Intelligent mold injection temperature adjusting system

Abstract

The application discloses a kind of mould injection temperature intelligent regulating system, it is related to mould injection temperature control technical field, the system includes: obtaining injection material information, product requirement information, carries out injection mould analysis and temperature analysis, determines ideal working temperature;Ideal working temperature is calculated according to mould monitoring temperature data deviation, determines mould temperature deviation distribution;Based on mould position mark, product requirement information carries out position demand constraint degree analysis, obtains temperature positioning constraint coefficient;Mould temperature deviation distribution is corrected, determines temperature control target, and matches control strategy and carries out temperature compensation control.The application solves the technical problems that the accuracy and uniformity of temperature control are poor due to the diversity of mould materials and the complexity of structure in the prior art, achieves temperature deviation analysis and compensation based on mould material characteristics and structure distribution, improves the accuracy and uniformity of temperature control.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of mold injection temperature control, and in particular to a mold injection temperature intelligent adjustment system. BACKGROUND

[0002] The mold temperature has a great influence on the mold filling flow of the plastic melt, solidification and shaping, production efficiency, and the shape and size accuracy of the plastic parts. The control of the mold temperature directly affects the quality, appearance, dimensional stability and production efficiency of the plastic parts. With the development of industrial intelligence, the temperature control of the injection mold is also transforming towards intelligence. Researchers have conducted in-depth research on mold heating and cooling systems and control methods to improve the intelligent level of mold temperature control. However, due to the diversity of mold materials and the complexity of the structure, there are still deficiencies in the precision and uniformity of the mold temperature control. SUMMARY

[0003] The present application provides a mold injection temperature intelligent adjustment system, which solves the technical problem of poor precision and uniformity of temperature control due to the diversity of mold materials and the complexity of the structure in the prior art.

[0004] In a first aspect, the present application provides a mold injection temperature intelligent adjustment system, which comprises: a basic information acquisition module, configured to acquire injection material information and product requirement information; an ideal working temperature determination module, configured to determine an ideal working temperature by analyzing the injection mold and the temperature based on the injection material information and the product requirement information; a mold monitoring temperature acquisition module, configured to configure a data acquisition module and acquire mold monitoring temperature data through temperature sensors arranged in the mold, wherein 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, configured to calculate the deviation of the mold monitoring temperature data based on the ideal working temperature, and determine a mold temperature deviation distribution; a position demand constraint degree analysis module, configured to analyze the position demand constraint degree based on the mold position identifier and the product requirement information, and obtain a temperature positioning constraint coefficient; a temperature control target determination module, configured to correct the mold temperature deviation distribution by using the temperature positioning constraint coefficient, and determine a temperature control target; and a temperature compensation control module, configured to match a control strategy according to the temperature deviation value of the temperature control target, and perform temperature compensation control based on the matched control strategy.

[0005] One or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0006] The mould injection temperature intelligent adjustment system provided by the application relates to the technical field of mould injection temperature control, and solves the technical problems of poor precision and uniformity of temperature control due to the diversity of mould materials and the complexity of the structure in the prior art by acquiring injection material information and product requirement information, performing injection mould analysis and temperature analysis, determining an ideal working temperature, performing deviation calculation on mould monitoring temperature data, determining mould temperature deviation distribution, correcting the mould temperature deviation distribution based on mould position identification and product requirement information, determining a temperature control target, and matching a control strategy to perform temperature compensation control. BRIEF DESCRIPTION OF DRAWINGS

[0007] In order to more clearly illustrate the technical solutions in the embodiments of the application, the drawings needed to be used in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative effort on the basis of these drawings.

[0008] Figure 1 A structure schematic diagram of the mould injection temperature intelligent adjustment system provided by the embodiment of the application is provided.

[0009] Figure 2 A flowchart of determining an ideal working temperature in the mould injection temperature intelligent adjustment system provided by the embodiment of the application is provided.

[0010] Figure 3 A flowchart of matching a control strategy according to a temperature deviation value of a temperature control target in the mould injection temperature intelligent adjustment system provided by the embodiment of the application is provided.

[0011] The reference signs are explained as follows: a basic information acquisition module 11, an ideal working temperature determination module 12, a mould monitoring temperature acquisition module 13, a mould temperature deviation calculation module 14, a position requirement constraint degree analysis module 15, a temperature control target determination module 16, and a temperature compensation control module 17. DETAILED DESCRIPTION

[0012] The mould injection temperature intelligent adjustment system provided by the application is used to solve the technical problems of poor precision and uniformity of temperature control due to the diversity of mould materials and the complexity of the structure in the prior art.

[0013] With reference to the drawings and the embodiments of the present application, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all the other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0014] It should be noted that the terms "first", "second", etc. in the specification of the present application and the above drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or server including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or modules that are not clearly listed or inherent to these processes, methods, products or devices.

[0015] Embodiment one

[0016] As shown in the Figure 1 The present application provides a mold injection temperature intelligent adjustment system, which comprises:

[0017] A basic information acquisition module 11 is configured to acquire injection material information and product requirement information.

[0018] Further, the basic information acquisition module 11 is further configured to perform the following steps:

[0019] P11: Acquire an abnormal case database of mold injection, extract product abnormal type data of different injection materials, and the product abnormal type data at least includes size abnormality, surface quality abnormality and internal stress abnormality;

[0020] P12: Take the product abnormal type data as a top event, decompose through abnormal case data, determine abnormal factors, and fit a function influence relationship between the abnormal factors and the top event;

[0021] P13: Extract an influence coefficient according to the function influence relationship, and cluster around the injection material to screen material abnormal factors;

[0022] P14: According to the material abnormal factor, the product abnormal type data configuration data screening dimension, the injection material information, the product requirement information are obtained, wherein the material abnormal factor corresponds to the screening dimension of the injection material information, and the product abnormal type data corresponds to the screening dimension of the product requirement information.

[0023] 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 material information and product requirement information. The specific collection process can be, first access the abnormal case database of mold injection, which stores various injection abnormal situations and related data occurred in history, extract the product abnormal type data related to different injection materials from the database, including but not limited to size abnormality, surface quality abnormality, internal stress abnormality, etc.

[0024] Further, the extracted product abnormal type data is used as the top event, and the abnormal case data is analyzed in depth, and the factors causing these abnormalities, i.e. abnormal factors, are determined through fault tree analysis (FTA) or similar methods. Further, the function influence relationship between the abnormal factors and the top event is fitted to quantify the influence degree of each abnormal factor on the final product abnormality. Further, according to the function 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 abnormality. And the clustering analysis is carried out with the injection material as the clustering center, and the abnormal factors closely related to the specific material, i.e. material abnormal factors, are selected according to the influence coefficient.

[0025] Further, based on the material abnormal factor and the product abnormal type data, the corresponding data screening dimension is configured to guide the accurate acquisition of the required injection material information and product requirement information from a wider data source. Among them, the material abnormal factor corresponds to the screening dimension of the injection material information one by one, and the product abnormal type data corresponds to the screening dimension of the product requirement information. Through this corresponding relationship, the module can ensure that the information obtained 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 material information and product requirement information, but also extract risk factors closely related to specific materials and product requirements by combining historical abnormal case data, providing strong data support for subsequent ideal working temperature determination, temperature control, etc.

[0026] The ideal working temperature determination module 12 is used for injection mold analysis and temperature analysis according to the injection material information and the product requirement information to determine the ideal working temperature.

[0027] Further, as Figure 2As shown, the ideal working temperature determination module 12 is also used to perform the following steps:

[0028] P21: extract the preset material analysis module, product requirement analysis module;

[0029] P22: respectively taking the injection molding material information and product requirement information as input, and performing material temperature relationship analysis and product demand temperature relationship analysis through the preset material analysis module and product requirement analysis module;

[0030] P23: according to the material temperature relationship analysis result and the product demand temperature relationship analysis result, performing temperature fusion analysis to determine the ideal working temperature.

[0031] Optionally, the ideal working temperature determination module 12 of the present application is responsible for determining the best injection mold working temperature, i.e. the ideal working temperature, according to the injection molding material information and the product requirement information. Specifically, two key analysis modules in the system are extracted: 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 injection molding materials and product requirements, which can be obtained through machine learning by collecting sample data. Further, the injection molding material information and the product requirement information are respectively taken as input and processed through the preset material analysis module and the product requirement analysis module. First, according to the physical and chemical properties of the injection molding material, the preset material analysis module analyzes the relationship between the material and the temperature, including the melting point, flow temperature, and curing temperature of the material, and obtains the performance of the material at different temperatures and the possible changes. Further, according to the specific requirements and specifications of the product, the product requirement analysis module analyzes the temperature requirements of the product, including the dimensional accuracy, surface quality, and mechanical properties of the product. These requirements are converted into specific temperature requirements, such as the minimum working temperature, the maximum working temperature, and the temperature uniformity.

[0032] Further, according to the material temperature relationship analysis result and the product demand temperature relationship analysis result, temperature fusion analysis is performed, that is, the dual factors of material performance and product requirements are considered, and the temperature is comprehensively analyzed. For example, a temperature range that can guarantee material performance and meet product requirements is found, or a specific temperature point is determined as the optimal working temperature. Further, one or more ideal working temperatures are determined as the basis for subsequent temperature control and adjustment, ensuring that the injection molding process can be carried out under the best temperature conditions.

[0033] Further, the step P22 of the embodiment of the present application further comprises:

[0034] P22-1: according to the injection molding material information, fitting a material characteristic temperature curve, the material characteristic temperature curve representing the relationship between the material shaping characteristics and the temperature;

[0035] P22-2: Obtain a material temperature relationship analysis result according to the material characteristic temperature curve;

[0036] P22-3: Obtain each demand characteristic temperature curve according to the product requirement information;

[0037] P22-4: Operate the demand characteristic temperature curve according to the model parameters in the product requirement analysis module to obtain a product demand temperature relationship analysis result.

[0038] Specifically, the specific process of analyzing the injection molding material information and the product requirement information can be that, according to the injection molding material information, a material characteristic temperature curve is fitted through a data analysis tool or professional software, and the material characteristic temperature curve can intuitively show the change relationship of the material shaping characteristics (such as flowability, viscoelasticity, etc.) with temperature. Further, according to the material characteristic temperature curve, the relationship between the material temperature and the injection molding process performance is further analyzed to obtain a material temperature relationship analysis result, and the material temperature relationship analysis result includes the best working temperature range of the material, the sensitive temperature interval, etc.

[0039] Further, according to the product requirement information, the key quality characteristics of the product are identified, such as size accuracy, surface quality, mechanical performance, etc., and for each characteristic, the corresponding demand characteristic temperature curve is obtained by consulting relevant materials, using professional software or performing simulation analysis, and the demand characteristic temperature curve can describe the performance of the key quality characteristics of the product at different temperatures.

[0040] Further, the demand characteristic temperature curve is input into the product requirement analysis module, and is operated according to the preset model parameters, and the product requirement analysis module performs curve superposition, weight distribution, etc. to comprehensively consider the temperature requirements of each key quality characteristic, and obtain a comprehensive product demand temperature relationship analysis result. This result will be one of the important bases for determining the ideal working temperature, including one or more recommended working temperature values or ranges, which can meet the requirements of the key quality characteristics of the product, while taking into account the material shaping characteristics.

[0041] Further, the step P23 of the embodiment of the present application further includes:

[0042] P23-1: Time sequence alignment fitting is performed on the material characteristic temperature curve and the demand characteristic temperature curves;

[0043] P23-2: A threshold baseline is set according to the product requirement information, and curve segment screening is performed on the time sequence fitting curve using the threshold baseline;

[0044] P23-3: According to the curve segment determined by screening, the intersection temperature and the overlapping time interval are obtained, and the intersection temperature with the maximum overlapping time interval is selected as the ideal working temperature.

[0045] Optionally, in order to compare the material characteristic temperature curve with each demand characteristic temperature curve, it is necessary to time-align and fit them. First, a common time or temperature range is determined as a reference. Then, the material characteristic temperature curve and each demand characteristic temperature curve are aligned in the reference 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.

[0046] Further, according to the specific requirements of the product on the material performance and product quality, one or more threshold baselines are set, which may include the requirements of the material flowability, curing speed, product size tolerance, surface quality, etc. The time-aligned and fitted curve is compared with the set threshold baseline to screen out the curve segment that meets the requirements. These curve segments should meet the material characteristics and product requirements at the same time and remain stable within a certain time or temperature range.

[0047] Further, in the screened curve segment, the intersection points of the material characteristic temperature curve and each demand 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, i.e. within the time interval, the material characteristics and product requirements remain stable and meet the requirements. Further, the maximum overlapping time interval is selected from all intersection points as the ideal working temperature. The ideal working temperature can not only guarantee the best 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 the injection molding process has higher stability and repeatability at this temperature.

[0048] Further, the ideal working temperature determination module 12 is further configured to perform the following steps:

[0049] P21a: According to the overlapping time interval, the region is divided to determine a plurality of time sequence regions;

[0050] P22a: Based on the plurality of time sequence regions, the change gradient of the corresponding intersection temperature is calculated;

[0051] P23a: According to the injection temperature flow, the time sequence neighborhood aggregation is performed in combination with the change gradient to obtain a plurality of adjusted temperature control time zones, and the ideal working temperature of the adjusted temperature control time zone is determined by using the intersection temperature of the adjusted temperature control time zone.

[0052] In a possible embodiment of the present application, to further refine the temperature control strategy, increase the calculation of the segmentation of the timing area and the change of the temperature gradient, and the timing neighborhood aggregation. Specifically, analyze the length, shape and distribution of the overlapping time interval, and according to these characteristics, segment the overlapping time interval to form multiple independent timing areas. These timing areas represent the stable state in which the material properties and product requirements are met at different times or temperature stages.

[0053] Further, based on the plurality of timing areas, in each timing area, the change gradient of the corresponding cross temperature is calculated. Numerical analysis or statistical methods can be used to calculate the derivative or difference of the cross temperature in each timing area, thereby obtaining the change gradient. The change gradient reflects the speed of the cross temperature changing with time or temperature, which is crucial for understanding the trend of the influence of temperature on material properties and product requirements.

[0054] Further, analyze the characteristics and requirements of the injection molding temperature process, such as heating, holding, cooling, etc. Then, according to the characteristics of different stages and the change gradient of the cross temperature, the adjacent timing areas are aggregated to form multiple adjustment temperature control time zones. In each adjustment temperature control time zone, select the temperature point with the longest overlapping time interval or the smallest change gradient in the cross temperature 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 adjustment 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.

[0055] The mold monitoring temperature acquisition module 13 is configured to configure a data acquisition module, acquire mold monitoring temperature data through temperature sensors arranged on the mold, and the mold monitoring temperature data has a mold position identifier, and the mold position identifier describes the arrangement position of the temperature sensor.

[0056] 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 sensors arranged on the mold. First, configure the data acquisition module, which is responsible for receiving and processing data from the temperature sensor. The configuration process includes setting data acquisition frequency, data format, transmission protocol, etc. to ensure the accuracy and efficiency of data acquisition.

[0057] Further, 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, the cavity, the mold wall, etc., to monitor the temperature changes at the key positions of the mold in real time. The temperature sensors are connected to the data acquisition module, and the temperature conditions at different positions and times of the mold are collected in real time to obtain mold monitoring temperature data. The mold monitoring temperature data has a mold position identifier that clearly indicates the specific position of the sensor corresponding to the temperature data on the mold. This can provide strong support for temperature control during the injection molding process.

[0058] Further, the mold monitoring temperature acquisition module 13 is further configured to perform the following steps:

[0059] P31a: Obtain the mold structure and determine the three-dimensional size of the mold;

[0060] P32a: Obtain the heating contact area and cooling contact area of the mold according to the mold structure;

[0061] P33a: Determine the thermal radiation depth according to the three-dimensional size of the mold;

[0062] P34a: Perform temperature control sensitivity analysis according to the heating contact area, cooling contact area, and thermal radiation depth to determine the temperature control sensitivity of each mold partition;

[0063] P35a: Perform demand target analysis according to the product requirement information to screen the core quality area;

[0064] P36a: Screen the non-sensitive area according to the temperature control sensitivity, and arrange the temperature sensors in the non-sensitive area and the core quality area.

[0065] Specifically, before obtaining the mold monitoring temperature data through the temperature sensors arranged in the mold, the temperature sensors need to be arranged. First, obtain the detailed structure information of the mold, including the size, shape, and relative position of each part, etc., and accurately determine the three-dimensional size of the mold through professional CAD software or mold design software. Further, according to the mold structure, calculate the contact area of the mold with the heating element and the cooling pipeline to obtain the heating contact area and the cooling contact area, which can reflect the heat exchange efficiency of the mold during heating and cooling. According to the three-dimensional size of the mold and the material characteristics, calculate the propagation depth of thermal radiation inside the mold, i.e., the thermal radiation depth. Further, perform temperature control sensitivity analysis according to the heating contact area, cooling contact area, and thermal radiation depth to evaluate the sensitivity of different areas of the mold to temperature changes and determine the temperature control sensitivity of each mold partition.

[0066] Further, the requirement target analysis is combined with the product requirement information. By evaluating the key quality characteristics of the product and the production process requirements, the core quality areas in the mold can be screened out. These areas will directly affect the quality and performance of the product, so special attention is needed in temperature control.

[0067] Finally, according to the analysis results of the temperature control sensitivity, the non-sensitive areas in the mold are screened out. In the non-sensitive areas and the core quality areas, the temperature sensor is laid out. In this way, under the limited number of sensors, the areas that have the greatest impact on the quality of the product are covered first, while the monitoring cost of the non-sensitive areas is reduced, and the accuracy and pertinence of the temperature data are improved.

[0068] The mold temperature deviation calculation module 14 is used to calculate the deviation of the mold monitoring temperature data according to the ideal working temperature, and determine the mold temperature deviation distribution.

[0069] It should be understood that the main function of the mold temperature deviation calculation module 14 of the present application is to calculate the deviation of 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 the real-time mold monitoring temperature data from the mold monitoring temperature acquisition module 13.

[0070] Further, 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 diagram to directly show the deviation between each area of the mold and the ideal working temperature, helping the operator to quickly identify the temperature abnormal area.

[0071] The position requirement constraint degree analysis module 15 is used to analyze the position requirement constraint degree based on the mold position identification and the product requirement information, and obtain the temperature positioning constraint coefficient.

[0072] Specifically, the position requirement constraint degree analysis module 15 mainly functions to perform in-depth position requirement constraint degree analysis based on the mold position identifier and the product requirement information to obtain a temperature positioning constraint coefficient, that is, to obtain the degree of dependence of different positions of the mold on ideal temperature. Based on the mold position identifier 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 is regarded as a high importance area. And the constraint conditions related to these important positions are identified, such as temperature range, temperature uniformity requirement, etc. Then, according to the position importance evaluation and constraint condition identification, the temperature positioning constraint coefficient is calculated for each mold position. This coefficient is a quantitative index reflecting the importance and constraint degree of different mold positions in temperature control, which can be calculated using weighted summation, fuzzy evaluation, etc. When the temperature positioning constraint coefficient is 0-1, it indicates that the importance of the corresponding position in temperature control is low, and when the temperature positioning constraint coefficient is greater than 1, it indicates that the importance of the corresponding position in temperature control is high.

[0073] The temperature control target determination module 16 is configured to correct the mold temperature deviation distribution using the temperature positioning constraint coefficient and determine a temperature control target.

[0074] Optionally, the temperature control target determination module 16 is configured to correct the mold temperature deviation distribution using the temperature positioning constraint coefficient, correct the mold temperature deviation of the position using the corresponding temperature positioning constraint coefficient according to the mold position identifier, and obtain a corrected mold temperature deviation distribution. Then, based on the corrected mold temperature deviation distribution, combined with the corrected temperature deviation distribution, the product requirement information, and the injection molding process requirement, the temperature control target of each key area is determined as the basis for subsequent temperature control strategy formulation.

[0075] The temperature compensation control module 17 is configured to match a control strategy according to the temperature deviation value of the temperature control target and perform temperature compensation control based on the matched control strategy.

[0076] Further, as shown in Figure 3 The temperature compensation control module 17 is further configured to perform the following steps:

[0077] P71: Set control parameters, including control proportion coefficient, integral coefficient, and differential coefficient;

[0078] P72: Fit a control function based on the control parameters, take the temperature deviation value as input, perform control parameter matching analysis through the control function, bias control strategy, integrate all bias control strategies according to the time corresponding relationship of the adjustment temperature control time zone to obtain the control strategy.

[0079] wherein the control function expression is: wherein, is a control proportional coefficient, is an integral coefficient, is a differential coefficient, is a temperature deviation value at time t, denotes the derivative with respect to time t, denotes the cumulative integral of the deviation over time.

[0080] 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 to perform temperature compensation control to ensure the stability and accuracy of the mold temperature. Specifically, first, control parameters are set, including 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.

[0081] Further, a control function is fitted based on the control parameters, and the control function expression is: wherein, is a control proportional coefficient, is an integral coefficient, is a differential coefficient, is a temperature deviation value at time t, denotes the derivative with respect to time t, denotes the cumulative integral of the deviation over time. The temperature deviation value is taken as input, and parameter matching analysis is performed through the control function, thereby obtaining a deviation control strategy. For temperature deviations at different time periods, there may be different control requirements. Therefore, all deviation control strategies are integrated according to the time correspondence of the adjustment 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. Based on the matched control strategy, temperature compensation control is performed to realize accurate control and adjustment of the temperature of the injection mold, which helps to improve the molding quality and production efficiency of the product.

[0082] In summary, the embodiments of the present application have at least the following technical effects:

[0083] The present application determines the ideal working temperature by obtaining injection material information and product requirement information, analyzes the injection mold and temperature, determines the ideal working temperature, calculates the deviation of the mold temperature data, determines the mold temperature deviation distribution, corrects the mold temperature deviation distribution based on the mold position mark and product requirement information, determines the temperature control target, and matches the control strategy for temperature compensation control.

[0084] The technical effect of improving the precision and uniformity of temperature control is achieved by analyzing and compensating temperature deviation based on the material properties and structure distribution of the mold.

[0085] 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. The above-mentioned specific embodiments of the present application are described. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are possible or can be advantageous.

[0086] The above-mentioned is only the preferred embodiment of the present application, and does not limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0087] The present specification and drawings are only exemplary of the present application, and any and all modifications, changes, combinations or equivalents within the scope of the present application are considered to be covered. Obviously, those skilled in the art can 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 belong to the scope of the present application and its equivalent technology, the present application is intended to include these modifications and variations.

Claims

1. A mold injection temperature intelligent regulating system, characterized in that, The mold injection temperature intelligent adjusting system comprises: a basic information acquisition module, which is used to acquire injection material information and product requirement information; an ideal working temperature determination module, which is used to determine an ideal working temperature by analyzing an injection mold and temperature according to the injection material information and product requirement information; a mold monitoring temperature acquisition module, which is used to configure a data acquisition module, acquire mold monitoring temperature data through temperature sensors arranged in the mold, and determine a mold position identifier of the mold monitoring temperature data, wherein the mold position identifier describes the arrangement position of the temperature sensors; a mold temperature deviation calculation module, which is used to calculate the mold monitoring temperature data according to the ideal working temperature to determine a mold temperature deviation distribution; a position demand constraint degree analysis module, which is used to analyze the position demand constraint degree based on the mold position identifier and the product requirement information to obtain a temperature positioning constraint coefficient; a temperature control target determination module, which is used to correct the mold temperature deviation distribution by using the temperature positioning constraint coefficient to determine a temperature control target; a temperature compensation control module, which is used to match a control strategy according to the temperature deviation value of the temperature control target and perform temperature compensation control based on the matched control strategy.

2. The intelligent mold injection temperature regulating system of claim 1, wherein, The injection material information and product requirement information are acquired by: acquiring an abnormal case database of mold injection, extracting product abnormal type data of different injection materials, and determining at least size abnormality, surface quality abnormality and internal stress abnormality in the product abnormal type data; taking the product abnormal type data as a top event, decomposing the abnormal case data to determine abnormal factors, and fitting a function influence relationship between the abnormal factors and the top event; extracting an influence coefficient according to the function influence relationship, clustering the injection materials as the center, and screening material abnormal factors; configuring a data screening dimension according to the material abnormal factors and the product abnormal type data to acquire the injection material information and product requirement information, wherein the material abnormal factors correspond to the screening dimension of the injection material information, and the product abnormal type data correspond to the screening dimension of the product requirement information.

3. The intelligent mold injection temperature regulating system of claim 2, wherein, The ideal working temperature is determined by analyzing an injection mold and temperature according to the injection material information and product requirement information, which comprises: extracting a preset material analysis module and a product requirement analysis module; respectively taking the injection material information and product requirement information as inputs, analyzing material temperature relationships and product demand temperature relationships through the preset material analysis module and the product requirement analysis module; performing temperature fusion analysis according to the material temperature relationship analysis result and the product demand temperature relationship analysis result to determine the ideal working temperature.

4. The intelligent mold injection temperature regulating system of claim 3, wherein, The material temperature relationship analysis and product demand temperature relationship analysis through the preset material analysis module and the product requirement analysis module comprise: According to the injection material information, a material characteristic temperature curve is fitted, the material characteristic temperature curve representing a relationship between a material shaping characteristic and temperature; According to the material characteristic temperature curve, a material temperature relationship analysis result is obtained; According to the product requirement information, a demand characteristic temperature curve is obtained; The demand characteristic temperature curve is operated according to a model parameter in a product requirement analysis module, and a product demand temperature relationship analysis result is obtained.

5. The intelligent mold injection temperature regulating system of claim 4, wherein, The temperature fusion analysis according to the material temperature relationship analysis result and the product demand temperature relationship analysis result is performed to determine the ideal working temperature, including: The material characteristic temperature curve and the demand characteristic temperature curve are time sequence aligned and fitted; A threshold baseline is set according to the product requirement information, and a curve segment is screened from the time sequence fitted curve according to the threshold baseline; According to the screened curve segment, a cross temperature and an overlapping time interval are obtained, and a cross temperature with the largest overlapping time interval is selected as the ideal working temperature.

6. The intelligent mold injection temperature regulating system of claim 5, wherein, Determining the ideal working temperature further includes: According to the overlapping time interval, a plurality of time sequence regions are divided; Based on the plurality of time sequence regions, a change gradient of the corresponding cross temperature is calculated; According to an injection temperature flow, a plurality of adjustment temperature control time zones are obtained by combining the change gradient and time sequence neighborhood aggregation, and the ideal working temperature of the adjustment temperature control time zone is determined according to the cross temperature of the adjustment temperature control time zone.

7. The intelligent mold injection temperature regulating system of claim 1, wherein, The temperature sensor arranged in the mold is used to obtain mold monitoring temperature data, and the foregoing includes: The mold structure is obtained, and the three-dimensional size of the mold is determined; According to the mold structure, the heating contact area and the cooling contact area of the mold are obtained; According to the three-dimensional size of the mold, the thermal radiation depth is determined; According to the heating contact area, the cooling contact area, and the thermal radiation depth, the temperature control sensitivity is analyzed, and the temperature control sensitivity of each mold partition is determined; According to the product requirement information, the demand target is analyzed, and the core quality area is screened; According to the temperature control sensitivity, the non-sensitive area is screened, and the temperature sensor is arranged in the non-sensitive area and the core quality area.

8. The intelligent mold injection temperature regulating system of claim 6, wherein, The temperature control target temperature deviation value matching control strategy includes: The control parameters include a control proportion coefficient, an integral coefficient, and a differential coefficient; Based on the control parameters, a control function is fitted, the temperature deviation value is taken as an input, the control parameter matching analysis is performed through the control function, a deviation control strategy is obtained, all deviation control strategies are integrated according to the time corresponding relationship of the adjustment temperature control time zone, and the control strategy is obtained. wherein the control function expression is: wherein, is a control proportional coefficient, is an integral coefficient, is a differential coefficient, is a temperature deviation value at time t, denotes a derivative with respect to time t, denotes a cumulative integral of the deviation over time.

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