Intelligent auxiliary system for improving design efficiency of complex injection mold

Abstract

The invention relates to the technical field of injection molding auxiliary control, in particular to an intelligent auxiliary system for improving the design efficiency of a complex injection mold, which comprises an intelligent sensing module, an injection molding model building module, an injection molding auxiliary analysis module and a self-adaptive injection molding module, performing multi-source data acquisition on the mold and the production process through a scanning unit, a cavity pressure sensor, a thermocouple and an infrared thermal imager; based on the three-dimensional point cloud data, automatically constructing and marking a digital model of a flow channel and a waterway; by calculating a flow complexity index and a cooling uniformity index, the molds are intelligently classified into high-speed balance, flow sensitivity or cooling leading and other process categories; according to the method, the conversion from experience trial and error to data-driven decision making is realized, and the injection molding stability and the product quality consistency of the complex mold are remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of injection molding auxiliary control, and in particular to an intelligent auxiliary system for improving the design efficiency of complex injection molding molds. BACKGROUND

[0002] With the increasing demand for product lightweight, functional integration and appearance refinement in the manufacturing industry, complex injection molding molds are increasingly widely used. Such molds usually have multiple cavities, deep cavities, thin walls or special-shaped structures, and their runner systems are complex and cooling water routes are densely arranged. In the production process, how to set and optimize injection molding process parameters to achieve high-quality molding. Currently, the process development and production debugging for complex molds mainly rely on the rich experience of engineers and repeated trial-and-error adjustments. Engineers need to preset hundreds of parameters such as injection speed, pressure, temperature and cooling time according to the mold drawings and product structure based on experience, and make manual corrections through observation of the defects of trial samples. This process not only takes a long time and costs a lot, but also depends heavily on personal experience, making it difficult to ensure reproducibility and optimality.

[0003] In addition, even if the initial process parameters are carefully adjusted, the production stability will still be disturbed by many dynamic factors such as material batch fluctuations, changes in environmental temperature and humidity, mold temperature field drift and equipment state decay during mass production. The open-loop or simple PID control mode used by traditional injection molding machines cannot actively perceive and adapt to these changes, which can easily lead to the recurrence of problems such as uneven filling and uneven cooling, causing product defect rate fluctuations and production cycle forced to extend. Some advanced process auxiliary technologies, such as mold flow analysis (CAE) software, can simulate and predict in the design stage, but there is a gap between the analysis results and the real production environment, and real-time closed-loop control cannot be achieved in production. SUMMARY

[0004] Therefore, the present application provides an intelligent auxiliary system for improving the design efficiency of complex injection molding molds to overcome the problem of lack of consideration of the actual shape of the mold affecting the injection molding process in the prior art, which directly leads to reduced product quality.

[0005] To achieve the above-mentioned purpose, the present application provides an intelligent auxiliary system for improving the design efficiency of complex injection molding molds, comprising:

[0006] An intelligent sensing module, which includes a scanning unit for collecting three-dimensional point cloud data of the mold cavity, a plurality of cavity pressure sensors arranged at key positions of the mold cavity to collect cavity pressure data, a multi-point thermocouple arranged inside the mold core, and an infrared thermal imager for detecting the temperature distribution of the outer surface of the mold;

[0007] an injection molding model construction module configured to construct a mold digital model according to the mold cavity three-dimensional point cloud data, and to mark injection molding runners and cooling water paths on the mold digital model;

[0008] an injection molding auxiliary analysis module configured to calculate a flow complexity index and a cooling uniformity index according to the mold digital model, to determine an injection molding auxiliary category, to generate a cavity pressure curve in real time according to the cavity pressure data, and to generate a temperature distribution image according to the outer surface temperature distribution obtained by the infrared thermal imager;

[0009] an adaptive injection molding module configured to determine an injection molding auxiliary control operation according to the injection molding auxiliary category, the cavity pressure data, and the temperature data, the injection molding auxiliary control operation including:

[0010] calling preset high-speed injection parameters to perform injection molding, and dynamically fine-tuning an injection speed according to a deviation between a real-time cavity pressure curve and an ideal predicted curve; or, enabling a multi-segment speed and pressure control curve to perform injection molding, and determining whether to adjust a V / P switching point according to data balance of each cavity pressure sensor; or, determining a hot spot region based on the multi-point thermocouple, and determining whether to trigger local high-pressure point cooling in combination with a corresponding temperature drop curve.

[0011] As a preferred technical solution of the intelligent auxiliary system for improving the design efficiency of complex injection molding molds, the adaptive injection molding module, in response to the injection molding auxiliary category of the cooling dominant category, is configured to:

[0012] The scanning unit is configured to perform non-contact three-dimensional scanning on the inner surface of the cavity of the injection molding mold.

[0013] The cavity pressure sensors are respectively arranged at the gate end region and the flow end region of the mold cavity, and the sensing end faces thereof are flush with the inner surface of the cavity.

[0014] The multi-point thermocouple is embedded in the mold core and the temperature measurement end thereof is in contact with the inner surface of the mold core.

[0015] The infrared thermal imager is configured to perform temperature distribution imaging on the outer surface of the parting surface of the injection molding mold and the outer surface of the ejector plate region.

[0016] As a preferred technical solution of the intelligent auxiliary system for improving the design efficiency of complex injection molding molds, the adaptive injection molding module, in response to the injection molding auxiliary category of the cooling dominant category, is configured to:

[0017] The injection molding model construction module reconstructs a three-dimensional entity model containing cavity surface geometric features from the mold cavity three-dimensional point cloud data, identifies and marks the path and cross-sectional profile of the injection molding runner based on the point cloud density variation characteristics, and identifies and marks the direction and interface position of the cooling water path based on the tubular structure features in the point cloud data.

[0018] As a preferred technical solution of the intelligent auxiliary system for improving the design efficiency of a complex injection mold, the adaptive injection mold module is configured to respond to the injection auxiliary category of the cooling dominant category:

[0019] The injection auxiliary analysis module performs flow analysis on the filling paths of the injection runners to the ends of the cavities according to the mold digital model, calculates the flow complexity index according to the ratio of the flow length to the average wall thickness of each filling path and the statistical distribution characteristics of the ratio between the filling paths, and determines the cooling time of each cooling loop corresponding to the cavity area according to the mold digital model.

[0020] The injection auxiliary analysis module performs flow analysis on the filling paths of the injection runners to the ends of the cavities according to the mold digital model, calculates the flow complexity index according to the ratio of the flow length to the average wall thickness of each filling path and the statistical distribution characteristics of the ratio between the filling paths, and determines the cooling time of each cooling loop corresponding to the cavity area according to the mold digital model.

[0021] As a preferred technical solution of the intelligent auxiliary system for improving the design efficiency of a complex injection mold, the adaptive injection mold module is configured to respond to the injection auxiliary category of the cooling dominant category:

[0022] The injection auxiliary analysis module determines the injection auxiliary category according to the flow complexity index and the cooling uniformity index, including:

[0023] If the flow complexity index is less than or equal to a first flow index threshold and the cooling uniformity index is less than or equal to a first cooling index threshold, it is determined to be a high-speed balance category;

[0024] If the flow complexity index is greater than the first flow index threshold and the cooling uniformity index is less than or equal to the second cooling index threshold, it is determined to be a flow-sensitive category;

[0025] If the cooling uniformity index is greater than the first cooling index threshold, it is determined to be a cooling dominant category.

[0026] As a preferred technical solution of the intelligent auxiliary system for improving the design efficiency of a complex injection mold, the adaptive injection mold module is configured to respond to the injection auxiliary category of the cooling dominant category:

[0027] The adaptive injection mold module is configured to determine the corresponding injection auxiliary control operation according to the determined injection auxiliary category:

[0028] If the injection auxiliary category is a high-speed balance category, the adaptive injection mold module calls a preset high-speed injection parameter for injection, and dynamically adjusts the injection speed according to the deviation of the real-time cavity pressure curve from the ideal predicted curve.

[0029] If the injection auxiliary category is a flow-sensitive category, the adaptive injection molding module enables multi-segment speed and pressure control curves for injection molding, and determines whether to adjust the V / P switching point according to the data balance of the cavity pressure sensor;

[0030] If the injection auxiliary category is a cooling-dominant category, the adaptive injection molding module determines whether to trigger local high-pressure point cooling based on the hot spot area identified by the multi-point thermocouple and its corresponding temperature drop curve.

[0031] As a preferred technical solution of the intelligent auxiliary system for improving the design efficiency of complex injection molding molds, the adaptive injection molding module, in response to the injection auxiliary category of the cooling-dominant category, is configured to:

[0032] The adaptive injection molding module, in response to the injection auxiliary category of the high-speed balance category, is configured to:

[0033] Obtain an ideal cavity pressure curve based on the mold digital model simulation and the real-time cavity pressure curve;

[0034] Calculate the pressure difference value of the real-time cavity pressure curve and the ideal cavity pressure curve at the same time node in the injection filling stage;

[0035] If the absolute value of the pressure difference value exceeds the preset pressure tolerance threshold, the injection speed is reduced or increased by a preset step according to the positive or negative of the pressure difference value.

[0036] As a preferred technical solution of the intelligent auxiliary system for improving the design efficiency of complex injection molding molds, the adaptive injection molding module, in response to the injection auxiliary category of the cooling-dominant category, is configured to:

[0037] The adaptive injection molding module, in response to the injection auxiliary category of the flow-sensitive category, is configured to:

[0038] Obtain real-time pressure data of each cavity pressure sensor arranged at the gate end region and the flow end region in the filling end section;

[0039] According to the real-time pressure data of each cavity pressure sensor, calculate the time when the pressure of each sensor reaches a preset pressure proportion threshold;

[0040] According to the difference between the times when each sensor reaches the preset pressure proportion threshold, determine the filling balance;

[0041] If the time difference exceeds a preset synchronization threshold, adjust the triggering time of the V / P switching point according to the time difference.

[0042] As a preferred technical solution of the intelligent auxiliary system for improving the design efficiency of complex injection molding molds, the adaptive injection molding module, in response to the injection auxiliary category of the cooling-dominant category, is configured to:

[0043] The adaptive injection molding module identifies hot spot areas based on the multi-point thermocouples, comprising:

[0044] Obtain the data of temperature drop over time collected by each thermocouple inside the mold core, and calculate the cooling rate of the area corresponding to each thermocouple;

[0045] Compare the cooling rates of the areas corresponding to each thermocouple;

[0046] Identify one or more areas with a cooling rate lower than a preset cooling rate threshold as a hot spot area.

[0047] As a preferred technical solution of the intelligent auxiliary system for improving the design efficiency of complex injection molding molds, the adaptive injection molding module, in response to the injection molding auxiliary category of the cooling dominant category, is configured to:

[0048] Obtain the temperature drop curve of the hot spot area in the cooling stage, and compare it with the preset standard cooling curve at the same cooling time node;

[0049] Calculate the difference between the actual temperature value of the temperature drop curve and the standard temperature value of the standard cooling curve;

[0050] If the difference exceeds a preset temperature difference trigger threshold, it is determined that local high-pressure point cooling needs to be triggered for the hot spot area.

[0051] Compared with the prior art, the system first utilizes the scanning unit, cavity pressure sensor, thermocouple and infrared thermal imager to perform multi-source data fusion sensing on the mold geometry and production state, and automatically constructs an accurate digital model containing the flow channel and waterway. Through calculating the flow complexity and cooling uniformity index, the system automatically classifies the mold into different categories such as high-speed balance, flow sensitivity or cooling dominance. Based on this classification, the adaptive module can execute precise matching control strategies, such as fine-tuning the injection speed according to the cavity pressure curve deviation in the high-speed balance category, dynamically adjusting the V / P switching point according to the multi-cavity pressure synchronicity in the flow sensitivity category, and triggering local point cooling according to the hot spot temperature curve deviation in the cooling dominant category. The present application significantly reduces the dependence on human experience for process debugging, shortens the trial and parameter optimization time, and can compensate for fluctuations in real time during mass production, thereby comprehensively improving the process development efficiency, production stability and product quality consistency of complex injection molding molds. BRIEF DESCRIPTION OF DRAWINGS

[0052] Figure 1 Structure diagram of the intelligent auxiliary system for improving the design efficiency of complex injection molding molds according to the embodiments of the present application;

[0053] Figure 2A logic diagram for determining an injection molding auxiliary category of an injection molding auxiliary analysis module of an embodiment of the present application;

[0054] Figure 3 A logic diagram for identifying a hot spot area by an adaptive injection molding module of an embodiment of the present application. DETAILED DESCRIPTION

[0055] In order to make the objects and advantages of the present application clearer, the present application will be further described in conjunction with embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and not to limit the present application.

[0056] The preferred embodiments of the present application will be described below with reference to the accompanying drawings. It should be understood by those skilled in the art that the embodiments are only used to explain the technical principles of the present application and not to limit the protection scope of the present application.

[0057] It should be noted that, in the description of the present application, the terms of "upper", "lower", "left", "right", "inner", "outer" and the like indicating the direction or positional relationship are based on the direction or positional relationship shown in the drawings, which is only for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation of the present application.

[0058] In addition, it should also be noted that, in the description of the present application, unless otherwise explicitly specified and limited, the terms of "mounting", "connecting", "connection" should be understood in a broad sense, for example, it can be fixed connection, or detachable connection, or integrally connected; it can be mechanical connection, or electrical connection; it can be directly connected, or indirectly connected through an intermediate medium, or the communication inside two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0059] Please refer to Figure 1 As shown in the figure, it is a structure schematic diagram of an intelligent auxiliary system for improving the design efficiency of a complex injection molding mold of an embodiment of the present application; the present application provides an intelligent auxiliary system for improving the design efficiency of a complex injection molding mold, comprising:

[0060] The intelligent sensing module comprises a scanning unit for collecting three-dimensional point cloud data of the mold cavity, a plurality of cavity pressure sensors arranged at key positions of the mold cavity for collecting cavity pressure data, a multi-point thermocouple arranged inside the mold core, and an infrared thermal imager for detecting the temperature distribution of the outer surface of the mold;

[0061] The injection molding model construction module is used to construct a digital mold model according to the three-dimensional point cloud data of the mold cavity, and mark the injection runner and the cooling waterway on the digital mold model;

[0062] an injection molding auxiliary analysis module configured to calculate a flow complexity index and a cooling uniformity index based on a digital mold model, to determine an injection molding auxiliary category, to generate a cavity pressure curve in real time based on cavity pressure data, and to generate a temperature distribution image based on an outer surface temperature distribution obtained by an infrared thermal imager;

[0063] an adaptive injection molding module configured to determine an injection molding auxiliary control operation based on the injection molding auxiliary category, the cavity pressure data, and the temperature data, the injection molding auxiliary control operation comprising:

[0064] calling preset high-speed injection parameters for injection molding and dynamically fine-tuning an injection speed based on a deviation between the real-time cavity pressure curve and an ideal predicted curve, or enabling a multi-segment speed and pressure control curve for injection molding and determining whether to adjust a V / P switching point based on data balance of each cavity pressure sensor, or determining a hot spot region based on a multi-point thermocouple and determining whether to trigger local high-pressure point cooling based on a corresponding temperature drop curve.

[0065] The system first uses a scanning unit, a cavity pressure sensor, a thermocouple, and an infrared thermal imager to perform multi-source data fusion sensing on the mold geometry and production state, and automatically constructs an accurate digital model containing a flow channel and a water channel. By calculating the flow complexity and cooling uniformity indexes, the system automatically classifies the mold into different categories such as high-speed balance, flow sensitivity, or cooling dominance. Based on this classification, the adaptive module can execute precise matching control strategies, such as fine-tuning the injection speed based on the cavity pressure curve deviation in the high-speed balance category, dynamically adjusting the V / P switching point based on the multi-cavity pressure synchronicity in the flow sensitivity category, and triggering local point cooling based on the hot spot temperature curve deviation in the cooling dominant category. The present application significantly reduces the dependence on human experience for process debugging, shortens the trial and parameter optimization time, and can compensate for fluctuations in real time during mass production, thereby comprehensively improving the process development efficiency, production stability, and product quality consistency of complex injection molding molds.

[0066] Specifically, the scanning unit is configured to perform non-contact three-dimensional scanning on the inner surface of the mold cavity of the injection mold;

[0067] The cavity pressure sensor is arranged at the gate end region and the flow end region of the mold cavity, and the sensing end surface is flush with the inner surface of the mold cavity;

[0068] The multi-point thermocouple is embedded in the mold core and the temperature measuring end is in contact with the inner surface of the mold core;

[0069] The infrared thermal imager is configured to perform temperature distribution imaging on the outer surface of the parting surface and the outer surface of the ejector plate region of the injection mold.

[0070] The scanning unit can acquire high-precision geometric data of the mold cavity and key structures, providing a foundation for constructing a digital model that reflects the true form of the physical entity. During implementation, a laser scanner or structured light scanning device can be used. By emitting a probe beam and receiving reflected signals, the spatial coordinates of dense points on the cavity surface are recorded, forming point cloud data. This avoids potential damage or interference to the precision mold surface caused by contact measurements, ensuring the accuracy and reliability of the constructed digital model and providing accurate geometric input for subsequent flow and cooling simulation analysis.

[0071] Cavity pressure sensors are installed at the gate end and flow end areas of the mold cavity, with their sensing faces flush with the inner surface of the cavity. These sensors monitor in real time the pressure build-up process as the melt fills to a critical position and the actual pressure distribution within the cavity during the holding pressure stage. In implementation, the cavity pressure sensors are embedded to ensure a smooth, flush end face with the cavity, avoiding interference with melt flow or flash. By sensing the pressure of the melt on the sensor's sensing diaphragm, they convert this pressure into an electrical signal, providing the system with the most direct process signal. This ensures that subsequent filling balance analysis, V / P switching point determination, and holding pressure curve optimization are all based on measured cavity pressure data, making it the core information source for achieving adaptive closed-loop process control.

[0072] Multi-point thermocouples are embedded inside the mold core with their sensing ends in contact with the inner surface of the mold core. This allows for direct measurement of the actual temperature changes at key locations on the cavity surface during the injection molding cycle. In implementation, the thermocouple sensing points need to be drilled to ensure they are as close as possible to and in direct contact with the cavity surface. Utilizing the thermoelectric effect, the temperature difference is converted into a potential difference for measurement, acquiring thermodynamic data during the cooling process. This provides irreplaceable direct measurement data for calculating the cooling rate, identifying actual hot spots, and optimizing cooling time, forming the foundation for precise thermal management and cycle optimization.

[0073] Infrared thermal imagers image the temperature distribution on the outer surface of the parting line and the outer surface of the ejector plate area of ​​injection molds, providing a global, non-contact monitoring of the overall thermal distribution of the mold during production. In practice, the thermal imager can be fixedly mounted on a robotic arm or independent support. During the mold opening stage, it scans the target area, converting the infrared radiation emitted from the object's surface into a temperature distribution image. This allows for the rapid and intuitive identification of localized temperature anomalies caused by blocked cooling water channels, abnormal hot runners, or mold thermal imbalance. As an important supplement to internal thermocouple point measurements, it provides a visual diagnostic tool for mold system status monitoring and preventative maintenance.

[0074] Specifically, the injection mold construction module constructs the three-dimensional point cloud data of the mold cavity, reconstructs a three-dimensional entity model containing the geometric features of the cavity surface, and identifies and labels the path and cross-sectional profile of the injection runner based on the point cloud density variation characteristics, and identifies and labels the direction and interface position of the cooling water channel based on the tubular structure characteristics in the point cloud data.

[0075] In implementation, the injection mold construction module uses Poisson reconstruction to process the point cloud data, generates a continuous triangular mesh surface by fitting the normal vector and spatial position of the point cloud, and further converts it into a boundary representation entity model. The original measurement data obtained by scanning is converted into a standardized digital model, providing an accurate and operable geometric carrier for subsequent simulation, analysis and optimization.

[0076] In specific implementation, the module uses a Poisson reconstruction algorithm for processing, the core of which is to construct a hidden function that can reflect the internal and external relationship of the object surface. Specifically, the algorithm first estimates a normal vector pointing to the outside of the surface for each three-dimensional point cloud data point. The algorithm discretizes the entire processing space into a three-dimensional grid and defines a vector field for each grid cell. Then, the algorithm contributes a vector value to its adjacent grid cells according to the position and normal vector information of each data point, and generates a discrete vector field on the three-dimensional grid by accumulating the contributions of all data points. The vector field is constructed as an approximation of the gradient field of the indicator function related to the surface to be reconstructed. The indicator function takes a positive value inside the object, a negative value outside, and zero at the surface. To solve this indicator function, a Poisson equation is established with the divergence of the generated discrete vector field as the source term. By solving this Poisson equation, the indicator function value of each grid cell can be obtained. Finally, by extracting the zero level surface of the indicator function, a continuous triangular mesh surface can be generated. This triangular mesh surface is the basis for converting to a boundary representation entity model later.

[0077] It can be understood that the point cloud spatial distribution of the injection runner area is generally more regular and smooth than the complex cavity surface, and presents a trend of continuity, uniformity and specific direction in point cloud density. By analyzing the local density gradient and spatial continuity of the point cloud, the area conforming to the geometric features of the runner is identified, and the centerline is extracted along its direction, and then the cross-sectional shape is reconstructed.

[0078] Meanwhile, the running direction and interface position of the cooling water channel are identified and labeled based on the tubular structure feature in the point cloud data, and the cooling pipe network inside the mold is identified, which is the basis for cooling effect simulation and optimization. It can be understood that the cooling water channel appears as a typical tubular cavity structure in the scanning data, and the inner wall point cloud forms a continuous tubular surface in space. By identifying the region with cylindrical surface geometric features and using Hough transform algorithm for fitting, the center axis, diameter and interface position of the inlet / outlet of the water channel are extracted, and the three-dimensional model of the cooling system hidden inside the mold is accurately reconstructed, laying a reliable geometric foundation for calculating the cooling uniformity index and conducting thermodynamic analysis.

[0079] Specifically, the module uses Hough transform for cylindrical surface detection, since the inner wall of the cooling water channel constitutes a cylindrical surface in the point cloud, Hough transform is used to search for the best cylindrical surface parameters in the three-dimensional parameter space. Specifically, the algorithm randomly samples seed points from the preprocessed point cloud, and calculates the normal vector distribution of the neighborhood points for each seed point. The cylindrical surface feature is that the normal vectors of the points on its surface converge to the central axis of the cylinder. Therefore, the voting process of Hough transform is: for each point and its normal vector, calculate all the spatial straight lines passing through the point and perpendicular to the normal vector of the point, and vote in the accumulator space representing the parameters of the straight lines. After a large number of votes, the parameter combination with the highest number of votes corresponds to the position and direction of the most likely existing cylinder central axis. Once the central axis is identified, the radius of the water channel can be estimated by calculating the distance distribution of the points to the axis, and the interface position of the water channel can be determined according to the start and end boundaries of the cylindrical surface.

[0080] Specifically, the injection molding auxiliary analysis module performs flow analysis on the filling paths of the injection runners to the ends of the cavities according to the mold digital model, calculates the flow complexity index according to the ratio of the flow length to the average wall thickness of each filling path and the statistical distribution characteristics of the ratio between the filling paths;

[0081] And, according to the mold digital model, the cooling water channel is simulated thermodynamically to determine the cooling time of each cooling loop corresponding to the cavity area, and the cooling uniformity index is calculated according to the statistical difference of the cooling time of each area.

[0082] In implementation, the flow complexity index is used to quantitatively evaluate the complexity and imbalance of the melt filling process in the mold cavity, which provides a key basis for subsequent process strategy classification. Based on the mold digital model, the system automatically identifies several main filling paths from the injection runner entrance to the end of each cavity. For each path, the algorithm calculates the ratio of the total flow length L to the average product wall thickness H of the area covered by the path, denoted as path flow ratio R=L / H, which reflects the resistance and cooling effect of the melt flowing along the path.

[0083] Subsequently, the system collects the flow ratios of all the main filling paths to form a data set . The standard deviation of this data set is calculated , which represents the dispersion of the flow ratios of the paths, i.e., the statistical intensity of the filling imbalance. To obtain a dimensionless and range-controlled index, the standard deviation is normalized. The reference value for normalization is set as the average of the flow ratios of all the paths. Finally, the flow complexity index .

[0084] The cooling uniformity index is used to evaluate the uniformity of the cooling effect of the mold cooling system on different cavity regions and identify the weak links of the cooling design. Based on the mold digital model marked with the cooling water path, transient thermodynamic simulation is performed. The initial melt temperature and cooling water parameters are set in the simulation, and the heat conduction equation is solved by numerical calculation to predict the temperature drop process of each region on the cavity surface over time. According to the simulation results, the module determines the main cavity region affected by each independent cooling loop and extracts the cooling time required for the temperature of the region to drop to the preset ejection temperature.

[0085] When calculating the cooling uniformity index, the standard deviation of the cooling time is calculated based on the statistical difference of all the region cooling times. Based on the thermodynamic simulation results, the module extracts the cooling time required for the main cavity region corresponding to each independent cooling loop to reach the preset ejection temperature, forming a cooling time data set . Then, the statistical standard deviation of this data set is calculated , which directly quantifies the dispersion of the cooling rates of each region. To generate a standardized index, the standard deviation needs to be normalized. The reference value for normalization can be set as the average cooling time in the data set. The cooling uniformity index .

[0086] Please refer to Figure 2 , which is a logic diagram for determining the injection molding auxiliary category by the injection molding auxiliary analysis module of the embodiment of the present application. The injection molding auxiliary analysis module determines the injection molding auxiliary category based on the flow complexity index combined with the cooling uniformity index, including:

[0087] If the flow complexity index is less than or equal to the first flow index threshold and the cooling uniformity index is less than or equal to the first cooling index threshold, it is determined as the high-speed balanced category;

[0088] If the flow complexity index is greater than the first flow index threshold and the cooling uniformity index is less than or equal to the second cooling index threshold, it is determined as the flow-sensitive category;

[0089] If the cooling uniformity index is greater than the first cooling index threshold, it is determined as the cooling-dominated category.

[0090] In implementation, the first flow index threshold, the first cooling index threshold and the second cooling index threshold are determined according to statistical analysis of historical mold sample database or limited field process test. For example:

[0091] The first flow index threshold is used to distinguish the mold with relatively balanced flow path and the mold with significantly complex flow path. The determination method is to collect a group of short shot test confirmations, verify the mold samples with good filling balance, calculate the flow complexity index, and take the upper quantile of the statistical distribution of the group of indexes as the initial threshold value, preferably 0.25.

[0092] The first cooling index threshold is used to define the basic threshold of whether the cooling is uniform. The determination method can be based on another group of mold samples with good cooling effect and small product warping deformation, analyze the distribution of the cooling uniformity index, and take the upper limit as the threshold, preferably 0.15.

[0093] The second cooling index threshold is higher than the first cooling index threshold, and is used to define the upper limit of the cooling condition that the system can still focus on flow regulation under the premise of flow sensitivity. The value can be obtained by analyzing mold samples with complex flow but still stable production through process adjustment, to avoid mold with slight uneven cooling being prematurely classified as cooling dominant. Preferably, it is 0.35.

[0094] In addition, the above three thresholds can still be periodically optimized and adaptively fine-tuned after the system is initialized, by continuously collecting actual production effect data and using linear regression or clustering analysis algorithm.

[0095] It can be understood that the system first compares the cooling uniformity index with the first cooling index threshold, and if the threshold is exceeded, it is determined as the cooling dominant category regardless of the flow complexity, because severe cooling unevenness is the primary contradiction that limits the shortening of cycle time and causes warping, sink mark and other root quality defects, and must be intervened first. Only when the cooling uniformity index does not exceed the first cooling index threshold, i.e. the cooling basis is qualified, the system further subdivides according to the flow complexity index. When the flow complexity index does not exceed the first flow index threshold, it is determined as the high-speed balanced category, indicating that under the condition of small filling resistance and good balance, high-speed strategy can be used to compress the filling time. When the flow complexity index exceeds the first flow index threshold but the cooling uniformity index still does not exceed the second cooling index threshold, it is determined as the flow sensitive category, which indicates that the main contradiction has been transformed into the management of complex flow pattern, and multi-segment fine control needs to be enabled to optimize the filling balance.

[0096] Specifically, the system first compares the cooling uniformity index with a first cooling index threshold. If the cooling uniformity index > the first cooling index threshold, it is directly determined as the cooling dominant category regardless of the flow complexity index. It can be understood that severe cooling non-uniformity is often the primary contradiction that limits the shortening of the cycle and leads to quality defects, and needs to be handled in priority. If the cooling uniformity index ≤ the first cooling index threshold, the flow characteristic judgment process is entered: when the flow complexity index is less than or equal to the first flow index threshold, it is determined as the high-speed balance category; when the flow complexity index is greater than the first flow index threshold and the cooling uniformity index is less than or equal to the second cooling index threshold, it is determined as the flow sensitive category.

[0097] In the present application, the quantitative geometric and thermodynamic analysis results are mapped to clear process strategy guidance, thereby realizing the differentiated and precise control of different characteristic molds. By comparing the two key indexes with the preset engineering threshold, the mold is divided into categories with significant process tendency, so as to match different adaptive control strategies subsequently. The threshold setting can balance the sensitivity and robustness of classification, effectively distinguishing molds with different process challenges, while avoiding frequent mis-switching of categories due to normal fluctuations in production.

[0098] Specifically, the adaptive injection molding module is configured to determine the corresponding injection assistance control operation according to the determined injection assistance category:

[0099] If the injection assistance category is the high-speed balance category, the adaptive injection molding module calls the preset high-speed injection parameters for injection, and dynamically fine-tunes the injection speed according to the deviation of the real-time cavity pressure curve from the ideal predicted curve;

[0100] If the injection assistance category is the flow sensitive category, the adaptive injection molding module enables the multi-segment speed and pressure control curve for injection, and judges whether to adjust the V / P switching point according to the data balance of each cavity pressure sensor;

[0101] If the injection assistance category is the cooling dominant category, the adaptive injection molding module determines whether to trigger local high-pressure point cooling based on the hot spot area identified by the multi-point thermocouple and the corresponding temperature drop curve.

[0102] It can be understood that when the category is high-speed balance, it indicates that the filling and cooling characteristics of the mold are relatively ideal, and the optimization direction needed is to maximize the production efficiency under the premise of ensuring stability. By comparing the real-time cavity pressure curve collected in real time with the ideal predicted curve generated based on the digital model of the mold, the deviation of the two at the pressure rising section and the peak pressure point is monitored. If the deviation of the real-time cavity pressure curve relative to the ideal curve continuously exceeds the pre-set tolerance threshold, the injection speed is reduced to seek a more stable filling process. In the pursuit of high-speed production, the filling instability caused by material or environmental fluctuations can be automatically inhibited through closed-loop feedback to maintain high-quality production status.

[0103] When the category is flow sensitive, it indicates that the flow path of the mold is complex or unbalanced, and then the multi-segment speed and pressure control curve designed in advance for multi-cavity or complex flow channel is enabled. By setting multiple cavity pressure sensors at the end of the gate and the end of the flow, the timing and amplitude of the pressure established at each key position are monitored and compared in real time to evaluate whether the pressure of different cavities or regions reaches a certain pre-set proportion synchronously at the end of the filling. It can be 80% of the peak pressure. If the pressure rise is not synchronized, the difference exceeds the pre-set balance threshold, the system will automatically delay or advance the V / P switching point to coordinate the progress of each flow front. By dynamically adjusting the switching time, actively compensating for the flow path difference, optimizing the filling balance, and reducing the internal stress or flash defects caused by flow stagnation or overpressure.

[0104] When the category is cooling dominant, it indicates that the uneven cooling of the mold is the bottleneck restricting efficiency and quality, and the auxiliary operation focuses on thermal management. Based on the data of multiple thermocouples, the hottest area with the slowest cooling rate is identified, and the temperature drop curve of the area in the cooling stage is analyzed as the basis for determining whether to trigger local high-pressure point cooling. Specifically, at half of the theoretical cooling time, the difference between the actual temperature of the hot spot area and the standard temperature is calculated. If this difference exceeds the pre-set trigger threshold, it is determined that local high-pressure point cooling needs to be triggered at the position corresponding to the hot spot on the back of the mold, that is, high-pressure cooling medium is injected instantaneously to strengthen heat dissipation, accurately and timely dynamically compensate for the cooling short board, and promote overall uniform cooling, thereby creating conditions for shortening the overall cooling period under the premise of ensuring quality.

[0105] Specifically, the adaptive injection molding module in response to the injection molding auxiliary category of high-speed balance category is configured to:

[0106] obtain an ideal cavity pressure curve simulated based on a digital model of the mold and a real-time cavity pressure curve;

[0107] calculate a pressure difference value of the real-time cavity pressure curve and the ideal cavity pressure curve at the same time node during the injection filling stage;

[0108] If the absolute value of the pressure difference exceeds the preset pressure tolerance threshold, the injection speed is reduced or increased by a preset step according to the positive or negative of the pressure difference.

[0109] In the implementation, an ideal cavity pressure curve generated based on the mold digital model is obtained from the injection mold model building module, and the ideal cavity pressure curve represents a trajectory of pressure change over time that should theoretically occur under preset high-speed injection parameters. At the same time, the system collects data through the cavity pressure sensor to generate a real-time cavity pressure curve for the current injection cycle. During the injection filling stage, the system time-aligns the two curves, and at multiple same time nodes, such as equally spaced sampling points or specific key phase points, or the time when the pressure rises to 80% of the peak pressure, the system calculates the difference between the real-time pressure value and the ideal pressure value.

[0110] The preset pressure tolerance threshold is determined through limited process experiments or statistical analysis of historical stable production data. The determination method is as follows: collect multiple injection cycle data in a high-speed balanced state that is determined to be stable in production and qualified in quality, calculate the absolute value of the pressure difference between the real-time cavity pressure curve and the ideal cavity pressure curve at each key node in these cycles, and statistically analyze the distribution. Generally, the 95th percentile of the distribution or the average value plus several times the standard deviation can be taken as the initial threshold recommendation value.

[0111] If the absolute value of the difference between the real-time pressure and the ideal pressure exceeds the threshold at any comparison node, the system determines that the current filling process has deviated from the ideal state and needs to intervene in the speed. If the real-time pressure is higher than the ideal pressure, i.e., the difference is positive, it indicates that the actual filling resistance is greater than expected, and the system reduces the injection speed by a preset, smaller fixed step, preferably 1% of the injection speed per second; on the contrary, if the real-time pressure is lower than the ideal pressure, i.e., the difference is negative, it may mean that the filling is too fast or the material flowability changes, and the system will appropriately increase the injection speed by the same step logic.

[0112] In the present application, the dynamic fine-tuning injection speed operation performed for the high-speed balanced category can form a closed-loop control mechanism of process feedback. Through continuous comparison and small reverse correction, the actual injection process can adaptively track and approach the optimal path predicted by the ideal model, effectively suppressing process deviations caused by factors such as material batch fluctuations, environmental temperature changes, or equipment state drift, thereby maintaining process stability and product quality consistency in high-speed state in long-term production.

[0113] Specifically, the adaptive injection molding module, in response to the flow-sensitive category of injection molding auxiliary categories, is configured to:

[0114] Obtain real-time pressure data of the cavity pressure sensors arranged at the gate end region and the flow end region at the end of filling;

[0115] According to the real-time pressure data of each cavity pressure sensor, the time when the pressure of each sensor reaches the preset pressure ratio threshold is calculated;

[0116] According to the difference in the time when each sensor reaches the preset pressure ratio threshold, the filling balance is judged;

[0117] If the time difference exceeds the preset synchronization threshold, the triggering time of the V / P switching point is adjusted according to the time difference.

[0118] In implementation, when the mold is classified into the flow-sensitive category, the system enables a preset multi-segment control curve during the injection process. At the end of the filling stage, when the cavity is about to be completely filled, the system synchronously collects pressure readings of multiple cavity pressure sensors respectively installed at the end of the gate and the end of each cavity or the most difficult flow area. The system calculates the difference between them by comparing the time when each sensor reaches the set pressure threshold. This time difference directly reflects the degree of imbalance in the speed of melt filling different cavities or areas. The system compares the calculated actual time difference with a preset synchronization threshold to determine whether process assistance intervention is needed. If the actual difference exceeds the preset synchronization threshold, it is determined that the filling is imbalanced, and the system will adjust the triggering time of the V / P switching point according to the size and direction of the difference, i.e., which sensor is delayed: if a certain end area sensor is significantly delayed, the system will appropriately delay the switching point to provide more time for the lagging melt front to complete the filling; otherwise, if the overall filling is too fast, the switching may be advanced.

[0119] The preset pressure ratio threshold is used to determine the key pressure state when filling is about to be completed. Its determination can be calibrated in combination with computer aided engineering (CAE) simulation and limited process experiments. By comparing simulation and measured data under multiple sets of different process parameters, the statistical proportional relationship between the pressure at the moment when the cavity is just completely filled and the final peak pressure at each key position is analyzed, and a percentage value that can stably represent this critical state and is sensitive to time difference is selected as a universal threshold. Generally, the preset pressure ratio threshold is 0.8, which usually corresponds to the critical state when the cavity is about to be filled but the melt still has flowability. The time point at this moment is very sensitive to flow path differences.

[0120] The preset synchronization threshold defines the limit of tolerable filling asynchronization. Its determination is based on statistical analysis of historical production data. A series of production cycle data with balanced filling and stable product quality in historical injection molding are collected, and the standard deviation of the time difference when each sensor reaches the above-mentioned pressure ratio threshold is calculated. Based on this statistical distribution, the average value plus twice the standard deviation is selected as the threshold for triggering adjustment. This ensures that the system only intervenes when the filling imbalance is sufficient to have a predictable impact on product quality, avoiding excessive adjustment due to normal process fluctuations.

[0121] In the present application, by establishing a closed-loop feedback control loop based on multi-point and multi-parameter real-time comparison, the system can automatically identify and compensate flow imbalance, dynamically optimize the filling end phase, promote the melt to fill each part of the cavity more synchronously, improve the surface defects or structural weak areas caused by flow stagnation, and also reduce the internal stress of the product by avoiding local overpressure, thereby significantly enhancing the process robustness and product quality consistency of complex molds under high-speed or high-demand production conditions.

[0122] Referring to Figure 3 The logic diagram of the adaptive injection molding module identifying hot spot areas is shown in the figure, and the adaptive injection molding module identifies hot spot areas based on multi-point thermocouples, which includes:

[0123] Obtain the data of the temperature drop over time collected by each thermocouple inside the mold core, and calculate the cooling rate of the area corresponding to each thermocouple;

[0124] Compare the cooling rates of the areas corresponding to each thermocouple;

[0125] Identify one or more areas with a cooling rate lower than the preset cooling rate threshold as hot spot areas.

[0126] In each injection molding cycle, the temperature-time curve data of each point is continuously obtained from the multi-point thermocouples pre-buried at different key positions inside the mold core, and based on these original temperature-time data, the cooling rate of the monitoring area corresponding to each thermocouple is calculated. The cooling rate is usually obtained by analyzing the average slope of the temperature drop curve during the main cooling stage, which directly reflects the strength of the heat dissipation capacity of the area, for example, from the end of melt injection to the temperature dropping to a certain set value.

[0127] The preset cooling rate threshold is the minimum value of the cooling rate, which is used to determine whether the cooling speed of a certain area is slow enough to be considered as a hot spot that needs special attention. The system compares the measured cooling rate of each area with this threshold, and identifies and marks all areas with a cooling rate lower than the threshold as hot spot areas.

[0128] The preset cooling rate threshold is initially set to 80% of the average cooling rate of all areas. Its determination method is as follows: collect multiple injection molding cycle data under stable production conditions with qualified product quality, calculate the cooling rates of the monitoring points of each thermocouple in these cycles, and analyze the statistical distribution. Through analysis, it is found that when the cooling rate of a certain area is lower than 80% of the average cooling rate of all areas, the area is likely to have visible sink marks or cause the cycle time to be forced to extend in subsequent production. Therefore, this proportion is used as the basis for setting the threshold. This threshold is set during system initialization and can be periodically re-optimized based on the accumulation of larger data sets in subsequent production.

[0129] Specifically, the adaptive injection molding module, in response to the cooling-dominant category of the injection-assisted category, is configured to:

[0130] obtain a temperature drop curve of the hot spot area in the cooling stage, and compare it with a preset standard cooling curve at the same cooling time node;

[0131] calculate the difference between the actual temperature value of the temperature drop curve and the standard temperature value of the standard cooling curve;

[0132] if the difference exceeds a preset temperature difference trigger threshold, it is determined that local high-pressure point cooling needs to be triggered for the hot spot area.

[0133] In implementation, when the system determines that the mold is in the cooling-dominant category and identifies a specific hot spot area, in the cooling stage of each production cycle, the system obtains the actual temperature drop curve of the hot spot area collected by the thermocouple over time. At the same time, the system calls a preset standard cooling curve as a reference for comparison. The standard curve represents the temperature drop trajectory that the hot spot area should exhibit under ideal cooling conditions of the mold, which is usually derived from the thermodynamic simulation results of the digital twin model of the mold, or is generated by averaging the temperature curves corresponding to multiple production stable and quality qualified injection cycles in history.

[0134] The system aligns the actual temperature drop curve with the standard curve on the time axis, and selects one or more same comparison time nodes during the cooling process, calculates the difference between the temperature value of the actual curve and the temperature value of the standard curve at the node. If the actual temperature difference exceeds the threshold, the system determines that the cooling lag of the hot spot area has exceeded the allowed range, and needs to trigger local high-pressure point cooling for the area immediately, that is, to instantaneously introduce high-pressure cooling medium for forced heat dissipation intervention.

[0135] The specific comparison time node is set to 50% of the cooling time, at which time the temperature difference effect is already obvious but there is still time for intervention.

[0136] The preset temperature difference trigger threshold is the key to determining whether to start point cooling. Its determination method is to collect a large number of injection cycle data of qualified products produced on the mold, extract the temperature difference of the hot spot area in the actual cooling curve and the standard curve at the given comparison node in these cycles, and analyze the statistical distribution of these difference values. The average value of the difference plus twice the standard deviation can be selected as the initial threshold value, which is initially set to 8°C.

[0137] The application makes the start of the local high-pressure point cooling, which is a strengthening cooling means, no longer a blind operation based on fixed time or experience, but a precise and on-demand triggering mechanism responding to actual cooling state deviation, thereby optimizing the energy consumption efficiency of the auxiliary cooling system while effectively compensating for uneven cooling, improving product quality and shortening the overall cooling time.

[0138] So far, the technical solutions of the application have been described in combination with the preferred embodiments shown in the drawings, but those skilled in the art can easily understand that the protection scope of the application is obviously not limited to these specific embodiments. Those skilled in the art can make equivalent changes or replacements to the related technical features without departing from the principles of the application, and the technical solutions after the changes or replacements will fall within the protection scope of the application.

[0139] The above description is only the preferred embodiments of the application and is not intended to limit the application; for those skilled in the art, the application can have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the application shall be included in the protection scope of the application.

Claims

1. An intelligent auxiliary system for improving the design efficiency of complex injection molds, characterized in that, include: The intelligent sensing module includes a scanning unit for collecting three-dimensional point cloud data of the injection mold cavity, several cavity pressure sensors set at key locations in the mold cavity to collect cavity pressure data, a multi-point thermocouple set inside the mold core, and an infrared thermal imager for detecting the temperature distribution on the outer surface of the mold. The injection model construction module is used to construct a digital model of the mold based on the three-dimensional point cloud data of the mold cavity, and to mark the injection flow channels and cooling water channels in the digital model of the mold. The injection molding auxiliary analysis module is used to calculate the flow complexity index and cooling uniformity index based on the mold digital model to determine the injection molding auxiliary category, generate the cavity pressure curve in real time based on the cavity pressure data, and generate the temperature distribution image based on the outer surface temperature distribution obtained by the infrared thermal imager. An adaptive injection molding module is used to determine injection molding auxiliary control operations based on the injection molding auxiliary category, the cavity pressure data, and the temperature data. The injection molding auxiliary control operations include: The system calls preset high-speed injection parameters for injection molding and dynamically fine-tunes the injection speed based on the deviation between the real-time cavity pressure curve and the ideal predicted curve. Alternatively, multiple speed and pressure control curves can be used for injection molding, and the V / P switching point can be adjusted based on the data balance of each cavity pressure sensor; or, hot spot areas can be identified based on the multi-point thermocouples, and the corresponding temperature drop curve can be used to determine whether to trigger local high-pressure cooling.

2. The intelligent auxiliary system for improving the design efficiency of complex injection molds according to claim 1, characterized in that, The scanning unit is configured to perform non-contact three-dimensional scanning of the inner surface of the injection mold cavity; The cavity pressure sensors are respectively installed in the gate end region and the flow end region of the mold cavity, and their sensing end face is flush with the inner surface of the cavity. The multi-point thermocouple is embedded inside the mold core and the temperature measuring end is in contact with the inner surface of the mold core; The infrared thermal imager is configured to image the temperature distribution on the outer surface of the parting surface of the injection mold and the outer surface of the ejector plate area.

3. The intelligent auxiliary system for improving the design efficiency of complex injection molds according to claim 2, characterized in that, The injection model construction module reconstructs the three-dimensional point cloud data of the mold cavity to generate a three-dimensional solid model containing the geometric features of the cavity surface. Based on the point cloud density change features, it identifies and marks the path and cross-sectional contour of the injection flow channel, and based on the tubular structure features in the point cloud data, it identifies and marks the direction and interface position of the cooling water channel.

4. The intelligent auxiliary system for improving the design efficiency of complex injection molds according to claim 3, characterized in that, The injection molding auxiliary analysis module performs flow analysis on the filling path from the injection runner to the end of each cavity based on the mold digital model, and calculates the flow complexity index based on the ratio of the flow length to the average wall thickness of each filling path and the statistical distribution characteristics of this ratio between filling paths. Furthermore, based on the digital model of the mold, a thermodynamic simulation of the cooling water circuit is performed to determine the cooling time of the cavity region corresponding to each cooling circuit, and the cooling uniformity index is calculated based on the statistical differences in the cooling time of each region.

5. The intelligent auxiliary system for improving the design efficiency of complex injection molds according to claim 4, characterized in that, The injection molding auxiliary analysis module determines the injection molding auxiliary category based on the flow complexity index and the cooling uniformity index, including: If the flow complexity index is less than or equal to the first flow index threshold and the cooling uniformity index is less than or equal to the first cooling index threshold, then it is determined to be a high-speed balance category; If the flow complexity index is greater than the first flow index threshold and the cooling uniformity index is less than or equal to the second cooling index threshold, then it is determined to be a flow-sensitive category. If the cooling uniformity index is greater than the first cooling index threshold, it is determined to be a cooling-dominant category.

6. The intelligent auxiliary system for improving the design efficiency of complex injection molds according to claim 5, characterized in that, The adaptive injection molding module is configured to determine the corresponding injection molding auxiliary control operation based on the determined injection molding auxiliary category: If the injection molding assistance category is the high-speed balance category, the adaptive injection molding module calls the preset high-speed injection parameters to perform injection molding, and dynamically fine-tunes the injection speed according to the deviation between the real-time cavity pressure curve and the ideal predicted curve. If the injection molding assistance category is a flow-sensitive category, the adaptive injection molding module uses multi-segment speed and pressure control curves for injection molding, and determines whether to adjust the V / P switching point based on the data balance of each cavity pressure sensor. If the injection molding auxiliary category is the cooling-dominant category, the adaptive injection molding module determines whether to trigger local high-pressure point cooling based on the hot spot area identified by the multi-point thermocouple and its corresponding temperature drop curve.

7. The intelligent auxiliary system for improving the design efficiency of complex injection molds according to claim 6, characterized in that, The adaptive injection molding module, in response to the injection assistance category of the high-speed balancing category, is configured as follows: Obtain the ideal cavity pressure curve and the real-time cavity pressure curve based on the simulation of the digital model of the mold; Calculate the pressure difference between the real-time cavity pressure curve and the ideal cavity pressure curve at the same time point during the injection filling stage; If the absolute value of the pressure difference exceeds the preset pressure tolerance threshold, the injection speed is decreased or increased by a preset step size depending on whether the pressure difference is positive or negative.

8. The intelligent auxiliary system for improving the design efficiency of complex injection molds according to claim 6, characterized in that, The adaptive injection molding module, in response to the injection assistance category of the flow-sensitive category, is configured as follows: Acquire real-time pressure data from each cavity pressure sensor located in the gate end region and the flow end region at the end of the filling stage; Based on the real-time pressure data of each chamber pressure sensor, calculate the time when the pressure of each sensor reaches a preset pressure ratio threshold. The filling balance is determined based on the difference in the time when each sensor reaches the preset pressure ratio threshold. If the time difference exceeds a preset synchronization threshold, the triggering timing of the V / P switching point is adjusted according to the time difference.

9. The intelligent auxiliary system for improving the design efficiency of complex injection molds according to claim 6, characterized in that, The adaptive injection molding module identifies hotspot areas based on the multi-point thermocouples, including: Data on the temperature decrease over time collected by each thermocouple inside the mold core are obtained, and the cooling rate of the corresponding region of each thermocouple is calculated. Compare the cooling rates of the regions corresponding to each thermocouple; One or more areas where the cooling rate is lower than a preset cooling rate threshold are identified as hotspot areas.

10. The intelligent auxiliary system for improving the design efficiency of complex injection molds according to claim 9, characterized in that, The adaptive injection molding module, in response to the injection auxiliary category of the cooling-dominant category, is configured as follows: Obtain the temperature drop curve of the hot spot area during the cooling phase and compare it with a preset standard cooling curve at the same cooling time point; Calculate the difference between the actual temperature value of the temperature drop curve and the standard temperature value of the standard cooling curve; If the difference exceeds the preset temperature difference trigger threshold, it is determined that local high-pressure cooling needs to be triggered in the hot spot area.

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