A plastic mold forming control device and method for electric toothbrush head

Abstract

The present invention discloses a control device and method for the plastic mold molding of an electric toothbrush head, and relates to the field of mold molding control technology. The device comprises: obtaining the production demand of an electric toothbrush head, designing a plastic mold, and obtaining the design parameters of the brush head plastic mold; according to the design parameters of the brush head plastic mold, activating the plastic mold production line, and obtaining the target brush head plastic mold; constructing a brush head molding process control space, and obtaining a brush head injection molding process solution set; using the production demand of the electric toothbrush head as a constraint parameter to perform traversal search and optimization, and output molding process control parameters; performing a brush head pre-molding test, obtaining a brush head molding test parameter, and performing brush head production optimization control. The present invention solves the technical problem of low product quality and production efficiency in the whole process of brush head production in the prior art, and achieves the technical effect of realizing accurate control and optimization of the whole process from production demand to brush head production, and improving product quality and production efficiency.

Description

Technical Field

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

[0002] As the oral care industry is booming, the market demand for electric toothbrushes is growing rapidly, and consumers' expectations for the quality of electric toothbrush heads continue to rise, including more refined appearance, more precise size and better performance. This has prompted manufacturers to continuously explore more efficient and higher-quality brush head plastic mold forming technology. Traditional brush head plastic mold forming technology has limitations in many aspects: in the mold design stage, it mainly relies on experience, and it is difficult to accurately respond to diverse appearance requirements, strict dimensional accuracy requirements and different material properties, resulting in poor adaptability of the mold structure to actual production, often causing problems such as uneven melt filling and poor demolding, affecting product quality and production efficiency; when optimizing the injection molding process, due to the lack of systematic analysis, it mostly relies on trial and error to adjust a single parameter, ignoring the complex interactions between parameters, and it is difficult to quickly and accurately obtain the global optimal parameter combination, resulting in a lengthy process optimization process and unsatisfactory results.

[0003] The existing technology has technical problems of low product quality and production efficiency in the whole process of brush head production. Summary of the invention

[0004] The present application provides a control device and method for forming a plastic mold for an electric toothbrush head, which is used to solve the technical problems of low product quality and production efficiency in the entire process of brush head production in the prior art.

[0005] In view of the above problems, the present application provides a control device and method for forming a plastic mold for an electric toothbrush head.

[0006] In a first aspect of the present application, a control device for forming a plastic mold of an electric toothbrush head is provided, the device comprising:

[0007] a design parameter acquisition module, the design parameter acquisition module is used to acquire the production requirements of electric toothbrush heads, design a plastic mold based on the production requirements of electric toothbrush heads, and obtain the design parameters of the brush head plastic mold; a target brush head plastic mold acquisition module, the target brush head plastic mold acquisition module is used to activate the plastic mold production line according to the brush head plastic mold design parameters, and perform mold production according to the brush head plastic mold design parameters through the plastic mold production line to obtain the target brush head plastic mold; an injection molding process solution acquisition module, the injection molding process solution acquisition module is used to construct a brush head molding process control space, matching and optimizing the brush head molding process control space based on the target brush head plastic mold to obtain a brush head injection molding process solution set; a molding process control parameter output module, the molding process control parameter output module is used to traverse and search the brush head injection molding process solution set with the electric toothbrush head production demand as a constraint parameter, and output the molding process control parameters; a brush head production control module, the brush head production control module performs a brush head pre-molding test based on the molding process control parameters to obtain brush head molding test parameters, and performs brush head production optimization control through the brush head molding test parameters.

[0008] The second aspect of the present application provides a method for controlling the molding of a plastic mold for an electric toothbrush head, the method comprising:

[0009] Acquire the production demand of electric toothbrush heads, design plastic molds based on the production demand of electric toothbrush heads, and obtain the design parameters of the brush head plastic mold; activate the plastic mold production line according to the design parameters of the brush head plastic mold, and produce the mold according to the design parameters of the brush head plastic mold through the plastic mold production line to obtain the target brush head plastic mold; construct a brush head molding process control space, match and optimize the brush head molding process control space based on the target brush head plastic mold, and obtain a brush head injection molding process solution set; use the electric toothbrush head production demand as a constraint parameter to traverse and search the brush head injection molding process solution set to output the molding process control parameters; perform a brush head pre-molding test based on the molding process control parameters to obtain the brush head molding test parameters, and perform brush head production optimization control through the brush head molding test parameters.

[0010] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0011] The design parameter acquisition module obtains the production demand of the electric toothbrush head, designs the plastic mold, and obtains the design parameters of the brush head plastic mold; the target brush head plastic mold acquisition module activates the plastic mold production line according to the brush head plastic mold design parameters, performs mold production, and obtains the target brush head plastic mold; the injection molding process solution set acquisition module constructs the brush head molding process control space and obtains the brush head injection molding process solution set; the molding process control parameter output module uses the electric toothbrush head production demand as a constraint parameter to traverse and search for the best in the brush head injection molding process solution set, and outputs the molding process control parameters; the brush head production control module performs a brush head pre-molding test based on the molding process control parameters to obtain the brush head molding test parameters, and optimizes the brush head production through the brush head molding test parameters. The technical effect of achieving precise control and optimization of the entire process from production demand to brush head production and improving product quality and production efficiency is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0013] Figure 1 A schematic diagram of the structure of a plastic mold forming control device for an electric toothbrush head provided in an embodiment of the present application;

[0014] Figure 2 A schematic flow chart of a method for controlling the plastic mold forming of an electric toothbrush head provided in an embodiment of the present application.

[0015] Explanation of the reference numerals: design parameter acquisition module 10 , target brush head plastic mold acquisition module 20 , injection molding process solution acquisition module 30 , molding process control parameter output module 40 , brush head production control module 50 . DETAILED DESCRIPTION

[0016] The present application provides a control device and method for forming a plastic mold for an electric toothbrush head, which is used to solve the technical problems of low product quality and production efficiency in the entire process of brush head production in the prior art.

[0017] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0018] Embodiment 1, as Figure 1 As shown, the present application provides a control device for forming a plastic mold of an electric toothbrush head, the device comprising:

[0019] The design parameter acquisition module 10 is used to obtain the production requirements of electric toothbrush heads, design a plastic mold based on the production requirements of the electric toothbrush heads, and obtain design parameters of the brush head plastic mold.

[0020] Specifically, the design parameter acquisition module 10 has the primary task of accurately acquiring the production demand for electric toothbrush heads. This demand comes from a wide range of sources, including market research data, customer customization requirements, and product planning within the company. After acquiring the demand, it immediately conducts in-depth analysis and uses professional classification algorithms to subdivide the demand into multiple key attribute categories. For example, in terms of appearance requirements, the specific design direction is clarified through comprehensive consideration of factors such as color preference, shape characteristics, and surface texture; for dimensional accuracy, the allowable tolerance range of each dimensional parameter is determined with the help of precision measurement technology and industry standards and specifications to ensure the perfect fit between the brush head and the electric toothbrush body; for material properties, the use scenarios of the brush head in the oral environment are fully considered, and the mechanical properties, chemical stability, and biocompatibility of various plastic materials are deeply studied to select the most suitable material types. Based on the above-mentioned detailed classification and accurate analysis of the brush head production demand attribute information, the design parameter acquisition module 10 starts the plastic mold design work, first accessing the built-in huge plastic mold design parameter library, which stores a large number of past successful design cases, industry general standard parameters, and experimentally verified material performance data. Using the intelligent matching algorithm, the production demand attributes of the current brush head are quickly compared and matched with the data in the library, and the plastic mold structure design parameters are initially generated. Subsequently, in order to ensure the effectiveness and feasibility of these parameters, advanced modeling and simulation software is used to build a virtual mold molding environment, simulate the entire process of filling, cooling, and curing of plastic in the mold cavity, and obtain the simulation effect of plastic mold debugging. Through the accurate evaluation of the molding accuracy (such as dimensional deviation, shape deformation degree, etc.) and molding reliability (such as the stability of the mold under multiple cycles, product qualification rate, etc.) in the simulation results, the design parameter acquisition module 10 can detect potential problems in a timely manner. In response to these problems, the optimization algorithm is used to fine-tune the plastic mold structure design parameters, and iterative optimization is continuously performed until the simulation effect reaches or exceeds the predetermined performance indicators, and finally successfully obtains high-quality brush head plastic mold design parameters that meet the production needs of electric toothbrush heads, providing solid and reliable technical support for subsequent mold production and injection molding processes.

[0021] The target brush head plastic mold acquisition module 20 is used to activate the plastic mold production line according to the brush head plastic mold design parameters, and to produce the mold according to the brush head plastic mold design parameters through the plastic mold production line to obtain the target brush head plastic mold.

[0022] Specifically, after receiving the accurate brush head plastic mold design parameters, the target brush head plastic mold acquisition module 20 immediately starts a series of coordinated and orderly operations. First, it sends an activation command to the control system of the plastic mold production line, prompting the production line to quickly switch from standby to working state. After the production line is activated, the target brush head plastic mold acquisition module 20 accurately transmits the carefully prepared brush head plastic mold design parameters to each key production link of the production line. In the mold manufacturing link, advanced CNC processing equipment accurately cuts, mills, drills, and polishes each component of the mold according to the size specifications, shape characteristics and other information in the design parameters to ensure that the processing accuracy of each component can be controlled within a very small tolerance range. For example, for the processing of the mold cavity, through high-precision CNC milling technology, it is possible to achieve accurate molding of complex curved surfaces, ensure that the roughness of the cavity surface meets the design requirements, and thus provide a high-quality cavity environment for subsequent plastic molding. In the mold assembly stage, each processed component is carefully assembled in strict accordance with the assembly sequence and matching requirements specified by the design parameters. During the entire production process, the target brush head plastic mold acquisition module 20 monitors the operating status of the production line in real time and collects production data through the sensor network, such as the operating parameters of the processing equipment, the processing progress of the mold parts, and the key dimension measurement data during the assembly process. Once any abnormal situation is found, such as the processing error exceeds the allowable range, the interference of parts during the assembly process, etc., the module immediately activates the emergency plan, adjusts the production parameters in time or guides the technicians to handle on-site to ensure that the production process is always on the right track. After a series of rigorous and efficient production processes, under the precise guidance of the design parameters and the strict monitoring of the module, the target brush head plastic mold that meets the design requirements is finally successfully manufactured, providing a solid and reliable mold foundation for the subsequent injection molding process, and effectively ensuring the high-quality production of electric toothbrush heads.

[0023] The injection molding process solution set acquisition module 30 is used to construct a brush head molding process control space, match and optimize the brush head molding process control space based on the target brush head plastic mold, and obtain a brush head injection molding process solution set.

[0024] Specifically, the injection molding process solution acquisition module 30 starts to construct the brush head molding process control space. This process involves comprehensive consideration and definition of many key factors in the injection molding process. It analyzes in detail various variables that affect the injection molding quality of the brush head, such as injection temperature, injection pressure, injection speed, holding time, cooling time and other process parameters, as well as mold temperature, ambient humidity and other external conditions. By setting a reasonable range of values ​​and change steps, a multi-dimensional process parameter space is constructed, which is the brush head molding process control space. After the control space is constructed, the target brush head plastic mold is used to match and optimize it, and the target brush head plastic mold is installed on an advanced injection molding machine for trial mold debugging. In this process, the injection molding machine performs trial mold operations according to a preset combination of multiple process parameters, and is equipped with high-precision sensors to collect various data in the injection molding process in real time, such as the position of the melt flow front, cavity pressure changes, actual product size and weight, etc. Through in-depth analysis of these data, the injection molding loss parameters are accurately obtained. These loss parameters can intuitively reflect the degree of deviation between the product molding quality and the ideal state under the current process parameter combination. For example, the process parameter combination corresponding to defects such as flash, material shortage, and shrinkage marks on the product has a higher loss parameter value. According to the injection molding loss parameters, the module further determines the molding process compensation parameters, which are intended to make targeted corrections and optimizations to the current process to reduce losses. At the same time, the module carefully classifies and marks the control space of the brush head molding process, and marks the numerous process control factor parameter sets (such as temperature, pressure, speed and other parameter sets) with the corresponding injection molding effect data (such as product appearance quality, dimensional accuracy, mechanical properties and other data) one by one. Then, taking each parameter in the control factor parameter set as a multi-factor independent variable and the injection molding effect data as the dependent variable, advanced data fitting algorithms such as multivariate linear regression and neural network fitting are used to deeply analyze and process a large amount of trial mold data to generate a set of multi-control factor molding curves. These curves intuitively show the complex relationship between different process parameter combinations and molding effects. Subsequently, each molding curve in the multi-control factor molding curve set is regressed and fused to construct a molding control factor regression model with high predictiveness and explanatory power. This model can accurately predict the molding effect of the product based on the input process parameters, and can also reversely analyze and deduce the appropriate range of process parameters based on the expected molding effect. Finally, the molding control factor regression model is used to conduct a deep compensation analysis of the injection molding loss parameters. By continuously adjusting the process parameters, the molding effect predicted by the model gradually approaches the actual expected effect, thereby determining the optimal molding process compensation parameter combination, and then obtaining the brush head injection molding process solution set.This solution set contains a series of optimized process parameter combinations, which provides a wealth of candidate solutions for the subsequent selection of the most suitable molding process control parameters, effectively ensuring the scientificity and efficiency of the brush head injection molding process.

[0025] The molding process control parameter output module 40 is used to use the production demand of the electric toothbrush head as a constraint parameter to traverse and search for the best solution in the brush head injection molding process solution set, and output the molding process control parameters.

[0026] Specifically, the molding process control parameter output module 40 quantifies the comprehensive and detailed production requirements of electric toothbrush heads and converts them into specific constraints, which cover product quality requirements (such as the allowable degree of appearance defects, the tolerance range of dimensional accuracy, the minimum value of mechanical properties, etc.), production efficiency indicators (such as production requirements per unit time) and cost control ranges (such as upper limits on raw material consumption, energy consumption limits, etc.). Subsequently, the module uses these strict production demand constraint parameters as a guide to conduct a comprehensive traversal search in the complex parameter space constructed by the solution set of the brush head injection molding process. In this process, it will evaluate each set of process parameter combinations in the solution set one by one, and calculate whether the product meets the production demand constraints under the current parameter combination. For example, for a specific combination of injection molding temperature, pressure, speed and time parameters, the appearance quality (whether there are defects such as flash, bubbles, flow marks, etc.), dimensional accuracy (whether the length, width, thickness and other dimensions are within the tolerance range), mechanical properties (such as hardness and toughness) and production efficiency (whether the molding cycle meets the requirements) and cost (whether the raw material and energy consumption exceed the standard) of the brush head produced under this combination are predicted through a pre-established process simulation model or an empirical model of actual production data. In the traversal search process, advanced optimization algorithms such as genetic algorithms and particle swarm optimization algorithms are used to optimize the process parameter combination. These algorithms evaluate and compare each set of parameter combinations based on the set objective function (usually taking into account multiple factors such as product quality, production efficiency and cost, such as a weighted sum function with a higher weight for quality and a lower weight for efficiency and cost), and select the parameter combination with better performance. As the traversal search continues, the candidate set of the optimal parameter combination is continuously updated, gradually approaching the global optimal solution. After a lot of calculations and comparisons, we finally determined a set of molding process control parameters that can achieve the best product quality, highest production efficiency and lowest cost under the constraints of electric toothbrush head production requirements. Then, this set of carefully selected parameters is accurately output to provide clear process guidance for the subsequent brush head production control module, ensuring that the entire brush head injection molding process can be carried out efficiently, stably and with high quality, so as to produce electric toothbrush head products that meet market demand and corporate standards.

[0027] The brush head production control module 50 performs a brush head pre-forming test based on the molding process control parameters to obtain brush head molding test parameters, and performs brush head production optimization control through the brush head molding test parameters.

[0028] Specifically, after the brush head production control module 50 receives the transmitted precise molding process control parameters, the module immediately starts the brush head pre-molding test process. During the test process, the plastic raw materials are precisely metered, plasticized and injected in strict accordance with the established molding process control parameters, and the plastic melt is injected into the target brush head plastic mold cavity. After a series of process processes such as pressure holding and cooling, a pre-molded brush head sample is obtained. During the pre-molding test process, various brush head molding test parameters are collected in real time through a high-precision sensor network deployed on the mold and production equipment. These parameters cover the temperature change curve of the brush head during the molding process (including the changes in melt temperature and mold temperature at different stages), pressure distribution data (such as injection pressure, dynamic changes in holding pressure and pressure differences at different positions in the cavity), dimensional accuracy parameters (the actual length, width, thickness of the pre-molded brush head and the deviation value between the dimensional ratio relationship between each part and the design requirements), appearance quality characteristics (such as surface roughness, glossiness, whether there are surface defects such as flash, bubbles, flow marks) and material performance indicators (such as the measured values ​​of mechanical performance parameters such as hardness and elastic modulus). After obtaining these rich brush head molding test parameters, the brush head production control module 50 conducts an in-depth analysis of the test parameters, and accurately identifies the problems and deficiencies in the current molding process by comparing them with the preset quality standards and ideal molding parameters. For example, if it is found that the dimensional deviation of a certain part of the brush head exceeds the allowable range, it may be due to the wear of the mold cavity of the part or the uneven pressure distribution during the injection molding process; if there are many bubbles on the surface, it may be due to the injection speed being too fast or the melt temperature being too high, causing too much gas to be mixed into the plastic. According to the analysis results, a clear brush head production optimization control direction is determined. For mold problems, measures such as mold repair and mold structure adjustment (such as optimizing gate design and improving cooling water channel layout) are taken; for situations where process parameters are not suitable, such as adjusting the set values ​​of injection pressure, speed, and temperature, optimizing holding time and cooling time, etc. In the optimization process, the experimental design (DOE) method is used to systematically change the relevant parameters, conduct multiple small batch trial productions, collect new test parameters, and determine the optimal parameter combination through comparative analysis. At the same time, machine learning algorithms are used to learn and train a large number of test parameters and optimization results, and an intelligent prediction model between process parameters and product quality is established. In the subsequent production, product quality can be quickly predicted based on real-time monitored parameters, and the production process can be adjusted in time to achieve continuous optimization and control of the brush head production process, ensuring that the electric toothbrush heads produced meet high standards in quality, performance and consistency.

[0029] In a possible implementation, the design parameter acquisition module further includes:

[0030] The production requirements of the electric toothbrush heads are classified by attributes to obtain the production requirements attribute information of the brush heads, wherein the production requirements attribute information of the brush heads include appearance requirements, dimensional accuracy and material properties; a plastic mold design parameter library is established, and matching design is performed on the production requirements attribute information of the brush heads based on the plastic mold design parameter library to obtain the plastic mold structure design parameters; modeling, simulation and debugging are performed on the plastic mold structure design parameters to obtain the plastic mold debugging simulation effect, wherein the plastic mold debugging simulation effect includes molding accuracy and molding reliability; based on the plastic mold debugging simulation effect, the plastic mold structure design parameters are optimized and adjusted to obtain the brush head plastic mold design parameters.

[0031] Specifically, first, the production demand for electric toothbrush heads is classified by attributes, and the demand is carefully classified according to different characteristics. From the perspective of appearance requirements, it is necessary to consider consumers' expectations for brush head shape (such as square, round, oval, etc.), color (various bright or elegant color options), surface texture (smooth, frosted or with a specific pattern texture), etc. In terms of dimensional accuracy, the length, width, thickness of the brush head, the size of the bristle area, the size and position of the mounting holes, etc. are accurately measured and specified. Any slight deviation may cause assembly problems between the brush head and the electric toothbrush body, affecting the overall performance of the product. The material properties need to be comprehensively weighed. For example, according to the frequency of use and functional requirements of the brush head, plastic materials with appropriate hardness (to ensure cleaning effect and not easy to damage), flexibility (to adapt to the complex environment of the oral cavity), wear resistance (to extend service life), corrosion resistance (to resist erosion by chemicals in the oral cavity) and biocompatibility (to ensure safety and harmlessness to the human body) are selected. Through such attribute classification, comprehensive information on the attributes of brush head production needs can be obtained.

[0032] Establishing a plastic mold design parameter library is a long-term and systematic project. It summarizes many successful mold design case data, common design standards and specifications in the industry, and performance parameters of different materials and structures obtained through a large number of experiments. Based on this rich parameter library, matching design is carried out for the acquired brush head production demand attribute information. According to the appearance, size and material requirements of the brush head, matching mold structure design parameters are selected from the library, such as cavity shape, parting surface position, mold wall thickness and other preliminary design solutions.

[0033] Modeling, simulation and debugging of plastic mold structure design parameters are carried out, and professional computer-aided engineering (CAE) software is used to build a virtual injection molding environment to simulate the flow, filling, cooling and solidification process of plastic in the mold cavity. In this virtual world, various phenomena and data in the molding process are observed and analyzed to obtain the simulation effect of plastic mold debugging. Among them, the molding accuracy is evaluated by comparing the difference between the brush head size after simulated molding and the design size. The smaller the difference, the higher the accuracy; the molding reliability considers whether the mold can stably produce qualified products under multiple simulated molding cycles, including the stability of the mold structure, the wear of parts, and the consistency of product quality.

[0034] Based on the simulation effect of plastic mold debugging, the plastic mold structure design parameters are optimized and adjusted. If the simulation results show that the molding accuracy is not up to standard, such as the deviation of a key dimension exceeds the allowable range, it is necessary to adjust the size of the mold cavity or modify the parameters of the pouring system to ensure that the plastic melt can fill the cavity more evenly, thereby improving the dimensional accuracy. If there are problems with molding reliability, such as large wear or deformation of parts after multiple simulation cycles, it is necessary to optimize the strength and rigidity design of the mold structure, or improve the cooling system of the mold so that the mold can maintain stable performance in actual production and extend its service life. After repeated simulation, evaluation and optimization, the design parameters of the brush head plastic mold that meet the production needs of electric toothbrush heads are finally obtained, laying a solid foundation for subsequent mold production and injection molding processes.

[0035] In a possible implementation, the injection molding process solution set acquisition module further includes:

[0036] The target brush head plastic mold is installed on an injection molding machine for trial mold debugging to obtain injection molding loss parameters; based on the injection molding loss parameters, the molding process compensation parameters are determined; based on the brush head plastic mold design parameters of the target brush head plastic mold and the brush head molding process control space, matching and dividing are performed to obtain a matching molding process control space; based on the molding process compensation parameters, the matching molding process control space is compensated and optimized to obtain the brush head injection molding process solution set.

[0037] Specifically, accurately installing the target brush head plastic mold on the injection molding machine is the first step in the trial mold debugging. It is necessary to ensure that the mold fits tightly with the installation surface of the injection molding machine, each positioning hole is accurately aligned with the positioning device of the injection molding machine, and the clamping mechanism firmly locks the mold to provide a stable foundation for the subsequent injection molding process. After the installation is completed, start the injection molding machine for trial mold debugging. During the injection molding process, the injection molding machine operates according to the preset initial process parameters, which include but are not limited to injection temperature, injection pressure, injection speed, holding time, cooling time, etc. At the same time, through high-precision temperature sensors distributed in key parts such as the injection molding machine barrel, mold cavity, cooling water channel, etc., the temperature changes of the plastic melt during the entire injection molding process are monitored in real time. Starting from the melting of the plastic raw material in the barrel, record its temperature rise rate, stability after reaching the set injection temperature, and heat loss during injection into the mold cavity, especially the sudden change in temperature when the melt flows through narrow parts such as runners and gates, until the temperature drop curve in the cooling and solidification stage in the mold cavity. In addition, pay close attention to the material loss. After the mold trial is completed, check the molded brush head product and the surrounding area of ​​the mold, count the excess plastic materials caused by flash, overflow, etc., and calculate their weight or volume as direct quantitative data for material loss. At the same time, observe whether there are traces of burning or decomposition on the surface of the product due to poor exhaust. If there are, it indicates that some plastic materials have undergone chemical changes due to local overheating during the injection molding process. This is also a manifestation of material loss. Although it is difficult to directly measure the amount of loss, it can indirectly reflect its impact on the molding process by evaluating the degree of product defects. Obtain accurate injection molding loss parameters through comprehensive monitoring and analysis of temperature and material loss. These loss parameters provide a key basis for the subsequent determination of molding process compensation parameters. For example, if it is found that the mold cavity temperature drops too fast in the later stage of filling, resulting in cold material marks on the product, or the product size shrinks severely due to excessive material loss, it is clear that the process parameters such as mold temperature and holding pressure need to be adjusted and compensated in order to optimize the injection molding process and improve product quality and production efficiency.

[0038] After obtaining the injection molding loss parameters, determining the molding process compensation parameters becomes a key step. First, the injection molding loss parameters are analyzed in depth. In terms of temperature loss, it is observed that the heat is lost too quickly during the heat transfer from the barrel to the mold cavity during the injection molding process, resulting in reduced fluidity of the plastic melt in the later stage of cavity filling, and defects such as short shots or cold material marks. At this time, it is necessary to consider increasing the mold temperature to compensate for the heat loss. The specific compensation amount is determined by calculating the temperature difference and combining the thermal performance parameters of the material. For example, based on the specific heat capacity, thermal conductivity coefficient and actual measured temperature drop of the plastic material, the heat balance equation is used to calculate the mold temperature increase value required to keep the melt in good fluidity. At the same time, the heating temperature of the injection molding machine barrel is adjusted to ensure that the melt has a suitable temperature before entering the cavity. For material loss, if the plastic melt shrinks excessively during the cooling process due to insufficient holding pressure, the product will have problems such as shrinkage marks and dimensional deviations. It is necessary to increase the holding pressure to compensate for the reduction in material volume. The compensation amount is calculated based on factors such as the product's dimensional tolerance requirements, the material's shrinkage characteristics and the actual measured shrinkage mark depth. By accurately measuring the dimensional changes of key parts of the product and comparing them with the design dimensions, the material shrinkage model is used to estimate the volume of material to be added, which is then converted into an adjustment value for the holding pressure. In addition, if the loss parameters show that the injection speed is too fast, the melt will generate turbulence in the cavity, and air will be drawn in to form bubbles, affecting the appearance and mechanical properties of the product. It is necessary to appropriately reduce the injection speed, and by simulating or experimentally comparing the molding effects at different injection speeds, find the optimal injection speed value that can ensure that the melt fills the cavity smoothly and meets the production efficiency requirements, as a compensation adjustment for the injection speed parameters. At the same time, according to actual conditions, other process parameters such as cooling time and ejection parameters may also need to be adjusted accordingly to ensure that the entire injection molding process can effectively reduce losses and improve product quality after compensation adjustment, so that the molding process reaches the optimal state and meets the high precision and high quality requirements of electric toothbrush head production.

[0039] Based on the target brush head plastic mold, the brush head plastic mold design parameters are matched and divided with the brush head molding process control space, and the key information in the mold design parameters, such as cavity structure characteristics, gate position and size, mold material thermal conductivity, etc., are correlated with the range of various process parameters in the pre-constructed brush head molding process control space. For example, the reasonable range of injection pressure is determined according to the complexity of the cavity, and the injection speed value range is adjusted according to the gate size, so as to divide the molding process control space that best matches the mold, which can narrow the scope of subsequent optimization search and improve efficiency.

[0040] Compensation and optimization of the matching molding process control space based on the molding process compensation parameters is the key link to achieve accurate optimization of the injection molding process of the brush head. First, the determined molding process compensation parameters are introduced into the matching molding process control space. For example, if the compensation parameters require an increase in the mold temperature, the value range of the mold temperature parameter is adjusted upward by a certain amount in the control space, and other parameter ranges associated with the temperature are adjusted accordingly. For example, the increase in temperature may affect the fluidity of the plastic melt, and then the injection speed range is fine-tuned to ensure that the melt can smoothly and efficiently fill the cavity under the new temperature conditions. For the holding pressure compensation parameters, if the holding pressure needs to be increased, the upper limit of the holding pressure is appropriately expanded in the control space, and the holding time is optimized and adjusted according to the pressure change to ensure that the product can get sufficient pressure supplement during the cooling process and reduce defects caused by material shrinkage. In the process of adjusting these key process parameters, the mutual influence relationship between the parameters is fully considered, and the experimental design (DOE) method or simulation analysis based on the physical model is adopted to systematically study the molding effect under different parameter combinations. Through multiple mold trials or simulation calculations, the injection molding result data under different process parameter combinations are obtained, including the product's appearance quality (such as surface flatness, glossiness, whether there are defects such as flash, bubbles, flow marks, etc.), dimensional accuracy (deviation value between the dimensions of each key part and the design dimensions), mechanical properties (such as hardness, toughness, tensile strength, etc.) and production efficiency indicators (such as injection cycle length). Then, according to the preset quality standards and production requirements, these result data are evaluated and screened, and the process parameter combinations that meet the quality requirements and are within the production efficiency and cost control range are selected to form a brush head injection molding process solution set. This solution set covers a variety of optimized process parameter combinations that can produce qualified brush head products, providing a wealth of options for the subsequent flexible selection of the most appropriate process parameters according to actual production conditions, ensuring that efficient, stable, and high-quality brush head injection molding production can be achieved under different production conditions and requirements.

[0041] In a possible implementation, the injection molding process solution set acquisition module further includes:

[0042] Data classification and labeling are performed on the brush head molding process control space to obtain a molding process control factor parameter set and corresponding injection molding effect data; each control factor parameter in the molding process control factor parameter set is used as a multi-factor independent variable, and the corresponding injection molding effect data is used as a molding dependent variable; the multi-factor independent variable and the molding dependent variable are respectively associated and fitted to generate a multi-control factor molding curve set; each molding curve in the multi-control factor molding curve set is regressed and fused to construct a molding control factor regression model; based on the molding control factor regression model, the injection molding loss parameters are compensated and analyzed to determine the molding process compensation parameters.

[0043] Specifically, data classification and labeling of the brush head molding process control space is a meticulous and critical task, which aims to lay a solid data foundation for the subsequent accurate determination of the molding process compensation parameters. First of all, comprehensively sort out all kinds of data information in the brush head molding process control space. Among the many parameters, the key factor of mold temperature is accurately extracted. It has a vital influence on the fluidity, solidification speed of the plastic melt, and the internal structure and appearance quality of the final product; the holding pressure should not be ignored. Its size is directly related to the material replenishment of the product during the cooling process, affecting the dimensional accuracy and density distribution of the product; injection time is also one of the important control factors. It determines the speed and filling amount of the melt entering the cavity, which is closely related to the molding integrity and quality of the product. In addition to these, it also covers many parameters such as injection speed, cooling time, cavity pressure, raw material temperature, etc., which are included in the molding process control factor parameter set. For each specific combination of molding process control factor parameters, there is a corresponding set of comprehensive injection molding effect data. In terms of injection molding effect data, appearance quality data is obtained through high-resolution image analysis technology and professional surface roughness measuring instruments, accurately recording whether there are defects such as flash, bubbles, flow marks on the product surface, as well as the surface roughness value. These data directly reflect the appearance of the product and the consumer's tactile experience; dimensional accuracy data is obtained by using a high-precision three-coordinate measuring instrument to accurately measure the key dimensions of the product such as length, width, height, aperture, wall thickness, etc., and compare them with the design dimensions. Detailed dimensional deviation values ​​are obtained to ensure that the product can be accurately assembled with other parts such as the electric toothbrush body; mechanical performance data are tested by professional material testing equipment, such as universal material testing machines, to test the product's mechanical properties such as tension, compression, bending, and impact to obtain its hardness, strength, toughness and other indicators, which are directly related to the cleaning effect and durability of the brush head during use; at the same time, advanced non-destructive testing technologies, such as X-ray flaw detection and ultrasonic testing, are used to analyze the internal structure quality of the product to check whether there are internal defects such as shrinkage marks, pores, and cracks, which may cause potential risks during the use of the product. These rich and diverse molding process control element parameter sets are marked one by one with the corresponding injection molding effect data, and the correlation between the two is clearly established. For example, when the mold temperature is 80℃, the holding pressure is 50MPa, and the injection time is 3s, the corresponding product surface is smooth without obvious defects, the dimensional deviation is within the range of ±0.05mm, the hardness reaches 80ShoreD, and there are no obvious shrinkage marks and pores inside.Through such comprehensive, detailed and accurate data classification and labeling work, sufficient and reliable data support is provided for the subsequent in-depth correlation fitting, model building and final molding process compensation parameter calculation, so that the optimization process of the entire brush head injection molding process can be steadily advanced based on scientific data, ensuring the production of high-quality electric toothbrush head products.

[0044] Each control factor parameter in the molding process control factor parameter set is used as a multi-factor independent variable, and the corresponding injection molding effect data is used as the molding dependent variable. This setting establishes a causal relationship model framework between process parameters and molding results. For example, mold temperature, holding pressure, injection time, etc. are regarded as influencing factors (independent variables), while the product's dimensional accuracy, appearance quality, mechanical properties and other molding effects (dependent variables) are the results of these factors. In this way, a variable basis is provided for subsequent mathematical modeling, aiming to reveal how changes in process parameters lead to changes in molding effects.

[0045] The multi-factor independent variables and the molding dependent variables are respectively fitted with correlations. For each molding dependent variable, statistical methods such as multivariate regression analysis are used, combined with a large amount of experimental data or actual production data, to determine the mathematical relationship between it and multiple independent variables. For example, for the dependent variable of product dimensional accuracy, by collecting dimensional accuracy data under different combinations of mold temperature, holding pressure and injection time, the regression coefficient is calculated to obtain a mathematical expression describing the relationship between dimensional accuracy and these control factor parameters. The same operation is performed on other dependent variables such as appearance quality and mechanical properties to generate a set of multi-control factor molding curves. These curves intuitively show the changing trend of the molding effect under different process parameter values. For example, when the mold temperature increases, the product dimensional accuracy may first increase and then decrease, forming a curve with a peak value; when the holding pressure increases, the mechanical properties of the product may show an upward trend, but too high a holding pressure may cause defects in the appearance, and the curve will turn.

[0046] Then, each molding curve in the multi-control factor molding curve set is regressed and fused, and a weighted average algorithm is used to comprehensively consider the relationship represented by each curve. By assigning different weights to different curves, the comprehensive impact of multiple control factors on the molding effect is integrated to construct a molding control factor regression model. This model can more comprehensively and accurately reflect the complex nonlinear relationship between process control factors and injection molding effects, overcoming the limitations of single curve fitting. For example, when predicting product quality, the model can simultaneously consider the interaction of mold temperature, holding pressure, and injection time, rather than just the influence of a single parameter.

[0047] Finally, based on the molding control factor regression model, the injection molding loss parameters are compensated and analyzed to determine the molding process compensation parameters. When injection molding losses occur, such as excessive product size deviation, surface defects, or substandard mechanical properties, these loss parameters are input into the regression model. Based on the pre-established relationship, the model reversely calculates the direction and amplitude of adjustment of each control factor parameter to determine the molding process compensation parameters. For example, if the product size is too large, the model analysis concludes that it is caused by too high holding pressure or too long injection time, and then calculates the specific values ​​of reducing the holding pressure and shortening the injection time, providing clear guidance for optimizing the injection molding process and ensuring the production of electric toothbrush head products that meet quality requirements.

[0048] In a possible implementation, the molding process control parameter output module further includes:

[0049] The production demand of the electric toothbrush heads is used as a constraint parameter to perform a traversal search in the solution set of the brush head injection molding process to obtain an injection molding process selection threshold; a molding benefit evaluation index set is obtained, and the brush head molding process control space is evaluated and fitted according to the molding benefit evaluation index set to construct a molding benefit fitness function; a plurality of molding control parameters are randomly selected within the injection molding process selection threshold, and the plurality of molding control parameters are evaluated using the molding benefit fitness function to obtain a plurality of molding parameter fitnesses; a global optimization of the plurality of molding control parameters is performed based on the plurality of molding parameter fitnesses, and the molding process control parameters are output.

[0050] Specifically, the production demand for electric toothbrush heads is converted into constraint parameters, and a traversal search is performed within the solution set for the injection molding process of the brush heads. The production demand covers product quality requirements (such as strict dimensional accuracy range, appearance defect restrictions, and specific mechanical performance indicators), production efficiency targets (specified unit time output), and cost control ranges (including upper limits such as raw material costs, energy consumption costs, and equipment depreciation costs). During the traversal process, based on these constraints, the range of process parameter combinations that meet the requirements is screened out to obtain the injection molding process selection threshold. For example, if the production demand stipulates that the tolerance of the brush head length is within ±0.1mm, then in the process solution set, only those process parameter combinations that can stably produce brush heads within this tolerance range are included in the selection threshold range;

[0051] Obtaining a set of molding benefit evaluation indicators is the key basis for constructing a molding benefit fitness function. This set of indicators covers multiple aspects and aims to comprehensively and accurately measure the comprehensive benefits of the brush head injection molding process. Quality indicators are the core part of it. The product qualification rate is obtained by counting the ratio of the number of qualified products in a certain production batch to the total production quantity. It directly reflects the stability and reliability of the process; the scrap rate provides information from the opposite perspective, which helps to clarify the possible problem areas in the process. The dimensional accuracy compliance rate is determined by measuring the ratio of the deviation between the key dimensions of the product and the design dimensions within the allowable range to ensure that the brush head can accurately match the electric toothbrush body. The appearance quality score is based on the evaluation of product surface defects (such as flash, bubbles, flow marks, etc.) by professionals and the comprehensive judgment of visual effects such as surface finish and color uniformity, which are converted into specific numerical scores. Mechanical performance indicators include test values ​​of hardness, toughness, wear resistance, etc. These properties are directly related to the cleaning effect and durability of the brush head during use. Production efficiency indicators are also an important component. The unit time output is reflected by recording the number of brush heads produced in a specific time period, which directly reflects the production speed; the injection molding cycle time is the time required to complete an injection molding process, and its length directly affects the production efficiency. A shorter injection molding cycle means higher equipment utilization and production capacity. In addition, cost indicators cannot be ignored. The cost of raw materials is calculated by multiplying the weight of plastic raw materials consumed by each batch of production by the unit raw material price; the energy consumption cost covers the electric energy consumed during the operation of the injection molding machine, the heat energy consumed by the heating and cooling system, etc., and the data is obtained and cost accounting is performed through metering devices such as electric meters and heat meters installed on the equipment; the equipment depreciation cost is calculated according to factors such as equipment purchase cost, expected service life, and number of production batches, reflecting the value loss of the equipment during the production process. Based on this comprehensive set of molding benefit evaluation indicators, the brush head molding process control space is evaluated and fitted. Mathematical statistical methods are used to analyze the complex relationship between various parameters in the process control space (such as mold temperature, injection pressure, holding time, cooling time, etc.) and the evaluation indicators. Through a large amount of experimental data or actual production data, a multivariate regression model is established to describe how process parameters affect various benefit indicators. Based on these models, a molding benefit fitness function is constructed. This function takes multiple benefit indicators into consideration and converts the values ​​of different indicators into a single fitness value through weighted summation. For example, the following is a formula example for describing the molding benefit fitness function:

[0052] ;

[0053] in: is the fitness function value of the forming benefit, is the weight coefficient corresponding to each indicator, and The size of the weight is determined according to the importance of each indicator to the overall benefit. For example, product quality related indicators have a higher weight, and cost related indicators also have a higher weight. The weight distribution can be adjusted according to actual conditions. is the product qualification rate, The maximum product qualification rate is set (ideally 100%, but in actual production a higher acceptable upper limit may be set based on industry standards or corporate goals). is production efficiency (such as output per unit time or other indicators that can reflect production speed), For maximum production efficiency (can be determined based on equipment performance and the maximum production capacity expected by the enterprise). is the raw material utilization rate, The maximum raw material utilization rate is 100% in the ideal case, but a reasonable upper limit is set according to the process characteristics and material characteristics. is the scrap rate, The maximum scrap rate is set (usually the lower the scrap rate, the better, and the upper limit is set according to the company's quality control goals). is the equipment depreciation cost, The maximum acceptable equipment depreciation cost (determined based on the equipment purchase cost, expected service life and enterprise cost budget). is the energy consumption cost, is the maximum acceptable energy consumption cost (set according to the enterprise's energy management goals and cost budget). Through such a formula, we can comprehensively consider factors such as product quality, production efficiency and cost, and conduct quantitative evaluation of different molding process control parameter combinations. The higher the fitness function value, the better the molding benefit of the parameter combination, thereby selecting the optimal process control parameters in the brush head injection molding process solution set.

[0054] Randomly selecting multiple molding control parameters within the selection threshold of the injection molding process is an important step in optimizing process parameters. The selection threshold is determined based on the production demand constraints of electric toothbrush heads to ensure that the selected parameter combination is within a reasonable range that meets product quality, production efficiency and cost requirements. For example, if the production demand stipulates that the dimensional accuracy tolerance of the brush head is ±0.1mm, then the parameter combination within the selection threshold should be able to stabilize the dimensional accuracy of the produced brush head within this range; at the same time, for production efficiency, if the unit time output is required to be no less than 100 pieces / hour, the parameters within the selection threshold should be able to support or approach this output target; in terms of cost, it is necessary to consider the comprehensive costs such as raw material costs and energy consumption costs within the acceptable range of the enterprise. In the random selection process, the diversity and randomness of process control parameters are fully considered, covering key parameters such as mold temperature (such as random values ​​between 40℃-80℃), injection pressure (randomly selected from 50MPa-150MPa), holding time (random value of 5s-20s), and cooling time (randomly determined from 10s-30s). The value range of each parameter is determined based on previous process research, empirical data and equipment performance to ensure that the randomly selected parameters are practical and representative. After selecting multiple molding control parameter combinations, the molding benefit fitness function is used to evaluate them. Substitute each parameter combination into the fitness function to calculate the corresponding molding parameter fitness. Taking the fitness function mentioned above as an example, for a specific parameter combination, such as a mold temperature of 60°C, an injection pressure of 100MPa, a holding time of 12s, and a cooling time of 20s, first obtain the product qualification rate (assuming 95%), production efficiency (assuming the unit time output is 120 pieces / hour), raw material utilization rate (assuming 85%), scrap rate (assuming 5%), equipment depreciation cost (assuming 0.5 yuan / piece) and energy consumption cost (assuming 0.3 yuan / piece) under the parameter combination according to the actual production situation. Then, substitute these data into the fitness function and calculate the fitness value according to the corresponding weights. If the product qualification rate weight in the function Production efficiency weight Raw material utilization weight Scrap rate weight Equipment depreciation cost weight Energy consumption cost weight And assuming the maximum product qualification rate Maximum production efficiency Maximum utilization of raw materials Maximum scrap rate Maximum equipment depreciation cost Maximum energy consumption cost Then the fitness value of this parameter combination is:

[0055] ;

[0056] Through such calculation, the fitness value of the parameter combination is obtained, which reflects the degree of its superiority in meeting production needs and achieving benefit goals. The above evaluation process is repeated for multiple randomly selected molding control parameter combinations to obtain multiple molding parameter fitness values, providing a data basis for subsequent global optimization, thereby screening out the optimal molding process control parameters to ensure that the injection molding process of the electric toothbrush head achieves the best balance in terms of quality, efficiency and cost.

[0057] Finally, a genetic algorithm is used to globally optimize the molding control parameters, randomly initialize a group of molding control parameter individuals, and calculate the fitness of each molding parameter. Then enter the iterative loop, select individuals according to the fitness value, and individuals with high fitness have a greater probability of being selected. Then perform a crossover operation on the selected individuals to simulate gene exchange to generate new parameter combinations. At the same time, introduce mutation operations to randomly change some parameters with a lower probability to avoid falling into local optimality. The newly generated individuals form the next generation population, and their fitness values ​​are calculated again. Repeat the selection, crossover, mutation and fitness evaluation steps until the preset upper limit of the number of iterations or the termination condition of minimal fitness change for multiple generations is reached. At this time, the molding control parameters corresponding to the individuals with the highest fitness in the population are the optimal molding process control parameters required, and they are output for the production process of electric toothbrush heads, thereby ensuring the efficiency of production and the stability of product quality.

[0058] In a possible implementation, the molding process control parameter output module further includes:

[0059] Based on the fitness of the multiple molding parameters, the multiple molding control parameters are optimized to determine a preset number of multiple optimal molding control parameters; the multiple optimal molding control parameters are used as multiple cluster centers, and the multiple molding control parameters are evenly clustered based on the multiple cluster centers to obtain multiple molding parameter clusters; iterative optimization and cluster center update are performed in the multiple molding parameter clusters to obtain multiple molding optimization parameter clusters; based on the multiple molding optimization parameter clusters, multiple updated cluster centers are determined, and global optimization is performed on the multiple updated cluster centers to obtain the molding process control parameters.

[0060] Specifically, optimizing multiple molding control parameters based on the fitness of multiple molding parameters is an important step to improve the efficiency and accuracy of optimization. First, sort the fitness of all molding parameters, and select a preset number of molding control parameters from high to low as the optimal molding control parameters. This preset number can be determined based on actual computing resources and optimization requirements, such as selecting parameter combinations that rank in the top 10% or top 20% in fitness. These optimal molding control parameters perform relatively well in meeting the production requirements of electric toothbrush heads, and have a high potential to become the final optimal solution or a near-optimal solution.

[0061] Next, the determined multiple optimal molding control parameters are used as multiple cluster centers. Based on these cluster centers, all molding control parameters are equally clustered, and the distance between each molding control parameter and each cluster center is calculated. The distance measurement method is selected according to the characteristics of the parameter, such as Euclidean distance. According to the distance, each molding control parameter is divided into the cluster to which the nearest cluster center belongs, thereby obtaining multiple molding parameter clusters. Such a clustering operation can bring together similar parameter combinations, which is helpful for fine-grained search in different areas in the subsequent optimization process.

[0062] Then, iterative optimization and cluster center update are performed in multiple molding parameter clusters. For each molding parameter cluster, the particle swarm optimization algorithm is used again in the cluster, with the cluster center as the initial point, to search for a better parameter combination in the cluster. In the iterative process, the fitness value of the parameter combination in the cluster is continuously updated, and the parameters are adjusted according to the change of the fitness value. At the same time, according to the distribution of the parameters in the cluster, the cluster center is recalculated so that the cluster center always represents the average or optimal trend of the parameters in the cluster. After multiple iterations, the parameter combination in each molding parameter cluster gradually evolves to a better direction, and multiple molding optimization parameter clusters are obtained.

[0063] Finally, based on multiple molding optimization parameter clusters, multiple updated cluster centers are determined. These updated cluster centers are representative points of each cluster after optimization. The advantageous characteristics of the parameters within the cluster are integrated, and these updated cluster centers are globally optimized. The global optimization algorithm is applied again, and a genetic algorithm is used to search for the optimal solution in the space formed by these updated cluster centers. After this stage of global optimization, the optimal molding control parameters are finally determined, which are the molding process control parameters. This parameter can achieve the best molding benefits in the production of electric toothbrush heads, including high-quality products, high production efficiency and low cost, provide accurate and reliable process guidance for actual production, and effectively improve the overall performance of the production process.

[0064] In a possible implementation, the brush head production control module further includes:

[0065] According to the multiple update cluster centers, an injection molding control parameter library is obtained; based on the brush head molding test parameters, the injection molding control parameter library is analyzed for improvement directions to determine the parameter improvement variation directions; according to the parameter improvement variation directions, the injection molding control parameter library is expanded with parameter variations to obtain an expanded molding control parameter library; according to the expanded molding control parameter library, the molding process optimization control parameters are determined by searching and optimizing, and brush head production optimization control is performed based on the molding process optimization control parameters.

[0066] Specifically, the injection molding control parameter library is obtained based on multiple updated cluster centers. This process integrates and organizes the molding control parameters represented by multiple updated cluster centers obtained in the early optimization process to build a more comprehensive and representative injection molding control parameter library. These cluster centers are parameter combinations that perform better in different areas after multiple rounds of optimization, covering a variety of process settings that may meet the production needs of electric toothbrush heads. They are aggregated and stored in the library, providing a rich initial parameter resource for subsequent production optimization.

[0067] The key link is to analyze the improvement direction of the injection molding control parameter library based on the brush head molding test parameters. When performing the brush head pre-molding test, detailed brush head molding test parameters will be obtained, such as the actual dimensional accuracy of the product (including the deviation between the dimensions of each part such as length, width, thickness and the design dimensions), appearance quality (whether there are defects such as flash, bubbles, flow marks on the surface, as well as surface finish, color uniformity, etc.), mechanical properties (actual test data such as hardness, toughness, wear resistance, etc.), etc. By comparing and analyzing these test parameters with the expected production goals and quality standards, the deficiencies of the process parameters in the current parameter library can be found. For example, if it is found that the dimensional deviation of a certain part of the brush head is always too large, it may mean that the mold cavity size design of the part or the process parameters such as injection pressure and holding time need to be adjusted during the injection molding process; if there are many bubbles on the surface of the product, it may be necessary to improve the parameters such as injection speed, mold exhaust system or raw material temperature. According to these comparative analysis results, the direction of parameter improvement variation is determined, and it is clear whether each process parameter should be increased or decreased, or what kind of structural or setting adjustment is needed.

[0068] Expanding the injection molding control parameter library by parameter variation according to the determined parameter improvement variation direction is an important measure to enrich the process parameter selection and explore better solutions. Each original molding control parameter combination is taken out from the injection molding control parameter library in turn. These parameter combinations include key process parameters such as mold temperature, injection pressure, holding time, cooling time, injection speed, etc. For each original parameter combination, the parameter variation operation is performed according to the previously determined parameter improvement variation direction. Taking the mold temperature as an example, if the variation direction is to increase the temperature, assuming that the original mold temperature is set to 50℃, according to the predetermined variation step length (such as 5℃), the mutated mold temperature will change to 55℃, 60℃, 65℃, etc.; if the variation direction is to reduce the temperature, it may change to 45℃, 40℃, 35℃, etc. For injection pressure, if the variation direction is to increase pressure, the original injection pressure is 80MPa, and the variation is carried out with a certain pressure increment (such as 5MPa), the pressure value after variation may be 85MPa, 90MPa, 95MPa, etc.; on the contrary, if the pressure is reduced, it may become 75MPa, 70MPa, 65MPa, etc. Similarly, similar variation operations are performed on parameters such as holding time, cooling time and injection speed according to their respective variation directions and step sizes. While performing single parameter variation, the mutual influence relationship between parameters is fully considered, and multi-parameter collaborative variation is performed. For example, when the mold temperature increases, the injection speed and holding time may need to be adjusted accordingly to maintain a good molding effect. If the mold temperature increases by 5°C, based on experience or simulation analysis, the injection speed may need to be reduced by 5% and the holding time may need to be extended by 1s, thereby generating a new set of collaboratively mutated parameter combinations. In this way, a comprehensive parameter variation is performed on each original parameter combination in the injection molding control parameter library, and a large number of new parameter combinations are generated, and all these new parameter combinations are included in the molding control expanded parameter library. Compared with the original parameter library, the expanded molding control parameter library obtained in this way contains more diverse parameter settings and more comprehensively covers the potential optimization space of process parameters. It not only retains the existing better parameter combinations in the original parameter library, but also introduces many new parameter combinations with potential through variation expansion, which provides a rich data basis for the subsequent optimization and determination of molding process optimization control parameters in a wider range, greatly increases the possibility of finding better production process parameters, and helps to further improve the injection molding quality, production efficiency and reduce costs of electric toothbrush heads.

[0069] Determining the optimal control parameters of the molding process according to the molding control expansion parameter library is the core link to achieve accurate, efficient and high-quality production of brush heads. Extract various parameter combinations from the molding control expansion parameter library, use genetic algorithms to comprehensively evaluate these parameter combinations, and calculate a fitness value for each parameter combination based on the preset objective function, which comprehensively considers multiple factors such as product quality (such as dimensional accuracy, appearance quality, mechanical properties, etc.), production efficiency (such as injection molding cycle, unit time output, etc.) and cost (including raw material cost, energy consumption cost, equipment loss cost, etc.) to measure its quality. In the optimization process, the algorithm compares the fitness values ​​of different parameter combinations through continuous iterative search, and selects parameter combinations with higher fitness as candidate solutions for the next generation. For example, in the genetic algorithm, through selection, crossover and mutation operations, the biological evolution process is simulated to make the parameter combination gradually evolve in the optimal direction. Based on the determined molding process optimization control parameters, the brush head production optimization control is carried out, and these optimized parameters are accurately applied to the actual brush head production process. In the injection molding machine control system, the mold temperature is accurately set to the optimized temperature value to ensure that the plastic melt flows and forms at the appropriate temperature to obtain good product quality and production efficiency; the injection pressure is adjusted to the optimal value so that the plastic melt can evenly and quickly fill the mold cavity to avoid defects such as short shots and flash; the holding time and holding pressure are accurately controlled to ensure that the product is properly replenished with materials during the cooling process to reduce problems such as shrinkage marks and deformation; the cooling time is reasonably set so that the product can be fully cooled and shaped, while avoiding excessive cooling time that leads to reduced production efficiency. Throughout the production process, the operating status of the production equipment, the actual execution of the process parameters, and the real-time detection data of the product quality are continuously monitored. Through sensors, data acquisition systems and monitoring software, possible parameter deviations, equipment failures or quality abnormalities can be discovered and handled in a timely manner. For example, if it is detected that the actual value of the mold temperature deviates from the set value by more than a certain range, it is adjusted immediately; if it is found that there are defects in the appearance of the product, the possible process parameter problems are traced back and corrected in a timely manner. Through this dynamic monitoring and adjustment mechanism, it is ensured that the brush head production process is always in the optimal state and high-quality electric toothbrush brush head products are stably produced.

[0070] Embodiment 2 is based on the same inventive concept as the electric toothbrush head plastic mold forming control device in the above embodiment. Figure 2 As shown, the present application provides a method for controlling the molding of a plastic mold for an electric toothbrush head. The method and the device embodiments in the present application are based on the same inventive concept. The method includes:

[0071] Step S100: Obtain production requirements for electric toothbrush heads, design a plastic mold based on the production requirements for electric toothbrush heads, and obtain design parameters for the plastic mold for the brush heads.

[0072] Step S200: activating a plastic mold production line according to the brush head plastic mold design parameters, and performing mold production according to the brush head plastic mold design parameters through the plastic mold production line to obtain a target brush head plastic mold.

[0073] Step S300: construct a brush head molding process control space, match and optimize the brush head molding process control space based on the target brush head plastic mold, and obtain a brush head injection molding process solution set.

[0074] Step S400: using the electric toothbrush head production demand as a constraint parameter to perform a traversal search and optimization in the brush head injection molding process solution set, and output molding process control parameters.

[0075] Step S500: performing a brush head pre-molding test based on the molding process control parameters to obtain brush head molding test parameters, and performing brush head production optimization control through the brush head molding test parameters.

[0076] Furthermore, step S100 also includes:

[0077] Step S110: classifying the properties of the electric toothbrush head production requirements to obtain the property information of the brush head production requirements, wherein the property information of the brush head production requirements includes appearance requirements, dimensional accuracy and material properties.

[0078] Step S120: establishing a plastic mold design parameter library, and matching and designing the brush head production requirement attribute information based on the plastic mold design parameter library to obtain plastic mold structure design parameters.

[0079] Step S130: Modeling and simulating the plastic mold structure design parameters to obtain a simulation effect of the plastic mold debugging, wherein the simulation effect of the plastic mold debugging includes molding accuracy and molding reliability.

[0080] Step S140: optimizing and adjusting the structural design parameters of the plastic mold based on the simulation effect of the plastic mold debugging to obtain the design parameters of the brush head plastic mold.

[0081] Furthermore, step S300 also includes:

[0082] Step S310: installing the target brush head plastic mold on an injection molding machine to perform mold trial debugging and obtain injection molding loss parameters.

[0083] Step S320: determining molding process compensation parameters according to the injection molding loss parameters.

[0084] Step S330: matching and dividing the brush head plastic mold design parameters of the target brush head plastic mold with the brush head molding process control space to obtain a matching molding process control space.

[0085] Step S340: Compensate and optimize the matching molding process control space based on the molding process compensation parameters to obtain the brush head injection molding process solution set.

[0086] Furthermore, step S320 also includes:

[0087] Step S321: classify and mark the brush head molding process control space to obtain a molding process control element parameter set and corresponding injection molding effect data.

[0088] Step S322: taking each control factor parameter in the molding process control factor parameter set as a multi-factor independent variable, and taking the corresponding injection molding effect data as a molding dependent variable.

[0089] Step S323: performing correlation fitting on the multi-factor independent variables and the forming dependent variables respectively to generate a multi-control factor forming curve set.

[0090] Step S324: regressing and fusing each forming curve in the multi-control factor forming curve set to construct a forming control factor regression model.

[0091] Step S325: performing compensation analysis on the injection molding loss parameters based on the molding control factor regression model to determine the molding process compensation parameters.

[0092] Furthermore, step S400 further includes:

[0093] Step S410: using the electric toothbrush head production demand as a constraint parameter to perform a traversal search in the brush head injection molding process solution set to obtain an injection molding process selection threshold.

[0094] Step S420: obtaining a set of molding benefit evaluation indicators, evaluating and fitting the brush head molding process control space according to the set of molding benefit evaluation indicators, and constructing a molding benefit fitness function.

[0095] Step S430: randomly selecting a plurality of molding control parameters within the injection molding process selection threshold, and evaluating the plurality of molding control parameters using the molding benefit fitness function to obtain a plurality of molding parameter fitnesses.

[0096] Step S440: performing global optimization on the multiple molding control parameters based on the multiple molding parameter fitnesses, and outputting the molding process control parameters.

[0097] Furthermore, step S440 further includes:

[0098] Step S441: optimizing the plurality of molding control parameters based on the fitness of the plurality of molding parameters, and determining a preset number of plurality of optimal molding control parameters.

[0099] Step S442: taking the plurality of optimal molding control parameters as a plurality of cluster centers, and equally clustering the plurality of molding control parameters based on the plurality of cluster centers to obtain a plurality of molding parameter clusters.

[0100] Step S443: performing iterative optimization and cluster center updating in the multiple molding parameter clusters respectively to obtain multiple molding optimization parameter clusters.

[0101] Step S444: determining a plurality of update cluster centers according to the plurality of molding optimization parameter clusters, and performing global optimization on the plurality of update cluster centers to obtain the molding process control parameters.

[0102] Furthermore, step S500 further includes:

[0103] Step S510: obtaining an injection molding control parameter library according to the multiple update cluster centers.

[0104] Step S520: analyzing the injection molding control parameter library for improvement based on the brush head molding test parameters to determine the parameter improvement variation direction.

[0105] Step S530: performing parameter variation expansion on the injection molding control parameter library according to the parameter improvement variation direction to obtain a molding control expanded parameter library.

[0106] Step S540: According to the molding control expansion parameter library, optimal molding process control parameters are determined, and brush head production optimization control is performed based on the molding process optimization control parameters.

[0107] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0108] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0109] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A plastic mold forming control device for an electric toothbrush head, characterized in that: The device comprises: A design parameter acquisition module, which is used to acquire production requirements of electric toothbrush heads, design a plastic mold based on the production requirements of the electric toothbrush heads, and acquire design parameters of the plastic mold for the brush heads; A target brush head plastic mold acquisition module, the target brush head plastic mold acquisition module is used to activate a plastic mold production line according to the brush head plastic mold design parameters, and to produce a mold according to the brush head plastic mold design parameters through the plastic mold production line to obtain a target brush head plastic mold; An injection molding process solution set acquisition module, which is used to construct a brush head molding process control space, match and optimize the brush head molding process control space based on the target brush head plastic mold, and obtain a brush head injection molding process solution set; A molding process control parameter output module, the molding process control parameter output module is used to use the electric toothbrush head production demand as a constraint parameter to traverse and search for optimization in the brush head injection molding process solution set, and output molding process control parameters; A brush head production control module, wherein the brush head production control module performs a brush head pre-forming test based on the forming process control parameters to obtain brush head forming test parameters, and performs brush head production optimization control through the brush head forming test parameters; Wherein, the molding process control parameter output module also includes: Using the electric toothbrush head production demand as a constraint parameter to perform a traversal search in the brush head injection molding process solution set to obtain an injection molding process selection threshold; Acquire a set of molding benefit evaluation indicators, evaluate and fit the brush head molding process control space according to the set of molding benefit evaluation indicators, and construct a molding benefit fitness function; Randomly selecting a plurality of molding control parameters within the injection molding process selection threshold, and evaluating the plurality of molding control parameters using the molding benefit fitness function to obtain a plurality of molding parameter fitnesses; Performing global optimization on the multiple molding control parameters based on the multiple molding parameter fitnesses, and outputting the molding process control parameters; Optimizing the plurality of molding control parameters based on the plurality of molding parameter adaptabilities to determine a preset number of plurality of optimal molding control parameters; Taking the plurality of optimal molding control parameters as a plurality of cluster centers, and equally clustering the plurality of molding control parameters based on the plurality of cluster centers to obtain a plurality of molding parameter clusters; Performing iterative optimization and cluster center update in the multiple molding parameter clusters respectively to obtain multiple molding optimization parameter clusters; Determining a plurality of update cluster centers according to the plurality of molding optimization parameter clusters, and performing global optimization on the plurality of update cluster centers to obtain the molding process control parameters; Wherein, the brush head production control module also includes: Obtaining an injection molding control parameter library according to the multiple updated cluster centers; Based on the brush head molding test parameters, the injection molding control parameter library is analyzed for improvement directions to determine the parameter improvement variation directions; Perform parameter variation expansion on the injection molding control parameter library according to the parameter improvement variation direction to obtain an expanded molding control parameter library; According to the molding control expansion parameter library, optimizing and determining molding process optimization control parameters, and performing brush head production optimization control based on the molding process optimization control parameters; The design parameter acquisition module also includes: Classifying the production requirements of electric toothbrush heads to obtain the production requirements attribute information of the brush heads, wherein the production requirements attribute information of the brush heads includes appearance requirements, dimensional accuracy and material properties; Establishing a plastic mold design parameter library, and matching and designing the brush head production demand attribute information based on the plastic mold design parameter library to obtain plastic mold structure design parameters; Modeling and simulating the plastic mold structure design parameters to obtain the plastic mold debugging simulation effect, wherein the plastic mold debugging simulation effect includes molding accuracy and molding reliability; Based on the simulation effect of the plastic mold debugging, the structural design parameters of the plastic mold are optimized and adjusted to obtain the design parameters of the brush head plastic mold.

2. The electric toothbrush head plastic mold forming control device according to claim 1, characterized in that: The injection molding process solution acquisition module also includes: The target brush head plastic mold is installed on an injection molding machine for mold trial debugging to obtain injection molding loss parameters; Determining molding process compensation parameters according to the injection molding loss parameters; Based on the design parameters of the brush head plastic mold of the target brush head plastic mold, the brush head molding process control space is matched and divided to obtain a matching molding process control space; The matching molding process control space is compensated and optimized based on the molding process compensation parameters to obtain the brush head injection molding process solution set.

3. The electric toothbrush head plastic mold forming control device according to claim 2, characterized in that: The injection molding process solution acquisition module also includes: Performing data classification and marking on the brush head molding process control space to obtain a molding process control element parameter set and corresponding injection molding effect data; Taking each control factor parameter in the molding process control factor parameter set as a multi-factor independent variable, and taking the corresponding injection molding effect data as a molding dependent variable; Performing correlation fitting on the multi-factor independent variables and the molding dependent variables respectively to generate a multi-control factor molding curve set; Regressing and fusing each forming curve in the multi-control factor forming curve set to construct a forming control factor regression model; The injection molding loss parameters are compensated and analyzed based on the molding control factor regression model to determine the molding process compensation parameters.

4. A method for controlling the plastic mold forming of an electric toothbrush head, characterized in that: The method is applied to the device according to any one of claims 1 to 3, and the method comprises: Obtaining production requirements for electric toothbrush heads, designing a plastic mold based on the production requirements for electric toothbrush heads, and obtaining design parameters for the plastic mold for the brush heads; According to the brush head plastic mold design parameters, a plastic mold production line is activated, and mold production is performed according to the brush head plastic mold design parameters through the plastic mold production line to obtain a target brush head plastic mold; Constructing a brush head molding process control space, matching and optimizing the brush head molding process control space based on the target brush head plastic mold, and obtaining a brush head injection molding process solution set; Taking the electric toothbrush head production demand as a constraint parameter to perform traversal search and optimization in the brush head injection molding process solution set, and outputting the molding process control parameters; A brush head pre-molding test is performed based on the molding process control parameters to obtain brush head molding test parameters, and brush head production optimization control is performed using the brush head molding test parameters.

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