Bluetooth earphone cabin shell gradient injection molding method

Abstract

The invention relates to the technical field of information, in particular to a Bluetooth earphone cabin shell gradient injection molding method in the field of an earphone cabin shell injection molding process, which comprises the following steps: determining initial parameters of material distribution in a gradient injection molding process according to a relation model, including set values of temperature, pressure and injection molding speed; through a finite element analysis tool, the flowing and curing process of the material in the mold is simulated, and the heat conduction performance and the mechanical strength of gradient distribution are predicted; in the injection molding process, temperature and pressure changes in the mold are monitored in real time through a sensor, and dynamic data of material flowing and curing are obtained; judging whether the gradient distribution is consistent with a preset model or not according to the dynamic data; a machine learning algorithm is adopted, a performance prediction model is trained based on historical injection molding data, initial setting of material distribution parameters is optimized, and the production efficiency is improved; and through automatic detection equipment, the heat dissipation performance and the structural strength of the formed shell are tested, and the consistency and the yield of products are ensured.

Description

Technical Field

[0001] The present invention relates to the field of information technology, specifically to the field of injection molding processes for earphone case housings, and particularly to a gradient injection molding method for a Bluetooth earphone case housing. Background Art

[0002] During the gradient injection molding process of a Bluetooth earphone case housing, traditional methods use single-material injection, resulting in difficulty in simultaneously meeting the requirements for heat dissipation performance and structural strength of the housing. A single material cannot balance thermal conductivity and mechanical strength. Poor heat dissipation performance can cause the temperature of internal electronic components to be too high, affecting the device's lifespan, while insufficient structural strength reduces the impact resistance of the housing and increases the risk of damage.

[0003] Although existing gradient injection molding technologies can achieve hierarchical optimization of material properties, they mostly rely on multi-layer mold switching. This process not only increases the complexity of mold design and manufacturing but also leads to an extended production cycle, increased costs, and difficulty in achieving continuous gradient transitions of material properties. In addition, problems such as poor interfacial bonding are likely to occur during the multi-layer mold switching process, affecting the mechanical properties and appearance quality of the final product.

[0004] Therefore, there is an urgent need for a new injection molding method that can achieve continuous gradient changes in material properties within a single mold to solve the technical contradiction of difficulty in balancing heat dissipation and structural strength. At the same time, this method also needs to reduce production costs, improve production efficiency, and ensure product consistency and yield rate while guaranteeing performance improvement. Summary of the Invention

[0005] The present invention provides a gradient injection molding method for a Bluetooth earphone case housing, including the following steps:

[0006] S101. Using a preset gradient material database, obtain the thermal conductivity and mechanical strength data of the material, and establish a relationship model between material properties and gradient distribution;

[0007] S102. According to the relationship model, determine the initial parameters of material distribution during gradient injection molding, including the set values of temperature, pressure, and injection speed;

[0008] S103. Through a finite element analysis tool, simulate the flow and curing process of the material in the mold, and predict the thermal conductivity and mechanical strength of the gradient distribution;

[0009] S104. If the simulation results do not meet the preset performance threshold, adjust the material distribution parameters and re-conduct finite element analysis until the requirements for heat dissipation and structural strength are met;

[0010] S105. Use an intelligent control system to adjust the injection molding parameters in real time within a single mold, achieving a continuous gradient transition of material properties and avoiding problems of poor interfacial bonding;

[0011] S106. During the injection molding process, use sensors to monitor the temperature and pressure changes in the mold in real time, obtaining dynamic data on material flow and curing;

[0012] S107. Based on the dynamic data, determine whether the gradient distribution is consistent with the preset model. If not, feedback it to the intelligent control system to optimize the injection molding parameters in real time;

[0013] S108. Adopt machine learning algorithms to train a performance prediction model based on historical injection molding data, optimize the initial setting of material distribution parameters, and improve production efficiency;

[0014] S109. Use automated detection equipment to test the heat dissipation performance and structural strength of the molded shell, ensuring product consistency and the yield rate.

[0015] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0016] The present invention discloses a gradient injection molding method for the shell of a Bluetooth headset case, which is used to manufacture shell products with continuous gradient properties. The method first establishes a relationship model between material properties and gradient distribution and determines the initial injection molding parameters. By finite element analysis to simulate the material flow and curing process, predict the performance of the gradient distribution. If the preset threshold is not reached, adjust the parameters and re-analyze. During the injection molding process, the present invention uses an intelligent control system to adjust the parameters in real time, achieving a continuous gradient transition of material properties. At the same time, monitor the dynamic data in the mold through sensors and compare it with the preset model to optimize the injection molding parameters in real time. In addition, the present invention also uses machine learning algorithms to optimize the initial parameter setting and ensures product quality through automated detection. This method can effectively solve the problem of poor interfacial bonding in traditional injection molding, improve the heat dissipation performance and structural strength of the product, and at the same time improve production efficiency and the yield rate. Description of the Drawings

[0017] Figure 1 It is a flowchart of a gradient injection molding method for the shell of a Bluetooth headset case according to the present invention.

[0018] Figure 2 It is a schematic diagram of a gradient injection molding method for the shell of a Bluetooth headset case according to the present invention.

[0019] Figure 3 It is another schematic diagram of a gradient injection molding method for the shell of a Bluetooth headset case according to the present invention. Detailed Embodiments

[0020] To further understand the content of the present invention, the present invention will be described in detail in combination with the accompanying drawings and embodiments. The following further elaborates on the present application with reference to the accompanying drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the relevant invention and are not intended to limit the invention. Additionally, it should be noted that for ease of description, only the parts related to the invention are shown in the accompanying drawings.

[0021] As Figures 1-3 , a gradient injection molding method for the shell of a Bluetooth headset case in this embodiment may specifically include:

[0022] Step S101, using a preset gradient material database, obtain the thermal conductivity and mechanical strength data of the material, and establish a relationship model between the material properties and the gradient distribution.

[0023] Obtain a preset gradient material database, where the gradient material database includes the thermal conductivity and mechanical strength data of the material; obtain the gradient distribution parameters of the material, and calculate the gradient distribution characteristics of the material according to the gradient distribution parameters; use a first machine learning algorithm to establish a first mapping relationship between the thermal conductivity and the gradient distribution characteristics; use a second machine learning algorithm to establish a second mapping relationship between the mechanical strength and the gradient distribution characteristics; if there is a correlation between the thermal conductivity and the mechanical strength, obtain the correlation data between the thermal conductivity and the mechanical strength, and generate a comprehensive performance model of the material according to the correlation data; determine the relationship between the material properties and the gradient distribution characteristics according to the comprehensive performance model; obtain the target gradient distribution parameters, and calculate the target gradient distribution characteristics according to the target gradient distribution parameters; determine the performance parameters of the material under the target gradient distribution characteristics according to the relationship between the material properties and the gradient distribution characteristics.

[0024] Specifically, the thermal conductivity in the gradient material database usually includes typical materials such as matrix metals and functional ceramics. For example, a titanium alloy matrix may have a low thermal conductivity, while titanium carbide ceramics have a relatively high thermal conductivity. The mechanical strength database records the strength changes of each component at different temperatures. For instance, titanium alloys have high tensile strength and fatigue strength at room temperature. The gradient distribution characteristics can be characterized by parameters such as volume fraction and component thickness. For example, in the thermal barrier coating of a turbine blade, the thickness of the zirconia ceramic coating gradually decreases from the blade surface to the matrix, while the volume fraction of the titanium alloy matrix increases correspondingly. This change trend can be described by a piecewise function or a continuous function. Through machine learning algorithms such as support vector machines, a mapping relationship between thermal conductivity and gradient distribution can be established. Taking the turbine blade as an example, when the thickness of the ceramic coating increases, the overall thermal conductivity decreases, thereby improving the heat insulation effect. At the same time, a neural network model is used to analyze the relationship between mechanical strength and gradient distribution, and it is found that as the thickness of the ceramic coating increases, the interfacial stress concentration phenomenon intensifies, affecting the coating bonding strength. In the application of turbine blades, there is indeed a correlation between thermal conductivity and mechanical strength. Although a thicker ceramic coating has better heat insulation performance, being too thick will cause interfacial peeling. Therefore, it is necessary to establish a comprehensive performance model to ensure good bonding strength of the coating while guaranteeing sufficient heat insulation effect. Through this model, the comprehensive performance under different gradient distributions can be predicted. Another typical application is armor protection materials, where the gradient change of ceramics and metal matrixes not only affects the propagation of impact energy but also determines the overall structural strength. The surface layer with a higher ceramic content provides good bulletproof performance, while the metal matrix ensures sufficient toughness. Through gradient distribution optimization, the best balance between protection performance and structural reliability can be achieved. According to the analysis results of the comprehensive performance model, the optimal gradient distribution of the material in various application scenarios can be determined. For example, under high-temperature working conditions, it may be necessary to increase the thickness of the surface layer ceramics to provide better thermal protection, while in the case of large mechanical loads, it is necessary to appropriately reduce the ceramic thickness to improve structural reliability. This optimized design provides important guidance for the practical application of materials.

[0025] Step S102, according to the relationship model, determine the initial parameters of the material distribution in the gradient injection molding process, including the set values of temperature, pressure, and injection speed.

[0026] Obtain historical data on the material distribution during the gradient injection molding process, and establish a correlation model for the temperature, the pressure, and the injection speed; according to the correlation model, calculate the optimal temperature value, pressure value, and speed value range of the material distribution; according to the optimal temperature value, pressure value, and speed value range, determine the initial set values of the temperature, the pressure, and the injection speed; obtain the material distribution state information, and judge whether there is an abnormality in the material distribution state; if there is an abnormality in the material distribution state, adjust the set values of the temperature, the pressure, and the injection speed, and recalculate the material distribution state; monitor the material distribution state in real time, and judge whether the set values of the temperature, the pressure, and the injection speed reach the predetermined target; if the set values of the temperature, the pressure, and the injection speed do not reach the predetermined target, automatically adjust the set values of the temperature, the pressure, and the injection speed to optimize the material distribution state; use a machine learning algorithm to learn the adjusted set values of the temperature, the pressure, and the injection speed, and update the correlation relationship between the temperature, pressure, and injection speed in the correlation model.

[0027] Specifically, the historical data of material distribution during the gradient injection molding process includes three core parameters: temperature, pressure, and injection speed. The historical data shows that when polypropylene material is injection molded, the mold cavity temperature ranges from 180 to 220 degrees Celsius, the injection pressure is controlled between 70 and 90 MPa, and the injection speed is maintained between 50 and 70 mm / s, the material distribution is the most uniform. By analyzing these data, a correlation model between parameters can be established. When calculating the optimal parameter range using a preset algorithm, a neural network model can be used. Taking polycarbonate as an example, through the analysis of a large amount of historical data, it is found that when the mold temperature is 280 degrees Celsius, the combination of an injection pressure of 100 MPa and an injection speed of 60 mm / s can achieve the best material distribution state. These parameters are used as initial settings to provide a basis for subsequent optimization. In actual production, if it is found that the material distribution is uneven, such as accumulation at the mold corners, the parameters need to be adjusted in a timely manner. For example, the injection pressure can be increased to 110 MPa while reducing the injection speed to 50 mm / s, which can improve the flow performance of the material at the corners. The real-time monitoring system continuously collects production data through pressure sensors and temperature sensors. When it detects that the temperature in a certain area of the mold is too low, the system will automatically increase the heating power of that area. Machine learning algorithms play an important role in parameter optimization. Taking nylon material as an example, by learning the parameters after multiple adjustments, the system finds that when the mold temperature increases, the injection pressure needs to be correspondingly reduced to maintain a good material distribution. This correlation is updated into the model to guide subsequent production. The evaluation of the material distribution state uses multiple indicators. For example, the pressure difference in each area of the mold does not exceed 5%, the temperature fluctuation is controlled within the range of plus or minus 3 degrees, and the wall thickness error of the finished product is less than 2%, etc. These indicators jointly determine the direction and amplitude of parameter adjustment. When it is found that the wall thickness is too thin in a certain place, the system will appropriately increase the holding pressure time and pressure in that area while ensuring that it does not affect the material distribution in other areas. Through continuous optimization and learning, the relationship model becomes more accurate. For example, for polyethylene materials, the system can already automatically recommend the most suitable process parameter combinations according to the structural characteristics of different products and make fine-tuning according to real-time feedback during the production process to ensure the stability of product quality. This intelligent parameter adjustment method greatly improves the efficiency and product quality of gradient injection molding.

[0028] Step S103, through a finite element analysis tool, simulate the flow and curing process of the material in the mold, and predict the heat conduction performance and mechanical strength of the gradient distribution.

[0029] A mathematical model of material flow and solidification is established using finite element tools to obtain data on the flow state and solidification state of the material within the mold. Based on the flow state and solidification state data, the thermal conductivity values and mechanical strength values inside the material are calculated through a gradient distribution algorithm. For the thermal conductivity values and mechanical strength values, a regression model in machine learning algorithms is used to predict the gradient distribution map of the material within the mold. If the predicted gradient distribution map matches the preset threshold range, it is determined that the performance values of the material meet the design requirements. If not, the simulation parameters of the flow state and solidification state are adjusted, and the thermal conductivity values and mechanical strength values are recalculated. According to the adjusted parameters, the flow and solidification processes of the material within the mold are simulated again using finite element tools to obtain new flow state and solidification state data. With the new flow state and solidification state data, a new gradient distribution map is regenerated to determine whether the material performance values meet the design requirements.

[0030] Specifically, finite element analysis tools play an important role in the analysis of material flow and curing processes. For example, in the production of polycarbonate injection-molded parts, by setting the initial material temperature to 230 degrees Celsius and the mold temperature to 80 degrees Celsius, the temperature distribution, velocity field, and pressure field changes during the material flow process can be simulated. During the curing process, the material transforms from a flowing state to a cured state, and volume shrinkage occurs during this stage. Through finite element analysis, product size deviations can be predicted. After obtaining the flowing state and cured state data, the gradient distribution algorithm can calculate the internal properties of the material. Taking polyamide injection-molded parts as an example, by analyzing the crystallinity and molecular orientation at different positions, thermal conductivity performance values can be obtained. For example, the thermal conductivity coefficient is 0.2 watts per meter Kelvin in the edge region of the product and 0.3 watts per meter Kelvin in the central region. The mechanical strength values are shown as a tensile strength of 50 megapascals in the edge region and 60 megapascals in the central region. The machine learning regression model can predict the material gradient distribution. In the production of polypropylene injection-molded parts, a support vector regression model is used. The process parameters such as the temperature and pressure in each region of the mold are used as input variables, and the local thermal conductivity coefficient and tensile strength are used as output variables to establish a prediction model. Through training with a large amount of historical data, the model can predict the performance distribution under different process parameters. The prediction results need to match the preset threshold range. For example, the thermal conductivity performance requirement is in the range of 0.2 to 0.4 watts per meter Kelvin, and the mechanical strength requirement is in the range of 40 to 70 megapascals. If the performance value in a certain region exceeds the range, the simulation parameters need to be adjusted. The performance distribution can be improved by increasing the mold temperature in that region or decreasing the injection speed. After adjustment, re-conduct finite element analysis to obtain new flowing state and cured state data. Through iterative optimization, finally, the material performance distribution meets the requirements. This method can effectively improve the quality stability of injection-molded products, reduce the number of trial molds, and lower production costs. In practical applications, different materials and products have different requirements for performance distribution, and reasonable threshold ranges and optimization strategies need to be set according to specific situations. Through this systematic analysis method, the performance distribution of injection-molded products can be accurately predicted and controlled to ensure product quality. At the same time, the accumulated data and experience can be used for the development of subsequent similar products to improve development efficiency. This method is particularly suitable for the production of precision injection-molded parts with high performance requirements, such as in the fields of medical devices and optical components.

[0031] Step S104, if the simulation result does not reach the preset performance threshold, adjust the material distribution parameters and re-conduct finite element analysis until the requirements for heat dissipation and structural strength are met.

[0032] Obtain the result value of the finite element analysis; compare the result value with a preset value to obtain a comparison result; if the comparison result indicates that the result value does not reach the preset value, calculate an adjustment amount, and adjust the material distribution parameters according to the adjustment amount to obtain adjusted material distribution parameters; according to the adjusted material distribution parameters, re - perform the finite element analysis to obtain a new result value; determine whether the new result value reaches the preset value; if the new result value reaches the preset value, output the material distribution parameters that meet the heat dissipation and structural strength; if the new result value does not reach the preset value, repeat the steps of adjusting the material distribution parameters, re - performing the finite element analysis, and determining whether the new result value reaches the preset value until the new result value reaches the preset value.

[0033] Specifically, finite element analysis is an effective numerical method for studying complex engineering problems and is widely used in material design. Taking the radiator design as an example, result values such as temperature distribution and heat flux density can be obtained through finite element analysis. If the highest temperature on the radiator surface is 85 degrees, while the preset safety threshold is 70 degrees, it indicates that the current design scheme does not meet the heat dissipation requirements. At this time, the adjustment amount needs to be calculated, such as increasing the heat dissipation area or changing the fin pitch. The determination of the adjustment amount is based on the relationship between the temperature difference and the thermal resistance. The greater the temperature difference, the greater the required adjustment amplitude. The adjustment of material distribution parameters involves multiple aspects. Taking a composite material structural part as an example, if the stress analysis shows that the stress concentration in a certain area exceeds the preset value, the stress distribution can be optimized by adjusting parameters such as fiber content and arrangement direction. For example, the fiber content is adjusted from 30% to 40%, or the fiber direction is changed from 0 degrees to plus or minus 45 degrees. After each adjustment, the finite element analysis is re - performed to evaluate the performance of the new scheme. The judgment of the result value needs to consider multiple indicators comprehensively. Taking the shell of an electronic product as an example, it is necessary to meet both the structural strength requirements and ensure the heat dissipation performance. Suppose the shell material is a modified engineering plastic, and through finite element analysis, the maximum deformation is 0.8 mm and the heat distortion temperature is 120 degrees. If the preset values are 1 mm and 100 degrees respectively, it indicates that the strength index meets the requirements but the heat dissipation performance needs to be improved. The performance can be optimized by adding heat - conducting fillers or adjusting the wall thickness distribution. Repeat the optimization process until the design goal is achieved. Taking the dashboard bracket of a car as an example, the first analysis shows insufficient stiffness, and it is optimized by increasing the number of ribs and adjusting the rib layout. After each round of iterative analysis, new stress distribution and deformation data are obtained and compared with the preset stiffness and strength requirements. If the maximum stress in a certain iteration result is 100 MPa and the deformation is 2 mm, both of which are lower than the preset threshold, it is confirmed that the design scheme is feasible. This process reflects the iterative optimization characteristics of finite element analysis in engineering design, and a satisfactory design scheme is finally obtained through continuous adjustment and verification.

[0034] Step S105: Use an intelligent control system to adjust the injection molding parameters in real time within a single mold to achieve a continuous gradient transition of material properties and avoid problems with poor interfacial bonding.

[0035] Obtain the current temperature distribution data within the mold, and combine it with the preset material property gradient curve to determine the initial adjustment range of the injection molding parameters. Real-time collect the pressure, temperature, and flow rate data during the injection molding process through the intelligent control system, and compare and analyze it with the preset gradient performance curve. If it is detected that the current material property distribution deviates from the preset curve, calculate the dynamic adjustment amount of the injection molding parameters according to the degree of deviation. Use the intelligent control system to adjust the injection pressure, injection speed, and mold temperature of the injection molding machine in real time, so that the material property distribution gradually approaches the preset curve. Continuously monitor the temperature change in the interfacial bonding area to determine whether there is a temperature mutation or stress concentration phenomenon. If it is detected that the interfacial bonding area is abnormal, immediately adjust the injection molding parameters in the adjacent area to form a smooth temperature transition zone. After the injection molding is completed, obtain the material property distribution data of the final molded part, compare it with the preset gradient performance curve, and evaluate the interfacial bonding quality.

[0036] Specifically, the injection molding process data collected by the intelligent control system includes three dimensions: material temperature, pressure, and flow rate. Taking the injection molding of polyetheretherketone parts as an example, multiple groups of temperature sensors are set on the inner cavity surface of the mold to monitor the temperature changes in different regions in real time. By collecting the temperature data of the combined area through the acquisition interface, it is found that there is a sudden temperature change at the junction of the two materials, and the temperature difference can reach 30°C, which will lead to a decrease in the interface bonding strength. By adjusting the injection molding parameters, a 20-mm-wide temperature transition zone is formed at the junction area, and the temperature difference is controlled within 10°C, effectively improving the interface bonding quality. The material property gradient curve reflects the continuous change of material properties from one end of the product to the other. Taking the elastic modulus as an example, at one end of the product, the elastic modulus is required to reach 2500 MPa, while at the other end, it is required to drop to 1500 MPa, and a smooth transition needs to be formed in the middle area. By adjusting the injection molding parameters, the material generates different degrees of orientation during the molding process, thus realizing the gradient change of properties. When it is detected that the elastic modulus of a certain area deviates from the preset curve by more than 10%, the system will automatically adjust the injection pressure and speed of that area. The core of the intelligent control system is to establish the mapping relationship between injection molding parameters and material properties. Taking the injection pressure as an example, in the range of 20 MPa to 40 MPa, for every 5 MPa increase in pressure, the crystallinity of the material will increase by about 3%, which directly affects the mechanical properties of the final product. The system judges whether the current pressure is within the target range according to the pressure data collected in real time. If it deviates from the target value, it will be dynamically adjusted through a proportional valve. In terms of temperature control in the interface bonding area, a zoning temperature control method is adopted. The surface of the mold cavity is divided into multiple temperature control areas, and the temperature of each area can be adjusted independently. Taking the injection molding of a two-color cover plate as an example, the mold temperature of the hard area is set at 180°C, and the soft area is set at 160°C. The junction is controlled by a temperature gradient so that the two materials can be combined under good temperature conditions. When the temperature in the interface area is abnormal, the system will first adjust the mold temperature of the adjacent area to ensure a uniform temperature distribution. The performance evaluation after injection molding adopts a non-destructive testing method. Through ultrasonic scanning technology, the internal structure information of the interface bonding area can be obtained. Combining infrared thermography analysis, the actual distribution of material properties can be determined. Taking the injection molding of a shock-absorbing bracket as an example, through scanning analysis, it is found that there are microscopic pores in the interface bonding area, accounting for 5%, exceeding the preset threshold of 3%. The system immediately adjusts the injection molding process parameters to control the porosity within the allowable range, ensuring the product quality.

[0037] Step S106, during the injection molding process, the temperature and pressure changes in the mold are monitored in real time through sensors to obtain the dynamic data of material flow and curing.

[0038] Obtain the temperature value and pressure value collected by the sensor in the mold to obtain real-time data; analyze the corresponding relationship between the material flow state and the solidification state for the data change amount in the real-time data; judge the material state conversion characteristics according to the dynamic changes of the temperature value and the pressure value; if the temperature value reaches the preset threshold, determine that the material enters the completion stage of the solidification state; if the pressure value shows a preset abnormal fluctuation, judge that there is a defect in the material flow state and determine the defect position; generate a complete state model of the material flow state and the solidification state according to the data distribution of the monitoring points; optimize the process parameter settings in the mold according to the state models of the flow state and the solidification state.

[0039] Specifically, the acquisition of temperature and pressure data by sensors inside the mold is a key link in obtaining dynamic information during the injection molding process. In practical applications, temperature and pressure can be monitored in real time by arranging thermocouples and pressure sensors on the surface of the mold cavity. Taking the common injection molding of polypropylene as an example, multiple monitoring points are arranged on the surface of the mold cavity to collect temperature change data during the injection molding process. If the measured temperature drops from the initial 230 degrees Celsius to 130 degrees Celsius, it indicates that the material has started to crystallize and solidify. The judgment of the material flow and solidification state can be achieved by analyzing the characteristics of the temperature and pressure change curves. For example, during the injection filling stage, the pressure value will show a rapid upward trend. When the pressure suddenly shows abnormal fluctuations, such as a sharp drop from 60 MPa to 40 MPa, it indicates that there may be defects such as poor weld or air cavity at this position. During the holding pressure stage, the change rate of the temperature curve can reflect the degree of material solidification. When the temperature drop rate is lower than 0.5 degrees Celsius per second, it indicates that this area has basically completed solidification. By establishing state models for the flowing state and solidified state, the optimization adjustment of process parameters can be realized. By arranging multiple monitoring points inside the mold cavity to form a distribution map of the temperature field and pressure field, combined with the analysis of the material flow path, potential defect areas can be identified. For example, if it is found that the pressure value in a local area is too low, which may lead to sink marks or warpage deformation, the holding pressure value in this area needs to be increased or the holding pressure time needs to be extended at this time. If the temperature drops too quickly at a certain place, which may cause stress concentration, the mold temperature should be appropriately increased or the cooling rate should be reduced. For products with different structural characteristics, different monitoring strategies can be adopted. For products with a relatively large wall thickness, focus on the temperature changes in the central area of the cavity to ensure full internal solidification. For thin-walled products, it is necessary to closely monitor the pressure changes at the flow front to avoid insufficient filling caused by excessive local pressure drop. Through the comprehensive analysis of the monitoring data, process anomalies can be detected in a timely manner and adjusted to improve the quality stability of the products. In actual production, a correlation model between process parameters and product quality can be established based on the collected historical data. For example, by analyzing the correspondence between the temperature field distribution in different areas and the product dimensional accuracy, a prediction model can be established to guide the optimization setting of process parameters. When abnormal data is detected, the system automatically adjusts relevant parameters, such as increasing or decreasing the local mold temperature, adjusting the injection speed, etc., to achieve closed-loop control of product quality. This intelligent control method based on sensor data can effectively improve the stability of the injection molding process and the product qualification rate.

[0040] Step S107: According to the dynamic data, determine whether the gradient distribution is consistent with the preset model. If not, feedback it to the intelligent control system to optimize the injection molding parameters in real time.

[0041] Obtain the dynamic data collected by sensors during the injection molding process, and extract the gradient distribution characteristics for the dynamic data; compare the gradient distribution characteristics with a preset model to determine whether there is a deviation between the gradient distribution characteristics and the preset model; if there is a deviation between the gradient distribution characteristics and the preset model, extract the deviation value and calculate the optimization amount; adjust the injection molding parameters of the intelligent control system according to the optimization amount to generate a new parameter combination; use the regression model in the machine learning algorithm to predict the effect corresponding to the new parameter combination to obtain a prediction result; if the prediction result meets the preset conditions, update the parameter configuration of the intelligent control system; record the optimized parameter configuration and the dynamic data through the data storage module.

[0042] Specifically, during the injection molding process, the dynamic data collected by sensors includes key process parameters such as mold wall temperature, injection pressure, and holding pressure. These data exhibit different gradient distribution characteristics. For example, the temperature gradient in the runner decreases gradually from the inlet to the end, while the pressure gradient shows a trend of first increasing and then decreasing. The preset model is usually established based on the ideal state under standard process conditions. For instance, the temperature gradient should be maintained at a decrease of two to three degrees per centimeter, and the pressure gradient should show a smooth transition. When there is a gradient distribution deviation in actual production, specific deviation characteristics need to be extracted. For example, during the production of a certain injection molded part, the temperature gradient in a certain area of the mold suddenly increases to five degrees per centimeter, far exceeding the preset standard of three degrees, and at the same time, the pressure gradient in this area shows violent fluctuations. In this case, the system will calculate the optimization amounts for temperature and pressure, such as reducing the mold temperature setting value in this area by ten degrees and appropriately increasing the holding pressure value. The intelligent control system automatically adjusts the parameters according to the optimization amounts to form a new parameter combination. For example, the mold temperature in this area is adjusted from the original 180 degrees to 170 degrees, the holding pressure is increased from 40 MPa to 45 MPa, and the injection speed is reduced from 50 mm / s to 45 mm / s. The coordinated adjustment of these parameters can help stabilize the molding process. When the regression model predicts the effect of the new parameters, it will consider the mutual influence of multiple process indicators. For example, a decrease in mold temperature will lead to a decrease in fluidity, so it is necessary to compensate by increasing the holding pressure to ensure the filling density of the product. The prediction model will calculate the product quality indicators such as warpage deformation amount and sink mark depth under this parameter combination based on historical data. After meeting the preset conditions, the system will update the parameter configuration and store it. For example, after optimization of a certain injection molded product, the quality indicators meet the requirements: the warpage deformation amount is controlled within 0.2 mm, the sink mark depth is less than 0.1 mm, and the product size tolerance is maintained within the range of plus or minus 0.05 mm. These data and the corresponding process parameters will be recorded for subsequent process optimization and quality traceability. In this way, a complete data closed-loop is formed to continuously improve the injection molding process level.

[0043] Step S108: Using a machine learning algorithm, train a performance prediction model based on historical injection molding data, optimize the initial setting of material distribution parameters, and improve production efficiency.

[0044] Obtain historical injection molding data, where the historical injection molding data includes material parameters and distribution parameters; based on the historical injection molding data, train a performance prediction model using a random forest algorithm; through the performance prediction model, predict the production efficiency under different combinations of material parameters and distribution parameters to obtain the predicted production efficiency; determine whether the predicted production efficiency is lower than a preset threshold. If so, use a support vector machine algorithm to optimize the initial setting of the material parameters and distribution parameters to obtain the optimized material parameters and distribution parameters; according to the optimized material parameters and distribution parameters, retrain the performance prediction model to obtain the retrained performance prediction model; through the retrained performance prediction model, predict the optimized production efficiency; determine whether the optimized production efficiency reaches the expected goal.

[0045] Specifically, in injection molding production, the acquisition of historical data includes material parameters such as the melting point and fluidity of plastic pellets, as well as distribution parameters such as temperature distribution and pressure distribution. Through the real-time data collected by various sensors during the production process, a complete historical data set can be formed. For example, key indicators such as the change in fluidity of polypropylene material at different temperatures and the pressure distribution in each area of the mold. The random forest algorithm predicts performance by constructing multiple decision trees, and each decision tree is trained based on different subsets of features. For example, parameter combinations such as setting the mold temperature to 230 degrees Celsius, the injection pressure to 80 megapascals, and the holding pressure time to 10 seconds can be used to predict the product qualification rate and production cycle under these conditions. By analyzing a large amount of historical data, the algorithm can identify the key parameter combinations that affect production efficiency. When the predicted production efficiency is lower than the target value, the initial parameter settings need to be adjusted. For example, the original set screw speed is 60 revolutions per minute, but the prediction shows that the production capacity is only 80 pieces per hour, which is lower than the target value of 100 pieces per hour. At this time, the parameter settings need to be optimized. The support vector machine algorithm can find a better parameter combination by establishing an optimal classification hyperplane. For example, increasing the screw speed to 70 revolutions per minute and appropriately raising the barrel temperature at the same time. After optimizing the parameters, the performance prediction model needs to be retrained. The new training data includes the optimized parameter combinations and their corresponding production performance. In this way, the model can more accurately predict the production efficiency under different parameter settings. For example, the original model prediction error was about 10%, and after retraining, the error can be reduced to within 5%. This dynamic optimization process ensures that the model can always accurately reflect the current production situation. For different plastic raw materials, the optimization strategy also needs to be adjusted accordingly. Taking polycarbonate as an example, it is more sensitive to temperature, and the temperature distribution parameters need to be optimized. For materials such as polyethylene, more attention needs to be paid to the optimization of pressure distribution parameters. By establishing the mapping relationship between material properties and process parameters, parameter optimization can be carried out more targeted. In practical applications, the influence of mold structure on parameter optimization also needs to be considered. For example, for thin-walled products, the flow path is longer, which requires special attention to the uniformity of pressure distribution during optimization. For thick-walled products, the holding pressure parameters need to be optimized to prevent defects such as shrinkage and warping. Through this multi-dimensional parameter optimization, the stability and efficiency of injection molding production can be significantly improved.

[0046] Step S109, use an automated detection device to test the heat dissipation performance and structural strength of the formed shell to ensure product consistency and yield.

[0047] Obtain the temperature distribution data and stress distribution data of the formed shell to obtain the first detection data set; preprocess the first detection data set to remove outliers and noise data, and generate the second detection data set; extract the heat dissipation performance index and structural strength index from the second detection data set, and construct a performance index matrix; according to the preset performance threshold, judge whether each index in the performance index matrix meets the standard to obtain a preliminary judgment result; use a machine learning algorithm to optimize the preliminary judgment result, correct misjudgments and missed judgments, and obtain the final judgment result; calculate the yield rate according to the final judgment result; perform correlation analysis on the yield rate and the performance index matrix to establish a quality control model.

[0048] Specifically, the automated detection equipment includes an infrared thermal imager and a stress and strain detector. The thermal imager can monitor the surface temperature distribution of the formed shell in real time, and the stress and strain detector measures the stress distribution of each key point of the shell through a sensor array. For example, multiple detection points are arranged on the surface of the shell, and temperature and stress data are collected at each point to form a multi-dimensional data matrix. The data preprocessing link focuses on identifying and removing abnormal data points. In the temperature data, if the temperature of a certain point drops or rises suddenly by more than three times the average value of adjacent points, it is determined as abnormal. If there are outliers in the stress data that deviate significantly from the normal distribution, they also need to be removed. Through such preprocessing, the data quality for subsequent analysis can be guaranteed. The performance index matrix includes two major categories of indicators: heat dissipation performance and structural strength. The heat dissipation performance mainly examines the temperature uniformity and the highest temperature point. For example, the temperature difference on the surface of the shell does not exceed ten degrees, and the highest temperature does not exceed seventy degrees. The structural strength indicators include the stress concentration coefficient and the deformation amount. For example, the stress concentration coefficient needs to be less than two, and the maximum deformation amount does not exceed 0.5 mm. The preliminary judgment uses the threshold method to compare each index with the preset standard. If the temperature uniformity exceeds the standard but the structural strength is qualified, it is determined that the heat dissipation performance does not meet the standard. The machine learning algorithm can be optimized using a support vector machine. A classification model is established through training data to improve the judgment accuracy. The yield rate calculation is based on the final judgment result. For example, if 95 out of 100 continuously produced products are qualified, the yield rate is 95%. The quality control model finds the key factors affecting product quality by analyzing the correlation between the yield rate and each performance index. If it is found that the temperature uniformity is strongly correlated with the yield rate, the temperature control parameters can be optimized. By establishing a closed-loop management of data collection, analysis, judgment, and optimization, continuous improvement of the production process can be achieved. For example, if it is found that the yield rate of a certain batch has decreased and it is caused by uneven mold temperature through analysis, the heating parameters are adjusted in time to bring the product quality back to the normal level. This data-based intelligent management method can effectively improve the stability of product quality.

[0049] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A gradient injection molding method for the shell of a Bluetooth headset case, characterized in that, The method includes the following steps: S101. Using a preset gradient material database, obtain the thermal conductivity and mechanical strength data of the material, and establish a relationship model between the material properties and the gradient distribution; S102. According to the relationship model, determine the initial parameters of the material distribution during the gradient injection molding process, including the set values of temperature, pressure, and injection speed; S103. Through a finite element analysis tool, simulate the flow and curing process of the material in the mold, and predict the thermal conductivity and mechanical strength of the gradient distribution; S104. If the simulation results do not meet the preset performance threshold, adjust the material distribution parameters and perform finite element analysis again until the requirements for heat dissipation and structural strength are met; S105. Using an intelligent control system, adjust the injection molding parameters in real time within a single mold to achieve a continuous gradient transition of the material properties and avoid problems with poor interfacial bonding; S106. During the injection molding process, use sensors to monitor the temperature and pressure changes in the mold in real time to obtain the dynamic data of the material flow and curing; S107. According to the dynamic data, determine whether the gradient distribution is consistent with the preset model. If not, feedback it to the intelligent control system to optimize the injection molding parameters in real time; S108. Adopt a machine learning algorithm to train a performance prediction model based on historical injection molding data, optimize the initial setting of the material distribution parameters, and improve production efficiency; S109. Through an automated inspection device, test the heat dissipation performance and structural strength of the molded shell to ensure the consistency and yield rate of the product.

2. The method according to claim 1, characterized in that, The S101 includes: Obtain a preset gradient material database, where the gradient material database includes the thermal conductivity and mechanical strength data of the material; Obtain the gradient distribution parameters of the material, and calculate the gradient distribution characteristics of the material according to the gradient distribution parameters; Adopt a first machine learning algorithm to establish a first mapping relationship between the thermal conductivity and the gradient distribution characteristics; Adopt a second machine learning algorithm to establish a second mapping relationship between the mechanical strength and the gradient distribution characteristics; If there is a correlation between the thermal conductivity and the mechanical strength, obtain the correlation data between the thermal conductivity and the mechanical strength, and generate a comprehensive performance model of the material according to the correlation data; According to the comprehensive performance model, determine the relationship between the material properties and the gradient distribution characteristics; Obtain the target gradient distribution parameters, and calculate the target gradient distribution characteristics according to the target gradient distribution parameters; According to the relationship between the material properties and the gradient distribution characteristics, determine the performance parameters of the material under the target gradient distribution characteristics.

3. The method according to claim 1, wherein The S102 includes: Obtain the historical data of the material distribution during the gradient injection molding process, and establish an association relationship model between the temperature, the pressure, and the injection speed; According to the association relationship model, calculate the optimal temperature value, pressure value, and speed value range of the material distribution; According to the optimal temperature value, pressure value, and speed value range, determine the initial setting values of the temperature, the pressure, and the injection speed; Obtain the material distribution state information, and judge whether there is an abnormality in the material distribution state; If there is an abnormality in the material distribution state, adjust the set values of the temperature, the pressure, and the injection speed, and recalculate the material distribution state; Monitor the material distribution state in real time, and determine whether the set values of the temperature, the pressure, and the injection speed reach the predetermined target; If the set values of the temperature, the pressure, and the injection speed do not reach the predetermined target, automatically adjust the set values of the temperature, the pressure, and the injection speed to optimize the material distribution state; Use a machine learning algorithm to learn the adjusted set values of the temperature, the pressure, and the injection speed, and update the association relationships of temperature, pressure, and injection speed in the association relationship model.

4. The method according to any one of claims 1 to 3, characterized in that, The S103 includes: Use a finite element tool to establish a mathematical model of material flow and curing, and obtain the flow state and curing state data of the material in the mold; According to the flow state and curing state data, calculate the heat conduction performance value and mechanical strength value inside the material through the gradient distribution algorithm; For the heat conduction performance value and mechanical strength value, use a regression model in the machine learning algorithm to predict the gradient distribution map of the material in the mold; If the predicted gradient distribution map matches the preset threshold range, it is determined that the performance value of the material meets the design requirements; If they do not match, adjust the simulation parameters of the flow state and curing state, and recalculate the heat conduction performance value and mechanical strength value; According to the adjusted parameters, use the finite element tool to simulate the flow and curing process of the material in the mold again, and obtain new flow state and curing state data; Use the new flow state and curing state data to regenerate the gradient distribution map, and judge whether the material performance value meets the design requirements.

5. The method according to any one of claims 1 to 3, characterized in that, The S104 includes: Obtain the result value of the finite element analysis; Compare the result value with the preset value to obtain a comparison result; If the comparison result indicates that the result value does not reach the preset value, calculate the adjustment amount, and adjust the material distribution parameters according to the adjustment amount to obtain the adjusted material distribution parameters; According to the adjusted material distribution parameters, perform the finite element analysis again to obtain a new result value; Judge whether the new result value reaches the preset value; If the new result value reaches the preset value, output the material distribution parameters that meet the heat dissipation and structural strength; If the new result value does not reach the preset value, repeat the steps of adjusting the material distribution parameters, performing the finite element analysis again, and judging whether the new result value reaches the preset value until the new result value reaches the preset value.

6. The method according to any one of claims 1-3, characterized in that, The S105 includes: Obtain the current temperature distribution data in the mold, and combine it with the preset material performance gradient curve to determine the initial adjustment range of the injection parameters; Through the intelligent control system, collect the pressure, temperature, and flow rate data during the injection process in real time, and compare and analyze them with the preset gradient performance curve. If it is detected that the current material performance distribution deviates from the preset curve, calculate the dynamic adjustment amount of the injection parameters according to the deviation degree; Use the intelligent control system to adjust the injection pressure, injection speed, and mold temperature of the injection molding machine in real time, so that the material performance distribution gradually approaches the preset curve; Continuously monitor the temperature change in the interface bonding area to determine whether there are temperature mutations or stress concentration phenomena. If abnormalities are detected in the interface bonding area, immediately adjust the injection molding parameters in the adjacent area to form a smooth temperature transition zone; After injection molding is completed, obtain the material property distribution data of the final molded part and compare it with the preset gradient property curve to evaluate the interface bonding quality.

7. The method according to any one of claims 1-3, characterized in that, The S106 includes: Obtain the temperature values and pressure values collected by the sensor in the mold to obtain real-time data; Analyze the corresponding relationship between the material flow state and the solidified state for the data change amount in the real-time data; Judge the material state conversion characteristics according to the dynamic changes of the temperature values and pressure values; If the temperature value reaches the preset threshold, it is determined that the material enters the completion stage of the solidified state; If the pressure value shows a preset abnormal fluctuation, it is judged that there are defects in the material flow state and the defect position is determined; Generate a complete state model of the material flow state and the solidified state according to the data distribution of the monitoring points; Optimize the process parameter settings in the mold according to the state models of the flow state and the solidified state.

8. The method according to any one of claims 1 to 3, characterized in that The S107 includes: Obtain the dynamic data collected by the sensor during the injection molding process and extract the gradient distribution characteristics for the dynamic data; Compare the gradient distribution characteristics with the preset model to judge whether there is a deviation between the gradient distribution characteristics and the preset model; If there is a deviation between the gradient distribution characteristics and the preset model, extract the deviation value and calculate the optimization amount; Adjust the injection molding parameters of the intelligent control system according to the optimization amount to generate a new parameter combination; Use the regression model in the machine learning algorithm to predict the effect corresponding to the new parameter combination to obtain a prediction result; If the prediction result meets the preset conditions, update the parameter configuration of the intelligent control system; Record the optimized parameter configuration and the dynamic data through the data storage module.

9. The method according to any one of claims 1 to 3, characterized in that The S108 includes: Obtain historical injection molding data, and the historical injection molding data includes material parameters and distribution parameters; Based on the historical injection molding data, train a performance prediction model using the random forest algorithm; Through the performance prediction model, predict the production efficiency under different combinations of material parameters and distribution parameters to obtain the predicted production efficiency; Judge whether the predicted production efficiency is lower than the preset threshold. If so, use the support vector machine algorithm to optimize the initial settings of the material parameters and distribution parameters to obtain the optimized material parameters and distribution parameters; Retrain the performance prediction model according to the optimized material parameters and distribution parameters to obtain the retrained performance prediction model; Predict the optimized production efficiency through the retrained performance prediction model; Judge whether the optimized production efficiency reaches the expected goal.

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