Intelligent control method and system for injection molding of router plastic shell

Abstract

The invention relates to the technical field of injection molding control, in particular to an intelligent control method and system for injection molding of a router plastic shell, and the method comprises the steps that the in-cylinder melt temperature, the nozzle temperature, the injected melt temperature, the cooling water flow and the quality score of all injection processes are obtained; the injection process is divided into a cooling participation process and a non-cooling participation process; obtaining each cooling subsequence of each cooling participation process; according to the quality score variable quantity and the cooling water flow variable quantity under the unit temperature change, in combination with the temperature difference characteristic between the temperature in the cylinder and the temperature of the injected melt when the cooling system does not operate in each cooling participation process and the number of the cooling subsequences, the injection regulation and control amplitude of each cooling participation process is obtained; setting the injection regulation amplitude of each non-cooling participation process as 0; and the initial proportionality coefficient of the current injection process is optimized. According to the method, the proportionality coefficient of the current injection process is adaptively adjusted, so that the injection molding quality of the plastic shell is improved.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of injection molding control, in particular to a router plastic shell injection intelligent control method and system. BACKGROUND

[0002] In the production process of the router shell, recycled waste ABS plastic is used as raw material for processing and recycling, which not only achieves the concept of environmental protection, but also meets the basic performance requirements of the router shell. In the injection control engineering of the router plastic shell, the traditional plastic mold design and control method has been difficult to meet the current mold upgrading, and cannot meet the production demand of high-quality plastic production. Therefore, it is of great significance to build a control method of the router plastic shell injection molding machine to realize accurate regulation and control of the injection process and improve product quality.

[0003] When using recycled plastic waste to injection mold the plastic shell of the router, the quality of the router shell is affected by the injection melt temperature. If the temperature is too low, the plastic melt will damage the screw and the machine injection port during injection; if the temperature is too high, the plastic is easy to burn. In order to ensure the quality stability of the router plastic shell, the prior art usually uses a fixed control parameter of the PID control algorithm to ensure the temperature stability of the injection melt. However, the prior art does not fully consider that as the injection process proceeds, the nozzle temperature of the injection molding machine will produce heat accumulation, thereby producing additional temperature rise of the injection melt, and since the nozzle temperatures of different batches are inconsistent, the temperature rise of the injection melt is also dynamically changing. Therefore, the PID control algorithm with fixed parameters is difficult to adapt to the dynamic injection process, resulting in poor control effect, which in turn affects the injection quality of the plastic shell. SUMMARY

[0004] In order to solve the above technical problems, the purpose of the present application is to provide a router plastic shell injection intelligent control method and system, and the technical solution adopted is as follows: In a first aspect, the application provides a router plastic shell injection intelligent control method, which comprises the following steps: Obtaining the barrel melt temperature data, nozzle temperature data, injection melt temperature data, and cooling water flow data of the injection molding machine in a preset number of injection processes, and the quality score corresponding to each injection process; According to the discrete degree of the cooling water flow data of each injection process, all injection processes are divided into cooling participating processes and non-cooling participating processes; the injection melt temperature of each cooling participating process is divided into a plurality of subsequences, and each cooling subsequence is extracted; According to the mass score change amount under the unit initial nozzle temperature change in all injection processes, and the difference between the change range of each cooling sub-sequence in each cooling participating process and the change range of the corresponding cooling water flow data in the same period, the injection control sensitivity of each cooling participating process is obtained; The data of the barrel melt temperature and the injection melt temperature data in the corresponding time period of each cooling sub-sequence in each cooling participating process is removed, and according to the change trend of the temperature difference between the remaining barrel melt temperature and the injection melt temperature, and the number of cooling sub-sequences in each cooling participating process and the injection control sensitivity, the injection control range of each cooling participating process is obtained. The injection control range of each non-cooling participating process is set to 0; the initial nozzle temperature of the current injection process is clustered with the initial nozzle temperatures of all injection processes, and the initial proportion coefficient of the current injection process is optimized according to the injection control range of all injection processes in the cluster where the initial nozzle temperature of the current injection process is located.

[0005] Preferably, the specific process of dividing all injection processes into cooling participating processes and non-cooling participating processes is that the variance of the cooling water flow data of each injection process is calculated, and the injection process with a variance greater than or equal to a preset segmentation threshold is recorded as a cooling participating process; the injection process with a variance less than the preset segmentation threshold is recorded as a non-cooling participating process.

[0006] Preferably, the specific process of extracting each cooling sub-sequence is that the injection melt temperature data of each cooling participating process is divided into multiple sub-sequences, and the sub-sequence with a fitting straight line slope less than a preset slope threshold is recorded as a cooling sub-sequence of each cooling participating process.

[0007] Preferably, the injection control sensitivity of each cooling participating process is the product of the first ratio of all injection processes and the flow change factor corresponding to each cooling participating process; wherein the process of obtaining the first ratio of all injection processes is that the injection process with a mass score greater than or equal to a preset score threshold in all injection processes is recorded as a good injection process; the absolute difference c1 between the mean value of the initial nozzle temperature of all good injection processes and all non-good injection processes, and the absolute difference c2 between the mean value of the mass score of all good injection processes and all non-good injection processes are calculated respectively, and the ratio of the absolute difference c2 to the absolute difference c1 is recorded as the first ratio of all injection processes.

[0008] Preferably, the flow change factor of each cooling process is obtained by: obtaining each flow subsequence in the flow sequence of each cooling process corresponding to each cooling subsequence; sorting all cooling subsequences and all flow subsequences in each cooling process according to time sequence; calculating the ratio of the internal range of each flow subsequence to the internal range of the cooling subsequence corresponding to the same position sequence in each cooling process, and taking the average of all ratios as the flow change factor of each cooling process.

[0009] Preferably, the injection control amplitude of each cooling process is positively correlated with the injection control sensitivity, the total number of all cooling subsequences, and the first slope.

[0010] Preferably, the first slope of each cooling process is obtained by: taking the sequence of the melt temperature data in the barrel and the melt temperature data discharged in each injection process according to time sequence as the barrel temperature sequence and the discharge temperature sequence of each injection process; removing the elements in the discharge temperature sequence of each cooling process belonging to the cooling subsequence, and taking the sequence of the remaining elements according to time sequence as the new discharge temperature sequence of each cooling process; and removing the elements in the barrel temperature sequence corresponding to the position sequence according to the position sequence of the elements in the cooling subsequence of each cooling process, and taking the sequence of the remaining elements in the barrel temperature sequence according to time sequence as the new barrel temperature sequence of each cooling process.

[0011] Preferably, the calculation formula for optimizing the initial proportion coefficient of the current injection process is: ; in the formula, is the proportion coefficient of the current injection process; is the preset initial proportion coefficient of the current injection process; is the control factor of the current injection process; is a normalization function; is a preset weight coefficient.

[0012] Preferably, the control factor of the current injection process is obtained by: obtaining the initial nozzle temperature w of the current injection process, and clustering the initial nozzle temperature w of the current injection process with the initial nozzle temperature of all injection processes; taking the average of the injection control amplitudes of all injection processes except the current injection process in the cluster to which the initial nozzle temperature w of the current injection process belongs as the control factor of the current injection process.

[0013] In a second aspect, the embodiments of the present application further provide a router plastic shell injection intelligent control system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, and the processor implements the steps of the injection intelligent control method of the router plastic shell when executing the computer program.

[0014] The embodiments of the present application have at least the following beneficial effects: The embodiments of the present application are aimed at the problem that the melt temperature is unstable and the product quality is affected due to the fact that the prior art does not fully consider the influence of the dynamic change of the nozzle temperature heat accumulation. The injection control sensitivity is constructed by analyzing the mass score change amount under the unit nozzle temperature change and the cooling water flow change amount under the unit injection melt temperature change, so as to reflect the influence degree of the temperature difference on the product quality and the adjustment range of the required cooling water flow. The injection control range is constructed by analyzing the change trend of the temperature difference between the barrel melt temperature and the injection melt temperature in the non-running stage of the cooling system, so as to reflect the rapid degree of the required response speed of the control system, and then the required proportional coefficient adjustment amount under different nozzle temperatures is calculated, the proportional coefficient of the current injection process is dynamically adjusted, the cooling water flow of the cooling system can dynamically adapt to the heat accumulation trend at the nozzle, the stability of the melt temperature is improved, and the injection quality of the plastic shell is improved. BRIEF DESCRIPTION OF DRAWINGS

[0015] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the drawings needed in the embodiments or the prior art description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creating any creative labor.

[0016] Figure 1 A step flowchart of the injection intelligent control method of the router plastic shell is provided for an embodiment of the present application. Figure 2 A flowchart of obtaining the injection control range of each cooling participating process is provided for an embodiment of the present application. DETAILED DESCRIPTION

[0017] In order to further illustrate the technical means and effects adopted by the present application to achieve the predetermined invention purposes, the specific embodiments, structures, features and effects of the injection intelligent control method and system of the router plastic shell according to the present application are described in detail as follows by combining with the drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0019] The specific scheme of the router plastic shell injection intelligent control method and system provided by the application is specifically described below in combination with the drawings.

[0020] Please refer to Figure 1 , which shows the step flow chart of a router plastic shell injection intelligent control method provided by an embodiment of the application, which comprises the following steps: Step 1: Obtain the melt temperature data in the barrel, nozzle temperature data, injected melt temperature data, cooling water flow data of the injection molding machine in a preset number of injection processes, and the quality score corresponding to each injection process.

[0021] The application uses recycled ABS plastic for injection molding of router shell. The injection molding machine heats the melt in the barrel through its own electromagnetic induction heating device, and cools and cools the melt in the barrel through its own water cooling system. The application collects the temperature data of the melt in the barrel through the temperature sensor of the injection molding machine; obtains the nozzle temperature data at the outlet of the barrel through the thermocouple; collects the temperature of the melt discharged from the nozzle through the infrared thermometer; and obtains the cooling water flow data through the cooling system of the injection molding machine. All data are collected synchronously and in real time. In this embodiment, the collection frequency of various data is 100 Hz.

[0022] The process of discharging the melt from the nozzle of the barrel until filling the entire router mold is recorded as an injection process. The application collects a total of a preset number T (300 in this embodiment) of injection processes. According to the barrel melt temperature data, nozzle temperature data, injected melt temperature data, and cooling water flow data collected in each injection process, the barrel temperature sequence, nozzle temperature sequence, injection temperature sequence, and flow sequence corresponding to each injection process are constructed in chronological order.

[0023] Then, after each injection process is completed, pressure holding, cooling and demolding, and quality detection operations are performed to obtain the quality score data of the finished product corresponding to each injection process. The specific process of obtaining the quality score is: using existing visual detection methods to detect the quality of the produced router plastic shell and obtain the quality score. The visual detection algorithm is not limited to image defect detection, classification recognition, shell matching, etc. In this embodiment, the quality detection is performed by shell matching: the image of the finished product shell after production is matched with the image of the standard sample shell. When the match is complete, the quality score is 100, and when the match is not complete, the quality score is 0.

[0024] To eliminate the influence of dimensions between data, all data sequences are normalized. Normalization methods include Z-score, maximum value normalization, and maximum-minimum value normalization. This embodiment uses maximum-minimum value normalization.

[0025] Step 2: Based on the dispersion of cooling water flow data for each injection process, all injection processes are divided into cooling-involved processes and non-cooling-involved processes; the injection melt temperature of each cooling-involved process is divided into multiple subsequences, and each cooling subsequence is extracted.

[0026] When injection molding the plastic casing of a router, the melt inside the barrel already has a high temperature. Furthermore, when it exits the nozzle under pressure, it generates significant shear force at the nozzle, causing the melt temperature at the nozzle and the nozzle temperature to rise. This results in the injected melt temperature exceeding the preset value, affecting the injection molding effect. Therefore, existing technologies typically employ cooling systems to lower the melt temperature, thereby improving the quality of the finished router plastic casing.

[0027] When the cooling system is not operating to lower the temperature, i.e., the temperature of the injected melt is within the preset temperature range, the flow rate of the cooling water is low and relatively stable. However, if the cooling system is operating to lower the temperature, the flow rate of the cooling water will increase and become more variable. Therefore, by analyzing the changes in the flow rate of the cooling water, we can initially extract the cooling data of the injected melt, thus providing a data basis for subsequent adjustment of the cooling water flow rate.

[0028] The dispersion of the flow sequence for each injection process is calculated, and the dispersion of all flow sequences is used as input to the Otsu thresholding method. The output is a segmentation threshold, which is denoted as the preset segmentation threshold. Injection processes corresponding to flow sequences with dispersion greater than or equal to the preset segmentation threshold are denoted as cooling-involved processes; injection processes corresponding to flow sequences with dispersion less than the preset segmentation threshold are denoted as non-cooling-involved processes. The calculation of dispersion is not limited to variance, coefficient of variation, and standard deviation; variance is used in this embodiment.

[0029] Let's take the i-th cooling process as an example for analysis.

[0030] During the injection molding process of router casing molds, if the cooling system is not running, the injected melt temperature data will show a stable or rising trend; however, if the cooling system is running and participating in the melt cooling process, the injected melt temperature data will show a local downward trend.

[0031] Therefore, the injection temperature sequence in the i-th cooling participating process is segmented into multiple subsequences by a sequence segmentation algorithm, then each segmented subsequence is fitted by a straight line fitting algorithm, and finally the subsequence with a fitting straight line slope less than a preset slope threshold is recorded as a cooling subsequence of the i-th cooling participating process. In this embodiment, the preset slope threshold is 0. The sequence segmentation algorithm is not limited to the BG segmentation algorithm or the MK segmentation algorithm, and the straight line fitting algorithm is not limited to the least square method or the polynomial fitting method.

[0032] In the same way, each cooling subsequence in each cooling participating process is obtained.

[0033] At this point, the cooling data in the injection melt temperature data during the operation of the cooling system is obtained, thereby providing data support for subsequent cooling water flow adjustment range evaluation.

[0034] Step three: according to the mass score change amount of each injection process under the unit initial nozzle temperature change, and the difference between the change range of each cooling subsequence in each cooling participating process and the change range of the corresponding cooling water flow data in the same period, the injection control sensitivity of each cooling participating process is obtained.

[0035] Further, since the injection molding process is a classic batch production process, the interval time between batches is short, and after a large number of injection processes, the nozzle temperature will gradually rise, thereby also affecting the temperature rise of the melt flowing through the nozzle. Therefore, in order to improve the stability of the router shell injection quality, the nozzle temperature difference at the beginning of injection needs to be combined for analysis.

[0036] An injection process with a quality score greater than or equal to a preset score threshold (the preset score threshold in this embodiment is 90) is recorded as a good injection process. The first element in the nozzle temperature sequence of each injection process is recorded as the initial nozzle temperature of each injection process. The absolute difference c1 between the mean of the initial nozzle temperatures of all good injection processes and all non-good injection processes, and the absolute difference c2 between the mean of the quality scores of all good injection processes and all non-good injection processes are calculated, and the ratio of the absolute difference c2 to the absolute difference c1 is recorded as the first ratio of all injection processes. The first ratio can quantify the influence of the unit temperature difference of the initial nozzle temperature on the injection quality when the router shell is injection molded; the greater the value, the greater the influence of the initial nozzle temperature on the quality of the shell product, and the greater the adjustment range of the control parameter.

[0037] It should be noted that during the calculation of the first ratio, when the denominator is 0, in order to ensure the normal operation of the first ratio, the denominator is taken from the value range [0.005, 0.01], which has little effect on the calculation and can be ignored. In this embodiment, 0.008 is taken.

[0038] Further, since the injection molding machine is used to adjust the cooling water flow to regulate the temperature control, the change degree of the cooling water flow required for unit temperature regulation is also needed to be analyzed to achieve accurate regulation control.

[0039] Still taking the i-th cooling participation process as an example for analysis. Since the injected melt temperature will be affected by shear force, nozzle temperature heat conduction and other factors to constantly rise, the cooling system may participate in operation multiple times in a injection process. Therefore, in order to improve the control accuracy, the flow sequence also needs to be processed.

[0040] According to the time period corresponding to each temperature drop sub-sequence in the i-th cooling participation process, each flow sub-sequence of the same time period in the flow sequence of the i-th cooling participation process is obtained; all temperature drop sub-sequences and all flow sub-sequences in the i-th cooling participation process are sorted in time sequence order, so that a flow sub-sequence corresponds to a temperature drop sub-sequence; the ratio of the internal range of each flow sub-sequence in the i-th cooling participation process to the internal range of the temperature drop sub-sequence corresponding to the same sequence is calculated, and the average of all ratios is recorded as the flow change factor of the i-th cooling participation process. The flow change factor can quantify the change degree of the cooling water flow required for unit temperature drop regulation of the injected melt, and the greater the value, the greater the change range of the cooling water flow required for regulating the injected melt temperature in the i-th cooling participation process, and the greater the adjustment degree of the control algorithm.

[0041] As a preferred embodiment, the injection control sensitivity of each cooling participation process is obtained according to the change amount of the quality score under the unit initial nozzle temperature change in all injection processes, and the difference between the change amplitude of each temperature drop sub-sequence in each cooling participation process and the change amplitude of the corresponding cooling water flow data in the same period, which is used to represent the sensitive degree of controlling the melt temperature in each cooling participation process.

[0042] In this embodiment, the injection control sensitivity of the i-th cooling participation process is denoted as , and the specific expression is: ; in the formula, is the injection control sensitivity of the i-th cooling participation process, is the first ratio of all injection processes, is the flow change factor of the i-th cooling participation process.

[0043] The injection control sensitivity can reflect the range of the cooling water flow rate that needs to be adjusted due to the initial nozzle temperature deviation; the greater the value, the greater the sensitivity of the router's injection shell product quality to the initial nozzle temperature, and the greater the degree of cooling water control needed to control the melt injection temperature, and the greater the need for large-scale adjustment when controlling the temperature through the cooling water flow rate.

[0044] Step four: remove the data of all cooling sub-sequences corresponding to the time period from the in-cylinder melt temperature and melt injection temperature data during each cooling participation process, and according to the change trend of the temperature difference between the remaining in-cylinder melt temperature and melt injection temperature, and the number of cooling sub-sequences during each cooling participation process and the injection control sensitivity, obtain the injection control range of each cooling participation process.

[0045] Further, in the melt injection process of router shell injection molding, the temperature of the melt injection will be affected by shear force, heat conduction of nozzle temperature and other factors, resulting in temperature rise. When the melt temperature is cooled, if the cooling water flow rate is small, the cooling degree is insufficient, and the melt injection temperature will still be high; if the cooling water flow rate is large, the cooling degree is excessive, which will cause the melt injection temperature to fail to reach the preset value, and all of these will affect the injection molding product quality. Therefore, it is necessary to further quantitatively analyze the temperature rise of the melt injection temperature in the injection process, and obtain the optimal cooling water flow rate.

[0046] Still taking the i-th cooling participation process as an example for analysis.

[0047] First, remove the elements in the melt injection temperature sequence of the i-th cooling participation process that belong to the cooling sub-sequences, and the sequence composed of the remaining elements in time sequence order is recorded as the new melt injection temperature sequence of the i-th cooling participation process; at the same time, according to the position sequence of the elements in the cooling sub-sequences in the melt injection temperature sequence, remove the elements with the corresponding position sequence from the in-cylinder temperature sequence, and the sequence composed of the remaining elements in the in-cylinder temperature sequence in time sequence order is recorded as the new in-cylinder temperature sequence of the i-th cooling participation process, so that the data length and sampling time of the new melt injection temperature sequence and the new in-cylinder temperature sequence are consistent. After removing the data of the cooling sub-sequences corresponding to the time period, the time corresponding to the remaining data of the melt injection temperature sequence and the in-cylinder temperature sequence is in the non-running stage of the cooling system, and the temperature of the melt injection temperature sequence is always greater than or equal to the temperature of the in-cylinder temperature sequence.

[0048] The absolute difference between the new injection temperature sequence and the new barrel temperature sequence is calculated, and all the absolute differences are sorted in time sequence order, and a straight line is fitted, and the slope of the fitted straight line is recorded as the first slope of the i-th cooling participation process. The absolute difference between each corresponding bit sequence can reflect the temperature change of the melt after passing through the nozzle; the larger the first slope, the greater the temperature rise rate of the injection melt caused by the influence of shear force and nozzle at the initial nozzle temperature of the i-th cooling participation process, and the greater the proportional coefficient needed for control adjustment, thereby improving the control response speed of the cooling control system.

[0049] Therefore, according to the change trend of the temperature difference between the new barrel temperature sequence and the new injection temperature sequence, and the number of cooling sub-sequences in each cooling participation process and the injection control sensitivity, the injection regulation amplitude of each cooling participation process is obtained, which is used to represent the rapid degree of the response speed required by the control system of each cooling participation process. The injection regulation amplitude of each cooling participation process is positively correlated with the injection control sensitivity of each cooling participation process, the total number of all cooling sub-sequences, and the first slope. The injection regulation amplitude of each cooling participation process is obtained as shown in the following table. Figure 2 The positive correlation means that the dependent variable increases (decreases) as the independent variable increases (decreases).

[0050] Preferably, in the present embodiment, the injection regulation amplitude of the i-th cooling participation process is recorded as , and the specific expression is: ; in the formula, is the injection regulation amplitude of the i-th cooling participation process; is the total number of all cooling sub-sequences of the i-th cooling participation process, the greater the total number, the more times the cooling system participates in the melt cooling at the initial nozzle temperature of the i-th cooling participation process, and the greater the proportional coefficient needed; is the injection control sensitivity of the plasticizing process; is the first slope of the i-th cooling participation process; is a preset constant, in order to avoid the problem that the parameter is 0, which makes other parameters meaningless, the value is taken from the value range (0.001, 0.01), the value has little effect on the calculation and can be ignored, and the present embodiment takes 0.008.

[0051] The injection regulation amplitude can reflect the amplitude of the proportional coefficient needed to be adjusted in the i-th cooling participation process; the greater the value, the greater the proportional coefficient needed at the initial nozzle temperature of the i-th cooling participation process, thereby obtaining a higher response speed and improving the injection quality of the finished product.

[0052] Step five: set the injection control amplitude of each non-cooling participating process to 0; cluster the initial nozzle temperature of the current injection process with the initial nozzle temperatures of all injection processes, and optimize the initial proportion coefficient of the current injection process according to the injection control amplitudes of all injection processes in the cluster cluster where the initial nozzle temperature of the current injection process is located.

[0053] Further, the proportion coefficient of the current injection process is adjusted, as follows.

[0054] The initial nozzle temperature w of the current injection process is obtained, and then the initial nozzle temperature w is clustered with the initial nozzle temperatures of all injection processes. The clustering algorithm is not limited to the k-means algorithm, the DPC algorithm, and the DBSCAN algorithm. In this embodiment, the k-means algorithm is used to obtain the optimal cluster number through the silhouette coefficient.

[0055] It should be noted that since the cooling system does not participate in the non-cooling participating process, the control parameters do not need to be adjusted in the cooling participating process, and therefore the injection control amplitudes of all non-cooling participating processes are set to 0 to avoid additional errors.

[0056] The cluster cluster where the initial nozzle temperature w of the current injection process is located is recorded as the adjustment cluster of the current injection process, the mean value of the injection control amplitudes of all injection processes other than the current injection process in the adjustment cluster is calculated, and is recorded as the control factor of the current injection process.

[0057] Further, the proportion coefficient of the current injection process is calculated according to the control factor of the current injection process , and the expression is: ; in the formula, is the proportion coefficient of the current injection process; is the preset initial proportion coefficient of the current injection process, which is taken as 1.2 in this embodiment; is the control factor of the current injection process; is a normalization function, and in this embodiment, the tanh normalization function is taken; is a preset weight coefficient, and in order to avoid excessive adjustment degree and affect the stability of the control system, 2 is taken in this embodiment.

[0058] In the current injection process, the control system generates a control signal in real time according to the calculated proportion coefficient in combination with the PID algorithm, which is used to adjust the cooling water flow in the cooling system, so as to dynamically adapt to the heat accumulation trend of the nozzle area, improve the response speed and stability of temperature control, and realize precise regulation of the actual temperature of the melt.

[0059] Based on the same inventive concept as the above method, the embodiment of the present application also provides a router plastic shell injection intelligent control system, comprising a memory, a processor and a computer program stored in the memory and running on the processor, and the processor implements the steps of any one of the above router plastic shell injection intelligent control method when executing the computer program.

[0060] 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 describes the specific embodiments of the present application. 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 also possible or can be advantageous.

[0061] Each of the embodiments in the specification is described in a progressive manner, and the same or similar parts between each embodiment can be referred to each other. Each embodiment focuses on the difference from other embodiments.

[0062] The above only describes the preferred embodiments of the present application, and does not limit the present application. Any modification, equivalent replacement, improvement, etc. made within the principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for intelligent control of injection molding of a plastic casing of a router, characterized in that, The method comprises the following steps: obtaining barrel melt temperature data, nozzle temperature data, injection melt temperature data, cooling water flow data of the injection molding machine in a preset number of injection processes, and quality scores corresponding to each injection process; dividing all injection processes into cooling participating processes and non-cooling participating processes according to the dispersion degree of the cooling water flow data of each injection process; dividing the injection melt temperature of each cooling participating process into multiple subsequences, and extracting each cooling subsequence therefrom; obtaining the injection control sensitivity of each cooling participating process according to the quality score change amount of all injection processes under the change of the unit initial nozzle temperature, and the difference between the change amplitude of each cooling subsequence and the change amplitude of the corresponding cooling water flow data in the same period; removing the data of all cooling subsequences in the barrel melt temperature and injection melt temperature data of each cooling participating process, obtaining the injection control amplitude of each cooling participating process according to the change trend of the temperature difference between the remaining barrel melt temperature and injection melt temperature, and the number of cooling subsequences and the injection control sensitivity of each cooling participating process; setting the injection control amplitude of each non-cooling participating process to 0; clustering the initial nozzle temperature of the current injection process with the initial nozzle temperature of all injection processes, and optimizing the initial proportion coefficient of the current injection process according to the injection control amplitude of all injection processes in the cluster to which the initial nozzle temperature of the current injection process belongs.

2. The intelligent control method for injection molding of a plastic shell of a router as claimed in claim 1, wherein, The specific process of dividing all injection processes into cooling participating processes and non-cooling participating processes is: calculating the variance of the cooling water flow data of each injection process, and recording the injection process with a variance greater than or equal to a preset segmentation threshold as a cooling participating process; recording the injection process with a variance less than the preset segmentation threshold as a non-cooling participating process.

3. The intelligent control method for injection molding of a plastic shell of a router as claimed in claim 1, wherein, The specific process of extracting each cooling subsequence therefrom is: dividing the injection melt temperature data of each cooling participating process into multiple subsequences, and recording the subsequence with a fitting straight line slope less than a preset slope threshold as the cooling subsequence of each cooling participating process.

4. The intelligent control method for injection molding of a plastic shell of a router as claimed in claim 1, wherein, The injection control sensitivity of each cooling participating process is the product of the first ratio of all injection processes and the flow change factor corresponding to each cooling participating process; wherein, the process of obtaining the first ratio of all injection processes is: recording the injection process with a quality score greater than or equal to a preset score threshold as a good injection process; calculating the absolute difference c1 between the mean value of the initial nozzle temperature of all good injection processes and all non-good injection processes, and the absolute difference c2 between the mean value of the quality score of all good injection processes and all non-good injection processes, respectively; recording the ratio of the absolute difference c2 to the absolute difference c1 as the first ratio of all injection processes.

5. The intelligent control method for injection molding of a plastic shell of a router as claimed in claim 4, wherein, The process of obtaining the flow change factor of each cooling participating process is: obtaining each flow subsequence in the flow sequence corresponding to each cooling participating process in the same time period according to the time period corresponding to each cooling subsequence in each cooling participating process; sorting all cooling subsequences and all flow subsequences in each cooling participating process in chronological order; The ratio of the internal range of each flow sub-sequence in each cooling participation process to the internal range of the temperature drop sub-sequence with the same position sequence is calculated, and the average of all the ratios is recorded as the flow change factor of each cooling participation process.

6. The intelligent control method for injection molding of a plastic shell of a router as claimed in claim 1, wherein, The injection control range of each cooling participation process is positively correlated with the injection control sensitivity of each cooling participation process, the total number of all temperature drop sub-sequences, and the first slope.

7. The intelligent control method for injection molding of a plastic shell of a router as claimed in claim 6, wherein, The first slope of each cooling participation process is obtained by: recording the sequence of the melt temperature data in the barrel and the melt temperature data of each injection process in time sequence order as the barrel temperature sequence and the injection temperature sequence of each injection process; removing the elements belonging to the temperature drop sub-sequences in the injection temperature sequence of each cooling participation process, and recording the sequence of the remaining elements in time sequence order as the new injection temperature sequence of each cooling participation process; and removing the elements with the corresponding position sequence from the barrel temperature sequence according to the position sequence of the elements in the temperature drop sub-sequences of each cooling participation process, and recording the sequence of the remaining elements in the barrel temperature sequence in time sequence order as the new barrel temperature sequence of each cooling participation process.

8. The intelligent control method for injection molding of a plastic shell of a router as claimed in claim 1, wherein, The calculation formula for optimizing the initial proportional coefficient of the current injection process is: ; wherein, is the proportional coefficient of the current injection process; is the preset initial proportional coefficient of the current injection process; is the control factor of the current injection process; is a normalization function; is a preset weight coefficient.

9. The intelligent control method for injection molding of a plastic shell of a router as claimed in claim 8, wherein, The control factor of the current injection process is obtained by: obtaining the initial nozzle temperature w of the current injection process, and clustering the initial nozzle temperature w with the initial nozzle temperature of all injection processes; recording the average of the injection control range of all injection processes except the current injection process in the cluster of the initial nozzle temperature w of the current injection process as the control factor of the current injection process. 10.A system for intelligent control of injection molding of a plastic shell of a router, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein, The processor executes the computer program to realize the steps of the intelligent control method for the injection of the plastic shell of the router as claimed in any one of claims 1-9.

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