Negative pressure injection molding plastic forming method and device

Abstract

The invention relates to the technical field of plastic molding processes, and discloses a negative pressure injection molding plastic molding method and device. The method comprises the steps that in the negative pressure injection molding process, gas parameter data of a plurality of monitoring points in a mold cavity are obtained in real time; based on the change of the gas parameter data of each monitoring point, identifying the state of the gas pushed by the melt, and determining the propulsion path of the melt flowing front edge in the cavity; according to the propelling path, the melt filling progress and the filling time sequence of all areas in the cavity are calculated; and based on the filling progress and the filling time sequence, generating evaluation information of the filling state of the melt in the cavity. According to the method, the real-time visual prediction of the melt filling process in the mold is realized, and the control accuracy of the negative pressure injection molding process and the stability of the product quality are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of plastic forming process, and in particular to a negative pressure injection molding plastic forming method and device. BACKGROUND

[0002] Injection molding plastic forming is a widely used plastic processing technology, and its basic principle is to melt plastic raw materials, inject them into a closed mold cavity, and then take out the molded product after cooling and solidification. The injection molding process involves complex melt flow, heat and mass transfer, and other physical phenomena. The control accuracy of these processes directly determines the molding quality of the final product. Therefore, it is crucial to effectively monitor the injection molding process to achieve process optimization and quality control.

[0003] The existing injection molding monitoring technology mainly relies on indirect measurement methods such as pressure sensors, temperature sensors, and vacuum degree detection. Sensors are installed at key positions such as injection cylinders, mold surfaces, and vacuum pipelines to obtain process parameter information. Although these monitoring methods can provide important information about the overall process state, a deep analysis of their monitoring principles reveals that the traditional monitoring methods obtain parameter data at boundary positions, which cannot directly reflect the actual flow process and spatial distribution state of the melt inside the cavity. Especially for products with complex geometric structures, such as multi-branch pipelines, deep cavity structures, or thin-walled complex parts, single-point pressure or temperature data cannot accurately describe the filling sequence and filling state of the melt in different regions, making it difficult for operators to determine which regions will be filled first and which regions may have filling difficulties. Based on the above analysis, the existing technology cannot achieve real-time visual prediction of the melt filling process inside the mold, which seriously hinders the accurate control and quality optimization of the negative pressure injection molding process. SUMMARY

[0004] The main purpose of the present application is to solve the problem that the existing injection molding monitoring technology can only obtain parameter data at boundary positions, which cannot directly reflect the actual flow process and spatial distribution state of the melt inside the cavity, resulting in the inability to achieve real-time visual prediction of the melt filling process inside the mold.

[0005] The first aspect of the present application provides a negative pressure injection molding plastic forming method, which comprises: in the negative pressure injection molding process, real-time acquisition of gas parameter data of multiple monitoring points in the mold cavity; based on the change of the gas parameter data of each monitoring point, identifying the state of the gas being pushed by the melt, determining the advancing path of the melt flow front in the cavity; according to the advancing path, calculating the melt filling progress and filling time sequence of each region in the cavity; based on the filling progress and filling time sequence, generating evaluation information of the melt filling state in the cavity.

[0006] Optionally, in the first implementation manner of the first aspect, the state of the gas being pushed by the melt at each monitoring point is identified based on changes in the gas parameter data of the monitoring points, and the method comprises: matching process characteristic parameters corresponding to the material to be injected, and performing time sequence segmentation on the gas parameter data based on the process characteristic parameters to obtain gas data segments; performing similarity calculation on the gas data segments based on a preset plastic gas fingerprint library to identify a gas state type representing a melt filling process; performing time sequence comparison on the gas state types of the monitoring points to identify a time node at which the gas state changes, and determining the state of the gas being pushed by the melt at each monitoring point based on the time node.

[0007] Optionally, in the second implementation manner of the first aspect, the advancing path of the melt flow front in the cavity is determined, and the method comprises: calculating a time difference of the melt arriving between adjacent monitoring points based on the state of the gas being pushed by the melt at each monitoring point and the time node; determining a propagation speed and a propagation direction of the melt in the cavity based on the time difference and point space coordinates corresponding to each monitoring point; constructing a velocity field distribution of the melt propagation according to the propagation speed and the propagation direction, and performing path reconstruction on the velocity field distribution based on geometric constraint parameters corresponding to the mold cavity to obtain a path reconstruction result; and identifying a main flow path and a branch flow path of the melt based on the path reconstruction result to generate the advancing path of the melt flow front in the cavity.

[0008] Optionally, in the third implementation manner of the first aspect, the preset plastic gas fingerprint library is established by: collecting gas parameter data and corresponding melt filling time sequences of different plastic materials at each monitoring point under standard negative pressure injection molding process conditions to obtain an associated data set; performing feature extraction and fingerprint template construction on the associated data set to construct a corresponding template of the gas state type and the filling process node, and form a gas fingerprint template of each material; and classifying and identifying the gas fingerprint template according to material types, process parameters, and cavity characteristics to form a plastic gas fingerprint library.

[0009] Optionally, in the fourth implementation manner of the first aspect, the melt filling progress and filling time sequence of each region in the cavity are calculated according to the advancing path, and the method comprises: predicting arrival times and filling sequences of the melt at a plurality of preset filling positions in the cavity based on melt fluid characteristics corresponding to the material to be injected and the advancing path to obtain filling time sequence data; performing comparative analysis of filling states of a plurality of filling regions pre-divided in the cavity based on the filling time sequence data to identify a filling uneven region; determining filling completion states of each region based on the filling time sequence data and the filling uneven region to obtain the melt filling progress and filling time sequence of each region in the cavity.

[0010] Optionally, in a fifth implementation form of the first aspect of the present application, the step of performing comparative analysis on filling states of the plurality of filling regions pre-divided in the cavity based on the filling time sequence data to identify the filling uneven regions comprises: determining standard filling time of each filling region based on the wall thickness variation characteristics and the flow passage cross-sectional area distribution corresponding to the cavity; performing deviation comparison between the filling time sequence data and the standard filling time of each region to obtain filling time deviation values of each region; performing state classification on the filling time deviation values based on a preset deviation threshold to identify the filling too fast regions, the filling lag regions and the filling abnormal regions, and obtaining the filling uneven regions.

[0011] Optionally, in a sixth implementation form of the first aspect of the present application, the step of determining filling completion states of each region based on the filling time sequence data and the filling uneven regions to obtain the melt filling progress and filling time sequence of each region in the cavity comprises: determining filling arrival time corresponding to each filling region according to the filling time sequence data; performing filling state marking on each filling region based on the filling uneven regions to obtain state marking results; calculating completion proportions of each region relative to the overall filling process based on the filling arrival time of each region and the filling state marking to obtain the melt filling progress and filling time sequence of each region in the cavity.

[0012] Optionally, in a seventh implementation form of the first aspect of the present application, the step of generating evaluation information of the melt filling state in the cavity based on the filling progress and filling time sequence comprises: performing filling completion degree evaluation on each filling region based on the filling progress and filling time sequence to determine the normal filling regions and the filling regions to be improved; performing defect risk level division on the filling too fast regions in the filling uneven regions, the filling lag regions and the filling abnormal regions respectively to obtain influence weights of each type of region; performing weighted comprehensive calculation on the influence weights of the normal filling regions, the filling regions to be improved and each type of uneven region based on a preset weight coefficient to generate the evaluation information representing the melt filling state in the cavity.

[0013] Optionally, in an eighth implementation form of the first aspect of the present application, the step of performing process control based on the evaluation information comprises: determining key process parameters for regulating gas flow state and process adjustment direction according to the evaluation information; predicting gas flow states corresponding to different adjustment amounts based on the key process parameters and process adjustment direction, and calculating parameter adjustment amounts corresponding to the preset target gas state based on the gas flow states; synchronously adjusting the negative pressure intensity and injection process parameters based on the parameter adjustment amounts to make the gas monitoring parameters return to the preset normal monitoring interval, thereby forming a closed-loop control based on gas monitoring feedback.

[0014] The second aspect of the present application provides a negative pressure injection plastic forming device, the negative pressure injection plastic forming device comprising: a gas collection module, configured to acquire gas parameter data of a plurality of monitoring points in a mold cavity in real time during a negative pressure injection process; a trajectory reconstruction module, configured to identify a state in which gas is pushed by melt based on changes in the gas parameter data of each monitoring point, and determine a pushing path of a melt flow front in the cavity; a flow modeling module, configured to calculate a melt filling progress and a filling timing of each region in the cavity according to the pushing path; and an evaluation module, configured to generate evaluation information of a melt filling state in the cavity based on the filling progress and the filling timing.

[0015] The negative pressure injection plastic forming method and device described above. In the embodiment of the present application, the gas parameter data of a plurality of monitoring points in a mold cavity is acquired in real time during a negative pressure injection process, and gas feature analysis is performed to identify changes in the state in which gas is pushed by melt, and the state conversion timing of each monitoring point is obtained; the melt propagation speed and direction are calculated based on the state conversion timing, and the pushing path of the melt flow front is constructed; then the melt filling progress and timing distribution of each region are predicted according to the pushing path, and the uneven filling region is identified; finally, quality risk assessment is performed based on the filling state, and evaluation information of the melt filling state in the cavity is generated. Through gas state monitoring and path reconstruction in a negative pressure environment, the problem that the traditional monitoring technology cannot directly reflect the melt flow process inside the cavity is solved, especially in terms of gas state identification, melt path tracking and filling prediction, the unique phenomenon that gas is pushed by melt in negative pressure injection is fully utilized, and real-time visual prediction of the filling process inside the complex cavity is realized; and the multi-point gas monitoring and timing analysis strategy is adopted, which not only realizes accurate conversion from gas changes to melt flow, but also enhances the reliability of filling state prediction.

[0016] Other features and advantages of the present application will be set forth in the following description, and in part will become apparent to those skilled in the art from the description, or can be learned by practice of the present application. The objects and other advantages of the present application will be realized and achieved by the structure particularly pointed out in the description, claims and drawings.

[0017] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the following preferred embodiments are specifically described below, and the accompanying drawings are described as follows. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 The first embodiment of the negative pressure injection plastic forming method in the embodiment of the present application is shown in the figure. Figure 2 The first embodiment of the negative pressure injection plastic forming method in the embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] The terms "comprising" and "having," and any variations thereof, used in the embodiments of this invention are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the steps or units listed, but may optionally include other steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0021] To facilitate understanding of this embodiment, the specific process of this embodiment is described below. Please refer to [link / reference]. Figure 1 The first embodiment of the negative pressure injection molding method for plastics in this invention includes: 101. During negative pressure injection molding, real-time gas parameter data from multiple monitoring points within the mold cavity are acquired; In this embodiment, when performing negative pressure injection molding of plastic melt, the original gas data corresponding to the multidimensional gas parameters of multiple gas collection points in the target injection mold cavity are collected using a preset gas collection point network; the original gas data is subjected to adaptive filtering of process noise under negative pressure environment to obtain filtered gas parameter data.

[0022] In practical applications, multiple gas collection points are pre-positioned at key locations within the mold cavity. These collection points use a micro-sensor array to synchronously collect gas parameters. The pre-defined gas collection point network refers to a set of sensor nodes arranged within the cavity according to the product's geometric features and predicted flow paths. Each collection point is equipped with a pressure sensor, a temperature sensor, and a humidity sensor. The multi-dimensional gas parameters include data on gas pressure, temperature, and humidity. In specific implementation, 3mm diameter sensor mounting holes are reserved during mold manufacturing. The micro-sensors are fixed within these holes using threaded connections, with the sensor probes flush with the inner surface of the cavity. The sensors are connected to an external data acquisition unit via high-temperature resistant wires. The acquisition unit synchronously reads the gas parameter values ​​from all collection points 20 times per second. During negative pressure injection molding, as the melt advances, it compresses and displaces the gas within the cavity, causing significant changes in the pressure, temperature, and humidity values ​​at each collection point. For example, for automotive dashboard injection molded parts, collection points are placed at the end of the main runner, in thin-walled areas, and in deep cavities to synchronously monitor abrupt changes in the gas state as the melt reaches each area. This multi-point distributed acquisition method obtains the time sequence of gas state changes in different regions within the cavity, providing accurate location and time information for identifying the melt flow front.

[0023] The collected raw gas data undergoes adaptive filtering to address process noise under negative pressure conditions. Process noise refers to irrelevant fluctuations in gas parameter data generated during negative pressure injection molding due to factors such as injection machine vibration, vacuum pump pulsation, and sensor circuit interference. Adaptive filtering is a data processing method that automatically adjusts the filtering intensity based on current process conditions. In practice, pure noise data is first collected through a no-melt injection mold test to calculate the typical fluctuation amplitude and frequency characteristics of the equipment noise. Then, the real-time collected raw data is compared with a preset effective signal threshold. When the data fluctuation exceeds the noise amplitude range, it is considered a valid gas state change signal; when the fluctuation is within the noise range, a moving average algorithm is used for smoothing. The moving average algorithm uses the average of several consecutive data points as the filtering result for the current moment. The average window size is dynamically adjusted according to the injection speed: a smaller window maintains a faster response speed when the injection speed is high, and a larger window improves the smoothing effect when the injection speed is low. After adaptive filtering, the influence of equipment vibration and circuit interference on gas parameter monitoring is eliminated, resulting in clean gas data that accurately reflects the melt propagation process.

[0024] 102. Based on the changes in gas parameter data at each monitoring point, identify the state of gas being pushed by the melt and determine the propulsion path of the melt flow front within the cavity; In this embodiment, the process characteristic parameters corresponding to the material to be injected are matched, and based on the process characteristic parameters, the gas parameter data is segmented into time sequences to obtain gas data segments. Based on a preset plastic gas fingerprint database (wherein, the preset plastic gas fingerprint database is established in the following way: collecting gas parameter data and corresponding melt filling time sequences of each monitoring point of different plastic materials under standard negative pressure injection molding process conditions to obtain associated data groups; performing feature extraction and fingerprint template construction on the associated data groups to construct corresponding templates of gas state types and filling process nodes, forming gas fingerprint templates for each material; classifying and labeling the gas fingerprint templates according to material type, process parameters, and cavity characteristics to form a plastic gas fingerprint database), the similarity of the gas data segments is calculated to identify the characteristics of melt filling. The process involves: identifying the gas state type at each monitoring point; comparing the gas state type over time to identify the time nodes when the gas state changes; determining the state at which the gas is pushed by the melt at each monitoring point based on the time nodes; calculating the time difference between adjacent monitoring points based on the state at which the gas is pushed by the melt and the time nodes; determining the propagation speed and direction of the melt within the mold cavity based on the time difference and the spatial coordinates of each monitoring point; constructing the velocity field distribution of the melt propagation based on the propagation speed and direction; reconstructing the path of the velocity field distribution based on the geometric constraint parameters corresponding to the mold cavity; and identifying the main flow path and branch flow paths of the melt based on the path reconstruction results, generating the propagation path of the melt flow front within the mold cavity.

[0025] In practical applications, the corresponding process characteristic parameters are matched from the material database based on the type of material to be injected, and the filtered gas parameter data is then processed by time-series segmentation based on these parameters. The process characteristic parameters here refer to the unique process indicators of different plastic materials during negative pressure injection molding, including key parameters such as the material's melt temperature, flowability index, and typical injection time. Specifically, before injection begins, the operator selects the material type using a human-machine interface, thereby retrieving the corresponding process parameters from the pre-stored material database. For example, the melt temperature of ABS material is 220-260℃, and the typical filling time is 8-15 seconds. Time-series segmentation refers to the processing method of dividing continuously collected gas parameter data into multiple independent data segments according to fixed time intervals. The time window for segmentation is determined based on the material's typical filling time, dividing the total filling time into 10-20 equal time periods, each time period serving as an independent gas data segment. For example, for polypropylene injection molding with a filling time of 12 seconds, the data is divided into 10 data segments at 1.2-second intervals, each data segment containing gas pressure, temperature, and humidity changes at all monitoring points within that time window. This segmentation method based on material properties ensures that each data segment corresponds to a complete stage in the melt filling process, avoiding arbitrary time-based segmentation that disrupts the continuity of gas state changes.

[0026] Based on a pre-defined plastic gas fingerprint database, similarity calculations are performed on various gas data segments to identify the gas state types characterizing the melt filling process. The establishment of this plastic gas fingerprint database involves three specific steps: First, gas parameter data and corresponding melt filling time sequences are collected from various monitoring points of different plastic materials under standard negative pressure injection molding conditions, resulting in associated data sets (data sets that pair gas parameter change data with actual melt arrival times). In practice, commonly used materials such as ABS, polypropylene, and nylon are selected, and injection molding experiments are conducted under uniform standard process conditions. Gas pressure, temperature, and humidity changes at each monitoring point, along with the actual melt arrival time observed through a transparent mold, are recorded simultaneously, forming paired data sets of "gas parameter change curve - melt arrival time". Then, feature extraction and fingerprint template construction are performed on the associated data sets, constructing corresponding templates for gas state types and filling process nodes, thus forming gas fingerprint templates for each material. Feature extraction refers to identifying representative numerical features from gas parameter change curves, including key indicators such as pressure drop magnitude, temperature rise rate, and humidity change gradient. Fingerprint template construction involves classifying and organizing the extracted feature values ​​according to different stages of the filling process to form standard reference templates. For example, the "contact extrusion state" template for ABS material includes feature ranges of pressure drop of 20-30 Pa, temperature rise of 5-8℃, and humidity drop of 10-15%. Finally, the gas fingerprint templates are classified and labeled according to material type, process parameters, and cavity characteristics to form a plastic gas fingerprint library. Classification and labeling involves assigning a unique code to each fingerprint template, containing key information such as material name, injection pressure, injection temperature, and cavity complexity, facilitating rapid retrieval and matching. Gas state type refers to the different change patterns of gas parameters during melt filling, mainly including three types: non-contact state, contact extrusion state, and complete displacement state. In practice, the currently collected gas data segment is numerically compared with the standard templates in the fingerprint library, and the similarity is determined by calculating the degree of matching between the gas parameter change trend in the data segment and the standard template. Similarity calculation employs a data point difference accumulation method. The parameter value at each time point in the gas data segment is compared with the corresponding parameter value in the template. The absolute values ​​of all differences are then summed to obtain the total difference. A smaller total difference indicates higher similarity. For example, when the gas data segment at a monitoring point shows a rapid pressure drop from 100 Pa to 20 Pa and a temperature rise from 25℃ to 45℃, the total difference with the "completely displaced state" template in the fingerprint database is the smallest, thus identifying the gas state type at that location as a completely displaced state. This template-matching-based identification method can accurately determine the degree to which the gas at different locations is affected by the melt.

[0027] A time-series comparative analysis of the gas state types identified at each monitoring point is performed to pinpoint the key time nodes for gas state transitions. Based on these time nodes, the state of gas being pushed by the melt at each monitoring point is determined. The time-series comparison involves examining the changes in gas state types at each monitoring point over a continuous time period to identify the time points where state types abruptly change. In practice, the gas state type sequence at each monitoring point is examined chronologically. When a state type at a given time point differs from the previous time point, that time point is marked as a state transition time node. For example, if monitoring point A is in a non-contact state from 0-8 seconds, a contact-extrusion state from 8-10 seconds, and a complete displacement state from 10-12 seconds, then 8 seconds and 10 seconds are the two state transition time nodes for that point. By collecting the state transition time nodes from all monitoring points, the accurate time when the melt arrives at each location can be determined. For automotive dashboard injection molded parts, when the main channel monitoring point undergoes a transition from non-contact to contact-extrusion at 5 seconds, it indicates that the melt arrives at that location at 5 seconds and begins to push the gas. This state transition recognition-based method can accurately pinpoint the arrival time and migration process of the melt front.

[0028] Based on the state transition time points of the gas being pushed by the melt at each monitoring point, the time difference of melt arrival between adjacent monitoring points is calculated. In practice, firstly, an adjacency table is established according to the spatial location of the monitoring points within the mold cavity, defining monitoring points within a preset spatial distance as adjacent monitoring points. The adjacency table records the number and spatial distance of each monitoring point and all its adjacent monitoring points. Then, the time points of gas state transition at each monitoring point are extracted, and the numerical difference between the time points of adjacent monitoring points is calculated. For example, if the main channel monitoring point P1 undergoes a state transition at 6 seconds, and its adjacent branch monitoring point P2 undergoes a state transition at 9 seconds, then the time difference between the two points is 3 seconds. By traversing all monitoring point pairs in the adjacency table, the melt propagation time difference between each adjacent position within the mold cavity is calculated. This adjacency-based time difference calculation method provides accurate time reference data for subsequent analysis of melt flow velocity.

[0029] Based on the calculated time difference and the spatial coordinates of each monitoring point, the propagation speed and direction of the melt within the mold cavity are determined. The propagation speed refers to the average speed at which the melt front edge moves between adjacent monitoring points, and the propagation direction refers to the spatial direction from the starting monitoring point to the target monitoring point. In practice, the straight-line distance between adjacent monitoring points is calculated using the three-dimensional coordinate data of the monitoring points. The calculation method is to take the square root of the sum of the squares of the differences in the X, Y, and Z coordinates between the two points. Then, the straight-line distance is divided by the corresponding time difference to obtain the propagation speed of that path segment. The propagation direction is determined by calculating the proportional relationship of the coordinate differences, i.e., the direction vector is obtained by subtracting the starting point coordinates from the target point coordinates. For example, if the coordinates of monitoring point A are (10, 20, 0) mm, and the coordinates of monitoring point B are (16, 28, 0) mm, the straight-line distance between the two points is 10 mm, and the time difference is 2 seconds, then the propagation speed is 5 mm / s, and the propagation direction is a vector of 6 mm in the X direction and 8 mm in the Y direction. By calculating the speed and direction for all adjacent monitoring point pairs, the speed and direction distribution data of the melt flow within the mold cavity are obtained. This calculation method based on measured data can accurately reflect the actual flow characteristics of the melt within the mold cavity.

[0030] Based on the calculated propagation speed and direction, a velocity field distribution for melt propagation is constructed, and path reconstruction is performed based on the geometric constraint parameters corresponding to the mold cavity. Here, the velocity field distribution refers to the complete distribution map of melt flow velocity and direction at each spatial location within the cavity; while the geometric constraint parameters include the cavity wall position, channel width variations, and internal obstacles that affect melt flow. In practice, the cavity space is divided into regular grid cells, and the velocity and direction data of the monitoring points are extended to adjacent grid cells using a distance-weighted averaging method. Distance-weighted averaging assigns different weights based on the distance between the grid cell and the monitoring point, with closer cells receiving larger weights. The final velocity and direction are the average of the data from each monitoring point, calculated according to their weights. Then, the velocity field is corrected based on the cavity wall boundaries; when the flow direction points towards the wall, it is adjusted to be parallel to the wall. Path reconstruction involves recalculating the complete flow trajectory of the melt from the injection point to each monitoring point based on the corrected velocity field distribution. Starting from the injection point, the flow proceeds step-by-step according to the flow direction at each location in the velocity field until the target location is reached, forming a complete flow path. This path reconstruction method, which incorporates geometric boundaries, ensures that the calculated flow path conforms to the actual physical constraints.

[0031] Based on the path reconstruction results, the main flow path and branch flow paths of the melt are identified, generating a complete path for the melt flow front to advance within the cavity. The main flow path refers to the flow line with the highest melt flow rate from the injection port to the main area of ​​the cavity, while branch flow paths refer to flow lines branching off from the main flow path to various secondary areas. In practice, the main flow path is identified by statistically analyzing the melt flow rate through each path line. The flow rate is calculated by multiplying the velocity and cross-sectional area at each point on the path line. The path line with the highest flow rate is marked as the main flow path, and the path lines branching off from the main flow path are marked as branch flow paths. For example, for automotive door panel injection molding parts, the main flow path extends from the injection port to the central area of ​​the door panel, while the branch flow paths extend from the central area to the edge and handle area of ​​the door panel, respectively. The identified main flow path and all branch flow paths are combined to form a complete path network diagram describing the advancement process of the melt flow front throughout the cavity. This hierarchical path identification method can clearly display the complete flow process of the melt in complex cavity structures, providing accurate path information for predicting the filling sequence and filling time of each area.

[0032] 103. Based on the propulsion path, calculate the melt filling progress and filling sequence of each region within the cavity; In this embodiment, based on the melt fluid characteristics and propulsion path of the material to be injected, the arrival time and filling sequence of the melt at multiple preset filling positions within the cavity are predicted, resulting in filling timing data. Based on this data, a comparative analysis of the filling states of multiple pre-divided filling regions within the cavity is performed to identify uneven filling regions (i.e., based on the wall thickness variation characteristics and flow channel cross-sectional area distribution corresponding to the cavity, the standard filling time for each filling region is determined). The filling timing data is then compared with the standard filling time for each region to obtain the filling time deviation value for each region. Based on a preset deviation threshold, the state of each filling time deviation value is classified. The system identifies regions that fill too quickly, are filled too slowly, or are filled abnormally, thus identifying unevenly filled regions. Based on the filling timing data and unevenly filled regions, the filling completion status of each region is determined, resulting in the melt filling progress and filling timing of each region within the cavity. (i.e., based on the filling timing data, the filling arrival time corresponding to each filling region is determined; based on the unevenly filled regions, the filling status of each filling region is marked, resulting in the status marking results; based on the filling arrival time and filling status markings of each region, the completion ratio of each region relative to the overall filling process is calculated, resulting in the melt filling progress and filling timing of each region within the cavity.)

[0033] In practical applications, based on the melt fluid characteristics and propagation path of the material to be injected, the arrival time and filling sequence of the melt at multiple preset filling positions within the mold cavity are predicted, resulting in filling timing data. Here, melt fluid characteristics refer to physical parameters affecting flow behavior, such as viscosity, fluidity, and cooling rate of different material melts; while filling positions refer to key monitoring points preset within the mold cavity according to the product's geometry, including important locations such as the end of the flow channel, thin-walled areas, and the bottom of deep cavities. Specifically, the fluid characteristic parameters of the current injection molding material are first obtained by consulting the material technical manual. For example, the melt index of ABS material at the injection temperature is 22 g / 10 min, indicating its fluidity level. Then, based on the propagation path information obtained in the previous steps, the propagation time of the melt along each flow path to the preset filling position is calculated segment by segment. The calculation method is to divide the length of each path segment by the measured average flow velocity of that segment to obtain the segment propagation time, and then sum the time of each segment to obtain the total arrival time. For example, the total path length from the injection point to a thin-walled region is 120mm, divided into three segments: the first segment is 40mm long, with a measured flow velocity of 10mm / s and a propagation time of 4 seconds; the second segment is 50mm long, with a measured flow velocity of 8mm / s and a propagation time of 6.25 seconds; and the third segment is 30mm long, with a measured flow velocity of 6mm / s and a propagation time of 5 seconds. The total arrival time is then 15.25 seconds. By performing similar calculations on all preset filling positions, the predicted arrival time for each position is obtained. The filling sequence is determined by sorting the arrival times of each position from smallest to largest, with the position with the shortest arrival time assigned to position 1, the next shortest to position 2, and so on, ultimately yielding the filling time sequence data (a complete data table containing the filling position number, arrival time, and filling sequence). This prediction method based on actual measurement data avoids the errors of theoretical calculations and can accurately reflect the actual filling time sequence of each region in a complex cavity structure.

[0034] Based on filling time sequence data, a comparative analysis of the filling status of multiple pre-divided filling regions within the cavity is performed to identify areas of uneven filling. Here, a filling region refers to an independent functional area within the cavity, divided according to the product's structural characteristics, with each region containing several filling positions; an uneven filling region refers to a problem area where the filling time significantly deviates from the expected time. In practice, the standard filling time for each filling region is first determined based on the cavity's wall thickness variation characteristics (referring to the wall thickness differences in different parts of the product, obtained by measuring the wall thickness values ​​of each region in the product design drawings) and the runner cross-sectional area distribution (referring to the cross-sectional area changes of the melt flow channel at different locations, calculated by measuring the width and depth of the mold runner). The method for determining the standard filling time is based on fluid mechanics principles, considering the influence of region wall thickness and runner cross-sectional area on the filling time. Specifically, regions with thicker walls require more melt filling, resulting in a correspondingly longer standard time; regions with smaller runner cross-sectional areas have greater melt flow resistance, also leading to a correspondingly longer standard time. For example, the standard filling time is set to 12 seconds for a region with a wall thickness of 3 mm, and 8 seconds for a thin-walled region with a wall thickness of 1.5 mm. The standard filling time is increased by 20% for regions where the flow channel cross-sectional area is reduced from 10 square millimeters to 5 square millimeters. The filling time sequence data is then compared with the standard filling time for each region to obtain the filling time deviation value. Deviation comparison refers to the process of comparing the actual measured or predicted filling time with the theoretical standard time. The filling time deviation value is calculated by subtracting the standard filling time from the actual filling time; the resulting value indicates the degree of deviation of the filling time for that region. A positive deviation value indicates that the filling time exceeds the standard time, i.e., filling lag; a negative deviation value indicates that the filling time is less than the standard time, i.e., filling too quickly. For example, if the standard filling time for a region is 10 seconds and the actual filling time is 13 seconds, the deviation value is +3 seconds, indicating a filling lag of 3 seconds. Finally, based on preset deviation thresholds, the deviation values ​​of each filling time are classified into three categories: excessively fast filling, delayed filling, and abnormal filling. The deviation thresholds are pre-set judgment standards based on product quality requirements and process control precision, used to classify deviation values ​​into different status categories. Specifically, the classification standards are as follows: deviation values ​​within ±25% of the standard time are considered normal filling areas; deviation values ​​less than -25% of the standard time are considered excessively fast filling areas, indicating that the filling speed in this area is too fast and prone to stress concentration; deviation values ​​greater than +25% of the standard time are considered delayed filling areas, indicating that the filling in this area is difficult and prone to short-shot defects; and the absolute value of the deviation value exceeding 50% of the standard time is considered an abnormal filling area, indicating a serious filling problem in this area. For example, in an area with a standard filling time of 8 seconds, a deviation value within -2 seconds to +2 seconds is normal, a deviation value less than -2 seconds is excessively fast, a deviation value greater than +2 seconds is delayed, and an absolute value of the deviation value greater than 4 seconds is abnormal.This systematic classification and identification process provides a complete picture of the distribution of unevenly filled areas, offering a precise basis for problem localization in subsequent process adjustments and quality control.

[0035] Based on the filling timing data and the identification results of uneven filling regions, the filling completion status of each region is determined (referring to the progress level and quality evaluation of each region in the entire filling process), thus obtaining the melt filling progress and filling timing of each region within the cavity. In specific implementation, firstly, based on the filling timing data, the filling arrival time corresponding to each filling region is determined (referring to the time point when the melt begins to enter the region; the method is to find the earliest arrival time among all filling positions contained in the region as the filling arrival time of that region). For example, region A contains five filling positions numbered P1, P2, P3, P4, and P5. According to the filling timing data, the arrival times of these positions are found to be 6 seconds, 4 seconds, 8 seconds, 7 seconds, and 9 seconds respectively. Therefore, the earliest time, 4 seconds, is taken as the filling arrival time of region A. By traversing all filling regions, the filling arrival time of each region is determined, forming a list of region filling arrival times. Then, based on the identification results of uneven filling regions, the filling status of each filling region is marked, obtaining the status marking results. Filling status labeling refers to assigning a corresponding status category label to each region based on the aforementioned deviation analysis results. The labeling method directly converts the deviation classification results of each region into status labels: normal filling regions are labeled "Normal," overly fast filling regions are labeled "Overly Fast," lagging filling regions are labeled "Lagging," and abnormal filling regions are labeled "Abnormal." The status labeling results are recorded in tabular form, including region number, region name, and corresponding status label. For example, region A with a deviation value of -1.5 seconds and within the normal range is labeled "Normal"; region B with a deviation value of +4 seconds and exceeding the lag threshold is labeled "Lagging"; and region C with a deviation value of -6 seconds and exceeding the abnormal threshold is labeled "Abnormal." Finally, based on the filling arrival time and filling status label of each region, the completion ratio of each region relative to the overall filling process is calculated, obtaining the melt filling progress and filling sequence of each region within the cavity. Filling progress refers to the percentage of completion of each region in the entire filling process, and filling sequence refers to the order in which the regions are arranged according to their filling arrival time. The calculation of the completion rate involves two steps: First, the basic completion rate is calculated using the formula: the area filling arrival time divided by the total filling time of the entire cavity, multiplied by 100%, where the total filling time is the latest filling arrival time among all areas. For example, if the filling arrival time for area A is 4 seconds, for area B it is 8 seconds, for area C it is 12 seconds, and for area D it is 15 seconds, then the total filling time is 15 seconds, and the basic completion rate for area A is 4 ÷ 15 × 100% = 26.7%. Then, the rate is adjusted based on the filling status markings: areas marked "normal" maintain their original completion rate; areas marked "too fast" have their completion rate multiplied by an adjustment factor of 0.85 to reflect their early completion; areas marked "lagging" have their completion rate multiplied by an adjustment factor of 1.15 to reflect their delayed completion; and areas marked "abnormal" have their completion rate set to a special value of 999, indicating that they require special attention and handling.The filling sequence is obtained by sorting each region according to its adjusted completion percentage from smallest to largest, with regions having a smaller completion percentage being filled earlier. This systematic calculation and sorting method accurately quantifies the progress and timing of each region throughout the filling process, providing a detailed data foundation for process optimization and quality control.

[0036] 104. Based on the filling progress and filling sequence, generate evaluation information on the melt filling state in the cavity; In this embodiment, based on the filling progress and filling sequence, the filling completion rate of each filling area is assessed to determine the normal filling area and the filling area to be improved. The areas of excessively rapid filling, delayed filling, and abnormal filling within the uneven filling areas are classified into defect risk levels to obtain the influence weight of each type of area. Based on preset weight coefficients, the influence weights of the normal filling area, the filling area to be improved, and various uneven areas are weighted and comprehensively calculated to generate assessment information characterizing the melt filling state within the cavity. Then, process control is performed based on the assessment information. This control process includes: determining the key process parameters and process adjustment directions for regulating the gas flow state based on the assessment information; predicting the gas flow state corresponding to different adjustment amounts based on the key process parameters and process adjustment directions, and calculating the parameter adjustment amount corresponding to achieving the preset target gas state based on the gas flow state; and simultaneously adjusting the negative pressure intensity and injection process parameters based on the parameter adjustment amount to bring the gas monitoring parameters back to the preset normal monitoring range, forming a closed-loop control based on gas monitoring feedback.

[0037] In practical applications, based on filling progress and filling sequence, the filling completion rate of each filling area is assessed to determine normal filling areas and areas requiring improvement. Defect risk levels are then assigned to areas with excessively rapid filling, delayed filling, and abnormal filling within the uneven filling areas, resulting in the influence weight of each type of area. The filling completion rate assessment refers to the evaluation process that comprehensively judges the filling quality of each area based on its filling progress value and filling sequence ranking. In specific implementation, the evaluation criteria are first set: areas with filling progress within the range of 80%-120% and whose filling sequence deviation from the design expectation does not exceed two positions are marked as normal filling areas; other areas are marked as areas requiring improvement. The filling sequence deviation is calculated by comparing the difference in ranking between the actual and design sequences. For example, if an area's design sequence is 3rd and its actual sequence is 5th, the deviation is 2 positions. Then, risk levels are assigned to various types of uneven filling areas. The defect risk level refers to a grading standard set according to the degree of impact of different filling problems on product quality. Areas with excessively rapid filling are classified as Level 2 risk, with a risk coefficient of 0.6; areas with delayed filling are classified as Level 3 risk, with a risk coefficient of 0.8; and areas with abnormal filling are classified as Level 4 risk, with a risk coefficient of 1.0. The weighting of the impact is calculated by multiplying the proportion of each area to the total cavity area by the corresponding risk coefficient. For example, in an automotive door panel injection molded part, the area of ​​the excessively rapid filling area is 150 square millimeters, the total cavity area is 1000 square millimeters, the area proportion is 15%, and the impact weight is 0.15 × 0.6 = 0.09. By performing similar calculations on all problem areas, the impact weight values ​​for each type of area are obtained. This hierarchical and quantitative method accurately reflects the degree of impact of different filling problems on product quality, providing an objective weighting basis for comprehensive evaluation.

[0038] Based on preset weighting coefficients, the influence weights of normal filling areas, filling areas requiring improvement, and various non-uniform areas are weighted and comprehensively calculated to generate evaluation information characterizing the melt filling state within the mold cavity. The weighting coefficients are calculation parameters pre-set according to product quality standards, used to balance the contribution of different types of areas to the overall evaluation. Specifically, the weighting coefficient for normal filling areas is 1.0, representing a positive contribution to overall quality; the weighting coefficient for filling areas requiring improvement is 0.5, representing a moderate quality impact; the influence weights of various non-uniform areas are directly used as negative factors in the calculation. The weighted comprehensive calculation uses numerical addition and subtraction operations. The calculation steps are as follows: First, calculate the positive contribution value, which equals the area ratio of the normal filling area multiplied by 1.0; second, calculate the moderate impact value, which equals the area ratio of the filling areas requiring improvement multiplied by 0.5; third, calculate the negative impact value, which equals the sum of the influence weights of various non-uniform areas; fourth, calculate the comprehensive evaluation score, which equals the positive contribution value plus the moderate impact value minus the negative impact value. For example, if a product has a 60% normally filled area, a 20% area requiring improvement, and the total weight of uneven areas is 0.15, then the overall evaluation score is 0.60 × 1.0 + 0.20 × 0.5 - 0.15 = 0.55. The evaluation information is graded according to the score: 0.7 or above is Grade A (Excellent), 0.5-0.7 is Grade B (Good), 0.3-0.5 is Grade C (Acceptable), and below 0.3 is Grade D (Unacceptable). The evaluation information also includes the specific location coordinates and area size of the problem area, providing clear improvement targets for process adjustments. This numerical evaluation method can objectively quantify the overall quality level of the melt filling state within the cavity, thus achieving real-time visual prediction of the filling process inside complex cavities.

[0039] Process control based on assessment information includes determining key process parameters and adjustment directions, predicting adjustment effects, calculating parameter adjustment amounts, and ultimately achieving closed-loop control. Key process parameters include three main parameters: negative pressure intensity, injection speed, and injection pressure. The process adjustment direction is determined based on the main problem types identified in the assessment information: when the area of ​​overfilled regions exceeds 20%, the adjustment direction is to reduce the injection speed by 10-20%; when the area of ​​underfilled regions exceeds 15%, the adjustment direction is to increase the negative pressure intensity by 15-25% or increase the injection pressure by 10-15%; when the area of ​​abnormally filled regions exceeds 10%, both the negative pressure intensity and injection pressure are adjusted simultaneously. In practice, the corresponding adjustment scheme is retrieved from a pre-set process adjustment data table. This data table, established through extensive experimental data, records the effective adjustment parameters and adjustment ranges corresponding to different problem types. Then, the effects of different adjustment amounts are predicted using a lookup table method. The lookup table method refers to finding the corresponding expected results in a pre-established parameter effect comparison table based on the current process parameter values ​​and planned adjustment amounts. For example, if the current injection pressure is 100 MPa and the plan is to increase it by 10% to 110 MPa, a table indicates that this adjustment is expected to reduce the area of ​​the filling lag zone by 25%. The target gas state is set so that the gas pressure variation at each monitoring point is controlled within ±15% of the standard value, and the temperature change rate is controlled within ±2℃ per second. The parameter adjustment amount is determined through step-by-step trial calculations: first, an initial adjustment amount is set, and the effect is predicted by referring to a table. If the predicted result does not reach the target range, the adjustment amount is increased; if it exceeds the target range, the adjustment amount is decreased. This process is repeated until the predicted result falls within the target range. Finally, the parameter adjustment is synchronously executed through the injection molding machine's PLC control system, which monitors the parameter changes at each gas monitoring point in real time. When the monitored value deviates from the preset normal range, the automatic fine-tuning function is triggered. The normal monitoring range is determined based on historical data statistics of high-quality products and is set as ±10% of the standard value. When the monitored parameter exceeds the normal range three times consecutively, parameter fine-tuning is automatically initiated, with an adjustment range of ±5% of the current parameter value. This closed-loop control system based on real-time monitoring data feedback can automatically maintain the stability of the injection molding process and ensure the consistency of product quality.

[0040] In this embodiment of the invention, gas parameter data from multiple monitoring points within the mold cavity are acquired in real time during negative pressure injection molding, and gas characteristic analysis is performed to identify the state changes of gas being pushed by the melt, obtaining the state transition sequence of each monitoring point. Based on the state transition sequence, the melt propagation speed and direction are calculated to construct the advancement path of the melt flow front. Then, the melt filling progress and timing distribution in each region are predicted according to the advancement path, and uneven filling areas are identified. Finally, a quality risk assessment is performed based on the filling state to generate assessment information of the melt filling state within the cavity. By monitoring the gas state and reconstructing the path under negative pressure, the problem that traditional monitoring technologies cannot directly reflect the melt flow process inside the cavity is solved. Especially in terms of gas state identification, melt path tracking, and filling prediction, the unique phenomenon of gas being pushed by the melt in negative pressure injection molding is fully utilized to achieve real-time visual prediction of the filling process inside complex cavities. Furthermore, the multi-point gas monitoring and timing analysis strategy not only achieves accurate conversion from gas changes to melt flow but also enhances the reliability of filling state prediction.

[0041] The above describes the negative pressure injection molding method for plastics in the embodiments of the present invention. The following describes the negative pressure injection molding apparatus for plastics in the embodiments of the present invention. Please refer to [link / reference]. Figure 2 One embodiment of the negative pressure injection molding plastic molding device of the present invention includes: The gas acquisition module 201 is used to acquire gas parameter data from multiple monitoring points in the mold cavity in real time during the negative pressure injection molding process. The trajectory reconstruction module 202 is used to identify the state of gas being pushed by the melt based on the changes in gas parameter data at each monitoring point, and to determine the propulsion path of the melt flow front in the cavity. The flow modeling module 203 is used to calculate the melt filling progress and filling sequence of each region in the cavity according to the propulsion path; Evaluation module 204 is used to generate evaluation information on the melt filling status in the cavity based on the filling progress and filling sequence.

[0042] In this embodiment of the invention, by monitoring the gas state and reconstructing the path under negative pressure, the problem that traditional monitoring technology cannot directly reflect the melt flow process inside the cavity is solved. In particular, in terms of gas state identification, melt path tracking and filling prediction, the unique phenomenon of gas being pushed by the melt in negative pressure injection molding is fully utilized to realize real-time visualization and prediction of the filling process inside complex cavities. Furthermore, by adopting a multi-point gas monitoring and time-series analysis strategy, the accurate conversion from gas changes to melt flow is realized, and the reliability of filling state prediction is enhanced.

[0043] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A negative pressure injection molding method for plastics, characterized in that, The negative pressure injection molding method for plastics includes: During negative pressure injection molding, gas parameter data from multiple monitoring points within the mold cavity are acquired in real time. Based on the changes in gas parameter data at each monitoring point, the state of gas being pushed by the melt is identified, and the propulsion path of the melt flow front in the cavity is determined. Based on the aforementioned propulsion path, calculate the melt filling progress and filling sequence of each region within the cavity; Based on the filling progress and filling sequence, evaluation information on the melt filling state in the cavity is generated.

2. The negative pressure injection molding method for plastics according to claim 1, characterized in that, The method of identifying the state of gas being pushed by the melt based on changes in gas parameter data at each monitoring point includes: Match the process characteristic parameters corresponding to the material to be injected with, and based on the process characteristic parameters, perform time-series segmentation on the gas parameter data to obtain gas data segments; Based on a pre-set plastic gas fingerprint database, similarity calculations are performed on the gas data segments to identify the gas state type that characterizes the melt filling process. By comparing the gas state types at each monitoring point over time, the time nodes when the gas state changes are identified, and based on the time nodes, the state of the gas being pushed by the melt at each monitoring point is determined.

3. The negative pressure injection molding method for plastics according to claim 2, characterized in that, Determining the advancement path of the melt flow front within the cavity includes: Based on the state of gas being pushed by the melt at each monitoring point and the time node, calculate the time difference of melt arrival between adjacent monitoring points; Based on the time difference and the spatial coordinates of each monitoring point, the propagation speed and direction of the melt in the cavity are determined. Based on the propagation speed and propagation direction, a velocity field distribution for melt propagation is constructed, and based on the geometric constraint parameters corresponding to the mold cavity, the velocity field distribution is reconstructed to obtain the path reconstruction result; Based on the path reconstruction results, the main flow path and branch flow paths of the melt are identified, and the propulsion path of the melt flow front in the cavity is generated.

4. The negative pressure injection molding method for plastics according to claim 2, characterized in that, The preset plastic gas fingerprint database is established in the following way: Gas parameter data and corresponding melt filling sequence were collected at various monitoring points for different plastic materials under standard negative pressure injection molding process conditions to obtain a related data set. Feature extraction and fingerprint template construction are performed on the associated data group to construct a template corresponding to the gas state type and the filling process node, thus forming a gas fingerprint template for each material. The gas fingerprint templates are classified and labeled according to material type, process parameters and cavity features to form a plastic gas fingerprint library.

5. The negative pressure injection molding method for plastics according to claim 1, characterized in that, The step of calculating the melt filling progress and filling sequence of each region within the cavity based on the propulsion path includes: Based on the melt fluid characteristics of the material to be injected and the propulsion path, the arrival time and filling sequence of the melt at multiple preset filling positions in the cavity are predicted to obtain filling timing data. Based on the filling timing data, the filling status of multiple pre-divided filling regions within the cavity is compared and analyzed to identify areas with uneven filling. Based on the filling timing data and the uneven filling area, the filling completion status of each area is determined, and the melt filling progress and filling timing of each area in the cavity are obtained.

6. The negative pressure injection molding method for plastics according to claim 5, characterized in that, Based on the filling timing data, a comparative analysis of the filling status of multiple pre-divided filling regions within the cavity is performed to identify areas of uneven filling, including: Based on the wall thickness variation characteristics and flow channel cross-sectional area distribution corresponding to the cavity, the standard filling time for each filling area is determined; The filling time sequence data is compared with the standard filling time of each region to obtain the filling time deviation value of each region. Based on a preset deviation threshold, the states of each filling time deviation value are classified to identify regions of excessively fast filling, regions of delayed filling, and regions of abnormal filling, thereby obtaining regions of uneven filling.

7. The negative pressure injection molding method for plastics according to claim 5, characterized in that, The process of determining the filling completion status of each region based on the filling timing data and the uneven filling area, and obtaining the melt filling progress and filling timing of each region in the cavity, includes: Based on the filling timing data, determine the filling arrival time corresponding to each filling area; Based on the uneven filling area, the filling status of each filling area is marked to obtain the status marking result; Based on the filling arrival time of each region and the filling status marker, the completion ratio of each region relative to the overall filling process is calculated, and the melt filling progress and filling sequence of each region in the cavity are obtained.

8. The negative pressure injection molding method for plastics according to claim 1, characterized in that, The process of generating evaluation information on the melt filling state within the cavity based on the filling progress and filling timing includes: Based on the filling progress and filling sequence, the filling completion rate of each filling area is evaluated to determine the normal filling area and the filling area that needs improvement. The defect risk levels of the overfilled area, the overfilled area, and the overfilled area in the uneven filling area are classified respectively, and the influence weight of each type of area is obtained. Based on preset weighting coefficients, the influence weights of the normal filling area, the filling area to be improved, and various non-uniform areas are weighted and comprehensively calculated to generate evaluation information characterizing the melt filling state in the cavity.

9. The negative pressure injection molding method for plastics according to claim 1, characterized in that, Process control based on the evaluation information includes: Based on the assessment information, determine the key process parameters for regulating gas flow and the direction of process adjustment; Based on the key process parameters and process adjustment direction, predict the gas flow state corresponding to different adjustment amounts, and based on the gas flow state, calculate the parameter adjustment amount corresponding to achieving the preset target gas state. Based on the parameter adjustment amount, the negative pressure intensity and injection process parameters are adjusted synchronously to bring the gas monitoring parameters back to the preset normal monitoring range, forming a closed-loop control based on gas monitoring feedback.

10. A negative pressure injection molding apparatus for plastics, characterized in that, The negative pressure injection molding plastic molding device includes: The gas acquisition module is used to acquire gas parameter data from multiple monitoring points within the mold cavity in real time during negative pressure injection molding. The trajectory reconstruction module is used to identify the state of gas being pushed by the melt based on the changes in gas parameter data at each monitoring point, and to determine the propulsion path of the melt flow front in the cavity. The flow modeling module is used to calculate the melt filling progress and filling sequence of each region within the cavity based on the propulsion path. The evaluation module is used to generate evaluation information on the melt filling status in the cavity based on the filling progress and filling sequence.

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