk assessment based on remote gate pressure fluctuation characteristic data to generate gas residual risk data;

[0054] Based on the gas residual risk data, the local flow field model of the exhaust slot is used to simulate the gas filling and discharging airflow, and the simulation data of the exhaust slot filling and discharging airflow is obtained;

[0055] Calculate the gas flow rate of the exhaust slot according to the simulation data of the filling and discharging airflow of the exhaust slot to obtain the gas flow rate data of the exhaust slot;

[0056] Based on the exhaust slot first pressure change time data, the exhaust slot gas flow rate data is used to analyze the exhaust pressure stabilization time of the pressure transfer increase characteristic data to obtain the exhaust pressure stabilization time data;

[0057] The effective exhaust time period is analyzed by using the exhaust pressure stabilization time data to the exhaust slot first pressure change time data to obtain the exhaust slot effective exhaust time period;

[0058] The actual exhaust capacity of the exhaust slot during the effective exhaust time period is predicted using the exhaust slot gas flow rate data to generate actual exhaust capacity prediction data.

[0059] The present invention uses the pressure transmission increase characteristic data to analyze the melt-venting groove time node data, identifies the time of the first pressure change in the venting groove, and uses this as the starting point of the exhaust process. Then, the fluctuation spectrum characteristics of the far gate venting groove pressure data are extracted to evaluate the risk of gas residue. On this basis, the local flow field model of the venting groove is used to simulate the gas filling and discharging airflow, calculate the gas flow rate of the venting groove, and combine the time of the first pressure change of the venting groove to analyze the moment when the exhaust pressure reaches a stable state. Finally, by analyzing the effective exhaust time period between the exhaust pressure stabilization moment and the first pressure change time, and combining the exhaust groove gas flow rate, the actual exhaust capacity of the exhaust groove is accurately predicted, so that the prediction result is more accurate, thereby better guiding the intelligent filling and exhaust control, and using the exhaust groove gas flow rate and the effective exhaust time period to predict the actual exhaust capacity, a more accurate prediction result is obtained.

[0060] Preferably, step S36 includes the following steps:

[0061] Step S361: Calculate the expected exhaust volume of the exhaust slot according to the gas accumulation situation data to obtain the expected exhaust volume data of the exhaust slot;

[0062] Step S362: performing exhaust capacity matching on the expected exhaust volume data of the exhaust slot and the actual exhaust capacity prediction data to generate exhaust capacity matching data;

[0063] Step S363: performing exhaust bottleneck position identification on the exhaust capacity matching data to obtain exhaust bottleneck position data;

[0064] Step S364: performing exhaust slot operation distribution processing according to the exhaust capacity matching data, and marking the high-load risk area through the exhaust bottleneck position data to obtain high-load risk area data;

[0065] Step S365: collecting the exhaust slot working condition data of the high-load risk area to obtain the high-risk exhaust slot working condition data;

[0066] Step S366: Based on the preset exhaust slot intelligent control strategy, a mapping relationship between the exhaust slot working condition and the exhaust capacity is established using a random forest algorithm, and model training is performed using preset historical exhaust slot adjustment data, thereby constructing an intelligent charging and exhaust control model;

[0067] Step S367: The exhaust capacity matching data, melt-exhaust slot time node data, and high-risk exhaust slot working condition data are transmitted to the intelligent charging and exhaust control model for intelligent charging and exhaust adjustment to obtain intelligent charging and exhaust timing control data.

[0068] The present invention realizes precise control of the exhaust process through an intelligent charging and exhaust control model, thereby optimizing the exhaust efficiency to the greatest extent and reducing production risks. By calculating the expected exhaust volume of the exhaust slot and matching it with the actual exhaust capacity, the potential exhaust bottleneck position can be identified, which provides a direction for subsequent targeted optimization. By distributing the exhaust slot operations and marking the high-load risk areas, the exhaust process can be managed more finely and the high-risk areas can be monitored in a focused manner. The high-risk exhaust slot working condition data is collected to provide important training data for the subsequent intelligent control model. The mapping relationship between the exhaust slot working condition and the exhaust capacity is established using the random forest algorithm, and the model is trained through historical exhaust slot adjustment data to construct an intelligent charging and exhaust control model. This data-driven model can more accurately predict the exhaust capacity under different working conditions and make intelligent adjustments accordingly. By inputting the exhaust capacity matching data, the melt-exhaust slot time node data, and the high-risk exhaust slot working condition data into the intelligent charging and exhaust control model, more accurate intelligent charging and exhaust timing control data can be obtained.

[0069] Preferably, step S4 comprises the following steps:

[0070] Step S41: mapping the intelligent charging and exhausting timing control data to the exhaust slot control process parameters to obtain the exhaust slot control process parameters;

[0071] Step S42: analyzing the influence of the process parameters of the plastic mold according to the exhaust groove control process parameters, and optimizing the control parameters to obtain optimized exhaust control parameters;

[0072] Step S43: generating a segmented control instruction sequence for the optimized exhaust control parameters to obtain segmented control instruction sequence data;

[0073] Step S44: Perform exhaust slot collaborative work control according to the segmented control instruction sequence data to generate intelligent exhaust control sequence data.

[0074] The present invention realizes the conversion from abstract control strategy to specific control parameters by mapping intelligent charging and exhaust timing control data to exhaust slot control process parameters, mapping intelligent charging and exhaust timing control data to specific exhaust slot control process parameters, and analyzing the influence of these parameters on the plastic mold process, thereby optimizing the control parameters to ensure that they meet the actual production needs. On this basis, the optimized exhaust control parameters are converted into a segmented control instruction sequence to facilitate more refined control of the exhaust slot. Finally, according to the segmented control instruction sequence, the collaborative work control of multiple exhaust slots is realized, and a complete intelligent exhaust control sequence data is generated. The control sequence data comprehensively considers the working state, exhaust capacity and gas distribution of each exhaust slot, and can realize dynamic and precise control of the exhaust process, maximize the exhaust efficiency, reduce product defects and improve product quality. The influence of the exhaust slot control process parameters on the plastic mold process parameters is analyzed, and the control parameters are optimized, which can further improve the exhaust control effect and avoid potential process problems. This reflects the attention and optimization of the process details of the scheme. The optimized exhaust control parameters are converted into a segmented control instruction sequence, and the collaborative work control of the exhaust slot is realized. This segmented control strategy can control the exhaust process more finely and make dynamic adjustments based on actual conditions, thereby improving the flexibility and adaptability of control.

[0075] Preferably, the present invention further provides an intelligent processing system for plastic mold process parameters, which executes the intelligent processing method for plastic mold process parameters as described above, and the intelligent processing system for plastic mold process parameters comprises:

[0076] The mold cavity state sensing module is used to collect the initial state of the mold cavity of the plastic mold equipment and generate the initial state data of the single mold cavity; calibrate the pressure sensor parameters according to the initial state data of the single mold cavity and generate the sensor parameter calibration data; perform mold cavity pressure testing according to the sensor parameter calibration data to obtain the mold cavity pressure distribution data;

[0077] The exhaust flow field simulation module is used to construct the exhaust slot local flow field model according to the cavity pressure distribution data; the exhaust slot local flow field model is used to simulate the gas flow and generate gas accumulation situation data;

[0078] An intelligent exhaust capacity prediction module is used to obtain real-time exhaust slot pressure data; perform cavity pressure gradient distribution analysis on the real-time exhaust slot pressure data to obtain real-time cavity pressure distribution data; perform pressure transmission characteristic analysis based on the real-time cavity pressure distribution data to generate pressure transmission increase characteristic data; perform actual exhaust capacity prediction on the exhaust slot based on the pressure transmission increase characteristic data to generate actual exhaust capacity prediction data; perform intelligent charging and exhaust control on the actual exhaust capacity prediction data through gas accumulation situation data to obtain intelligent charging and exhaust timing control data;

[0079] The exhaust collaborative control module is used to optimize the control parameters of the intelligent charging and exhaust timing control data to obtain optimized exhaust control parameters; the exhaust slot collaborative work control is performed according to the optimized exhaust control parameters to generate intelligent exhaust control sequence data. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Figure 1 It is a schematic diagram of the steps of the intelligent processing method of the plastic mold process parameters of the present invention;

[0081] Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart;

[0082] Figure 3 for Figure 1 Detailed implementation steps of step S4 in FIG.

[0083] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0084] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0085] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0086] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0087] To achieve this, please refer to Figures 1 to 3 The present invention provides an intelligent processing method for plastic mold process parameters, comprising the following steps:

[0088] Step S1: collecting the initial state of the mold cavity of the plastic mold equipment to generate the initial state data of the single mold cavity; calibrating the pressure sensor parameters according to the initial state data of the single mold cavity to generate the sensor parameter calibration data; performing the mold cavity pressure test according to the sensor parameter calibration data to obtain the mold cavity pressure distribution data;

[0089] Step S2: constructing a local flow field model of the exhaust groove according to the cavity pressure distribution data; using the local flow field model of the exhaust groove to simulate the gas flow and generate gas accumulation situation data;

[0090] Step S3: acquiring real-time exhaust groove pressure data; performing cavity pressure gradient distribution analysis on the real-time exhaust groove pressure data to obtain real-time cavity pressure distribution data; performing pressure transmission characteristic analysis based on the real-time cavity pressure distribution data to generate pressure transmission increase characteristic data; predicting the actual exhaust capacity of the exhaust groove based on the pressure transmission increase characteristic data to generate actual exhaust capacity prediction data; performing intelligent charging and exhaust control on the actual exhaust capacity prediction data through gas accumulation situation data to obtain intelligent charging and exhaust timing control data;

[0091] Step S4: Optimize the control parameters of the intelligent charging and exhaust timing control data to obtain optimized exhaust control parameters; perform exhaust slot collaborative work control according to the optimized exhaust control parameters to generate intelligent exhaust control sequence data.

[0092] In the embodiment of the present invention, reference Figure 1 The above is a schematic flow chart of the steps of the intelligent processing method of the plastic mold process parameters of the present invention. In this embodiment, the intelligent processing method of the plastic mold process parameters includes the following steps:

[0093] Step S1: collecting the initial state of the mold cavity of the plastic mold equipment to generate the initial state data of the single mold cavity; calibrating the pressure sensor parameters according to the initial state data of the single mold cavity to generate the sensor parameter calibration data; performing the mold cavity pressure test according to the sensor parameter calibration data to obtain the mold cavity pressure distribution data;

[0094] In the embodiment of the present invention, it is assumed that the plastic mold is used to manufacture mobile phone cases, including but not limited to mobile phone cases, and the material is ABS plastic. After the mobile phone case ABS plastic mold is molded and before the melt is injected, the initial state data such as mold temperature, cavity vacuum, and clamping force are collected using temperature sensors, vacuum gauges, clamping force sensors and other equipment to generate single cavity initial state data. The micro pressure sensor array arranged around the exhaust groove is calibrated using known pressures, a mapping relationship between the sensor output value and the actual pressure value is established, and sensor parameter calibration data is generated. When the mold cavity is in a stable state, a series of preset pressure values ​​are applied to the mold cavity using a pressure source device, and the pressure data of each point in the mold cavity are collected using a calibrated pressure sensor array, and the mold cavity pressure distribution data is generated in combination with an interpolation algorithm.

[0095] Step S2: constructing a local flow field model of the exhaust groove according to the cavity pressure distribution data; using the local flow field model of the exhaust groove to simulate the gas flow and generate gas accumulation situation data;

[0096] In an embodiment of the present invention, based on the pressure distribution data of the mobile phone case mold cavity obtained in step S1, a finite element analysis software (such as COMSOL, ANSYS Fluent) is used to construct a local flow field model of the exhaust groove. The pressure distribution data is applied to the model as a boundary condition, and the physical properties of the ABS plastic melt and the gas are set. The model is used to simulate the flow and accumulation of gas in the mold cavity during the filling process of the ABS plastic melt, and the concentration, pressure and other data of the gas in the mold cavity at different times are obtained. Combined with the preset critical pressure value, the risk of gas accumulation is evaluated, and gas accumulation situation data is generated, such as the division of high-risk, medium-risk and low-risk areas.

[0097] Step S3: acquiring real-time exhaust groove pressure data; performing cavity pressure gradient distribution analysis on the real-time exhaust groove pressure data to obtain real-time cavity pressure distribution data; performing pressure transmission characteristic analysis based on the real-time cavity pressure distribution data to generate pressure transmission increase characteristic data; predicting the actual exhaust capacity of the exhaust groove based on the pressure transmission increase characteristic data to generate actual exhaust capacity prediction data; performing intelligent charging and exhaust control on the actual exhaust capacity prediction data through gas accumulation situation data to obtain intelligent charging and exhaust timing control data;

[0098] In an embodiment of the present invention, during the ABS plastic injection molding process of the mobile phone shell, a pressure sensor array arranged near the exhaust groove is used to collect pressure data in real time. The collected pressure data is subjected to time series analysis and interpolation processing to obtain real-time cavity pressure distribution data. The distribution and change of the pressure gradient are analyzed, and the characteristics of pressure transfer, such as the propagation speed of the pressure wave, the change of the pressure peak, etc., are extracted to generate pressure transfer increase characteristic data. Combined with the pressure transfer characteristics and the local flow field model of the exhaust groove, the actual exhaust capacity of the exhaust groove is predicted. According to the gas accumulation situation data generated in step S2 and the predicted exhaust capacity, it is determined whether the exhaust parameters need to be adjusted, such as the opening degree and opening time of the exhaust valve. If adjustment is required, the intelligent charging and exhaust timing control data is generated according to the preset control strategy.

[0099] Step S4: Optimize the control parameters of the intelligent charging and exhaust timing control data to obtain optimized exhaust control parameters; perform exhaust slot collaborative work control according to the optimized exhaust control parameters to generate intelligent exhaust control sequence data.

[0100] In an embodiment of the present invention, the intelligent filling and exhaust timing control data is analyzed and mapped to specific exhaust slot control process parameters, such as voltage, current, etc. The finite element analysis software is used to simulate the influence of different control parameters on the pressure, temperature and other parameters in the mold cavity, and the control parameters are optimized to obtain the optimized exhaust control parameters. According to the optimized control parameters and the flow of the melt, the coordinated work of each exhaust slot is controlled, such as adjusting the opening time and opening degree of different exhaust slots, generating intelligent exhaust control sequence data, and sending it to the control system of the injection molding machine, so as to realize the precise control of the exhaust process, and finally improve the production quality of the mobile phone case.

[0101] Preferably, step S1 comprises the following steps:

[0102] Step S11: Identify the exhaust groove area of ​​the mold cavity to obtain exhaust groove area data;

[0103] Step S12: deploying micro pressure sensors around the exhaust slots according to the exhaust slot area data to obtain pressure sensor array data;

[0104] Step S13: After the mold is closed, the mold cavity temperature, mold cavity vacuum, mold clamping force and exhaust groove air tightness of the mold cavity are collected to generate the single cavity initial state data;

[0105] Step S14: performing parameter range verification on the single cavity initial state data by using a preset standard cavity operation threshold value to generate cavity state verification data;

[0106] Step S15: calibrating the pressure sensor parameters on the pressure sensor array data using the mold cavity state verification data to generate sensor parameter calibration data;

[0107] Step S16: Performing a mold cavity pressure test using a pressure source device based on preset pressure value test sequence data, and controlling a micro pressure sensor to perform mold cavity pressure array acquisition according to sensor parameter calibration data to obtain mold cavity pressure test data;

[0108] Step S17: performing time series pressure distribution interpolation processing according to the mold cavity pressure test data to obtain mold cavity pressure distribution data.

[0109] In the embodiment of the present invention, taking the ABS plastic mold for producing a mobile phone shell as an example, first use a three-dimensional scanner to scan the mold cavity to obtain high-precision point cloud data, and then import the point cloud data into reverse engineering software such as Geomagic Studio, perform point cloud processing, mesh reconstruction and surface fitting, and obtain a three-dimensional CAD model of the mold cavity. Next, open the model in CAD software (such as SolidWorks, UG, etc.), and use the measurement tools and analysis functions of the software to identify the pre-designed or processed exhaust groove area, such as a narrow groove area with a depth of less than 0.02mm and a width of less than 0.5mm. Based on the exhaust groove area data of the mobile phone shell mold obtained in step S11, such as the position and size of the exhaust groove, determine the installation position of the micro pressure sensor. Select a suitable micro pressure sensor, such as a MEMS pressure sensor, which is small enough to be arranged around the narrow exhaust groove. Use a high-precision adhesive to fix the sensor near the exhaust groove, such as at the end or side wall of the exhaust groove, to ensure that the sensor is flush with the cavity surface to avoid affecting the flow of the plastic melt. The number and arrangement of sensors are determined according to the shape and size of the exhaust groove. For example, for linear exhaust grooves, multiple sensors can be arranged at equal intervals along the length of the exhaust groove; for surface exhaust grooves, a matrix arrangement can be adopted. The numbers, position coordinates and connection methods of all sensors are recorded to form pressure sensor array data. After the mobile phone case mold is closed and before the ABS plastic melt is injected, the initial state data of the mold cavity is collected. The mold temperature data is collected using the temperature sensor pre-installed on the mold, for example, thermocouples are arranged at key positions such as the cavity, runner, and gate to monitor temperature changes in real time. The vacuum degree of the mold cavity is measured using a vacuum gauge. The clamping force data is obtained through the sensor on the mold clamping device of the mold. For the air tightness of the exhaust groove, the pressure decay method can be used for detection, that is, a certain pressure of gas is filled into the cavity, and then the air inlet valve is closed to observe the speed of pressure drop, so as to judge the air tightness of the exhaust groove. The collected mold temperature, cavity vacuum degree, mold clamping force and exhaust groove air tightness data are integrated to generate the initial state data of the single cavity. According to the production process requirements of ABS mobile phone shells, preset standard cavity operation thresholds, such as mold temperature range of 180℃-200℃, cavity vacuum greater than -0.08MPa, clamping force range of 100-120 tons, exhaust groove airtightness leakage rate less than 5%. Compare the single cavity initial state data collected in step S13 with the preset threshold to determine whether each parameter is within the allowable range. For example, if the mold temperature is 190℃, the parameter is within the preset range and the verification result is "qualified"; if the cavity vacuum is -0.07MPa, the parameter is not within the preset range and the verification result is "unqualified". Record the verification results of all parameters to generate cavity state verification data.According to the cavity state verification data generated in step S14, if all parameters are qualified, it is considered that the initial state of the cavity meets the requirements, and the pressure sensor parameters can be calibrated. When the cavity is in a stable state, parameters such as temperature, vacuum, and clamping force are all stable near the set value, and the pressure sensor array deployed in step S12 is calibrated. The specific method is: fill a series of gases with known pressures into the cavity, such as 0.1MPa, 0.2MPa, 0.3MPa, etc., and record the output value of each sensor at each pressure value. Using fitting methods such as the least squares method, establish the relationship between each sensor output value and the actual pressure value, such as a linear relationship, a polynomial relationship, etc. The parameters obtained by fitting, such as slope, intercept, polynomial coefficient, etc., are saved as sensor parameter calibration data. In the injection molding process of ABS mobile phone shells, based on the preset pressure value test sequence data, for example, increasing from 0MPa to 1MPa at intervals of 0.1MPa, a pressure source device (such as a nitrogen bottle, an air compressor, etc.) is used to apply pressure to the cavity. At the same time, according to the sensor parameter calibration data generated in step S15, the micro pressure sensor is controlled to collect the cavity pressure array. At each pressure value, the output value of each sensor is converted into an actual pressure value, and the corresponding timestamp is recorded to finally obtain the cavity pressure test data. The cavity is discretized into a number of grid points, and the pressure value of each grid point is calculated according to the pressure value and position information of the sensor array. For areas where no sensors are arranged, the pressure value is estimated by the interpolation algorithm, and finally the cavity pressure distribution data is obtained.

[0110] As an example of the present invention, refer to Figure 2 As shown, Figure 1 Detailed implementation steps of step S2 in the embodiment are shown in the flowchart. In this embodiment, step S2 includes:

[0111] Step S21: dividing the exhaust groove influence area according to the cavity pressure distribution data to obtain exhaust groove influence area data;

[0112] In an embodiment of the present invention, the exhaust slot influence area is divided using indicators such as pressure gradient. For example, a pressure gradient threshold is set, and the area where the pressure gradient is greater than the threshold is defined as the exhaust slot influence area, and it is considered that the gas flow in this area is mainly affected by the exhaust slot. Programming languages ​​such as MATLAB and Python can be used in combination with numerical calculation methods to calculate the pressure gradient of each grid point. The boundary of the exhaust slot influence area is extracted, and its geometric shape, position, area and other data are saved, so as to obtain the exhaust slot influence area data.

[0113] Step S22: performing finite element local flow field analysis based on the cavity pressure distribution data, and constructing a two-dimensional flow field model to obtain a local flow field model of the exhaust groove;

[0114] In an embodiment of the present invention, according to the CAD model of the mold cavity, the geometric model of the exhaust groove influence area is extracted and simplified into a two-dimensional model, for example, the analysis is performed on a plane perpendicular to the exhaust groove direction. Then, the pressure distribution data obtained in step S17 is applied to the two-dimensional model as a boundary condition. The physical properties of the ABS plastic melt, such as viscosity, density, etc., are set. Finally, finite element meshing is performed, and a suitable solver is selected for calculation to obtain a local flow field model of the exhaust groove, including information such as velocity field and pressure field. For example, using finite element analysis software, such as ANSYS Fluent, the three-dimensional model of the mold cavity obtained in step S11 is imported, and the local flow field analysis area is defined according to the exhaust groove influence area data obtained in step S21. The flow characteristic parameters of the ABS plastic melt, such as viscosity, density, etc., and the injection molding process parameters, such as injection speed, melt temperature, etc., are input into the software. Based on the cavity pressure distribution data, the boundary conditions of the analysis area, such as pressure inlet, pressure outlet, etc., are set. Finite element simulation calculation is performed to obtain the distribution of flow field velocity, pressure and other parameters in the exhaust groove influence area. The simulation results are processed in two dimensions to construct a local flow field model of the exhaust slot, for example, the flow field direction and velocity magnitude are displayed in the form of a vector diagram.

[0115] Step S23: performing gas flow simulation using the exhaust slot local flow field model according to the exhaust slot influence area data to obtain regional gas accumulation trend data;

[0116] In the embodiment of the present invention, the gas generation rate is set according to the pyrolysis characteristics of ABS plastic, and the exhaust groove influence area data obtained in step S21 is used as the simulation area boundary. The flow, diffusion and discharge process of the gas in the exhaust groove influence area during the injection molding process is simulated. The gas concentration, pressure and other parameters at various positions in the simulation area are recorded at different times to obtain regional gas accumulation trend data, for example, the change trend of the gas concentration in the simulation area over time is displayed in the form of a dynamic curve chart.

[0117] Step S24: Calculate the critical pressure time according to the regional gas accumulation trend data to obtain the critical pressure time data;

[0118] In the embodiment of the present invention, according to the production process requirements of ABS plastic mobile phone shell, the critical pressure value of gas accumulation is set, for example, 200Pa. The regional gas accumulation trend data obtained in step S23 is analyzed to find the time corresponding to when the gas pressure in the simulated area reaches the critical pressure value, which is the critical pressure time. The critical pressure time at different positions is recorded to generate critical pressure time data. For example, the data can be stored in the form of a two-dimensional matrix, where each element represents the critical pressure time of a grid node.

[0119] Step S25: Perform gas accumulation curve analysis on the regional gas accumulation trend data and critical pressure time data, and perform gas accumulation risk assessment based on the exhaust slot impact area data to generate gas accumulation situation data.

[0120] In an embodiment of the present invention, a gas accumulation curve is drawn based on the gas accumulation trend data of the mobile phone case mold area obtained in step S23 and the critical pressure time data calculated in step S24, such as a curve showing the change of gas pressure over time in different regions. The slope, peak value and other characteristics of the curve are analyzed to evaluate the speed and degree of gas accumulation. Combined with the exhaust groove impact area data obtained in step S21, such as the area and position of the area, the risk of gas accumulation is evaluated. For example, areas with larger areas and locations close to key parts have a higher risk of gas accumulation. The gas accumulation trend, critical pressure time and risk assessment results are integrated to generate gas accumulation situation data. For example, the gas accumulation risk levels of different areas can be divided into high risk, medium risk, low risk, etc.

[0121] Preferably, step S3 comprises the following steps:

[0122] Step S31: real-time monitoring of the melt injection speed of the injection molding machine to generate melt injection speed data;

[0123] Step S32: monitoring the exhaust slot pressure in real time according to the pressure sensor array data to obtain real-time exhaust slot pressure data;

[0124] Step S33: performing cavity pressure time series aggregation on the real-time exhaust groove pressure data, and performing pressure gradient distribution analysis to obtain real-time cavity pressure distribution data;

[0125] Step S34: extracting the near-gate and far-gate venting groove pressure data from the real-time venting groove pressure data, and obtaining near-gate venting groove pressure data and far-gate venting groove pressure data respectively;

[0126] Step S35: Calculate the time when the melt reaches the venting groove according to the near-gate venting groove pressure data and the melt injection speed data to obtain the melt-venting groove time node data; perform pressure transmission characteristic analysis on the real-time cavity pressure distribution data through the melt-venting groove time node data to generate pressure transmission increase characteristic data; predict the actual venting capacity of the venting groove on the far-gate venting groove pressure data through the pressure transmission increase characteristic data to generate actual venting capacity prediction data;

[0127] Step S36: Based on the melt-exhaust slot time node data, the actual exhaust capacity prediction data is intelligently controlled through the gas accumulation situation data to obtain intelligent exhaust timing control data.

[0128] In an embodiment of the present invention, for example, in the process of ABS plastic injection molding of a mobile phone shell, the displacement and speed of the screw or plunger are monitored in real time using a sensor or an external sensor provided by the injection molding machine, and converted into a melt injection speed. For example, a displacement sensor can be used to measure the advancement distance of the screw, and the volume change of the melt can be calculated according to the geometric parameters of the screw, thereby obtaining the melt injection speed. The melt injection speed data is associated with a timestamp and saved in real time to generate melt injection speed data. The pressure sensor array deployed in step S12 is used to monitor the pressure changes near the exhaust groove in real time. The signal collected by the sensor is converted into a pressure value after amplification, filtering, etc. The pressure data of each sensor is associated with a timestamp and a sensor number, and saved in real time to obtain real-time exhaust groove pressure data. Based on the real-time exhaust groove pressure data of the mobile phone shell mold obtained in step S32, the pressure data at different time points are aggregated using a time series analysis method, such as calculating the average pressure and maximum pressure at each time point. Then, based on the aggregated pressure data and the position information of the sensor, the pressure values ​​at other positions in the cavity are calculated using an interpolation algorithm (such as Kriging interpolation, inverse distance weighted interpolation, etc.) to obtain real-time cavity pressure distribution data. According to the distance between the gate position and the venting groove position, the venting groove is divided into a near-gate venting groove and a far-gate venting groove. When the near-gate venting groove pressure data shows a significant increase, it indicates that the melt has reached the venting groove. Combined with the melt injection speed data obtained in step S31, the time for the melt to reach the venting groove from the gate can be calculated to obtain the melt-venting groove time node data. Using the time node data, the real-time cavity pressure distribution data obtained in step S33 is analyzed to extract the characteristics of pressure transmission, such as the propagation speed of the pressure wave, the change of the pressure peak, etc., to generate pressure transmission increase characteristic data. Then, based on the pressure transmission increase characteristic data and the far-gate venting groove pressure data, the actual exhaust capacity of the venting groove is predicted to generate actual exhaust capacity prediction data. For example, if the exhaust capacity is predicted to be insufficient to exhaust the accumulated gas, the exhaust valve opening or opening time can be adjusted according to the gas accumulation situation data to increase the exhaust volume; if the exhaust capacity is predicted to be excessive, the exhaust volume can be reduced to save energy. According to the control strategy, intelligent charging and exhaust timing control data, such as the exhaust valve opening, opening time, etc., are generated and sent to the injection molding machine control system to achieve precise control of the exhaust process.

[0129] Preferably, step S35 includes the following steps:

[0130] Step S351: Calculate the pressure change rate of the venting groove pressure data near the gate to generate pressure change rate data near the gate;

[0131] Step S352: performing melt advancement speed analysis according to the near-gate pressure change rate data and the melt injection speed data to obtain melt advancement speed data;

[0132] Step S353: Calculate the time it takes for the melt to reach the exhaust groove using the melt advancement speed data to obtain melt-exhaust groove time node data;

[0133] Step S354: determining the melt filling state according to the melt-venting groove time node data, and generating the melt filling state data near the gate;

[0134] Step S355: performing a near-gate exhaust efficiency attenuation evaluation on the near-gate melt filling state data to obtain near-gate exhaust efficiency attenuation data;

[0135] Step S356: performing pressure transmission characteristic analysis on the near-gate exhaust efficiency attenuation data through the melt-exhaust groove time node data and the real-time cavity pressure distribution data to generate pressure transmission increase characteristic data;

[0136] Step S357: extract the pressure fluctuation spectrum characteristics of the far gate vent groove pressure data to obtain the far gate pressure fluctuation characteristic data; predict the actual exhaust capacity of the far gate pressure fluctuation characteristic data based on the pressure transfer increase characteristic data using the vent groove local flow field model to generate actual exhaust capacity prediction data.

[0137] In an embodiment of the present invention, a numerical differential method, such as a finite difference method, is used to calculate the rate of change of pressure over time. Methods such as forward difference, backward difference or central difference can be used, and a suitable differential method can be selected according to the sampling frequency and data accuracy. The calculated pressure change rate is associated with the corresponding timestamp to generate near-gate pressure change rate data. The near-gate pressure change rate data obtained by analyzing step S351 is used to find the inflection point where the pressure change rate changes from small to large. The inflection point usually corresponds to the moment when the front end of the melt arrives near the exhaust groove, causing the exhaust resistance to change. Combined with the melt injection speed data obtained in step S31 and the mold runner geometry information, such as the runner cross-sectional area, the actual advancement speed of the melt in the runner is calculated to generate the melt advancement speed data. For example, according to the principle of constant melt volume, the melt injection speed and the runner cross-sectional area can be used to calculate the advancement speed of the melt in the runner. Knowing the melt advancement speed and the distance from the gate to the exhaust groove, the time when the melt reaches the exhaust groove can be calculated. For example, the path from the gate to the venting groove can be discretized into several small segments, and the time for the melt to pass through the segment can be calculated according to the melt advancement speed of each segment, and then the time of all segments can be accumulated to obtain the time when the melt reaches the venting groove. The time point is recorded as the melt-venting groove time node data. The melt-venting groove time node data indicates that the melt begins to enter the venting groove and exhaust gas. According to the time node, the melt filling state of the near-gate area can be judged. For example, before the time node, the near-gate area is in an incompletely filled state; after the time node, the near-gate area is gradually filled with the melt. The melt filling state at different time points is recorded to generate near-gate melt filling state data, such as "not filled", "partially filled", "completely filled", etc. As the melt gradually fills the near-gate area, the effective exhaust area of ​​the venting groove will gradually decrease, resulting in a decrease in exhaust efficiency. According to the near-gate melt filling state data, the degree of exhaust efficiency attenuation is evaluated. For example, a model of exhaust efficiency attenuation can be established according to the proportion of melt filling. The exhaust efficiency attenuation values ​​at different time points are recorded to generate near-gate exhaust efficiency attenuation data. Combine the melt-venting groove time node data, real-time cavity pressure distribution data and near-gate exhaust efficiency attenuation data to analyze the characteristics of pressure transmission. For example, the propagation speed of the pressure wave, the change of the pressure peak, and the relationship between these characteristics and the exhaust efficiency attenuation can be analyzed. Quantify the analysis results to generate pressure transmission increase characteristic data. Perform spectral analysis such as Fourier transform on the far gate exhaust groove pressure data to extract the frequency, amplitude and other characteristics of the pressure fluctuation to obtain the far gate pressure fluctuation characteristic data. Combine the pressure transmission increase characteristic data generated in step S356 and the exhaust groove local flow field model constructed in step S22 to predict the actual exhaust capacity of the far gate exhaust groove. For example, a relationship model between pressure fluctuation characteristics, pressure transmission characteristics and exhaust capacity can be established, and the model can be used for prediction to generate actual exhaust capacity prediction data.

[0138] Preferably, step S356 includes the following steps:

[0139] Perform time difference calculation on melt-exhaust slot time node data to obtain exhaust slot time difference data;

[0140] Perform exhaust groove pressure difference analysis on real-time cavity pressure distribution data through exhaust groove time difference data to generate exhaust groove pressure difference data;

[0141] Calculate the pressure transfer rate according to the exhaust slot time difference data and the exhaust slot pressure difference data to obtain the pressure transfer rate data;

[0142] Extract the exhaust slot spacing according to the exhaust slot area data to obtain the exhaust slot spacing data;

[0143] Based on the exhaust groove spacing data and the pressure transfer rate data, the pressure transfer increase characteristic coefficient is calculated for the near-gate exhaust efficiency attenuation data to obtain the pressure transfer increase characteristic data.

[0144] In an embodiment of the present invention, the mobile phone case mold usually has multiple exhaust grooves. In step S353, the time node data of the melt arriving at each exhaust groove is calculated. The melt arrival time of adjacent exhaust grooves is compared, and the time difference between them is calculated, for example, the melt arrival time of the latter exhaust groove minus the melt arrival time of the previous exhaust groove. These time differences are recorded to generate exhaust groove time difference data. After obtaining the exhaust groove time difference data, the pressure value of the melt arriving at each exhaust groove at the corresponding time is extracted according to the real-time cavity pressure distribution data. Then, the pressure difference between adjacent exhaust grooves is calculated. For example, the pressure value at the time when the melt arrives at the latter exhaust groove is subtracted from the pressure value at the time when the melt arrives at the previous exhaust groove. These pressure differences are recorded to generate exhaust groove pressure difference data. Using the exhaust groove time difference data and the exhaust groove pressure difference data, the pressure transfer rate of the melt between the exhaust grooves can be calculated. The pressure transfer rate can be expressed by the pressure difference divided by the time difference, that is, the pressure transfer rate = pressure difference / time difference. According to the exhaust groove area data of the mobile phone case mold obtained in step S11, the distance between the exhaust grooves, that is, the exhaust groove spacing, can be extracted. For example, the distance between the center points of adjacent exhaust grooves can be calculated, or the shortest distance between the edges of adjacent exhaust grooves can be calculated. For example, the pressure transfer increase characteristic coefficient can be defined as the ratio of the pressure transfer rate to the exhaust groove spacing, multiplied by the percentage of the near-gate exhaust efficiency attenuation. Finally, the pressure transfer increase characteristic coefficient is calculated to characterize the degree of influence of the near-gate exhaust efficiency attenuation on the pressure transfer in the mold cavity, and generate pressure transfer increase characteristic data.

[0145] Preferably, step S357 includes the following steps:

[0146] The pressure transfer increase feature data is used to analyze the gas pressure transfer of the melt-exhaust groove time node data, and the first pressure change of the exhaust groove is identified to generate the first pressure change time data of the exhaust groove;

[0147] Extract the pressure fluctuation spectrum characteristics of the remote gate exhaust groove pressure data to obtain the remote gate pressure fluctuation characteristic data;

[0148] Perform gas residual risk assessment based on remote gate pressure fluctuation characteristic data to generate gas residual risk data;

[0149] Based on the gas residual risk data, the local flow field model of the exhaust slot is used to simulate the gas filling and discharging airflow, and the simulation data of the exhaust slot filling and discharging airflow is obtained;

[0150] Calculate the gas flow rate of the exhaust slot according to the simulation data of the filling and discharging airflow of the exhaust slot to obtain the gas flow rate data of the exhaust slot;

[0151] Based on the exhaust slot first pressure change time data, the exhaust slot gas flow rate data is used to analyze the exhaust pressure stabilization time of the pressure transfer increase characteristic data to obtain the exhaust pressure stabilization time data;

[0152] The effective exhaust time period is analyzed by using the exhaust pressure stabilization time data to the exhaust slot first pressure change time data to obtain the exhaust slot effective exhaust time period;

[0153] The actual exhaust capacity of the exhaust slot during the effective exhaust time period is predicted using the exhaust slot gas flow rate data to generate actual exhaust capacity prediction data.

[0154] In an embodiment of the present invention, based on the melt-vent groove time node data of the mobile phone case mold, the moment when the melt reaches each vent groove can be determined. Combined with the pressure transfer increase characteristic data, how the pressure is transmitted in the mold cavity after the melt reaches the vent groove is analyzed. By analyzing the changes in the pressure data of the far gate vent groove, the moment when the pressure change first occurs in each vent groove is identified, which indicates that the gas begins to be discharged from the vent groove. These moments are recorded to generate the time data of the first pressure change of the vent groove. The pressure data of the far gate vent groove of the mobile phone case mold is subjected to spectral analysis such as Fourier transform to extract characteristic parameters such as the frequency and amplitude of the pressure fluctuation. These characteristic parameters can reflect the dynamic process of gas discharge, and these characteristic parameters are recorded to generate the far gate pressure fluctuation characteristic data. The characteristics of the pressure fluctuation can reflect the difficulty of gas discharge. For example, a higher frequency and a larger amplitude indicate that the gas discharge is not smooth and there is a risk of gas residue. According to the far gate pressure fluctuation characteristic data, combined with experience or machine learning models, the risk of gas residue is evaluated, and the evaluation results are quantified to generate gas residue risk data. For example, the risk level can be divided into three levels: high, medium, and low. According to the gas residual risk data, adjust the parameters of the local flow field model of the exhaust groove, such as the size, shape, and position of the exhaust groove, to simulate the gas charging and discharging process under different risk levels. Use finite element analysis software, such as Fluent or COMSOL, to simulate the gas charging and discharging airflow, obtain the flow rate, pressure, concentration and other data of the gas near the exhaust groove at different times, and generate the simulation data of the exhaust groove charging and discharging airflow. According to the obtained simulation data of the exhaust groove charging and discharging airflow, extract the simulation results at the outlet of the exhaust groove far from the gate, such as the gas velocity vector, calculate the gas flow rate at this place, and generate the exhaust groove gas flow rate data. Combined with the exhaust groove first pressure change time data, exhaust groove gas flow rate data and pressure transmission increase characteristic data, analyze the time when the exhaust groove pressure reaches a stable state. For example, a pressure change rate threshold can be set. When the pressure change rate is lower than the threshold, it is considered that the exhaust pressure reaches a stable state. The effective exhaust time period refers to the time period from the first pressure change of the exhaust groove to the exhaust pressure reaching a stable state. Using the exhaust groove first pressure change time data and exhaust pressure stabilization time data, the length of the effective exhaust time period can be calculated. According to the gas flow rate data of the exhaust slot and the effective exhaust time period, the volume of gas discharged by the exhaust slot during the time period can be calculated. For example, the flow rate at each moment in the effective exhaust time period can be integrated to obtain the discharged gas volume. This volume is used as the predicted value of the actual exhaust capacity to generate the actual exhaust capacity prediction data.

[0155] Preferably, step S36 includes the following steps:

[0156] Step S361: Calculate the expected exhaust volume of the exhaust slot according to the gas accumulation situation data to obtain the expected exhaust volume data of the exhaust slot;

[0157] Step S362: performing exhaust capacity matching on the expected exhaust volume data of the exhaust slot and the actual exhaust capacity prediction data to generate exhaust capacity matching data;

[0158] Step S363: performing exhaust bottleneck position identification on the exhaust capacity matching data to obtain exhaust bottleneck position data;

[0159] Step S364: performing exhaust slot operation distribution processing according to the exhaust capacity matching data, and marking the high-load risk area through the exhaust bottleneck position data to obtain high-load risk area data;

[0160] Step S365: collecting the exhaust slot working condition data of the high-load risk area to obtain the high-risk exhaust slot working condition data;

[0161] Step S366: Based on the preset exhaust slot intelligent control strategy, a mapping relationship between the exhaust slot working condition and the exhaust capacity is established using a random forest algorithm, and model training is performed using preset historical exhaust slot adjustment data, thereby constructing an intelligent charging and exhaust control model;

[0162] Step S367: The exhaust capacity matching data, melt-exhaust slot time node data, and high-risk exhaust slot working condition data are transmitted to the intelligent charging and exhaust control model for intelligent charging and exhaust adjustment to obtain intelligent charging and exhaust timing control data.

[0163] In an embodiment of the present invention, the gas accumulation situation data includes information such as the risk level, pressure, and volume of gas accumulation in each area of ​​the mold cavity. Based on this information, the amount of gas that needs to be discharged from each exhaust groove in order to avoid defects can be estimated. For example, an exhaust ratio can be set according to the volume and risk level of gas accumulation to calculate the expected exhaust volume of each exhaust groove. These expected exhaust volumes are recorded to generate the expected exhaust volume data of the exhaust groove. The expected exhaust volume data of the exhaust groove calculated in step S361 is compared with the actual exhaust capacity prediction data predicted in step S357 to evaluate whether the exhaust capacity of each exhaust groove meets the expected demand. For example, the ratio or difference of the actual exhaust capacity to the expected exhaust volume can be calculated. The comparison results are recorded to generate exhaust capacity matching data, such as "match", "mismatch" or a specific difference. The exhaust capacity matching data is analyzed to identify the exhaust slot positions with insufficient exhaust capacity. These positions are the bottlenecks of exhaust, which limit the overall exhaust efficiency. The position information of these exhaust bottlenecks is recorded to generate exhaust bottleneck position data. According to the exhaust capacity matching data generated in step S362, the exhaust tasks of the exhaust slots are reallocated. For example, part of the exhaust tasks can be transferred from the exhaust slots with insufficient exhaust capacity to the exhaust slots with sufficient exhaust capacity. At the same time, according to the exhaust bottleneck position identified in step S363, the exhaust load conditions in the surrounding area are analyzed, and the areas with excessively high exhaust loads are marked as high-load risk areas, and high-load risk area data are generated. According to the high-load risk area data obtained in step S364, the relevant working condition data of the exhaust slots in the area, such as exhaust pressure, exhaust time, exhaust flow, etc., are collected in real time to generate high-risk exhaust slot working condition data. According to the preset exhaust slot intelligent control strategy, such as giving priority to the exhaust demand of the exhaust bottleneck position, a mapping relationship model between the exhaust slot working condition data and the exhaust capacity is established using machine learning algorithms such as random forest algorithms. The model is trained using the pre-collected historical exhaust slot adjustment data, such as the adjustment values ​​of parameters such as exhaust pressure and exhaust time, and the corresponding exhaust effect data, to construct an intelligent charging and exhaust control model. The exhaust capacity matching data, melt-exhaust slot time node data and high-risk exhaust slot working condition data are input into the intelligent charging and exhaust control model constructed in step S366. Based on these input data, the model calculates the exhaust slot parameters that need to be adjusted, such as the opening degree and opening time of the exhaust valve. The adjustment values ​​of these parameters and the corresponding execution time are recorded, and the intelligent charging and exhaust timing control data is generated and sent to the control system of the injection molding machine to achieve real-time control of the exhaust process, such as controlling the opening and closing of the exhaust valve to optimize the exhaust effect and avoid defects in the mobile phone case.

[0164] As an example of the present invention, refer to Figure 3 As shown, Figure 1Detailed implementation steps of step S4 in the embodiment are shown in the flowchart. In this embodiment, step S4 includes:

[0165] Step S41: mapping the intelligent charging and exhausting timing control data to the exhaust slot control process parameters to obtain the exhaust slot control process parameters;

[0166] In an embodiment of the present invention, the intelligent charging and exhaust timing control data includes the timing information of the exhaust slot control, such as the exhaust valve opening and opening time that need to be adjusted at different time points. These timing control data need to be mapped to specific exhaust slot control process parameters, such as voltage, current, air pressure, etc. A mapping table or function can be established to convert the timing control data into corresponding control process parameters. For example, the opening of the exhaust valve can be mapped to the voltage value that controls the valve opening, and the opening time can be mapped to the current value that controls the power-on time of the solenoid valve.

[0167] Step S42: analyzing the influence of the process parameters of the plastic mold according to the exhaust groove control process parameters, and optimizing the control parameters to obtain optimized exhaust control parameters;

[0168] In an embodiment of the present invention, the adjustment of the exhaust groove control process parameters will affect various process parameters of the plastic mold, such as cavity pressure, temperature, melt flow rate, etc. It is necessary to analyze these influences and optimize the control parameters to achieve the best exhaust effect. For example, finite element analysis software can be used to simulate the pressure and temperature distribution in the mold cavity under different exhaust groove control parameters, and the control parameters can be adjusted according to the simulation results to ensure smooth exhaust while avoiding negative effects on product quality, such as burning, deformation, etc. Through optimization, a set of optimal exhaust groove control parameters can be obtained, that is, the exhaust control parameters are optimized to achieve the best exhaust effect and ultimately improve the production quality of the mobile phone case.

[0169] Step S43: generating a segmented control instruction sequence for the optimized exhaust control parameters to obtain segmented control instruction sequence data;

[0170] In the embodiment of the present invention, the optimized exhaust control parameters are segmented in time sequence, and the control parameters of each segment are converted into specific control instructions, such as the voltage value for controlling the valve opening, the current value for controlling the power-on time of the solenoid valve, etc. These segmented control instructions are arranged in time sequence to generate segmented control instruction sequence data. For example, the entire injection molding cycle can be divided into multiple time periods, each time period corresponds to a set of control instructions, so as to achieve fine control of the exhaust process.

[0171] Step S44: Perform exhaust slot collaborative work control according to the segmented control instruction sequence data to generate intelligent exhaust control sequence data.

[0172] In an embodiment of the present invention, a mobile phone case mold usually has multiple exhaust grooves, which need to be coordinated and controlled to achieve the best exhaust effect. According to the segmented control instruction sequence data, the coordinated work of each exhaust groove is controlled. For example, the opening time and opening degree of different exhaust grooves can be controlled according to the flow of the melt, so that the gas can be discharged smoothly from the mold cavity. The control instructions of each exhaust groove are combined in time sequence to generate intelligent exhaust control sequence data, and sent to the control system of the injection molding machine, so as to realize the coordinated control of multiple exhaust grooves and ultimately improve the production quality of the mobile phone case.

[0173] Preferably, the present invention further provides an intelligent processing system for plastic mold process parameters, which executes the intelligent processing method for plastic mold process parameters as described above, and the intelligent processing system for plastic mold process parameters comprises:

[0174] The mold cavity state sensing module is used to collect the initial state of the mold cavity of the plastic mold equipment and generate the initial state data of the single mold cavity; calibrate the pressure sensor parameters according to the initial state data of the single mold cavity and generate the sensor parameter calibration data; perform mold cavity pressure testing according to the sensor parameter calibration data to obtain the mold cavity pressure distribution data;

[0175] The exhaust flow field simulation module is used to construct the exhaust slot local flow field model according to the cavity pressure distribution data; the exhaust slot local flow field model is used to simulate the gas flow and generate gas accumulation situation data;

[0176] An intelligent exhaust capacity prediction module is used to obtain real-time exhaust slot pressure data; perform cavity pressure gradient distribution analysis on the real-time exhaust slot pressure data to obtain real-time cavity pressure distribution data; perform pressure transmission characteristic analysis based on the real-time cavity pressure distribution data to generate pressure transmission increase characteristic data; perform actual exhaust capacity prediction on the exhaust slot based on the pressure transmission increase characteristic data to generate actual exhaust capacity prediction data; perform intelligent charging and exhaust control on the actual exhaust capacity prediction data through gas accumulation situation data to obtain intelligent charging and exhaust timing control data;

[0177] The exhaust collaborative control module is used to optimize the control parameters of the intelligent charging and exhaust timing control data to obtain optimized exhaust control parameters; the exhaust slot collaborative work control is performed according to the optimized exhaust control parameters to generate intelligent exhaust control sequence data.

[0178] The present application is to accurately collect the initial state data of the mold cavity, including key parameters such as mold temperature, mold cavity vacuum, clamping force and exhaust groove air tightness, so that the present invention can provide accurate basic data for subsequent process control. The pressure sensor parameters are calibrated using these data to ensure the accuracy of the mold cavity pressure test, thereby obtaining more reliable mold cavity pressure distribution data. By constructing a local flow field model of the exhaust groove, simulating the gas flow situation, and generating gas accumulation situation data, the risk of gas accumulation inside the mold cavity is predicted, which provides a scientific basis for preventing and reducing molding defects. In addition, by real-time monitoring of the exhaust groove pressure data and combining the analysis of the mold cavity pressure gradient distribution, the present invention can dynamically evaluate the actual exhaust capacity of the exhaust groove, predict the problems that occur during the exhaust process, and perform intelligent filling and exhaust control accordingly, optimize the exhaust timing and parameters, ensure the effective discharge of the gas inside the mold cavity, and avoid defects such as bubbles and gas marks on the product surface. By optimizing the control parameters of the intelligent filling and exhaust timing control data, the coordinated work control of the exhaust groove is realized, and the intelligent exhaust control sequence data is generated. This collaborative work mechanism can give full play to the exhaust efficiency of each exhaust groove, avoid exhaust blind spots and gas residues, and further improve exhaust efficiency and product quality. Through intelligent control strategies, the exhaust operation can be dynamically adjusted according to the real-time pressure changes in the mold cavity, achieving precise control of the exhaust process, thereby maximizing product quality and production efficiency.

[0179] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0180] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. An intelligent processing method for plastic mold process parameters, characterized in that: The following steps are involved: Step S1: collecting the initial state of the mold cavity of the plastic mold equipment to generate the initial state data of the single mold cavity; calibrating the pressure sensor parameters according to the initial state data of the single mold cavity to generate the sensor parameter calibration data; performing the mold cavity pressure test according to the sensor parameter calibration data to obtain the mold cavity pressure distribution data; Step S2: constructing a local flow field model of the exhaust groove according to the cavity pressure distribution data; using the local flow field model of the exhaust groove to simulate the gas flow and generate gas accumulation situation data; Step S3: acquiring real-time exhaust groove pressure data; performing cavity pressure gradient distribution analysis on the real-time exhaust groove pressure data to obtain real-time cavity pressure distribution data; performing pressure transmission characteristic analysis based on the real-time cavity pressure distribution data to generate pressure transmission increase characteristic data; predicting the actual exhaust capacity of the exhaust groove based on the pressure transmission increase characteristic data to generate actual exhaust capacity prediction data; performing intelligent charging and exhaust control on the actual exhaust capacity prediction data through gas accumulation situation data to obtain intelligent charging and exhaust timing control data; Step S4: Optimize the control parameters of the intelligent charging and exhaust timing control data to obtain optimized exhaust control parameters; perform exhaust slot collaborative work control according to the optimized exhaust control parameters to generate intelligent exhaust control sequence data.

2. The intelligent processing method of plastic mold process parameters according to claim 1, characterized in that: The plastic mold equipment includes a mold cavity, an exhaust groove and a mold, and step S1 includes the following steps: Step S11: Identify the exhaust groove area of ​​the mold cavity to obtain exhaust groove area data; Step S12: deploying micro pressure sensors around the exhaust slots according to the exhaust slot area data to obtain pressure sensor array data; Step S13: After the mold is closed, the mold cavity temperature, mold cavity vacuum, mold clamping force and exhaust groove air tightness of the mold cavity are collected to generate the single cavity initial state data; Step S14: performing parameter range verification on the single cavity initial state data by using a preset standard cavity operation threshold value to generate cavity state verification data; Step S15: calibrating the pressure sensor parameters on the pressure sensor array data using the mold cavity state verification data to generate sensor parameter calibration data; Step S16: Performing a mold cavity pressure test using a pressure source device based on preset pressure value test sequence data, and controlling a micro pressure sensor to perform mold cavity pressure array acquisition according to sensor parameter calibration data to obtain mold cavity pressure test data; Step S17: performing time series pressure distribution interpolation processing according to the mold cavity pressure test data to obtain mold cavity pressure distribution data.

3. The intelligent processing method of plastic mold process parameters according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: dividing the exhaust groove influence area according to the cavity pressure distribution data to obtain exhaust groove influence area data; Step S22: performing finite element local flow field analysis based on the cavity pressure distribution data, and constructing a two-dimensional flow field model to obtain a local flow field model of the exhaust groove; Step S23: performing gas flow simulation using the exhaust slot local flow field model according to the exhaust slot influence area data to obtain regional gas accumulation trend data; Step S24: Calculate the critical pressure time according to the regional gas accumulation trend data to obtain the critical pressure time data; Step S25: Perform gas accumulation curve analysis on the regional gas accumulation trend data and critical pressure time data, and perform gas accumulation risk assessment based on the exhaust slot impact area data to generate gas accumulation situation data.

4. The intelligent processing method of plastic mold process parameters according to claim 2, characterized in that: Step S3 includes the following steps: Step S31: real-time monitoring of the melt injection speed of the injection molding machine to generate melt injection speed data; Step S32: monitoring the exhaust slot pressure in real time according to the pressure sensor array data to obtain real-time exhaust slot pressure data; Step S33: performing cavity pressure time series aggregation on the real-time exhaust groove pressure data, and performing pressure gradient distribution analysis to obtain real-time cavity pressure distribution data; Step S34: extracting the near-gate and far-gate venting groove pressure data from the real-time venting groove pressure data, and obtaining near-gate venting groove pressure data and far-gate venting groove pressure data respectively; Step S35: Calculate the time when the melt reaches the venting groove according to the near-gate venting groove pressure data and the melt injection speed data to obtain the melt-venting groove time node data; perform pressure transmission characteristic analysis on the real-time cavity pressure distribution data through the melt-venting groove time node data to generate pressure transmission increase characteristic data; predict the actual venting capacity of the venting groove on the far-gate venting groove pressure data through the pressure transmission increase characteristic data to generate actual venting capacity prediction data; Step S36: Based on the melt-exhaust slot time node data, the actual exhaust capacity prediction data is intelligently controlled through the gas accumulation situation data to obtain intelligent exhaust timing control data.

5. The intelligent processing method of plastic mold process parameters according to claim 4, characterized in that: Step S35 includes the following steps: Step S351: Calculate the pressure change rate of the venting groove pressure data near the gate to generate pressure change rate data near the gate; Step S352: performing melt advancement speed analysis according to the near-gate pressure change rate data and the melt injection speed data to obtain melt advancement speed data; Step S353: Calculate the time it takes for the melt to reach the exhaust groove using the melt advancement speed data to obtain melt-exhaust groove time node data; Step S354: determining the melt filling state according to the melt-venting groove time node data, and generating the melt filling state data near the gate; Step S355: performing a near-gate exhaust efficiency attenuation evaluation on the near-gate melt filling state data to obtain near-gate exhaust efficiency attenuation data; Step S356: performing pressure transmission characteristic analysis on the near-gate exhaust efficiency attenuation data through the melt-exhaust groove time node data and the real-time cavity pressure distribution data to generate pressure transmission increase characteristic data; Step S357: extract the pressure fluctuation spectrum characteristics of the far gate vent groove pressure data to obtain the far gate pressure fluctuation characteristic data; predict the actual exhaust capacity of the far gate pressure fluctuation characteristic data based on the pressure transfer increase characteristic data using the vent groove local flow field model to generate actual exhaust capacity prediction data.

6. The intelligent processing method of plastic mold process parameters according to claim 5, characterized in that: Step S356 includes the following steps: Perform time difference calculation on melt-exhaust slot time node data to obtain exhaust slot time difference data; Perform exhaust groove pressure difference analysis on real-time cavity pressure distribution data through exhaust groove time difference data to generate exhaust groove pressure difference data; Calculate the pressure transfer rate according to the exhaust slot time difference data and the exhaust slot pressure difference data to obtain the pressure transfer rate data; Extract the exhaust slot spacing according to the exhaust slot area data to obtain the exhaust slot spacing data; Based on the exhaust groove spacing data and the pressure transfer rate data, the pressure transfer increase characteristic coefficient is calculated for the near-gate exhaust efficiency attenuation data to obtain the pressure transfer increase characteristic data.

7. The intelligent processing method of plastic mold process parameters according to claim 5, characterized in that: Step S357 includes the following steps: The pressure transfer increase feature data is used to analyze the gas pressure transfer of the melt-exhaust groove time node data, and the first pressure change of the exhaust groove is identified to generate the first pressure change time data of the exhaust groove; Extract the pressure fluctuation spectrum characteristics of the remote gate exhaust groove pressure data to obtain the remote gate pressure fluctuation characteristic data; Perform gas residual risk assessment based on remote gate pressure fluctuation characteristic data to generate gas residual risk data; Based on the gas residual risk data, the local flow field model of the exhaust slot is used to simulate the gas filling and discharging airflow, and the simulation data of the exhaust slot filling and discharging airflow is obtained; Calculate the gas flow rate of the exhaust slot according to the simulation data of the filling and discharging airflow of the exhaust slot to obtain the gas flow rate data of the exhaust slot; Based on the exhaust slot first pressure change time data, the exhaust slot gas flow rate data is used to analyze the exhaust pressure stabilization time of the pressure transfer increase characteristic data to obtain the exhaust pressure stabilization time data; The effective exhaust time period is analyzed by using the exhaust pressure stabilization time data to the exhaust slot first pressure change time data to obtain the exhaust slot effective exhaust time period; The actual exhaust capacity of the exhaust slot during the effective exhaust time period is predicted using the exhaust slot gas flow rate data to generate actual exhaust capacity prediction data.

8. The intelligent processing method of plastic mold process parameters according to claim 4, characterized in that: Step S36 includes the following steps: Step S361: Calculate the expected exhaust volume of the exhaust slot according to the gas accumulation situation data to obtain the expected exhaust volume data of the exhaust slot; Step S362: performing exhaust capacity matching on the expected exhaust volume data of the exhaust slot and the actual exhaust capacity prediction data to generate exhaust capacity matching data; Step S363: performing exhaust bottleneck position identification on the exhaust capacity matching data to obtain exhaust bottleneck position data; Step S364: performing exhaust slot operation distribution processing according to the exhaust capacity matching data, and marking the high-load risk area through the exhaust bottleneck position data to obtain high-load risk area data; Step S365: collecting the exhaust slot working condition data of the high-load risk area to obtain the high-risk exhaust slot working condition data; Step S366: Based on the preset exhaust slot intelligent control strategy, a mapping relationship between the exhaust slot working condition and the exhaust capacity is established using a random forest algorithm, and model training is performed using preset historical exhaust slot adjustment data, thereby constructing an intelligent charging and exhaust control model; Step S367: The exhaust capacity matching data, melt-exhaust slot time node data, and high-risk exhaust slot working condition data are transmitted to the intelligent charging and exhaust control model for intelligent charging and exhaust adjustment to obtain intelligent charging and exhaust timing control data.

9. The intelligent processing method of plastic mold process parameters according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: mapping the intelligent charging and exhausting timing control data to the exhaust slot control process parameters to obtain the exhaust slot control process parameters; Step S42: analyzing the influence of the process parameters of the plastic mold according to the exhaust groove control process parameters, and optimizing the control parameters to obtain optimized exhaust control parameters; Step S43: generating a segmented control instruction sequence for the optimized exhaust control parameters to obtain segmented control instruction sequence data; Step S44: Perform exhaust slot collaborative work control according to the segmented control instruction sequence data to generate intelligent exhaust control sequence data.

10. An intelligent processing system for plastic mold process parameters, characterized in that: The method for intelligently processing the process parameters of a plastic mold according to claim 1, wherein the intelligent processing system for the process parameters of a plastic mold comprises: The mold cavity state sensing module is used to collect the initial state of the mold cavity of the plastic mold equipment and generate the initial state data of the single mold cavity; calibrate the pressure sensor parameters according to the initial state data of the single mold cavity and generate the sensor parameter calibration data; perform mold cavity pressure testing according to the sensor parameter calibration data to obtain the mold cavity pressure distribution data; The exhaust flow field simulation module is used to construct the exhaust slot local flow field model according to the cavity pressure distribution data; the exhaust slot local flow field model is used to simulate the gas flow and generate gas accumulation situation data; An intelligent exhaust capacity prediction module is used to obtain real-time exhaust slot pressure data; perform cavity pressure gradient distribution analysis on the real-time exhaust slot pressure data to obtain real-time cavity pressure distribution data; perform pressure transmission characteristic analysis based on the real-time cavity pressure distribution data to generate pressure transmission increase characteristic data; perform actual exhaust capacity prediction on the exhaust slot based on the pressure transmission increase characteristic data to generate actual exhaust capacity prediction data; perform intelligent charging and exhaust control on the actual exhaust capacity prediction data through gas accumulation situation data to obtain intelligent charging and exhaust timing control data; The exhaust collaborative control module is used to optimize the control parameters of the intelligent charging and exhaust timing control data to obtain optimized exhaust control parameters; the exhaust slot collaborative work control is performed according to the optimized exhaust control parameters to generate intelligent exhaust control sequence data.

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