Intelligent control method for improving forming efficiency of injection mold

Abstract

The invention provides an intelligent control method for improving the forming efficiency of an injection mold, and relates to the technical field of injection molding control, and the intelligent control method comprises the following steps: performing mold flow analysis through initial experiment parameters, and configuring an initial experiment parameter range; the quality index factor parameters are executed in an experiment to obtain quality index experiment response, and an optimal experiment parameter combination is obtained; constructing a quality index prediction model; constructing an initial population to perform explosion search; acquiring a quality index production response according to the optimal process parameter combination, performing deviation analysis on the quality index production response and the quality index prediction response, and adjusting the optimal process parameter combination; and injection mold forming is controlled based on the actual process parameter combination. By means of the method and device, the technical problems that in the prior art, parameter setting depends on artificial experience, and interaction of multiple parameters is difficult to effectively optimize can be solved, the technical purposes of optimizing the injection molding process parameters and dynamically adjusting the parameters are achieved, and the technical effect of improving the production efficiency is achieved.

Description

Technical Field

[0001] The present application relates to the field of injection molding control technology, and in particular to an intelligent control method for improving the molding efficiency of an injection mold. Background Art

[0002] Injection mold molding technology is an indispensable and important process in modern industrial production and is widely used in the fields of automobiles, electronics, medical care and consumer goods.

[0003] At present, traditional technologies usually rely on manual experience to set process parameters, such as injection pressure, mold temperature, filling time, and holding time. This experience-driven approach is not only prone to inaccurate parameter settings, but also requires a lot of trial and error and adjustment processes, which prolongs the product development cycle and increases production costs. In addition, since manual settings make it difficult to optimize the interaction of multiple parameters at the same time, the final product quality is often unstable, and defects such as warping, shrinkage, and core shift may occur, resulting in a high defective rate.

[0004] In summary, the prior art has technical problems in that parameter setting relies on manual experience and it is difficult to effectively optimize the interaction of multiple parameters, resulting in a long process parameter adjustment process, high cost, and unstable product quality, which further affects production efficiency and final product qualification rate. Summary of the invention

[0005] The purpose of this application is to provide an intelligent control method for improving the efficiency of injection mold molding, so as to solve the technical problems in the prior art that the parameter setting relies on manual experience and it is difficult to effectively optimize the interaction of multiple parameters, resulting in a long process parameter adjustment process, high cost and unstable product quality, which further affects the production efficiency and the final product qualification rate.

[0006] In view of the above problems, the present application provides an intelligent control method for improving the efficiency of injection mold molding, including: performing mold flow analysis through initial experimental parameters, and configuring the initial experimental parameter range according to the mold flow analysis results; extracting quality indicator factor parameters according to the initial experimental parameter range, experimentally executing the quality indicator factor parameters to obtain quality indicator experimental responses, and comparing the quality indicator experimental responses to obtain the optimal experimental parameter combination; constructing a quality indicator prediction model according to the optimal experimental parameter combination and the quality indicator experimental responses; constructing an initial population for explosion search to obtain an optimal population, and the optimal population is an optimal process parameter combination; collecting quality indicator production responses according to the optimal process parameter combination, performing deviation analysis on the quality indicator production responses and the quality indicator prediction responses obtained by predicting the optimal process parameter combination based on the quality indicator prediction model, adjusting the optimal process parameter combination according to the deviation results to obtain an actual process parameter combination; and controlling injection mold molding based on the actual process parameter combination.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages: Perform mold flow analysis through initial experimental parameters, and configure the initial experimental parameter range according to the mold flow analysis results; extract quality indicator factor parameters according to the initial experimental parameter range, experimentally execute the quality indicator factor parameters to obtain quality indicator experimental responses, and compare the quality indicator experimental responses to obtain the optimal experimental parameter combination; construct a quality indicator prediction model according to the optimal experimental parameter combination and the quality indicator experimental responses; construct an initial population for explosion search to obtain the optimal population, which is the optimal process parameter combination; collect quality indicator production responses according to the optimal process parameter combination, perform deviation analysis on the quality indicator production responses and the quality indicator prediction responses obtained by predicting the optimal process parameter combination based on the quality indicator prediction model, adjust the optimal process parameter combination according to the deviation results, and obtain the actual process parameter combination; control injection mold molding based on the actual process parameter combination, that is, by achieving the technical goal of optimizing injection molding process parameters and dynamically adjusting them, the technical effects of improving production efficiency, reducing product defective rate and stabilizing product quality are achieved.

[0008] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented according to the contents of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically cited below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0010] Figure 1 A schematic flow chart of an intelligent control method for improving the molding efficiency of an injection mold in this application; Figure 2 A schematic diagram of a process for obtaining the optimal experimental parameter combination in an intelligent control method for improving injection mold molding efficiency in this application. DETAILED DESCRIPTION

[0011] This application provides an intelligent control method for improving the molding efficiency of injection molds, which solves the technical problem in the prior art that the process parameter adjustment process is time-consuming, costly, and the product quality is unstable because the parameter setting depends on manual experience and it is difficult to effectively optimize the interaction of multiple parameters, which further affects the production efficiency and the final product qualification rate. The technical goal of optimizing the injection molding process parameters and dynamically adjusting them is achieved, and the technical effect of improving production efficiency, reducing the defective product rate, and stabilizing product quality is achieved.

[0012] Below, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application. It should also be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, rather than all of them.

[0013] For example, please refer to the attached Figure 1 , the present application provides an intelligent control method for improving the molding efficiency of injection molds, which specifically includes the following steps: Step 1: Perform mold flow analysis using initial experimental parameters and configure the initial experimental parameter range based on the mold flow analysis results.

[0014] Specifically, the preliminarily set injection molding process parameters are input into the CAE (computer-aided engineering) simulation tool for analysis, simulating factors such as flow, cooling, and pressure distribution during the process. The mold flow analysis results provide detailed data on the performance of the mold under the initial experimental parameters, such as temperature, pressure distribution, filling time, etc., which can help understand the defects or problems that may occur in the mold, such as bubbles, warping, or core offset. The initial experimental parameters can be adjusted according to the analysis results. For example, if the analysis results show that the temperature of a certain part is too high or the filling is uneven, the mold temperature or injection pressure may need to be adjusted. Finally, through the feedback of the mold flow analysis results, a reasonable initial experimental parameter range is configured to ensure that the actual production within the parameter range can minimize defects and improve production stability.

[0015] Step 2: extracting quality indicator factor parameters according to the initial experimental parameter range, experimentally executing the quality indicator factor parameters to obtain quality indicator experimental responses, and comparing the quality indicator experimental responses to obtain the optimal experimental parameter combination.

[0016] Specifically, according to the initial experimental parameter range, select the key factors that affect product quality, such as injection pressure, holding time, mold temperature, etc. These factors are the key parameters that need to be controlled and optimized during the experiment. Then, in actual production, configure the quality indicator factor parameters according to the extracted factors to perform the corresponding experiments, and test the influence of different factor parameter level combinations on product quality through actual injection molding operations to obtain the quality indicator experimental response, that is, the results obtained through experiments, such as warpage and wall thickness uniformity error. Finally, by comparing all experimental results, compare the quality indicator experimental responses, and find the best experimental parameter combination. The comparison process may be to compare the quality data such as warpage and dimensional accuracy of different experimental groups, and finally determine the optimal parameter combination to ensure the most ideal product quality.

[0017] Step three: construct a quality indicator prediction model based on the optimal experimental parameter combination and the quality indicator experimental response.

[0018] Specifically, the optimal experimental parameter combination screened out is used as the input variable, and the quality indicator experimental response corresponding to the optimal experimental parameter combination is used as the output variable, such as warpage and wall thickness error. The quality indicator experimental response is the quality result measured in the actual experiment, reflecting the specific impact of the parameter combination on product performance. Then, using the input parameter combination and the output experimental response, a quality indicator prediction model is established using data modeling methods such as regression analysis or neural networks. The quality indicator prediction model can quickly predict the impact of new parameter combinations on quality indicators in the future. Through the prediction capability, process parameters can be quickly adjusted during the production process to improve efficiency and product quality.

[0019] Step 4: construct an initial population for explosive search to obtain the optimal population, which is the optimal process parameter combination.

[0020] Specifically, constructing an initial population for explosive search refers to generating a set of initial solutions in the set parameter space as the initial population, and each solution corresponds to a possible combination of process parameters. The initial solutions are uniformly or randomly distributed within the parameter range. Explosive search refers to expanding each initial solution based on certain rules, that is, generating multiple new solutions (sparks), which are distributed near the original solution, while considering the balance of local and global searches, so as to explore better solutions and obtain the optimal population, which means that the optimal solution is selected from all sparks through multiple rounds of iterative explosive search and fitness evaluation. The optimal solution has a higher fitness value, indicating that it performs better in the process optimization goal. The screening process usually retains a fixed number of solutions to maintain the population size. The optimal population is the optimal process parameter combination, indicating that the selected optimal solution set can be directly used for production or further analysis.

[0021] Step 5: Collect quality indicator production responses according to the optimal process parameter combination, perform deviation analysis on the quality indicator production responses and the quality indicator prediction responses obtained by predicting the optimal process parameter combination based on the quality indicator prediction model, adjust the optimal process parameter combination according to the deviation results, and obtain the actual process parameter combination.

[0022] Specifically, in actual production, trial production is carried out according to the optimal process parameter combination that has been screened out, and product quality indicators such as warpage, dimensional deviation or mechanical strength are collected through sensors or testing equipment. The production response is a collection of actual production data that reflects the actual quality performance of the product under the current parameter conditions. The quality indicators collected in actual production are compared with the corresponding indicators given by the prediction model, and the difference between the two is calculated to evaluate the accuracy of the prediction model and whether the performance of the optimal process parameters in actual production meets the requirements. When the deviation exceeds the preset threshold, the current process parameters need to be adjusted to reduce the deviation and improve product quality to further optimize the production effect. A set of improved parameter combinations is determined through adjustment, which will be applied in actual production to ensure that the quality indicators of the product meet the requirements and the deviation is controlled within the preset range.

[0023] Step six: Control the injection mold molding based on the actual process parameter combination.

[0024] Specifically, the actual process parameters adjusted and optimized are applied to the injection molding machine to guide the molding process of the mold, including injection pressure, mold temperature, filling time, holding time, etc. The control process requires the parameters to be input into the operating system of the injection molding machine to ensure that the equipment executes according to the set values. During the production process, the equipment monitors the molding status in real time through sensors to maintain a stable process environment. Injection mold molding refers to injecting heated molten plastic material into the mold cavity in the injection molding machine, and forming the desired product through cooling and solidification. The application of the actual process parameter combination directly affects the quality of the final product. For example, too low injection pressure may lead to insufficient filling, while too high mold temperature may cause warping defects, so strict control is required.

[0025] The intelligent control method for improving the molding efficiency of injection molds can achieve the technical goal of optimizing injection molding process parameters and dynamically adjusting them, thereby achieving the technical effects of improving production efficiency, reducing product defective rate and stabilizing product quality.

[0026] Further, if Figure 2 As shown, this application also includes: Acquire quality indicator factors, and extract quality indicator factor parameters based on factor levels from the initial experimental parameter range, wherein the quality indicator factor parameters correspond to the quality indicator factors; enumerate and combine the quality indicator factor parameters to obtain a quality indicator experimental combination; experimentally execute the quality indicator experimental combination to obtain the quality indicator experimental response; compare the quality indicator experimental responses to obtain the optimal experimental parameter combination.

[0027] Specifically, obtaining quality indicator factors means determining the factors that affect product quality by analyzing key quality indicators in the process (such as warpage, wall thickness uniformity, dimensional accuracy, etc.). Quality indicator factors can be injection pressure, mold temperature, injection speed, etc. The impact of different values ​​of quality indicator factors on product quality is evaluated, and the corresponding factor levels are extracted from the initial experimental parameter range according to the impact of the quality indicator factors to form a set of quality indicator factor parameters. The quality indicator factor parameters correspond to different process conditions that may occur in actual production, and can provide a data basis for subsequent optimization; Next, the quality indicator factor parameters are enumerated and combined, that is, all possible factor levels are combined together to form multiple groups of experimental parameter combinations. Each group of experimental parameter combinations corresponds to a different set of process parameters, which are used to test and analyze their impact on product quality. By enumerating, all possible parameter configurations can be covered, so as to obtain comprehensive data under different process conditions. For example, the experimental parameter combinations are shown in Table 1 below: ; ; Then, the quality index experimental combination is executed, that is, the actual production test is carried out according to the experimental combination in the previous step. The test will be based on different parameter combinations, actual injection molding production, and measure the impact of each parameter combination on the quality index, for example, measuring the warpage and dimensional accuracy of the product after injection molding. Each set of experimental combinations will generate a set of experimental response data, which can reflect the actual performance under the conditions of the combination.

[0028] Finally, by comparing the experimental responses of the quality indicators, we can find the parameter combination that performs best in all experiments, that is, the optimal experimental parameter combination. The optimal experimental parameter combination can maximize the quality of the product, reduce defects, and improve production efficiency. The comparison process determines the process parameters that best meet the product quality requirements by analyzing the advantages and disadvantages of each group of experimental response data.

[0029] By obtaining quality index factors and extracting relevant parameters from the initial experimental parameter range, enumeration combinations and actual experiments are performed to finally obtain the optimal process parameter combination. By continuously comparing experimental responses, the optimal parameter configuration is selected to optimize the production process and improve product quality.

[0030] Furthermore, the present application also includes: Initialize the parameter space, generate the initial population based on the parameter space, the initial population includes multiple initial solutions; extract the first initial solution according to the multiple initial solutions; perform an explosion search on the first initial solution to generate a first spark; evaluate the first spark through the quality indicator prediction model to generate a first fitness; based on the first fitness, traverse the multiple initial solutions to obtain a preset number of optimal solutions, and integrate to obtain the optimal population.

[0031] Specifically, initializing the parameter space refers to setting the range of all possible parameters, such as injection pressure range and mold temperature range. The parameter space defines all possible search areas and provides a basis for subsequent optimization. Multiple initial solutions are randomly generated in the parameter space until a population containing multiple initial solutions is formed.

[0032] The first initial solution is randomly extracted based on multiple initial solutions. Explosion search of the first initial solution refers to local perturbation near the first initial solution to generate multiple new solutions, i.e., sparks. For example, with the first initial solution as the center, the injection pressure is increased or decreased by 0.5 MPa, and the mold temperature is increased or decreased by 1 degree Celsius, thereby generating multiple new spark solutions.

[0033] Evaluating the first spark through the quality index prediction model means inputting each spark solution into the quality index prediction model, calculating the response data of the corresponding quality index, such as warpage or dimensional error, and performing fitness evaluation on the response data to generate a fitness value, i.e., the first fitness. According to the method for generating the first fitness, multiple initial solutions are accessed in turn for optimization calculation, and the fitness is evaluated. A preset number of solutions are selected based on multiple fitnesses to obtain a preset number of optimal solutions, i.e., a global comparison is performed on all initial solutions and the spark solutions generated by them, and several solutions with higher fitness are selected. Finally, these optimal solutions are integrated into a new optimal population for the next iteration.

[0034] It starts by initializing the parameter space to generate the initial population, gradually extracting the initial solution, performing explosion search, and using the quality indicator prediction model to evaluate the spark fitness. Finally, the optimal solution is selected to build a new population. Through continuous iteration and updating of the population, the global optimal solution is gradually approached, thereby finding the optimal process parameter combination.

[0035] Furthermore, the present application also includes: An explosion is performed according to the first initial mass of the first initial solution to generate the first spark, wherein the first spark has a first spark amount, a first spark density and a first spark range, wherein a correlation coefficient between the first spark amount and the first initial mass is a positive value, a correlation coefficient between the first spark density and the first initial mass is a positive value, and a correlation coefficient between the first spark range and the first initial mass is a negative value.

[0036] Specifically, exploding according to the first initial quality of the first initial solution means triggering an explosion operation based on the first initial solution as the center and its quality (such as fitness value) to generate a new solution. The first initial quality reflects the quality of the solution. For example, the higher the quality, the better the solution performs in the optimization objective and is suitable as the basis for explosion. Generating the first spark means generating a set of new solutions (i.e., sparks) by applying a certain disturbance to the first initial solution. The distribution and number of sparks are controlled by the explosion parameters and are used to explore the surrounding area of ​​the solution to find a possible better solution.

[0037] The first spark has a first spark amount, a first spark density, and a first spark range, which indicate the number of sparks generated per explosion, the distribution of sparks in space, and the search area range for spark generation. For example, the first spark amount determines how many new solutions are generated by the explosion, the first spark density determines the concentration of these new solutions in a local area, and the first spark range indicates the maximum search radius of the new solution from the initial solution. The correlation coefficient between the first spark amount and the first initial quality is positive, which means that the higher the quality of the initial solution, the more sparks are generated, thereby more fully searching the surrounding area of ​​the high-quality solution.

[0038] The correlation coefficient between the first spark density and the first initial mass is positive, indicating that the higher the initial solution mass, the denser the sparks are distributed in its vicinity, reflecting a refined search for high-quality solutions, while at lower masses, the spark distribution is more dispersed, allowing for a more extensive exploration.

[0039] The correlation coefficient between the first spark range and the first initial quality is a negative value, which means that the higher the initial solution quality, the smaller the spark search range, and vice versa, which is used to increase the probability of finding a better solution in the low-quality solution area.

[0040] Sparks are generated by the quality of the initial solution, and combined with the control mechanism of the spark quantity, density and range, search resources are reasonably allocated. High-quality solutions obtain more sparks and are searched intensively, while low-quality solutions conduct more extensive exploration, thereby gradually improving the overall optimization effect.

[0041] Furthermore, the present application also includes: Based on the first fitness, the first spark is iteratively exploded according to the first spark range until the explosion spark converges to generate a first explosion spark; according to the first explosion spark, the multiple initial solutions are traversed to obtain multiple explosion sparks; the fitness of the multiple explosion sparks is evaluated, and the preset number of explosion sparks is extracted based on the multiple explosion fitnesses to obtain the optimal solution.

[0042] Specifically, based on the first fitness, the first spark is iteratively exploded according to the first spark range, which means that after the first explosion, the fitness of each spark is used to determine whether the search area needs to be further optimized. If the sparks with higher fitness still have potential for improvement, these sparks are exploded again to generate new solutions. The iterative explosion gradually narrows the search area according to the first spark range, and eventually converges, indicating that the change in the solution is no longer significant, and the explosion operation is stopped to generate the first explosion spark.

[0043] According to the first explosion spark, multiple initial solutions are traversed to obtain multiple explosion sparks, which means that not only the current explosion result is retained, but also the search range is expanded in combination with other solutions in the initial population to screen out possible high-quality sparks, ensuring that the optimization process is not limited to a single solution, but makes full use of the diversity of the initial population to avoid falling into the local optimum.

[0044] Evaluating the fitness of multiple explosion sparks means predicting and evaluating the quality indicators of all generated sparks one by one, such as calculating the warpage, wall thickness uniformity and other indicators corresponding to each spark solution, and generating a fitness value. Extracting a preset number of explosion sparks based on multiple explosion fitness means selecting several solutions with the highest fitness from all sparks as the optimal solution for the next round of iteration or as the final optimization result.

[0045] By iteratively exploding the sparks to gradually refine the search, combining traversing the initial solution to expand the search range, and then screening the optimal solution through fitness evaluation, we achieve a gradual approximation from the initial population to the final optimization result, which not only ensures a detailed search of the solution, but also takes into account the global exploration, increasing the possibility of finding the global optimal solution.

[0046] Furthermore, the present application also includes: The first spark is input into the quality indicator prediction model for response analysis, and a first quality indicator optimization response is output; and fitness calculation is performed on the first quality indicator optimization response to generate the first fitness.

[0047] Specifically, the spark solution generated by the first explosion is brought into the quality index prediction model. The quality index prediction model quickly estimates the possible quality index results according to the parameter combination of the spark, such as the warpage, wall thickness uniformity or dimensional deviation of the product, and then outputs the first quality index optimization response, which represents the prediction result of the output model and is used to measure the performance of each spark in the optimization target. The optimization response can be a single indicator or a comprehensive performance value of multiple indicators, such as the smaller the warpage, the better the quality, or a weighted combination of multiple indicators.

[0048] Based on the first quality index, the optimal response is converted into a fitness value, which is used to compare the quality of different sparks. For example, the warpage and dimensional deviation are normalized separately, and then weighted summed according to the preset weights to generate a fitness value. The higher the fitness value, the better the overall performance of the spark solution, and the more suitable it is for optimization.

[0049] The first fitness is generated through fitness calculation, which means that the fitness is used as the final score of each spark solution to evaluate the quality of the solution and screen the optimal solution.

[0050] By inputting the spark solution into the prediction model for response analysis, the quality index response can be quickly obtained, and then the fitness value is calculated based on these responses to evaluate the quality of the spark solution, thereby providing data support for the next step of screening the optimal solution.

[0051] Furthermore, the present application also includes: The initial experimental parameters are set; a mold flow analysis is performed on the initial experimental parameters by a CAE simulation tool to obtain the mold flow analysis results; and the initial experimental parameters are adjusted based on the mold flow analysis results to determine the range of the initial experimental parameters.

[0052] Specifically, set the initial experimental parameters, including injection pressure, holding time, mold temperature, etc. The initial settings are determined based on previous experience or engineering design requirements and are used to initially start the mold manufacturing process.

[0053] Next, the initial experimental parameters are analyzed by using CAE simulation tools to simulate the flow, cooling and molding behaviors during the injection molding process. Mold flow analysis can help understand the performance of the mold under different parameters, such as the filling speed of the plastic melt, temperature distribution, and possible defects such as bubbles and warpage. The mold flow analysis results provide data support and theoretical basis for subsequent parameter adjustments.

[0054] After obtaining the mold flow analysis results, adjust the initial experimental parameters according to the mold flow analysis results to optimize various indicators in the injection molding process. For example, if the temperature of some areas in the mold flow analysis results is too high or the filling time is too long, adjust the mold temperature or injection speed. Adjust through actual performance to find the best process parameters.

[0055] Finally, based on the adjusted data, a preliminary parameter range was determined, which provided a reasonable process parameter interval for subsequent actual production. Further testing and optimization can be carried out within this range to ultimately ensure product quality and production efficiency.

[0056] By setting the initial experimental parameters and using CAE simulation tools to perform mold flow analysis, relevant data can be obtained and parameters can be adjusted to ultimately determine the effective parameter range, which can then be used to optimize the injection molding process and improve product quality and production efficiency.

[0057] Furthermore, the present application also includes: Preset a deviation threshold; calculate the deviation between the quality indicator production response and the quality indicator prediction response to obtain the deviation result; if the deviation result does not meet the deviation threshold, adjust the optimal process parameter combination to obtain the actual process parameter combination.

[0058] Specifically, the preset deviation threshold refers to setting an allowable error range in actual production to measure whether the deviation between the actual result of the quality indicator and the predicted result is within an acceptable range. For example, for the dimensional deviation of the product, the deviation threshold may be set to within 5%, that is, the error of the actual size relative to the target size shall not exceed 5%. The preset deviation threshold is the judgment standard for subsequent adjustments, and its value is directly related to the production accuracy and adjustment difficulty.

[0059] Deviation calculation is performed on the production response of quality indicators and the predicted response of quality indicators, which means comparing the quality indicators (such as warpage, dimensional deviation, etc.) obtained in actual production with the quality indicators given by the prediction model and calculating the difference between the two. The purpose of deviation calculation is to quantify the degree of agreement between the production results and the prediction model, which is used to determine whether further optimization of process parameters is needed.

[0060] Obtaining a deviation result means obtaining a numerical value for evaluation after calculation. The numerical value of the deviation result indicates the deviation of the current production status. For example, if the deviation result is 6% and the deviation threshold is 5%, it means that the current production status does not meet the preset quality requirements and needs to be adjusted. If the deviation result is less than or equal to the threshold, it means that the current process parameters can meet the production requirements; If the deviation result does not meet the deviation threshold, the optimal process parameter combination is adjusted, which means that when the deviation exceeds the threshold, the previously determined optimal process parameters need to be readjusted to further optimize the production effect. For example, the adjustment process may be shown in Table 2 below to ensure the efficiency and effect of the adjustment; ; Obtaining the actual process parameter combination means that after adjustment, a set of improved parameter combinations are determined, and the actual process parameter combination is used in actual production to further reduce deviations and improve product quality.

[0061] By presetting deviation thresholds, calculating actual and predicted deviation results, and adjusting parameters when the deviation exceeds the standard, the process combination is gradually optimized to ensure production quality, thus achieving a high degree of consistency between actual production and the predicted model.

[0062] In summary, the intelligent control method for improving the molding efficiency of injection molds provided by the present application has the following technical effects: Perform mold flow analysis through initial experimental parameters, and configure the initial experimental parameter range according to the mold flow analysis results; extract quality indicator factor parameters according to the initial experimental parameter range, experimentally execute the quality indicator factor parameters to obtain quality indicator experimental responses, and compare the quality indicator experimental responses to obtain the optimal experimental parameter combination; construct a quality indicator prediction model according to the optimal experimental parameter combination and the quality indicator experimental responses; construct an initial population for explosion search to obtain the optimal population, which is the optimal process parameter combination; collect quality indicator production responses according to the optimal process parameter combination, perform deviation analysis on the quality indicator production responses and the quality indicator prediction responses obtained by predicting the optimal process parameter combination based on the quality indicator prediction model, adjust the optimal process parameter combination according to the deviation results, and obtain the actual process parameter combination; control injection mold molding based on the actual process parameter combination, that is, by achieving the technical goal of optimizing injection molding process parameters and dynamically adjusting them, the technical effects of improving production efficiency, reducing product defective rate and stabilizing product quality are achieved.

[0063] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0064] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and 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 also intended to include these modifications and variations.

Claims

1. An intelligent control method for improving the molding efficiency of injection molds, characterized in that: include: Perform mold flow analysis using initial experimental parameters and configure the initial experimental parameter range based on the mold flow analysis results; Extracting quality indicator factor parameters according to the initial experimental parameter range, experimentally executing the quality indicator factor parameters to obtain quality indicator experimental responses, and comparing the quality indicator experimental responses to obtain an optimal experimental parameter combination; Constructing a quality indicator prediction model according to the optimal experimental parameter combination and the quality indicator experimental response; Constructing an initial population for explosive search to obtain an optimal population, wherein the optimal population is an optimal process parameter combination; Collecting a quality indicator production response according to the optimal process parameter combination, performing a deviation analysis between the quality indicator production response and a quality indicator prediction response obtained by predicting the optimal process parameter combination based on the quality indicator prediction model, and adjusting the optimal process parameter combination according to the deviation result to obtain an actual process parameter combination; The injection mold molding is controlled based on the actual process parameter combination.

2. An intelligent control method for improving injection mold molding efficiency as claimed in claim 1, characterized in that: The step of extracting quality indicator factor parameters according to the initial experimental parameter range, experimentally executing the quality indicator factor parameters to obtain quality indicator experimental responses, and comparing the quality indicator experimental responses to obtain an optimal experimental parameter combination includes: Acquire a quality indicator factor, and extract a quality indicator factor parameter based on a factor level from the initial experimental parameter range, the quality indicator factor parameter corresponding to the quality indicator factor; Enumerating and combining the quality indicator factor parameters to obtain a quality indicator experimental combination; The quality indicator experimental combination is experimentally executed to obtain the quality indicator experimental response; The quality indicator experimental responses are compared to obtain the optimal experimental parameter combination.

3. The intelligent control method for improving the molding efficiency of injection molds according to claim 1, characterized in that: The step of constructing an initial population and performing explosive search to obtain an optimal population includes: Initializing a parameter space, generating the initial population based on the parameter space, wherein the initial population includes a plurality of initial solutions; extracting a first initial solution according to the plurality of initial solutions; Performing an explosion search on the first initial solution to generate a first spark; Evaluate the first spark by using the quality indicator prediction model to generate a first fitness; Based on the first fitness, the multiple initial solutions are traversed to obtain a preset number of optimal solutions, and the optimal population is obtained by integration.

4. An intelligent control method for improving injection mold molding efficiency as claimed in claim 3, characterized in that: An explosion is performed according to the first initial mass of the first initial solution to generate the first spark, wherein the first spark has a first spark amount, a first spark density and a first spark range, wherein a correlation coefficient between the first spark amount and the first initial mass is a positive value, a correlation coefficient between the first spark density and the first initial mass is a positive value, and a correlation coefficient between the first spark range and the first initial mass is a negative value.

5. An intelligent control method for improving injection mold molding efficiency as claimed in claim 4, characterized in that: The step of traversing the plurality of initial solutions based on the first fitness to obtain a preset number of optimal solutions includes: Based on the first fitness, iteratively explode the first spark according to the first spark range until the explosion sparks tend to converge, thereby generating a first explosion spark; According to the first explosion spark, traverse the multiple initial solutions to obtain multiple explosion sparks; The fitness of the plurality of explosion sparks is evaluated, and the preset number of explosion sparks is extracted based on the plurality of explosion fitnesses to obtain the optimal solution.

6. The intelligent control method for improving the molding efficiency of injection molds according to claim 3, characterized in that: The step of evaluating the first spark by using the quality indicator prediction model to generate a first fitness includes: Inputting the first spark into the quality index prediction model for response analysis, and outputting a first quality index optimization response; Perform fitness calculation on the first quality indicator optimization response to generate the first fitness.

7. The intelligent control method for improving the molding efficiency of injection molds according to claim 1, characterized in that: The mold flow analysis is performed using the initial experimental parameters, and the initial experimental parameter range is configured according to the mold flow analysis results, including: Setting the initial experimental parameters; Performing mold flow analysis on the initial experimental parameters by using a CAE simulation tool to obtain the mold flow analysis results; The initial experimental parameters are adjusted based on the mold flow analysis results to determine the initial experimental parameter range.

8. The intelligent control method for improving the molding efficiency of injection molds according to claim 1, characterized in that: The obtaining of the actual process parameter combination comprises: Preset deviation thresholds; Calculating the deviation between the quality indicator production response and the quality indicator prediction response to obtain the deviation result; If the deviation result does not meet the deviation threshold, the optimal process parameter combination is adjusted to obtain the actual process parameter combination.

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