CN118849366A - An intelligent mold injection temperature adjustment system - Google Patents

Abstract

The invention discloses an intelligent regulation system for the injection temperature of a mold, which relates to the technical field of mold injection temperature control, and comprises the following components: acquiring injection molding material information and product requirement information, analyzing an injection mold and analyzing temperature to determine an ideal working temperature; performing deviation calculation on the die monitoring temperature data according to the ideal working temperature, and determining die temperature deviation distribution; performing position demand constraint degree analysis based on the mold position identification and the product requirement information to obtain a temperature positioning constraint coefficient; correcting the temperature deviation distribution of the die, determining a temperature control target, and matching a control strategy to perform temperature compensation control. The invention solves the technical problems of poor temperature control accuracy and uniformity due to the diversity of die materials and the complexity of structures in the prior art, and achieves the technical effects of analyzing and compensating temperature deviation based on the characteristics and the structural distribution of the die materials and improving the temperature control accuracy and uniformity.

Description

An intelligent mold injection temperature adjustment system

Technical Field

The invention relates to the technical field of mold injection temperature control, and in particular to an intelligent mold injection temperature regulating system.

Background Art

Mold temperature has a great influence on the filling flow of plastic melt, solidification and shaping, production efficiency, and the shape and dimensional accuracy of plastic parts. The control of mold temperature directly affects the quality, appearance, dimensional stability and production efficiency of plastic parts. With the development of industrial intelligence, the temperature control of injection molds is also transforming towards intelligence. Researchers have conducted in-depth research on mold heating and cooling systems and control methods to improve the intelligence level of mold temperature control. However, due to the diversity of mold materials and the complexity of structure, the accuracy and uniformity of mold temperature control are still lacking.

Summary of the invention

The present application provides an intelligent mold injection temperature adjustment system for solving the technical problem in the prior art of poor temperature control accuracy and uniformity due to the diversity of mold materials and complexity of structure.

In a first aspect of the present application, a mold injection temperature intelligent adjustment system is provided, the system comprising: a basic information acquisition module, the basic information acquisition module is used to acquire injection material information and product requirement information; an ideal working temperature determination module, the ideal working temperature determination module is used to perform injection mold analysis and temperature analysis based on the injection material information and product requirement information to determine the ideal working temperature; a mold monitoring temperature acquisition module, the mold monitoring temperature acquisition module is used to configure a data acquisition module, and acquire mold monitoring temperature data through a temperature sensor arranged in the mold, the mold monitoring temperature data has a mold position identifier, and the mold position identifier describes the arrangement position of the temperature sensor; a mold temperature deviation calculation module, the ... The mold temperature deviation calculation module is used to perform deviation calculation on the mold monitoring temperature data according to the ideal working temperature, and determine the mold temperature deviation distribution; the position requirement constraint degree analysis module is used to perform position requirement constraint degree analysis based on the mold position identification and the product requirement information, and obtain the temperature positioning constraint coefficient; the temperature control target determination module is used to correct the mold temperature deviation distribution using the temperature positioning constraint coefficient and determine the temperature control target; the temperature compensation control module is used to match the control strategy according to the temperature deviation value of the temperature control target, and perform temperature compensation control based on the matched control strategy.

One or more technical solutions provided in this application have at least the following technical effects or advantages:

The present application provides an intelligent mold injection temperature adjustment system, which relates to the technical field of mold injection temperature control. By acquiring injection molding material information and product requirement information, the injection mold is parsed and the temperature is analyzed to determine the ideal working temperature. The deviation of the mold monitoring temperature data is calculated to determine the mold temperature deviation distribution. The mold temperature deviation distribution is corrected based on the mold position identification and product requirement information, the temperature control target is determined, and the control strategy is matched to perform temperature compensation control. The technical problem of poor temperature control accuracy and uniformity in the prior art due to the diversity of mold materials and the complexity of structure is solved, and the technical effect of temperature deviation analysis and compensation based on mold material characteristics and structural distribution is realized to improve the accuracy and uniformity of temperature control.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

FIG1 is a schematic diagram of the structure of a mold injection temperature intelligent adjustment system provided in an embodiment of the present application;

FIG2 is a schematic diagram of a process for determining an ideal working temperature in a mold injection temperature intelligent adjustment system provided in an embodiment of the present application;

3 is a flow chart of a temperature deviation value matching control strategy according to a temperature control target in a mold injection temperature intelligent adjustment system provided in an embodiment of the present application.

Explanation of the accompanying drawings: basic information acquisition module 11, ideal working temperature determination module 12, mold monitoring temperature acquisition module 13, mold temperature deviation calculation module 14, position requirement constraint analysis module 15, temperature control target determination module 16, temperature compensation control module 17.

DETAILED DESCRIPTION

The present application provides an intelligent mold injection temperature adjustment system for solving the technical problem in the prior art of poor temperature control accuracy and uniformity due to the diversity of mold materials and complexity of structure.

The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

It should be noted that the terms "first", "second", etc. in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices.

Embodiment 1

As shown in FIG1 , the present application provides a mold injection temperature intelligent adjustment system, the system comprising:

The basic information acquisition module 11 is used to acquire injection molding material information and product requirement information.

Furthermore, the basic information acquisition module 11 is also used to perform the following steps:

P11: Obtain an abnormal case database of mold injection molding, and extract product abnormality type data of different injection molding materials, wherein the product abnormality type data at least includes size abnormality, surface quality abnormality, and internal stress abnormality;

P12: Taking the product abnormality type data as the top event, decomposing it through the abnormal case data, determining the abnormal factor, and fitting the functional influence relationship between the abnormal factor and the top event;

P13: Extract the influence coefficient according to the functional influence relationship, cluster it with the injection molding material as the center, and screen the material abnormality factors;

P14: According to the material abnormality factor and the product abnormality type data, the data screening dimension is configured to obtain the injection molding material information and the product requirement information, wherein the material abnormality factor corresponds to the screening dimension of the injection molding material information, and the product abnormality type data corresponds to the screening dimension of the product requirement information.

It should be understood that the basic information acquisition module 11 of the present application is mainly responsible for collecting key information related to the injection molding process, including injection molding material information and product requirement information. The specific collection process can be, first accessing the mold injection abnormal case database, which stores various injection molding abnormalities and related data that have occurred in history, and extracting product abnormality type data related to different injection molding materials from the database, the abnormality type data including but not limited to size abnormality, surface quality abnormality, internal stress abnormality, etc.

Furthermore, the extracted product abnormality type data is used as the top event, and the abnormal case data is deeply analyzed. Through fault tree analysis (FTA) or similar methods, the factors that cause these abnormalities, namely abnormal factors, are determined. The functional influence relationship between abnormal factors and top events is further fitted to quantify the degree of influence of each abnormal factor on the final product abnormality. Furthermore, based on the functional influence relationship obtained by fitting, the influence coefficient of each abnormal factor is extracted. These coefficients reflect the influence weight of different abnormal factors on product abnormalities. Cluster analysis is performed with injection molding materials as the cluster center, and abnormal factors closely related to specific materials, namely material abnormal factors, are screened out according to the size of the influence coefficient.

Furthermore, based on the material abnormality factor and product abnormality type data, corresponding data screening dimensions are configured to guide the accurate acquisition of the required injection molding material information and product requirement information from a wider range of data sources. Among them, the material abnormality factor corresponds one-to-one to the screening dimension of the injection molding material information, and the product abnormality type data corresponds to the screening dimension of the product requirement information. Through this correspondence, the module can ensure that the acquired information is closely related to the key risk factors in the injection molding process. The basic information acquisition module 11 can not only collect basic injection molding material information and product requirement information, but also combine historical abnormal case data to extract risk factors closely related to specific materials and product requirements, and provide strong data support for subsequent ideal working temperature determination, temperature control, etc.

The ideal working temperature determination module 12 is used to perform injection mold analysis and temperature analysis according to the injection material information and product requirement information to determine the ideal working temperature.

Further, as shown in FIG2 , the ideal operating temperature determination module 12 is further configured to perform the following steps:

P21: Extract preset material analysis module and product requirement analysis module;

P22: taking the injection molding material information and the product requirement information as input respectively, and performing material temperature relationship analysis and product requirement temperature relationship analysis through the preset material analysis module and the product requirement analysis module;

P23: Based on the material temperature relationship analysis results and the product requirement temperature relationship analysis results, perform temperature fusion analysis to determine the ideal operating temperature.

Optionally, the ideal working temperature determination module 12 of the present application is responsible for determining the optimal working temperature of the injection mold, that is, the ideal working temperature, according to the injection molding material information and the product requirement information. Specifically, two key analysis modules preset in the extraction system are: the material analysis module and the product requirement analysis module. These two modules respectively contain the algorithms and rules required for in-depth analysis of the injection molding materials and product requirements, which can be obtained by collecting sample data for machine learning. Further, the injection molding material information and product requirement information are respectively used as inputs and processed by the preset material analysis module and product requirement analysis module. First, the preset material analysis module analyzes the relationship between the injection molding material and the temperature according to the physical and chemical properties of the injection molding material, including key parameters such as the melting point, flow temperature, and curing temperature of the material, and obtains the performance of the material at different temperatures and possible changes. Furthermore, the product requirement analysis module analyzes its demand for temperature according to the specific requirements and specifications of the product, including the dimensional accuracy, surface quality, mechanical properties, etc. of the product. And these requirements are converted into specific requirements for temperature, such as the minimum working temperature, the maximum working temperature, the temperature uniformity, etc.

Furthermore, based on the results of the material temperature relationship analysis and the product demand temperature relationship analysis, a temperature fusion analysis is performed, that is, a comprehensive analysis of the temperature is performed considering the dual factors of material performance and product requirements. For example, a temperature range that can ensure both material performance and product requirements is sought, or a specific temperature point is determined as the optimal working temperature. Then one or more ideal working temperatures are determined as the basis for subsequent temperature control and adjustment to ensure that the injection molding process can be carried out under the optimal temperature conditions.

Furthermore, step P22 of the embodiment of the present application also includes:

P22-1: fitting a material characteristic temperature curve according to the injection molding material information, wherein the material characteristic temperature curve represents the relationship between the plastic properties of the material and the temperature;

P22-2: Obtaining material temperature relationship analysis results based on the material characteristic temperature curve;

P22-3: According to the product requirement information, obtain the temperature curves of each requirement characteristic respectively;

P22-4: Calculate each of the demand characteristic temperature curves according to the model parameters in the product requirement analysis module to obtain the product requirement temperature relationship analysis result.

Specifically, the specific process of parsing the injection molding material information and product requirement information may be to fit the material characteristic temperature curve according to the injection molding material information through data analysis tools or professional software, and the material characteristic temperature curve may intuitively show the relationship between the material shaping characteristics (such as fluidity, viscoelasticity, etc.) and temperature. Furthermore, according to the material characteristic temperature curve, the relationship between the material temperature and the injection molding process performance is further analyzed to obtain the material temperature relationship analysis result, and the material temperature relationship analysis result includes the material's optimal operating temperature range, sensitive temperature range, etc.

Furthermore, based on the product requirement information, the key quality characteristics of the product, such as dimensional accuracy, surface quality, mechanical properties, etc., are identified, and for each characteristic, the corresponding demand characteristic temperature curve is obtained by consulting relevant information, using professional software or performing simulation analysis. The demand characteristic temperature curve can describe the performance of the key quality characteristics of the product at different temperatures.

Furthermore, the demand characteristic temperature curve is input into the product requirement analysis module, and the calculation is performed according to the preset model parameters. The product requirement analysis module performs curve superposition, weight distribution, etc., so as to comprehensively consider the temperature requirements of each key quality characteristic and obtain a comprehensive product demand temperature relationship analysis result. This result will serve as one of the important bases for determining the ideal operating temperature, including one or more recommended operating temperature values or ranges that can meet the requirements of the key quality characteristics of the product while taking into account the plasticity characteristics of the material.

Furthermore, step P23 of the embodiment of the present application also includes:

P23-1: Use the material characteristic temperature curve and the required characteristic temperature curves to perform timing alignment fitting;

P23-2: Setting a threshold baseline according to the product requirement information, and using the threshold baseline to screen curve segments of the time series fitting curve;

P23-3: According to the curve segments determined by screening, the crossover temperature and the overlapping time interval are obtained, and the crossover temperature with the largest overlapping time interval is selected as the ideal operating temperature.

Optionally, in order to compare the material characteristic temperature curve with each required characteristic temperature curve, they need to be time-series aligned and fitted. First, a common time or temperature range is determined as a benchmark. Then the material characteristic temperature curve is aligned with each required characteristic temperature curve within the benchmark range to ensure that they are consistent in the time or temperature dimension. Then, a suitable fitting method, such as linear interpolation, spline interpolation, etc., is used to fit the curve.

Furthermore, according to the specific requirements of the product for material performance and product quality, one or more threshold baselines are set, which may include requirements for material fluidity, curing speed, product dimensional tolerance, surface quality, etc. The curve after time series alignment fitting is compared with the set threshold baseline to screen out the curve segments that meet the requirements. These curve segments should meet both material properties and product requirements and remain stable within a certain time or temperature range.

Furthermore, in the screened curve segments, the intersection points of the material characteristic temperature curve and each required characteristic temperature curve are found. These intersection points represent the state where the material characteristics and product requirements are met at the same time at different times or temperatures. Then, the overlapping time interval corresponding to each intersection point is calculated, that is, within this time interval, the material characteristics and product requirements remain stable and meet the requirements. Further, the one with the largest overlapping time interval is selected from all the intersection points as the ideal operating temperature. The ideal operating temperature can not only ensure the optimal performance of the material, but also meet the key quality requirements of the product. At the same time, since the overlapping time interval is the largest, it means that at this temperature, the injection molding process has higher stability and repeatability.

Furthermore, the ideal operating temperature determination module 12 is further configured to perform the following steps:

P21a: performing region segmentation according to the overlapping time intervals to determine a plurality of time series regions;

P22a: Based on the multiple time series regions, calculating the change gradient of the corresponding cross temperature;

P23a: According to the injection molding temperature process, time-series neighborhood aggregation is performed in combination with the change gradient to obtain multiple temperature-controlled time zones, and the ideal working temperature of the temperature-controlled time zone is determined using the cross-temperature of the temperature-controlled time zone.

In a possible embodiment of the present application, in order to further refine the temperature control strategy, the segmentation of the time series area and the calculation of the temperature gradient change, as well as the time series neighborhood aggregation, are added. Specifically, the length, shape and distribution of the overlapping time intervals are analyzed, and based on these characteristics, the overlapping time intervals are segmented to form multiple independent time series areas. These time series areas represent the stable state in which material properties and product requirements are simultaneously met in different time or temperature segments.

Furthermore, based on the multiple time series regions, the change gradient of the corresponding cross temperature is calculated in each time series region, and the derivative or difference of the cross temperature in each time series region can be calculated by numerical analysis or statistical methods to obtain the change gradient. The change gradient reflects the speed of the cross temperature change with time or temperature, which is crucial for understanding the influence trend of temperature on material properties and product requirements.

Furthermore, the characteristics and requirements of the injection molding temperature process are analyzed, such as the heating, insulation, cooling and other stages. Then, according to the characteristics of different stages and the gradient of the cross temperature change, the adjacent timing areas are aggregated to form multiple temperature control time zones. And in each temperature control time zone, the temperature point with the longest overlapping time interval or the smallest gradient of the cross temperature is selected as the ideal working temperature of the time zone to ensure that the material properties and product requirements are met in the time zone. Through the above steps, the temperature control strategy is refined into multiple temperature control time zones, and the corresponding ideal working temperature is determined for each time zone, which helps to improve the flexibility and adaptability of the injection molding process and better meet the needs of actual production.

The mold monitoring temperature acquisition module 13 is used to configure the data acquisition module to acquire the mold monitoring temperature data through the temperature sensor arranged in the mold. The mold monitoring temperature data has a mold position mark, and the mold position mark describes the arrangement position of the temperature sensor.

Optionally, the mold monitoring temperature acquisition module 13 of the present application is mainly responsible for configuring the data acquisition module, and acquiring the monitoring temperature data of the mold in real time through the temperature sensor arranged on the mold. First, the data acquisition module is configured, and the data acquisition module is responsible for receiving and processing the data from the temperature sensor. The configuration process includes setting the data acquisition frequency, data format, transmission protocol, etc. to ensure the accuracy and efficiency of data acquisition.

Furthermore, according to the specific structure of the mold and the requirements of the injection molding process, temperature sensors are arranged at different positions of the mold. These temperature sensors are placed at key positions of the mold, such as the gate, cavity, mold wall, etc., to monitor the temperature changes of the mold at key positions in real time. The temperature sensor is connected to the data acquisition module, and the temperature conditions of the mold at different positions and times are collected in real time to obtain the mold monitoring temperature data. The mold monitoring temperature data is marked with a mold position mark, which clearly indicates the specific position of the sensor corresponding to the temperature data on the mold. It can provide strong support for the temperature control of the injection molding process.

Furthermore, the mold monitoring temperature acquisition module 13 is also used to perform the following steps:

P31a: Obtain the mold structure and determine the three-dimensional size of the mold;

P32a: Obtaining a mold heating contact area and a cooling contact area according to the mold structure;

P33a: Determine the heat radiation depth according to the three-dimensional dimensions of the mold;

P34a: Perform temperature control sensitivity analysis based on the heating contact area, cooling contact area, and heat radiation depth to determine the temperature control sensitivity of each mold partition;

P35a: Analyze the demand objectives based on the product requirement information and select the core quality areas;

P36a: Screen the non-sensitive area according to the temperature control sensitivity, and deploy the temperature sensors in the non-sensitive area and the core quality area.

Specifically, before obtaining the mold monitoring temperature data through the temperature sensors arranged in the mold, it is necessary to arrange the temperature sensors. First, obtain the detailed structural information of the mold, including the size, shape and relative position of each part, and accurately determine the three-dimensional size of the mold through professional CAD software or mold design software. Furthermore, according to the mold structure, the contact area between the mold and the heating element and the cooling pipe is calculated, and the heating contact area and cooling contact area of the mold are obtained, which can reflect the heat exchange efficiency of the mold during the heating and cooling process. According to the three-dimensional size and material characteristics of the mold, the propagation depth of the heat radiation inside the mold, that is, the heat radiation depth, is calculated. Then, based on the heating contact area, cooling contact area, and heat radiation depth, a temperature control sensitivity analysis is performed to evaluate the sensitivity of different areas of the mold to temperature changes and determine the temperature control sensitivity of each mold partition.

Furthermore, by combining the product requirement information with the demand target analysis, by evaluating the key quality characteristics and production process requirements of the product, the core quality areas in the mold can be screened out. These areas will directly affect the quality and performance of the product, and therefore require special attention in temperature control.

Finally, based on the analysis results of the temperature control sensitivity, the non-sensitive areas in the mold are screened out, and temperature sensors are deployed in the non-sensitive areas and the core quality areas. This ensures that with a limited number of sensors, the areas that have the greatest impact on product quality are covered first, while reducing the monitoring cost of non-sensitive areas and improving the accuracy and pertinence of temperature data.

The mold temperature deviation calculation module 14 is used to perform deviation calculation on the mold monitoring temperature data according to the ideal working temperature to determine the mold temperature deviation distribution.

It should be understood that the main function of the mold temperature deviation calculation module 14 of the present application is to perform deviation calculation on the mold monitoring temperature data according to the ideal working temperature, so as to determine the temperature deviation distribution of the mold. Specifically, the mold temperature deviation calculation module 14 first receives the ideal working temperature value calculated by the ideal working temperature determination module 12, and receives real-time mold monitoring temperature data from the mold monitoring temperature acquisition module 13.

Furthermore, for each monitoring point, the real-time monitored temperature value is compared with the ideal working temperature to calculate the temperature deviation value. At the same time, the temperature deviation values of all monitoring points are comprehensively analyzed to obtain the temperature deviation distribution of the entire mold, such as generating a temperature deviation distribution map to intuitively display the deviation between each area of the mold and the ideal working temperature, helping operators quickly identify abnormal temperature areas.

The position requirement constraint degree analysis module 15 is used to perform position requirement constraint degree analysis based on the mold position identification and the product requirement information to obtain a temperature positioning constraint coefficient.

Specifically, the main function of the position requirement constraint degree analysis module 15 of the present application is to conduct an in-depth position requirement constraint degree analysis based on the mold position identification and product requirement information to obtain the temperature positioning constraint coefficient, that is, to obtain the degree of dependence of different positions of the mold on the ideal temperature. Based on the mold position identification and the product requirement information, the importance of different positions on the mold is evaluated. For example, the mold area directly related to the key quality characteristics of the product will be regarded as a high-importance area. And identify the constraints related to these important positions, such as temperature range, temperature uniformity requirements, etc. Then, according to the position importance evaluation and constraint identification, the temperature positioning constraint coefficient is calculated for each mold position. This coefficient is a quantitative indicator that reflects the importance and degree of constraint of different mold positions in temperature control. It can be calculated using weighted summation, fuzzy evaluation and other methods. When the temperature positioning constraint coefficient is 0~1, it means that the corresponding position is less important in temperature control. When the temperature positioning constraint coefficient is greater than, it means that the corresponding position is more important in temperature control.

The temperature control target determination module 16 is used to modify the mold temperature deviation distribution by using the temperature positioning constraint coefficient to determine the temperature control target.

Optionally, the temperature control target determination module 16 of the present application is used to correct the mold temperature deviation distribution using the temperature positioning constraint coefficient, and according to the mold position identifier, the mold temperature deviation at the position is corrected using the corresponding temperature positioning constraint coefficient to obtain a corrected mold temperature deviation distribution. Based on the corrected mold temperature deviation distribution, combined with the corrected temperature deviation distribution, product requirement information, and injection molding process requirements, the temperature control target of each key area is determined as the basis for the subsequent temperature control strategy formulation.

The temperature compensation control module 17 is used to match the control strategy according to the temperature deviation value of the temperature control target, and perform temperature compensation control based on the matched control strategy.

Further, as shown in FIG3 , the temperature compensation control module 17 is further configured to perform the following steps:

P71: Set control parameters, including control proportional coefficient, integral coefficient, and differential coefficient;

P72: Fitting a control function based on the control parameter, taking the temperature deviation value as input, performing control parameter matching analysis and deviation control strategy through the control function, integrating all deviation control strategies according to the time correspondence of adjusting the temperature control time zone, and obtaining the control strategy;

Wherein, the control function expression is: ,in, To control the proportionality factor, is the integral coefficient, is the differential coefficient, is the temperature deviation at time t, represents the derivative with respect to time t, Represents the cumulative integral of the deviation over time.

It should be understood that the main function of the temperature compensation control module 17 of the present application is to match the corresponding control strategy according to the temperature deviation value of the temperature control target and perform temperature compensation control to ensure the stability and accuracy of the mold temperature. Specifically, the control parameters are first set, including the control proportional coefficient, integral coefficient, and differential coefficient. These parameters are the core of the PID (proportional-integral-differential) control algorithm, which is used to adjust the sensitivity and stability of the control.

Furthermore, a control function is fitted based on the control parameters, and the control function expression is: ,in, To control the proportionality factor, is the integral coefficient, is the differential coefficient, is the temperature deviation at time t, represents the derivative with respect to time t, Represents the cumulative integral of the deviation over time. The temperature deviation value is used as input, and parameter matching analysis is performed through the control function to obtain the deviation control strategy. There may be different control requirements for temperature deviations in different time periods. Therefore, all deviation control strategies are integrated according to the time correspondence of adjusting the temperature control time zone to obtain the final control strategy, which may include adjusting the power of the heating or cooling equipment, changing the circulating water flow of the mold, etc. Temperature compensation control is performed based on the matched control strategy to achieve precise control and adjustment of the injection mold temperature, which helps to improve the molding quality and production efficiency of the product.

In summary, the embodiments of the present application have at least the following technical effects:

This application obtains injection molding material information and product requirement information, performs injection mold analysis and temperature analysis, determines the ideal working temperature, calculates the deviation of the mold monitoring temperature data, determines the mold temperature deviation distribution, corrects the mold temperature deviation distribution based on the mold position identification and product requirement information, determines the temperature control target, and matches the control strategy for temperature compensation control.

The technical effect of analyzing and compensating for temperature deviation based on mold material characteristics and structural distribution and improving the accuracy and uniformity of temperature control is achieved.

It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims (8)

1. A mold injection temperature intelligent adjustment system, characterized in that the mold injection temperature intelligent adjustment system comprises: A basic information acquisition module, which is used to acquire injection molding material information and product requirement information; An ideal working temperature determination module, the ideal working temperature determination module is used to perform injection mold analysis and temperature analysis according to the injection material information and product requirement information to determine the ideal working temperature; A mold monitoring temperature acquisition module, which is used to configure a data acquisition module to acquire mold monitoring temperature data through a temperature sensor arranged in the mold. The mold monitoring temperature data has a mold position identifier, and the mold position identifier describes the arrangement position of the temperature sensor; A mold temperature deviation calculation module, the mold temperature deviation calculation module is used to perform deviation calculation on the mold monitoring temperature data according to the ideal working temperature to determine the mold temperature deviation distribution; A position requirement constraint degree analysis module, wherein the position requirement constraint degree analysis module is used to perform position requirement constraint degree analysis based on the mold position identifier and the product requirement information to obtain a temperature positioning constraint coefficient; A temperature control target determination module, the temperature control target determination module is used to modify the mold temperature deviation distribution by using the temperature positioning constraint coefficient to determine the temperature control target; A temperature compensation control module is used to match a control strategy according to a temperature deviation value of the temperature control target, and perform temperature compensation control based on the matched control strategy. 2. The mold injection temperature intelligent adjustment system according to claim 1, characterized in that the acquisition of injection material information and product requirement information comprises: Obtaining a database of abnormal cases of mold injection molding, extracting product abnormality type data of different injection molding materials, wherein the product abnormality type data at least includes size abnormality, surface quality abnormality, and internal stress abnormality; Taking the product abnormality type data as the top event, decomposing it through the abnormal case data, determining the abnormal factor, and fitting the functional influence relationship between the abnormal factor and the top event; Extract the influence coefficient according to the functional influence relationship, cluster the injection molding material as the center, and screen the material abnormal factors; According to the material abnormality factor and the product abnormality type data, the data screening dimension is configured to obtain the injection molding material information and the product requirement information, wherein the material abnormality factor corresponds to the screening dimension of the injection molding material information, and the product abnormality type data corresponds to the screening dimension of the product requirement information. 3. The mold injection temperature intelligent adjustment system according to claim 2, characterized in that the injection mold is analyzed and the temperature is analyzed according to the injection material information and product requirement information to determine the ideal working temperature, including: Extract preset material parsing module and product requirement parsing module; The injection molding material information and product requirement information are respectively used as input, and the material temperature relationship analysis and product requirement temperature relationship analysis are performed through the preset material analysis module and the product requirement analysis module; Based on the material temperature relationship analysis results and the product requirement temperature relationship analysis results, a temperature fusion analysis is performed to determine the ideal operating temperature. 4. The mold injection temperature intelligent adjustment system according to claim 3, characterized in that the material temperature relationship analysis and product requirement temperature relationship analysis are performed by the preset material analysis module and the product requirement analysis module, including: Fitting a material characteristic temperature curve according to the injection molding material information, wherein the material characteristic temperature curve represents a relationship between a plastic property of the material and a temperature; Obtaining material temperature relationship analysis results according to the material characteristic temperature curve; According to the product requirement information, obtaining temperature curves of each requirement characteristic respectively; The demand characteristic temperature curves are calculated according to the model parameters in the product requirement analysis module to obtain the product requirement temperature relationship analysis result. 5. The intelligent mold injection temperature adjustment system according to claim 4, characterized in that the temperature fusion analysis is performed according to the material temperature relationship analysis results and the product requirement temperature relationship analysis results to determine the ideal working temperature, including: Using the material characteristic temperature curve and each required characteristic temperature curve to perform time sequence alignment fitting; Setting a threshold baseline according to the product requirement information, and using the threshold baseline to screen curve segments of the time series fitting curve; According to the curve segments determined by screening, the crossover temperature and the overlapping time interval are obtained, and the crossover temperature with the largest overlapping time interval is selected as the ideal operating temperature. 6. The intelligent mold injection temperature adjustment system according to claim 5, characterized in that determining the ideal working temperature further comprises: Performing region segmentation according to the overlapping time intervals to determine a plurality of time series regions; Based on the multiple time series regions, calculating a change gradient of a corresponding cross temperature; According to the injection molding temperature process, time-series neighborhood aggregation is performed in combination with the change gradient to obtain multiple temperature-controlled time zones, and the ideal working temperature of the temperature-controlled time zone is determined using the cross temperature of the temperature-controlled time zone. 7. The mold injection temperature intelligent adjustment system according to claim 1, characterized in that the mold monitoring temperature data is obtained by a temperature sensor arranged in the mold, which comprises: Obtain the mold structure and determine the three-dimensional size of the mold; Obtaining a mold heating contact area and a cooling contact area according to the mold structure; Determining the heat radiation depth according to the three-dimensional dimensions of the mold; Perform temperature control sensitivity analysis based on the heating contact area, cooling contact area, and heat radiation depth to determine the temperature control sensitivity of each mold partition; Conduct demand target analysis based on the product requirement information and select core quality areas; The non-sensitive area is screened according to the temperature control sensitivity, and the temperature sensors are arranged in the non-sensitive area and the core quality area. 8. The intelligent mold injection temperature adjustment system according to claim 6, characterized in that the control strategy is matched according to the temperature deviation value of the temperature control target, comprising: Set control parameters, including control proportional coefficient, integral coefficient, and differential coefficient; Fitting a control function based on the control parameter, taking the temperature deviation value as input, performing control parameter matching analysis and deviation control strategy through the control function, integrating all deviation control strategies according to the time correspondence of adjusting the temperature control time zone, and obtaining the control strategy; Wherein, the control function expression is: ,in, To control the proportionality factor, is the integral coefficient, is the differential coefficient, is the temperature deviation at time t, represents the derivative with respect to time t, Represents the cumulative integral of the deviation over time.