A mold injection control method and device for electric toothbrush plastic molding

Abstract

The present invention provides a mold injection molding control method and device for electric toothbrush plastic molding, which relates to the field of plastic injection molding technology, including: obtaining electric toothbrush geometric data and plastic component physical performance data, initializing the electric toothbrush mold model; initializing particle swarm parameters, optimizing material flow path, iteratively obtaining optimized mold model; performing injection molding simulation, obtaining mold injection molding performance influencing parameters; constructing an injection molding control strategy library, performing matching retrieval, and obtaining matching injection molding control strategies; performing selection interval division, obtaining mold injection molding control strategy parameter intervals; constructing a fitness function; performing global optimization, determining target mold injection molding control strategy parameters, and performing mold injection molding control for electric toothbrush plastic molding. The present invention solves the technical problem that the injection molding parameter optimization of the prior art often starts after the mold manufacturing is completed, and the control parameters are adjusted by trial and error, resulting in high cost and low efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of plastic injection molding, and in particular to a mold injection molding control method and device for electric toothbrush plastic molding. Background Art

[0002] As a consumer electronic product, electric toothbrushes usually have the following characteristics: plastic parts need to be precisely matched and have high surface quality requirements; the material is required to have good mechanical properties and durability after molding, while remaining lightweight; during large-scale production, it is necessary to improve production efficiency and reduce manufacturing costs while ensuring quality.

[0003] However, the existing mold design and injection molding process still have some problems when facing complex structures, high quality requirements and efficient production needs. On the one hand, the mold design of the existing technology usually relies on the experience of design engineers. The rationality of key links such as mold cooling system and runner design often requires multiple tests and adjustments to achieve the best effect. Each mold modification and test consumes a lot of time, materials and resources, increasing production costs and time cycles; on the other hand, the setting of injection molding process parameters is usually carried out by operators based on experience, lacking data support, and it is difficult to achieve the best parameter configuration. Due to the lack of precise process control and real-time adjustment, product quality often fluctuates, such as dimensional deviation, surface defects and other problems. Summary of the invention

[0004] The present application provides a mold injection control method and device for electric toothbrush plastic molding, aiming to solve the technical problems that traditional mold design relies too much on experience, lacks in-depth analysis of the plastic flow path, and injection molding parameter optimization often begins after the mold is manufactured. The control parameters are adjusted through trial and error, resulting in high cost and low efficiency.

[0005] The first aspect disclosed in the present application provides a mold injection control method for electric toothbrush plastic molding, the method comprising: obtaining electric toothbrush geometric data and plastic component physical performance data used for electric toothbrush plastic molding, performing mold analysis, and initializing an electric toothbrush mold model; initializing particle swarm parameters based on the electric toothbrush mold model, optimizing the material flow path of the particle swarm parameters according to a predetermined optimization target, and iteratively obtaining an electric toothbrush optimized mold model, wherein the predetermined optimization target includes optimizing cooling efficiency and optimizing internal stress; performing mold injection simulation of electric toothbrush plastic molding based on the electric toothbrush optimized mold model, and obtaining mold injection performance influencing parameters, wherein the mold injection performance influencing parameters include temperature distribution influencing parameters, pressure distribution influencing parameters, and cooling rate influencing parameters; constructing an injection control A control strategy library is provided, based on the electric toothbrush geometric data and the physical performance data of the plastic parts, combined with the mold injection molding performance influencing parameters, a matching search is performed in the injection molding control strategy library to obtain a matching injection molding control strategy; based on the matching injection molding control strategy, the mold injection molding performance influencing parameters are selected and divided into intervals to obtain a mold injection molding control strategy parameter interval; mold injection molding evaluation indexes are extracted for the predetermined optimization target to obtain a mold injection molding evaluation index set, and a mold injection molding optimization effect fitness function is constructed based on the mold injection molding evaluation index set; based on the mold injection molding optimization effect fitness function, a global optimization is performed within the mold injection molding control strategy parameter interval to determine the target mold injection molding control strategy parameters, and the mold injection molding control and management of the electric toothbrush plastic molding is performed through the target mold injection molding control strategy parameters.

[0006] The second aspect disclosed in the present application provides a mold injection control device for electric toothbrush plastic molding, the device is used for the above-mentioned mold injection control method for electric toothbrush plastic molding, the device includes: a mold model construction module, the mold model construction module is used to obtain electric toothbrush geometric data and plastic component physical performance data used for electric toothbrush plastic molding, perform mold analysis, and initialize the electric toothbrush mold model; a mold model optimization module, the mold model optimization module is used to initialize particle group parameters based on the electric toothbrush mold model, optimize the material flow path of the particle group parameters according to a predetermined optimization target, and iteratively obtain the electric toothbrush optimized mold model, wherein the predetermined optimization target includes optimizing cooling efficiency and optimizing internal stress; a mold injection simulation module, the mold injection simulation module is used to perform mold injection simulation of electric toothbrush plastic molding based on the electric toothbrush optimized mold model, and obtain mold injection performance influencing parameters, wherein the mold injection performance influencing parameters include temperature distribution influencing parameters, pressure distribution influencing parameters, and cooling rate influencing parameters; strategy A matching retrieval module, wherein the strategy matching retrieval module is used to construct an injection molding control strategy library. Based on the electric toothbrush geometric data and the physical performance data of the plastic component, combined with the mold injection molding performance influencing parameters, a matching retrieval is performed in the injection molding control strategy library to obtain a matching injection molding control strategy; a selection interval division module, wherein the selection interval division module is used to divide the mold injection molding performance influencing parameters into selection intervals based on the matching injection molding control strategy, and obtain a mold injection molding control strategy parameter interval; a fitness function construction module, wherein the fitness function construction module is used to extract mold injection molding evaluation indicators for the predetermined optimization target, obtain a mold injection molding evaluation indicator set, and construct a mold injection molding optimization effect fitness function based on the mold injection molding evaluation indicator set; a mold injection molding control module, wherein the mold injection molding control module is used to perform global optimization within the mold injection molding control strategy parameter interval based on the mold injection molding optimization effect fitness function, determine the target mold injection molding control strategy parameters, and perform mold injection molding control of electric toothbrush plastic molding through the target mold injection molding control strategy parameters.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0008] By obtaining the geometric data of the electric toothbrush and the physical performance data of the plastic parts, accurate mold analysis and initialization can be performed, and the basic mold model can be quickly constructed to ensure the accuracy of the mold design and avoid repeated modifications and experiments caused by insufficient data or design errors; the material flow path is optimized based on the particle swarm optimization algorithm, which significantly improves the intelligence level of mold design. The optimized mold model can better adapt to the material characteristics and geometric requirements, and ensure the cooling efficiency and reasonable distribution of internal stress; through the precise simulation of key influencing parameters such as temperature, pressure, cooling rate, etc., potential problems can be discovered in advance to avoid quality or efficiency problems in actual production. By matching the injection molding control strategy, standardized management of complex production parameters can be achieved. This method can not only quickly respond to different design requirements, but also improve the production process. stability; based on the matching injection molding control strategy, the parameters affecting the injection molding performance of the mold are divided into selected intervals, and the mold injection molding control strategy parameter interval is obtained to ensure the precise control of parameters in the production process, which helps to reduce the defect rate of the product. By extracting the injection molding evaluation indicators and constructing the optimization fitness function, the quantitative evaluation method is directly linked to the optimization goal, which improves the scientificity and reliability of the molding quality; global optimization is performed within the parameter interval, and the algorithm automatically searches for the optimal injection molding control strategy parameters, which reduces the test and adjustment time. This global optimization not only reduces the trial and error cost of mold design and production, but also improves the utilization efficiency of resources. The optimized mold and injection molding control strategy can reduce the molding cycle, reduce energy consumption, and reduce the defective rate, thereby reducing the overall cost and improving production quality in the long run.

[0009] 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 in accordance with the contents of the specification. 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 listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 A schematic flow chart of a mold injection molding control method for plastic molding of an electric toothbrush provided in an embodiment of the present application.

[0011] Figure 2 A schematic diagram of the structure of a mold injection control device for plastic molding of an electric toothbrush provided in an embodiment of the present application.

[0012] Explanation of the accompanying drawings: mold model construction module 10, mold model optimization module 20, mold injection simulation module 30, strategy matching retrieval module 40, selection interval division module 50, fitness function construction module 60, mold injection control module 70. DETAILED DESCRIPTION

[0013] The embodiments of the present application provide a method and device for controlling the injection molding of an electric toothbrush plastic molding mold, thereby solving the technical problems that traditional mold design relies heavily on experience, lacks in-depth analysis of the plastic flow path, and injection molding parameter optimization often begins only after mold manufacturing is completed, and control parameters are adjusted through trial and error, resulting in high cost and low efficiency.

[0014] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically introduced below in conjunction with the accompanying drawings of the specification. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0015] Embodiment 1, as Figure 1 As shown, the embodiment of the present application provides a mold injection molding control method for electric toothbrush plastic molding, the method comprising:

[0016] Obtain the electric toothbrush geometry data and plastic component physical performance data used for electric toothbrush plastic molding, perform mold analysis, and initialize the electric toothbrush mold model.

[0017] Obtain the geometric data of the electric toothbrush, including the size, shape and structural layout of each component of the electric toothbrush. These data can be obtained through the model in the CAD software, or generated by using 3D scanning of existing toothbrush entities. Obtain the physical performance data of electric plastic components, including the mechanical properties, thermal properties, rheological properties, etc. of the material. These data can be obtained from the material supplier or through physical experiments and material database queries.

[0018] Using the geometric data and physical property data obtained above, the mold design is analyzed through mold design software to determine the various partitions of the mold, the gate system (such as gate position and size), the selection of parting surfaces, etc., and a preliminary electric toothbrush mold model is constructed in the software based on the analysis results. This model should include all mold components, such as cavity, core, slider, lifter, etc.

[0019] Initialize the particle swarm parameters based on the electric toothbrush mold model, optimize the material flow path of the particle swarm parameters according to the predetermined optimization target, and iteratively obtain the electric toothbrush optimized mold model, wherein the predetermined optimization target includes optimizing cooling efficiency and optimizing internal stress.

[0020] The particle swarm optimization algorithm is used to achieve optimization by simulating the process of a group of particles searching for the optimal solution in the solution space. Each particle represents a mold design scheme, including the material flow path. Based on the electric toothbrush mold model, the position and speed of each particle are set according to the specific needs of the mold. The position includes the material flow path configuration, and the speed determines the rate of change of these parameters during the search process.

[0021] The optimization goals include improving cooling efficiency and reducing internal stress. These goals are to ensure efficiency and product quality during the plastic molding process. The material flow path has a direct impact on the filling pattern, cooling uniformity, and the final internal stress distribution of the product. Optimizing these paths can reduce defects in the molding process, such as warping and stress concentration. Based on the fitness evaluation, the position and speed of the particles are updated. The particles will move to a better solution based on their own experience and the group's best solution. The particles are repeatedly updated until the termination conditions are met, such as reaching the maximum number of iterations. After the iteration ends, the mold design represented by the particle with the highest fitness is selected as the final electric toothbrush optimized mold model.

[0022] Based on the electric toothbrush optimized mold model, mold injection simulation of electric toothbrush plastic molding is performed to obtain mold injection performance influencing parameters, wherein the mold injection performance influencing parameters include temperature distribution influencing parameters, pressure distribution influencing parameters, and cooling rate influencing parameters.

[0023] The injection molding simulation is performed using an electric toothbrush to optimize the mold model. Specifically, the initial conditions of the simulation are set, including the type of plastic, parameters of the injection molding machine (such as injection pressure, injection speed, holding time, etc.), mold temperature, etc. The flow, cooling and solidification process of the plastic in the mold is simulated according to the set conditions, and the key data in the process is captured to obtain the parameters affecting the mold injection molding performance.

[0024] Among them, the temperature distribution of plastics and molds in the simulation is analyzed, including the location of the highest and lowest temperature points, temperature gradient, etc., to obtain the temperature distribution influencing parameters, which have a direct impact on the molding quality and cooling efficiency of the product; the pressure distribution during the plastic flow process is monitored, paying attention to the pressure peak, pressure fluctuation and pressure uniformity, to obtain the pressure distribution influencing parameters, and uneven pressure distribution may cause deformation of the product or increase internal stress; the cooling rate of the plastic from injection to solidification, as well as the efficiency of the mold cooling channel, are analyzed to obtain the cooling rate influencing parameters, and the cooling rate has an important influence on the crystallinity, dimensional stability and physical properties of the finished product.

[0025] An injection molding control strategy library is constructed, and based on the electric toothbrush geometry data and the physical performance data of the plastic component, combined with the mold injection molding performance influencing parameters, a matching search is performed in the injection molding control strategy library to obtain a matching injection molding control strategy.

[0026] Construct an injection molding control strategy library, which includes a variety of injection molding control strategies. Each strategy is optimized based on different mold designs, material types, and production requirements. These strategies involve control parameters such as injection pressure, injection speed, holding time, cooling time, and mold temperature. In the control strategy library, the geometric data of the electric toothbrush and the physical performance data of the plastic parts are used, combined with the parameters affecting the mold injection molding performance, to perform strategy retrieval, such as through database query or algorithm matching, in order to find the injection molding control strategy that best suits the current product requirements, and obtain the matching injection molding control strategy after retrieval.

[0027] Based on the matching injection molding control strategy, the parameters affecting the injection molding performance of the mold are divided into selection intervals to obtain the mold injection molding control strategy parameter intervals.

[0028] Determine the various parameters included in the selected matching injection molding control strategy, such as injection pressure, speed, temperature setting, etc. Based on these performance parameters, divide the operating range of each mold injection molding performance influencing parameter, determine the optimal range and limit range of each control parameter, and obtain the mold injection molding control strategy parameter range, where the optimal range refers to the parameter setting within this range that can obtain the best production efficiency and product quality; the limit range refers to the parameter setting that may cause production problems or quality degradation if it exceeds this range.

[0029] The mold injection molding evaluation index is extracted for the predetermined optimization target to obtain a mold injection molding evaluation index set, and a mold injection molding optimization effect fitness function is constructed based on the mold injection molding evaluation index set.

[0030] The predetermined optimization goals include optimizing cooling efficiency and optimizing internal stress. Based on these goals, specific evaluation indicators that can reflect the degree to which these goals are achieved are selected, such as cooling time and cooling efficiency, injection pressure and temperature uniformity.

[0031] Based on the set of mold injection molding evaluation indicators, a fitness function for mold injection molding optimization effect is constructed. The fitness function is a mathematical expression used to evaluate the quality of an injection molding parameter setting. It calculates a value based on the predetermined optimization goal and the extracted evaluation indicators. The higher the value, the closer the solution is to the ideal state. In practical applications, it is usually necessary to consider multiple evaluation indicators. At this time, weighted average, priority ranking or multi-objective optimization methods can be used to assign corresponding weights according to the importance of different indicators in the overall optimization goal. The weight setting is usually based on experience or adjusted through experimental data, so as to integrate different evaluation indicators into a fitness function.

[0032] In the process of injection molding parameter optimization, the fitness function is used to evaluate the impact of different parameter changes on the optimization effect. This can be achieved through computer simulation or small batch trial production. According to the score obtained by the fitness function, the mold design or injection molding parameters are adjusted and evaluated again. This process is repeated until the solution with the highest fitness is found.

[0033] Based on the mold injection optimization effect fitness function, a global optimization is performed within the mold injection control strategy parameter range to determine the target mold injection control strategy parameters, and the mold injection control of the electric toothbrush plastic molding is performed using the target mold injection control strategy parameters.

[0034] In the process of injection molding parameter optimization, the fitness function is used to evaluate the impact of different parameter changes on the optimization effect. This can be achieved through computer simulation or small batch trial production. According to the score obtained by the fitness function, the mold design or injection molding parameters are adjusted to find the parameter combination that can maximize the fitness function value within the entire control strategy parameter range. In each iteration of the optimization algorithm, the fitness function value is calculated according to the current parameter combination to evaluate the performance of the parameter combination. Each iteration selects a better parameter combination according to the fitness function value, gradually approaching the optimal solution until a certain convergence condition is met, such as reaching a predetermined number of iterations, and the parameter combination with the highest fitness function value is selected as the target mold injection molding control strategy parameter.

[0035] Furthermore, the method further comprises:

[0036] The target mold injection molding control strategy parameters are simulated and evaluated to obtain a mold injection molding optimization simulation effect; based on the mold injection molding optimization simulation effect, the target mold injection molding control strategy parameters are optimized in an optimization direction and parameter optimization rules are determined; based on the parameter optimization rules, the target mold injection molding control strategy parameters are mutated and expanded to obtain a mold injection molding control strategy parameter set; parameter comparison and optimization are performed within the mold injection molding control strategy parameter set to obtain optimized mold injection molding control strategy parameters and complete feedback optimization of the target mold injection molding control strategy parameters.

[0037] The injection molding simulation software is used to set the injection molding control strategy parameters for the target mold for simulation experiments. The simulation includes a complete injection molding cycle, from plastic melting, injection, pressure holding to cooling and demolding. The goal is to verify the performance of the control strategy in the actual injection molding process, especially its impact on product quality and production efficiency. Multiple simulations are performed to ensure the reliability of the results, and key performance indicators such as filling time, cooling time, internal stress distribution of the product, degree of warpage, injection pressure and temperature are recorded as the simulation effect of mold injection optimization.

[0038] Based on the simulation results, the performance and existing problems of the current control strategy parameters are analyzed, including which parameters most affect product quality and production efficiency, and the possible interactions between these parameters. According to the simulation effects and analysis results, specific parameter optimization rules are formulated. For example, if it is found that increasing the mold temperature can reduce product surface defects, then a new temperature range can be set as an optimization rule.

[0039] According to the parameter optimization rules, the target mold injection molding control strategy parameters are mutated, that is, small changes are introduced on the basis of the existing parameters to create new control strategy parameter combinations. This variation can be an increase or decrease in parameters, or an adjustment of the parameter operating range. This method generates multiple new control strategy parameter combinations to form an extended mold injection molding control strategy parameter set, which contains the original parameters plus the new parameters obtained through the variation, thereby increasing the possibility of finding a better solution.

[0040] In the mold injection molding control strategy parameter set, each parameter combination is evaluated. By comparing the performance of each parameter combination, the combination with the highest fitness function value is selected as the optimized mold injection molding control strategy parameter. This process requires multiple rounds of iterations, and the parameters may be adjusted in each round to gradually approach the optimal solution. Through this feedback optimization method, the continuous optimization of the injection molding control strategy can be ensured to improve production efficiency and product quality.

[0041] Furthermore, the particle swarm parameters are initialized based on the electric toothbrush mold model, the material flow path of the particle swarm parameters is optimized according to a predetermined optimization target, and the electric toothbrush optimized mold model is iteratively obtained. The method includes:

[0042] According to the particle swarm parameters, the parameter particle position and the parameter particle speed are determined, and the fitness of the particle swarm parameters is evaluated using the mold injection optimization effect fitness function to obtain a particle swarm fitness set; based on the particle swarm fitness set, the parameter particle position and the parameter particle speed are iteratively updated until a preset number of iterations is reached, and the optimal parameter particle with the largest fitness is determined, wherein the optimal parameter particle includes a material flow optimization path; based on the material flow optimization path, the electric toothbrush mold model is optimized and updated to obtain the electric toothbrush optimized mold model.

[0043] In the particle swarm optimization algorithm, each particle represents a potential solution, and its position represents a point in the solution space, that is, a set of specific mold injection control parameters. The speed of the particle determines the direction and speed of its search in the solution space. The speed is adjusted based on the individual historical best position of the particle and the historical best position of the entire particle swarm.

[0044] The current position of each particle, that is, the mold injection control strategy parameter it represents, is evaluated using the fitness function. The higher the output value of the fitness function, the better the solution. The fitness evaluation result of each particle is obtained to form a particle swarm fitness set. This set includes the performance evaluation of all current particles and is the basis for subsequent optimization iterations.

[0045] The movement strategy of particles is guided by the fitness set of the particle swarm. The position of particles with high fitness will be imitated by other particles, and particles with low fitness will adjust their search strategy to explore possible better solutions. Specifically, the position and speed of each particle are updated according to its individual best solution and the group best solution. The position update formula is usually: the new position is equal to the current position plus the new speed. The calculation of the new speed includes the current speed, the speed adjustment brought by the particle's personal best position, and the speed adjustment brought by the group's best position. The speed update also involves adjustment coefficients, including cognitive factors and social factors, as well as a random factor to increase the randomness and diversity of the search, which helps to avoid local optimality and miss the global optimality.

[0046] After each particle position is updated, the fitness is recalculated to evaluate the performance of the new position. If the fitness of a particle in the new position exceeds its historical best fitness, its individual best position is updated. At the same time, if the fitness of the particle is also the highest among all current particles, the group best position is updated. The iterative update continues until the preset number of iterations is reached, indicating that the optimal solution is close to being reached. The optimal parameter particle with the highest fitness is determined. The determined optimal parameter particle includes the material flow optimization path, because the material flow path directly affects the quality of the product and the efficiency of production.

[0047] According to the material flow optimization path, the electric toothbrush mold model is designed and updated, including adjusting the injection point position and changing the runner system design to obtain the optimized mold model of the electric toothbrush.

[0048] Furthermore, the method further comprises:

[0049] Based on the structural layout characteristics of the electric toothbrush mold model, a three-dimensional layout diagram is constructed; the three-dimensional layout diagram is rasterized in combination with a predetermined flow channel width to obtain a three-dimensional layout grid diagram; material flow path fitting is performed within the three-dimensional layout grid diagram to complete the particle group parameter initialization.

[0050] Analyze the structural features of the electric toothbrush mold, including the size, shape, and complex internal geometry of the mold. These features will directly affect the design of the material flow and mold cooling system, and determine the relative positions of the various components in the mold, such as injection points, cooling channels, cavities, etc. Use CAD software to create a detailed three-dimensional layout diagram based on the analyzed data. This diagram shows the position and structure of all relevant components in the mold, providing a basis for subsequent rasterization and material flow path analysis.

[0051] The flow channel width is predetermined according to the specific requirements of mold injection molding and material properties. The flow channel width has an important influence on material flow. It is necessary to ensure that it can effectively transfer plastic without causing excessive pressure loss. Based on the flow channel width, the three-dimensional layout diagram is converted into a grid diagram, that is, the continuous geometric space is discretized into a regular grid, each grid represents a small area in the mold, such processing can simplify the calculation and optimization of the material flow path.

[0052] In the rasterized three-dimensional layout diagram, the particle swarm parameters are initialized, including the initial position and speed of each particle. These particles represent different material flow path schemes. The particle swarm algorithm is used to search for the optimal material flow path in the raster diagram, including determining the optimal injection point and runner design, to ensure that the material can fill the cavity evenly and quickly and maximize the cooling efficiency.

[0053] Furthermore, based on the mold injection optimization effect fitness function, a global optimization is performed within the mold injection control strategy parameter interval to determine the target mold injection control strategy parameters, and the method includes:

[0054] In the mold injection molding control strategy parameter interval, strategy parameters are randomly selected to obtain multiple random strategy parameters; based on the mold injection molding optimization effect fitness function, the fitness of the multiple random strategy parameters is evaluated to obtain multiple random strategy parameter fitnesses; based on the multiple random strategy parameter fitnesses, the mold injection molding control strategy parameter interval is searched and focused to determine the local optimization area of ​​the strategy parameters; according to the local optimization area of ​​the strategy parameters, the strategy parameter search step is set; according to the strategy parameter search step, the strategy parameter search and evaluation is performed in the local optimization area of ​​the strategy parameters, and the search range is iteratively focused according to the strategy parameter search and evaluation results until a preset number of iterations is reached, and the result with the largest fitness is determined as the target mold injection molding control strategy parameter.

[0055] Using the determined mold injection control strategy parameter range, including pressure, temperature, injection speed, cooling time, etc., as the initial range of the search, multiple parameter combinations are randomly generated within the control parameter range. Each combination contains the specific values ​​of all control strategy parameters. These randomly selected parameter combinations are the starting point for exploring the solution space, which can effectively avoid the search being limited to a fixed pattern.

[0056] The fitness function of mold injection optimization effect constructed by application is used to substitute each set of randomly generated strategy parameters into the fitness function, and the fitness value of each set of parameters is calculated. The higher the fitness value, the better the effect of the parameter combination in achieving the optimization goal. The fitness value of each random strategy parameter is recorded, and the fitness of multiple random strategy parameters is obtained for subsequent optimization analysis.

[0057] Sort the fitness values ​​of multiple random strategy parameters, identify the parameter combinations with the highest fitness values, and the range of the parameter values ​​of these combinations, and gradually shrink the control strategy parameter interval to the area where the parameter combinations with higher fitness values ​​are located. For example, redefine the upper and lower limits of the control parameters according to the parameter distribution of high fitness values ​​to narrow the search range; by analyzing the parameters that contribute most to the optimization target in the fitness results, focus on the range of these parameters first. The final local optimization area of ​​the strategy parameters is defined as a narrower parameter interval, concentrated in the area with high fitness values.

[0058] According to the local optimization area of ​​the strategy parameters, analyze the range of each parameter, that is, the upper and lower limits, as well as the degree of detail that needs to be explored within the range. The search step determines the sampling density of the parameters in the local optimization area. The step can be adjusted according to the search process. For example, in the initial stage, a larger step can be used for fast search to cover the entire local area. When gradually narrowing the search range, the step should be reduced to improve the accuracy of parameter optimization.

[0059] According to the set strategy parameter search step, a series of new parameter combinations are generated in the local optimization area. Each combination is substituted into the mold injection optimization effect fitness function, its effect is evaluated, and the fitness value is recorded. The parameter combination with the highest fitness value in the current search is found, and the sub-area where the high fitness value is located is determined. According to the position of the high fitness point, the local optimization area is further narrowed to the vicinity of the high fitness point to form a new search range. At the same time, the search step is dynamically adjusted. The search process is repeated until the preset number of iterations is reached. The parameter combination with the highest fitness value is selected from all iteration results as the final target mold injection control strategy parameter.

[0060] Through this process of gradually narrowing the search range and refining parameter optimization, it can be ensured that the final selected mold injection control strategy parameters are both efficient and accurate, which can effectively improve injection molding quality and production efficiency while reducing manufacturing costs.

[0061] Furthermore, the method further comprises:

[0062] A preset retrieval cycle is obtained, and historical electric toothbrush plastic molding records are retrieved based on the preset retrieval cycle, wherein the historical electric toothbrush plastic molding records include a historical molding state information set; a historical abnormal representation set is extracted from the historical molding state information set, and an event analysis is performed as a top event to obtain a plastic molding accident tree, and a minimum cut set set is constructed based on the plastic molding accident tree; according to the minimum cut set set, clustering is performed with the injection molding material as the center to obtain an abnormal material factor; data screening is performed with the abnormal material factor as a constraint to obtain injection molding material information, and the physical property data of the plastic component is obtained based on the injection molding material information.

[0063] Set the retrieval time range according to the production cycle. For example, historical records can be retrieved quarterly, half-yearly or annually. The selection of the retrieval cycle should be combined with the timeliness and representativeness of the data to ensure that enough molding cases and potential problems are covered. Obtain the historical records of electric toothbrush plastic molding from the production management system, equipment monitoring system or factory database. These records include production process parameters (such as injection pressure, temperature, cooling time, etc.), product quality inspection results, and production equipment status information.

[0064] Statistical analysis is performed on historical data to identify common abnormal phenomena in the molding process and their frequency of occurrence. Typical abnormalities include incomplete filling, product warping, uneven cooling, unstable injection, etc. The key influencing factors of each abnormality are extracted, such as too high or too low injection pressure, insufficient cooling, and too high mold temperature. These abnormal phenomena and their influencing factors are summarized to form a historical abnormality representation set.

[0065] Certain anomalies that seriously affect production or product quality, such as product warping and uneven cooling, are defined as top events. Based on causal analysis, the anomaly representations are constructed into an accident tree according to logical relationships. Each node represents an anomaly representation or its cause. The minimum cut set is the minimum combination of causes that lead to the top event. Each cut set consists of one or more nodes in the accident tree. The accident tree is analyzed to identify the minimum cut set.

[0066] Through the construction of the accident tree, problems and risk points that occurred in historical production can be effectively identified, and the problems can be logically sorted out in the form of accident trees and minimum cut sets, providing systematic support for subsequent mold design improvements and process parameter optimization.

[0067] The minimum cut set contains the minimum combination of causes that cause anomalies. Some cut sets are related to the characteristics of injection molding materials. Factors related to the characteristics of injection molding materials (such as fluidity, thermal stability, and filling) are extracted from the minimum cut set. For example, insufficient material fluidity leads to incomplete filling, and poor material thermal stability causes internal stress concentration. Material-related factors are used as feature vectors. For example, the K-means clustering algorithm is used to cluster material-related factors and find correlation patterns between factors. The goal is to classify factors with similar characteristics and influences into one category. Each clustering result represents a group of abnormal material factors, such as low fluidity or high residual stress tendency. These factors describe the influence pattern of material characteristics on anomalies.

[0068] Extract data records related to abnormal material factors from the injection molding material database. The data sources include data provided by material suppliers, laboratory test results, and material performance data in production records. Filter out relevant material information, for example, filter out materials with low fluidity characteristics, and further screen their melt index, processing temperature range and other detailed parameters. Through data screening, determine the injection molding material information related to abnormal material factors, including its brand, model, specification, and key performance parameters. Extract physical performance data from the screened injection molding material information, such as tensile strength, elongation at break, thermal expansion coefficient, density, etc., as physical performance data of plastic parts, to provide a basis for subsequent mold design and injection molding process optimization.

[0069] Furthermore, the method further comprises:

[0070] Based on the historical molding state information set, first historical molding state information is extracted, wherein the first historical molding state information includes first historical injection molding machine state information, first historical mold state information, and first historical injection molding material state information; the first historical molding state information is analyzed to obtain a first historical molding state analysis result, wherein the first historical molding state analysis result includes multiple first historical abnormal representations.

[0071] An analysis object is randomly extracted from the historical molding state information set as the first historical molding state information, which includes the first historical injection molding machine state information, the first historical mold state information, and the first historical injection molding material state information. The injection molding machine state includes pressure curve, speed curve, holding time, cooling time, etc.; the mold state includes mold temperature, runner design, cavity distribution, cooling channel layout, etc.; the injection molding material state includes material type, batch characteristics, melt index, thermal stability, etc.

[0072] By analyzing the first historical injection molding machine status information, the first historical mold status information, and the first historical injection molding material status information in the first historical molding status information, the characteristics and rules related to the abnormal molding status are mined, and the key factors affecting the production efficiency or product quality are identified. For example, basic statistics are performed on the injection molding machine status information, the mold status information, and the material status information, such as mean, variance, and outlier analysis. The correlation between different status information is analyzed through association rule mining, such as the correlation between injection pressure fluctuations and mold cooling efficiency, the relationship between material batch changes and product size deviations, etc. According to the analysis results, the abnormal phenomena that affect the molding quality are characterized, and according to the type and impact of the abnormal phenomena, multiple first historical abnormal characterizations are obtained to form the first historical molding state analysis results.

[0073] In summary, the mold injection molding control method for electric toothbrush plastic molding provided in the embodiment of the present application has the following technical effects:

[0074] By obtaining the geometric data of the electric toothbrush and the physical performance data of the plastic parts, accurate mold analysis and initialization can be performed, and the basic mold model can be quickly constructed to ensure the accuracy of the mold design and avoid repeated modifications and experiments caused by insufficient data or design errors; the material flow path is optimized based on the particle swarm optimization algorithm, which significantly improves the intelligence level of mold design. The optimized mold model can better adapt to the material characteristics and geometric requirements, and ensure the cooling efficiency and reasonable distribution of internal stress; through the precise simulation of key influencing parameters such as temperature, pressure, cooling rate, etc., potential problems can be discovered in advance to avoid quality or efficiency problems in actual production. By matching the injection molding control strategy, standardized management of complex production parameters can be achieved. This method can not only quickly respond to different design requirements, but also improve the production process. stability; based on the matching injection molding control strategy, the parameters affecting the injection molding performance of the mold are divided into selected intervals, and the mold injection molding control strategy parameter interval is obtained to ensure the precise control of parameters in the production process, which helps to reduce the defect rate of the product. By extracting the injection molding evaluation indicators and constructing the optimization fitness function, the quantitative evaluation method is directly linked to the optimization goal, which improves the scientificity and reliability of the molding quality; global optimization is performed within the parameter interval, and the algorithm automatically searches for the optimal injection molding control strategy parameters, which reduces the test and adjustment time. This global optimization not only reduces the trial and error cost of mold design and production, but also improves the utilization efficiency of resources. The optimized mold and injection molding control strategy can reduce the molding cycle, reduce energy consumption, and reduce the defective rate, thereby reducing the overall cost and improving production quality in the long run.

[0075] Embodiment 2, based on the same inventive concept as the mold injection molding control method of an electric toothbrush plastic molding in the aforementioned embodiment, such as Figure 2As shown, the embodiment of the present application provides a mold injection control device for electric toothbrush plastic molding, the device comprising:

[0076] A mold model construction module 10, the mold model construction module 10 is used to obtain electric toothbrush geometric data and plastic component physical performance data used for electric toothbrush plastic molding, perform mold analysis, and initialize the electric toothbrush mold model; a mold model optimization module 20, the mold model optimization module 20 is used to initialize particle swarm parameters based on the electric toothbrush mold model, optimize the material flow path of the particle swarm parameters according to a predetermined optimization target, and iteratively obtain the electric toothbrush optimized mold model, wherein the predetermined optimization target includes optimizing cooling efficiency and optimizing internal stress; a mold injection simulation module 30, the mold injection simulation module 30 is used to perform mold injection simulation of electric toothbrush plastic molding based on the electric toothbrush optimized mold model, and obtain mold injection performance influencing parameters, wherein the mold injection performance influencing parameters include temperature distribution influencing parameters, pressure distribution influencing parameters, and cooling rate influencing parameters; a strategy matching retrieval module 40, the strategy matching retrieval module 40 is used to construct an injection control strategy library based on the The electric toothbrush geometric data and the physical performance data of the plastic parts are combined with the mold injection molding performance influencing parameters, and a matching search is performed in the injection molding control strategy library to obtain a matching injection molding control strategy; a selection interval division module 50 is used to divide the mold injection molding performance influencing parameters into selection intervals based on the matching injection molding control strategy, and obtain the mold injection molding control strategy parameter interval; a fitness function construction module 60, the fitness function construction module 60 is used to extract the mold injection molding evaluation index for the predetermined optimization target, obtain a mold injection molding evaluation index set, and construct a mold injection molding optimization effect fitness function based on the mold injection molding evaluation index set; a mold injection molding control module 70, the mold injection molding control module 70 is used to perform global optimization within the mold injection molding control strategy parameter interval based on the mold injection molding optimization effect fitness function, determine the target mold injection molding control strategy parameters, and perform mold injection molding control of electric toothbrush plastic molding through the target mold injection molding control strategy parameters.

[0077] Furthermore, the device further includes a feedback optimization module to perform the following operation steps:

[0078] The target mold injection molding control strategy parameters are simulated and evaluated to obtain a mold injection molding optimization simulation effect; based on the mold injection molding optimization simulation effect, the target mold injection molding control strategy parameters are optimized in an optimization direction and parameter optimization rules are determined; based on the parameter optimization rules, the target mold injection molding control strategy parameters are mutated and expanded to obtain a mold injection molding control strategy parameter set; parameter comparison and optimization are performed within the mold injection molding control strategy parameter set to obtain optimized mold injection molding control strategy parameters and complete feedback optimization of the target mold injection molding control strategy parameters.

[0079] Furthermore, the device also includes an electric toothbrush optimized mold model acquisition module to perform the following operation steps:

[0080] According to the particle swarm parameters, the parameter particle position and the parameter particle speed are determined, and the fitness of the particle swarm parameters is evaluated using the mold injection optimization effect fitness function to obtain a particle swarm fitness set; based on the particle swarm fitness set, the parameter particle position and the parameter particle speed are iteratively updated until a preset number of iterations is reached, and the optimal parameter particle with the largest fitness is determined, wherein the optimal parameter particle includes a material flow optimization path; based on the material flow optimization path, the electric toothbrush mold model is optimized and updated to obtain the electric toothbrush optimized mold model.

[0081] Furthermore, the device also includes a particle swarm parameter initialization module to perform the following operation steps:

[0082] Based on the structural layout characteristics of the electric toothbrush mold model, a three-dimensional layout diagram is constructed; the three-dimensional layout diagram is rasterized in combination with a predetermined flow channel width to obtain a three-dimensional layout grid diagram; material flow path fitting is performed within the three-dimensional layout grid diagram to complete the particle group parameter initialization.

[0083] Furthermore, the device also includes a target mold injection control strategy parameter acquisition module to perform the following operation steps:

[0084] In the mold injection molding control strategy parameter interval, strategy parameters are randomly selected to obtain multiple random strategy parameters; based on the mold injection molding optimization effect fitness function, the fitness of the multiple random strategy parameters is evaluated to obtain multiple random strategy parameter fitnesses; based on the multiple random strategy parameter fitnesses, the mold injection molding control strategy parameter interval is searched and focused to determine the local optimization area of ​​the strategy parameters; according to the local optimization area of ​​the strategy parameters, the strategy parameter search step is set; according to the strategy parameter search step, the strategy parameter search and evaluation is performed in the local optimization area of ​​the strategy parameters, and the search range is iteratively focused according to the strategy parameter search and evaluation results until a preset number of iterations is reached, and the result with the largest fitness is determined as the target mold injection molding control strategy parameter.

[0085] Furthermore, the device also includes a plastic component physical property data acquisition module to perform the following operation steps:

[0086] A preset retrieval cycle is obtained, and historical electric toothbrush plastic molding records are retrieved based on the preset retrieval cycle, wherein the historical electric toothbrush plastic molding records include a historical molding state information set; a historical abnormal representation set is extracted from the historical molding state information set, and an event analysis is performed as a top event to obtain a plastic molding accident tree, and a minimum cut set set is constructed based on the plastic molding accident tree; according to the minimum cut set set, clustering is performed with the injection molding material as the center to obtain an abnormal material factor; data screening is performed with the abnormal material factor as a constraint to obtain injection molding material information, and the physical property data of the plastic component is obtained based on the injection molding material information.

[0087] Furthermore, the device further includes a historical abnormality representation acquisition module to perform the following operation steps:

[0088] Based on the historical molding state information set, first historical molding state information is extracted, wherein the first historical molding state information includes first historical injection molding machine state information, first historical mold state information, and first historical injection molding material state information; the first historical molding state information is analyzed to obtain a first historical molding state analysis result, wherein the first historical molding state analysis result includes multiple first historical abnormal representations.

[0089] Through the above-mentioned detailed description of the mold injection molding control method for electric toothbrush plastic molding in this specification, those skilled in the art can clearly know the mold injection molding control device for electric toothbrush plastic molding in this embodiment. Since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0090] 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.

Claims

1. A mold injection molding control method for electric toothbrush plastic molding, characterized in that: The method comprises: Obtain the electric toothbrush geometry data and plastic component physical performance data used for electric toothbrush plastic molding, perform mold analysis, and initialize the electric toothbrush mold model; Initializing particle swarm parameters based on the electric toothbrush mold model, optimizing the material flow path of the particle swarm parameters according to a predetermined optimization goal, and iteratively obtaining an optimized mold model of the electric toothbrush, wherein the predetermined optimization goal includes optimizing cooling efficiency and optimizing internal stress; Based on the electric toothbrush optimized mold model, mold injection simulation of electric toothbrush plastic molding is performed to obtain mold injection performance influencing parameters, wherein the mold injection performance influencing parameters include temperature distribution influencing parameters, pressure distribution influencing parameters, and cooling rate influencing parameters; Constructing an injection molding control strategy library, based on the electric toothbrush geometry data and the physical performance data of the plastic component, combined with the mold injection molding performance influencing parameters, performing matching retrieval in the injection molding control strategy library to obtain a matching injection molding control strategy; Based on the matching injection molding control strategy, the parameters affecting the injection molding performance of the mold are divided into selection intervals to obtain the mold injection molding control strategy parameter interval; Extracting mold injection molding evaluation indicators for the predetermined optimization target to obtain a mold injection molding evaluation indicator set, and constructing a mold injection molding optimization effect fitness function based on the mold injection molding evaluation indicator set; Based on the mold injection optimization effect fitness function, a global optimization is performed within the mold injection control strategy parameter interval to determine the target mold injection control strategy parameters, and the mold injection control of the electric toothbrush plastic molding is performed by using the target mold injection control strategy parameters; Performing simulation evaluation on the target mold injection molding control strategy parameters to obtain the mold injection molding optimization simulation effect; Based on the mold injection optimization simulation effect, the optimization direction of the target mold injection control strategy parameters is analyzed to determine the parameter optimization rules; Based on the parameter optimization rule, the target mold injection molding control strategy parameters are mutated and expanded to obtain a mold injection molding control strategy parameter set; Parameter comparison and optimization are performed within the mold injection molding control strategy parameter set to obtain optimized mold injection molding control strategy parameters, and feedback optimization of the target mold injection molding control strategy parameters is completed.

2. A method for controlling mold injection molding of electric toothbrush plastic molding according to claim 1, characterized in that: The method of initializing particle swarm parameters based on the electric toothbrush mold model, optimizing the material flow path of the particle swarm parameters according to a predetermined optimization target, and iteratively obtaining the electric toothbrush optimized mold model comprises: According to the particle swarm parameters, the parameter particle position and the parameter particle speed are determined, and the fitness of the particle swarm parameters is evaluated using the mold injection optimization effect fitness function to obtain a particle swarm fitness set; Iteratively updating the parameter particle position and the parameter particle velocity based on the particle swarm fitness set until a preset number of iterations is reached, and determining an optimal parameter particle with the maximum fitness, wherein the optimal parameter particle includes a material flow optimization path; The electric toothbrush mold model is optimized and updated based on the material flow optimization path to obtain the electric toothbrush optimized mold model.

3. A method for controlling mold injection molding of electric toothbrush plastic molding according to claim 2, characterized in that: The method further comprises: Based on the structural layout features of the electric toothbrush mold model, a three-dimensional layout diagram is constructed; rasterizing the three-dimensional layout diagram in combination with a predetermined flow channel width to obtain a three-dimensional layout grid diagram; Material flow path fitting is performed in the three-dimensional layout grid diagram to complete the particle group parameter initialization.

4. The method for controlling mold injection molding of electric toothbrush plastic molding according to claim 1, characterized in that: Based on the mold injection molding optimization effect fitness function, a global optimization is performed within the mold injection molding control strategy parameter interval to determine the target mold injection molding control strategy parameters, and the method includes: Randomly selecting strategy parameters within the mold injection control strategy parameter interval to obtain multiple random strategy parameters; Based on the mold injection optimization effect fitness function, the fitness of the multiple random strategy parameters is evaluated to obtain the fitness of multiple random strategy parameters; Based on the fitness of the multiple random strategy parameters, focusing the search range of the mold injection control strategy parameter interval to determine the local optimization area of ​​the strategy parameters; According to the local optimization area of ​​the strategy parameters, setting the strategy parameter search step size; A strategy parameter search and evaluation is performed in the strategy parameter local optimization area according to the strategy parameter search step, and the search range is iteratively focused according to the strategy parameter search and evaluation result until a preset number of iterations is reached, and the result with the largest fitness is determined as the target mold injection control strategy parameter.

5. The method for controlling mold injection molding of electric toothbrush plastic molding according to claim 1, characterized in that: The method further comprises: Obtaining a preset search cycle, and retrieving and obtaining historical electric toothbrush plastic molding records based on the preset search cycle, wherein the historical electric toothbrush plastic molding records include a historical molding state information set; Extracting a historical abnormal representation set from the historical molding state information set and performing event analysis as a top event to obtain a plastic molding accident tree, and constructing a minimum cut set set based on the plastic molding accident tree; According to the minimum cut set set, clustering is performed with the injection molding material as the center to obtain abnormal material factors; Taking the abnormal material factor as a constraint, data screening is performed to obtain injection molding material information, and the physical property data of the plastic component is obtained based on the injection molding material information.

6. A method for controlling mold injection molding of electric toothbrush plastic molding according to claim 5, characterized in that: The method further comprises: Based on the historical molding state information set, extracting first historical molding state information, wherein the first historical molding state information includes first historical injection molding machine state information, first historical mold state information, and first historical injection molding material state information; The first historical forming state information is analyzed to obtain a first historical forming state analysis result, wherein the first historical forming state analysis result includes a plurality of first historical abnormal representations.

7. A mold injection control device for electric toothbrush plastic molding, characterized in that: A method for controlling mold injection molding of electric toothbrush plastic molding according to any one of claims 1 to 6, the device comprising: A mold model building module, the mold model building module is used to obtain electric toothbrush geometric data and plastic component physical performance data used for electric toothbrush plastic molding, perform mold analysis, and initialize the electric toothbrush mold model; A mold model optimization module, the mold model optimization module is used to initialize particle swarm parameters based on the electric toothbrush mold model, optimize the material flow path of the particle swarm parameters according to a predetermined optimization target, and iteratively obtain an optimized mold model of the electric toothbrush, wherein the predetermined optimization target includes optimizing cooling efficiency and optimizing internal stress; A mold injection simulation module, the mold injection simulation module is used to perform mold injection simulation of electric toothbrush plastic molding based on the electric toothbrush optimization mold model to obtain mold injection performance influencing parameters, wherein the mold injection performance influencing parameters include temperature distribution influencing parameters, pressure distribution influencing parameters, and cooling rate influencing parameters; A strategy matching retrieval module, wherein the strategy matching retrieval module is used to construct an injection molding control strategy library, and based on the electric toothbrush geometry data and the plastic component physical performance data, combined with the mold injection molding performance influencing parameters, a matching search is performed in the injection molding control strategy library to obtain a matching injection molding control strategy; A selection interval division module, wherein the selection interval division module is used to divide the parameters affecting the injection molding performance of the mold into selection intervals based on the matching injection molding control strategy, and obtain the parameter interval of the mold injection molding control strategy; A fitness function construction module, wherein the fitness function construction module is used to extract mold injection molding evaluation indicators for the predetermined optimization target, obtain a mold injection molding evaluation indicator set, and construct a mold injection molding optimization effect fitness function based on the mold injection molding evaluation indicator set; A mold injection control module, the mold injection control module is used to perform global optimization within the mold injection control strategy parameter interval based on the mold injection optimization effect fitness function, determine the target mold injection control strategy parameters, and perform mold injection control of electric toothbrush plastic molding through the target mold injection control strategy parameters; The device also includes a feedback optimization module to perform the following operation steps: The target mold injection molding control strategy parameters are simulated and evaluated to obtain a mold injection molding optimization simulation effect; based on the mold injection molding optimization simulation effect, the target mold injection molding control strategy parameters are optimized in an optimization direction and parameter optimization rules are determined; based on the parameter optimization rules, the target mold injection molding control strategy parameters are mutated and expanded to obtain a mold injection molding control strategy parameter set; parameter comparison and optimization are performed within the mold injection molding control strategy parameter set to obtain optimized mold injection molding control strategy parameters and complete feedback optimization of the target mold injection molding control strategy parameters.

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