Intelligent toy injection molding process optimization method

Abstract

The invention provides an intelligent toy injection molding process optimization method which comprises the following steps: aiming at a key temperature control area, calculating an optimized cooling time value and a cooling water temperature set value through a correlation model of thermodynamic attribute parameters of toy materials, cooling time and cooling water temperature, and issuing the cooling time value and the cooling water temperature set value to an injection molding machine control system, meanwhile, according to the temperature fluctuation amplitude of the key area, the forming pressure value is finely adjusted, so that the temperature fluctuation of the key area is reduced; in the injection molding process, temperature data of multiple positions on the surface of the mold are collected in real time and transmitted to the central processing unit, the central processing unit conducts filtering and denoising on the collected temperature data and then judges whether the temperature data exceed the optimized temperature control range or not, if yes, an alarm mechanism is triggered, and meanwhile operation parameters of the injection molding machine are automatically adjusted. And the time for deviating from the temperature control range is shortened to the minimum.

Description

Technical Field

[0001] The present invention relates to the field of information technology, in particular to injection molding, and specifically relates to an intelligent optimization method for toy injection molding process. Background Art

[0002] During the toy injection production process, how to accurately control the mold temperature distribution according to the structural characteristics and material properties of the toy to ensure product quality. First, we need to obtain information such as the complexity of the toy structure, wall thickness distribution, etc., as well as parameters such as the heat distortion temperature and thermal conductivity of the material. Through a pre-established correlation model, we can initially determine the setting ranges of the heating temperature, cooling time, and molding pressure. However, in actual production, the mold temperature distribution is often uneven, which may lead to product defects. Therefore, we need to perform numerical simulation of the mold temperature field and analyze the temperature distribution map through image processing algorithms. If temperature abnormal areas are found, we need to determine whether they are located in parts with complex toy structures or large wall thickness changes. For these key temperature control areas, we need to optimize the cooling time and cooling water temperature, and at the same time fine-tune the molding pressure. During this process, we also need to monitor the mold surface temperature in real time and dynamically adjust the injection molding machine parameters according to the actual situation. Finally, through the detection and feedback of the finished product quality, we can continuously optimize the process parameter model to improve production accuracy and efficiency. Summary of the Invention

[0003] The present invention provides an intelligent optimization method for toy injection molding process, mainly including:

[0004] Obtain information such as the complexity of the toy structure, wall thickness distribution, size, and shape characteristics in the current production task, and at the same time obtain thermodynamic property parameters such as the heat distortion temperature, thermal conductivity, specific heat capacity, and melting temperature of the toy material. Through a pre-established correlation model between the toy structural characteristics, material thermodynamic properties, and process parameters, determine the initial setting ranges of the heating temperature value, cooling time value, and molding pressure value during the injection molding process;

[0005] According to the number of mold cavities and the cooling water channel layout, use the finite element analysis method to perform numerical simulation on the mold temperature field during the injection molding process, obtain a temperature distribution map reflecting the mold surface temperature distribution state, and for the temperature distribution map, judge the uniformity of its temperature distribution through image processing algorithms. If there are situations where the temperature fluctuation exceeds the preset threshold or the temperature distribution unevenness exceeds the preset ratio, automatically extract the position coordinates and temperature fluctuation amplitude values of the temperature abnormal area;

[0006] Based on the position coordinates of the temperature anomaly area, determine whether it is located in a part of the toy with complex structure or large wall thickness variation. If so, mark this area as a key temperature control area. At the same time, according to the temperature fluctuation amplitude value and the duration of temperature anomaly, determine whether the temperature anomaly is caused by uneven coating material on the mold surface or uneven heater power. If so, generate corresponding mold repair or heater replacement instructions;

[0007] For the key temperature control area, through the correlation model between the thermodynamic property parameters of the toy material, cooling time, and cooling water temperature, calculate the optimized cooling time value and cooling water temperature setting value, and send them to the injection molding machine control system. At the same time, according to the temperature fluctuation amplitude of the key area, fine-tune the molding pressure value to reduce the temperature fluctuation in the key area;

[0008] During the injection molding process, real-time collect the temperature data at multiple positions on the mold surface and transmit it to the central processing unit. After the central processing unit filters and denoises the collected temperature data, determine whether it exceeds the optimized temperature control range. If it exceeds, trigger the alarm mechanism, and at the same time automatically adjust the operating parameters of the injection molding machine to minimize the time deviating from the temperature control range;

[0009] After the injection molding is completed, automatically detect the size, shape, and surface quality of the toy finished product, obtain the actual structural characteristic parameters of the toy finished product, compare the actual structural characteristic parameters with the design parameters, calculate the structural deviation value. If the deviation value exceeds the preset threshold, feedback the deviation data to the process parameter optimization model to dynamically correct the initial setting range of the process parameters for the next production task.

[0010] The technical solution provided by the embodiments of the present invention may include the following beneficial effects:

[0011] The present invention obtains the toy structural characteristics and material thermodynamic properties, establishes a process parameter correlation model, and determines the initial process parameter range. Simulate the mold temperature field using finite element analysis, and identify the temperature anomaly area through image processing. For the key temperature control area, optimize the cooling time and water temperature, and adjust the molding pressure. Monitor the temperature in real time during the injection process, and automatically adjust the parameters to control the temperature deviation. After molding, detect the toy, and feedback the structural deviation to the parameter optimization model. The present invention realizes precise temperature control in the toy injection molding process, effectively improves product quality and production efficiency, reduces the defective rate, and provides an intelligent and automated production solution for the toy manufacturing industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 It is a flowchart of an intelligent toy injection molding process optimization method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0013] To further understand the content of the present invention, the present invention will be described in detail with reference to the accompanying drawings and embodiments. The following further elaborates on the present invention with reference to the drawings and embodiments. It can be understood that the specific embodiments described herein are only for explaining the relevant invention and not for limiting the invention. Additionally, it should be noted that for the sake of description, only the parts related to the invention are shown in the drawings.

[0014] As Figure 1 , a method for optimizing the injection molding process of an intelligent toy in this embodiment may specifically include:

[0015] Step S101: Obtain information such as the structural complexity, wall thickness distribution, size, and shape characteristics of the toy in the current production task. At the same time, obtain thermodynamic property parameters such as the heat distortion temperature, thermal conductivity, specific heat capacity, and melting temperature of the toy material. Through a pre-established correlation model between the toy's structural characteristics, material thermodynamic properties, and process parameters, determine the initial setting ranges of the heating temperature value, cooling time value, and molding pressure value during the injection molding process.

[0016] Obtain the toy structure complexity, wall thickness distribution, size characteristics, and shape characteristic data of the current production task to form a toy structure feature set. Obtain the heat distortion temperature, thermal conductivity, specific heat capacity, and melting temperature data of the toy material to form a material thermodynamic property set. Input the toy structure feature set and the material thermodynamic property set into the pre-established correlation model to obtain the initial values of the process parameters. If the initial values of the process parameters exceed the preset threshold, adjust the weight coefficients in the correlation model and recalculate the initial values of the process parameters. Determine the setting ranges of the heating temperature, cooling time, and molding pressure based on the adjusted initial values of the process parameters. Optimize the setting ranges using a regression algorithm to obtain the optimal process parameter combination. Input the optimal process parameter combination into the injection molding control system to complete the process parameter configuration.

[0017] Specifically, the key to toy injection molding lies in understanding the relationship between structural features and process parameters. Take a children's building block toy as an example. Its structure has an uneven wall thickness distribution, with an outer wall thickness of 3 mm and an inner rib wall thickness of 2 mm. This structural feature will cause uneven shrinkage during injection molding. In terms of shape features, the surface of the building block has fine concave and convex textures, with dimensions of 50 by 50 by 20 mm, belonging to a structure of medium complexity. Polypropylene is selected as the material, with a heat distortion temperature of 145 degrees, a thermal conductivity of 0.22 watts per meter Kelvin, a specific heat capacity of 1920 joules per kilogram Kelvin, and a melting temperature of 165 degrees. These thermodynamic properties determine the fluidity and cooling characteristics of the material during the molding process. There is a close relationship between material properties and structural features. For example, the wall thickness distribution will affect the heat conduction efficiency, which in turn affects the molding quality. The correlation model adopts a neural network structure, and the input layer includes nodes of structural features and material properties. Taking the building block toy as an example, the initial calculation gives a heating temperature of 220 degrees, a cooling time of 20 seconds, and a molding pressure of 80 MPa. When it is found that the temperature exceeds the threshold (200 degrees), by adjusting the weights of the structural features in the model, reducing the influence of the wall thickness factor, and recalculating, the temperature is obtained as 195 degrees. The determination of the process parameter setting range needs to consider product quality and production efficiency. The temperature range is set from 185 to 195 degrees, which can avoid material degradation and ensure sufficient melting at the same time. The cooling time range is 18 to 22 seconds, balancing the molding cycle and product warpage control. The molding pressure range is 75 to 85 MPa, which can ensure complete filling without generating flash. Response surface method is used for regression optimization, with the appearance quality, dimensional accuracy, and production efficiency of the product as the optimization objectives. Sample data are obtained through multiple experiments, and a mathematical model of process parameters and product performance is established. Finally, the optimal parameters are obtained through optimization: temperature 190 degrees, cooling time 20 seconds, and pressure 80 MPa. This set of parameters not only ensures stable product quality but also achieves high production efficiency. Input the optimized parameters into the injection molding machine control system, and the system needs to perform closed-loop control according to the actual processing process. The process parameters are monitored in real time through temperature sensors and pressure sensors to ensure that they are stable within the set value range. This precise control can ensure product consistency during mass production, reduce the defective rate, and improve production efficiency.

[0018] Step S102: According to the number of mold cavities and the cooling water channel layout, use the finite element analysis method to numerically simulate the mold temperature field during the injection molding process to obtain a temperature distribution map reflecting the temperature distribution state on the mold surface. For the temperature distribution map, judge the uniformity of its temperature distribution through image processing algorithms. If there is a situation where the temperature fluctuation exceeds the preset threshold or the temperature distribution unevenness exceeds the preset ratio, automatically extract the position coordinates and temperature fluctuation amplitude values of the temperature abnormal area.

[0019] Obtain the geometric data of the number of mold cavities and the cooling water channel layout as the input conditions for finite element analysis. Use the finite element analysis method to numerically simulate the mold temperature field and obtain the mold surface temperature distribution map. For the temperature distribution map, calculate the temperature uniformity value through an image processing algorithm to determine whether the temperature fluctuation exceeds a preset threshold. If the temperature fluctuation exceeds the preset threshold, further analyze whether the temperature distribution ratio exceeds a preset ratio. When the temperature distribution unevenness exceeds the preset ratio, automatically extract the position coordinates of the temperature abnormal area. Combine the position coordinates and the temperature distribution map to calculate the fluctuation amplitude value of the temperature abnormal area. Generate an analysis report for the temperature abnormal area based on the fluctuation amplitude value and the position coordinates.

[0020] Specifically, the number of mold cavities is usually directly related to production efficiency. For example, a two-cavity mold can produce two identical plastic toy parts simultaneously, improving production efficiency. The cooling water channel layout is the key to mold temperature control. For example, a certain baby toy mold adopts a combined water channel layout of straight and spiral types, with the water channel spacing maintained at about twenty-five millimeters. This layout can ensure uniform cooling in each area of the mold. Finite element analysis can divide grid units. For example, the mold surface is divided into five thousand quadrilateral units, and the size of each unit is about zero point five millimeters. The temperature field distribution is obtained by solving the heat transfer equation. The temperature distribution map usually uses different colors to represent different temperature areas. For example, red represents a high-temperature area that may reach one hundred and eighty degrees, and blue represents a low-temperature area that may be sixty degrees. The temperature uniformity calculation uses the standard deviation method to statistically analyze the mold surface temperature data. The preset temperature fluctuation threshold is usually plus or minus five degrees. If the temperature in a certain area exceeds this range, it needs to be focused on. The temperature distribution ratio refers to the percentage of the area of the abnormal temperature area in the total area, and the preset ratio threshold is generally ten percent. The temperature abnormal area positioning uses the region growing algorithm, starting from the highest temperature point and expanding to the surrounding until the temperature difference is lower than the set threshold. For example, in a certain toy wheel mold, it is found that there is a temperature abnormality at the corner. The coordinates of this area are located one hundred millimeters to the left of the mold center, and the temperature is eight degrees higher than the surrounding area. The fluctuation amplitude value is obtained by calculating the difference between the highest temperature in the abnormal area and the surrounding average temperature. The analysis report includes information such as the position description, temperature value, and fluctuation range of the abnormal area. For example, it is found that the temperature fluctuation amplitude in the right cavity of the mold reaches ten degrees, and the cooling water channel in this area needs to be optimized. This analysis helps to improve the design rationality of the mold cooling system and ensure stable product quality. The problems found through temperature field analysis can directly guide mold improvement. For example, the analysis of a certain building block mold shows that overheating is likely to occur at the corners, and the temperature is seven to nine degrees higher than the surrounding area. By adding cooling water channels or adjusting the water channel layout at this place, the temperature distribution can be made more uniform. The uniformity of the mold surface temperature field directly affects the internal stress distribution and dimensional stability of the product, and is of great significance for improving the product qualification rate.

[0021] Step S103: Based on the position coordinates of the temperature anomaly area, determine whether it is located in a part of the toy with complex structure or large wall thickness variation. If so, mark this area as a key temperature control area. At the same time, based on the temperature fluctuation amplitude value and the duration of the temperature anomaly, determine whether the temperature anomaly is caused by uneven coating material on the mold surface or uneven heater power. If so, generate corresponding mold repair or heater replacement instructions.

[0022] Obtain the position coordinate information of the temperature anomaly area, and match it with the pre-established coordinate database of the complex toy structure area and the wall thickness variation area. If the match is successful, mark this area as a key temperature control area. For the key temperature control area, extract its temperature fluctuation amplitude and anomaly duration data, and conduct a comparative analysis by combining the preset temperature fluctuation threshold and duration threshold. If the threshold is exceeded, enter the cause judgment link. According to the specific values of the temperature fluctuation amplitude and the anomaly duration, use the pre-established mold surface coating material defect judgment model for analysis. If the model output is a coating material problem, generate a mold repair instruction. Use the pre-established heater power uneven judgment model, and combine the specific values of the temperature fluctuation amplitude and the anomaly duration for judgment. If the model output is a heater power uneven problem, generate a heater replacement instruction. For the marked key temperature control area, obtain its historical temperature data, and use the time series analysis method to predict the future temperature change trend to obtain the temperature change prediction result. According to the temperature change prediction result, combine the pre-established key temperature control area optimization strategy model to obtain the temperature control optimization plan. Integrate the temperature control optimization plan with the mold repair instruction or heater replacement instruction to generate a complete temperature anomaly handling plan and output it.

[0023] Specifically, in toy injection molding production, the positioning and control of abnormal temperature areas are key links. For example, for a multi-cavity snap-together toy mold, through coordinate matching, it can be found that the abnormal temperature positions at the connection parts highly coincide with the pre-stored complex structure areas. Such areas often appear at the wall thickness mutation points such as the snaps and bosses of toy parts, and their temperature control needs to be focused on. In practical applications, assume that the temperature of a snap part of a snap-together toy fluctuates. The monitoring data shows that the temperature fluctuation range in this area reaches ±8°C during the molding process, and the duration exceeds 20 seconds, which is significantly higher than the preset thresholds of ±5°C and 15 seconds. In this case, it is necessary to further analyze the root cause of the abnormal temperature. Through the analysis of the coating material defect judgment model, it is found that when the temperature fluctuation range is large and the duration is long, it is very likely that the local coating material of the mold surface is damaged. For example, a certain toy mold uses a nickel-based alloy coating. After long-term operation in a high-temperature and high-pressure environment, microscopic cracks appear in local areas, resulting in a decrease in heat conduction performance, and then causing abnormal temperature. At this time, it is necessary to generate a mold repair instruction to repair or replace the damaged coating. Uneven heater power is also an important factor leading to abnormal temperature. For example, the heating system of a certain toy mold uses multiple groups of heaters to work together. When the power output of a certain group of heaters is unstable, it will cause temperature fluctuations in local areas. Through the established judgment model, combined with the temperature fluctuation characteristics, the faulty heater can be accurately identified and a replacement instruction can be generated in time. For the determined key temperature control areas, it is necessary to predict the temperature change trend. Taking a certain toy mold as an example, collect the recent temperature change data of this area, and predict the temperature change trend within the next 4 hours through time series analysis methods. The prediction results show that the temperature fluctuation range has an expanding trend, and it is necessary to make timely optimization adjustments. The formulation of the temperature control optimization plan needs to consider multiple factors. For example, the optimization plan for a certain toy mold includes multiple aspects such as adjusting the cooling water flow rate, optimizing the heater power distribution, and improving the mold surface treatment process. These optimization measures are combined with the equipment maintenance instructions to form a complete temperature abnormality handling plan to ensure the stability of the mold temperature field distribution and improve the product quality.

[0024] Step S104: For the key temperature control areas, calculate the optimized cooling time value and the set value of the cooling water temperature through the correlation model between the thermodynamic property parameters of the toy material, the cooling time, and the cooling water temperature, and send them to the injection molding machine control system. At the same time, according to the temperature fluctuation range of the key area, fine-tune the molding pressure value to reduce the temperature fluctuation in the key area.

[0025] Obtain the thermodynamic property parameters of the toy material, and calculate the optimized values of the cooling time and the cooling water temperature based on a preset correlation model. Transmit the optimized cooling time and the set value of the cooling water temperature to the injection molding machine control system to complete the parameter configuration. Monitor the temperature fluctuation amplitude in the key area and determine whether it exceeds the preset fluctuation threshold. If the temperature fluctuation amplitude exceeds the threshold, calculate the adjustment value of the molding pressure according to the fluctuation amplitude. Send the adjustment value of the molding pressure to the injection molding machine control system to update the molding pressure parameter. Continuously monitor the temperature in the key area and determine whether the temperature fluctuation tends to be stable. If the temperature fluctuation tends to be stable, end the adjustment process; otherwise, repeat the calculation and adjustment steps.

[0026] Step S105: During the injection molding process, collect the temperature data at multiple positions on the mold surface in real time and transmit it to the central processing unit. After filtering and denoising the collected temperature data by the central processing unit, determine whether it exceeds the optimized temperature control range. If it exceeds, trigger the alarm mechanism and automatically adjust the operating parameters of the injection molding machine to minimize the time deviated from the temperature control range.

[0027] Obtain the temperature value at the preset collection points on the mold surface, and transmit the collected temperature value to the central processing unit. The central processing unit uses a preset filter to denoise the temperature value to obtain the filtered temperature value. According to the pre-established control limit, determine whether the filtered temperature value exceeds the control limit range. If the filtered temperature value exceeds the control limit range, trigger the alarm to send an alarm signal. Calculate the adjustment amount of the operating parameters of the injection molding machine according to the deviation degree of the temperature value. Automatically adjust the operating parameters of the injection molding machine according to the calculated adjustment amount. Use the optimization method to iteratively update the temperature control process to shorten the time when the temperature value deviates from the control limit range.

[0028] Specifically, when collecting temperature values on the mold surface, thermocouples or infrared sensors are usually used. Thermocouples have the characteristics of fast response speed and high reliability, and multiple collection points can be arranged at key positions of the mold, such as the cavity surface, near the cooling channels, etc. The collected temperature data is transmitted to the central processor through the data acquisition module, and the sampling frequency is generally set to ten times per second to ensure continuous monitoring of temperature changes. To eliminate interference and noise during the sensor collection process, the central processor uses a digital filter for signal processing. Commonly used filtering methods include mean filtering and Kalman filtering, which can be selected according to the actual working conditions. Taking mean filtering as an example, the moving average of ten continuously collected temperature values can effectively remove random fluctuations and obtain a smoother temperature curve. The setting of the control limit is based on product quality requirements and process experience. For example, when injection molding a toy shell, the mold surface temperature is controlled between eighty degrees and ninety degrees. Exceeding this range may cause product warping or poor surface quality. When the temperature value exceeds the control limit, the system emits an audible and visual alarm signal to alert the operator. The temperature deviation refers to the difference between the actual temperature and the target temperature. When it is detected that the temperature is too high, the system automatically increases the cooling time or reduces the cooling water temperature. For example, when the temperature exceeds the upper limit by five degrees, the cooling time is extended by two seconds and the cooling water temperature is reduced by three degrees. This automatic adjustment mechanism can quickly respond to temperature anomalies and reduce the production of defective products. The adjustment of the injection molding machine operating parameters includes mold temperature, pressure, speed, etc. Taking mold temperature control as an example, when it is found that the temperature in a local area is too high, the temperature distribution can be improved by adjusting the cooling water flow near that area. By increasing the water flow by twenty percent, the local temperature can be reduced by three to five degrees. The optimization of the temperature control process is carried out in an iterative manner. The system records the temperature change trend after each adjustment and analyzes the adjustment effect. If the time for the temperature to return to the control limit exceeds the expectation, the adjustment amount is increased in the next round of adjustment. Through continuous optimization, the time for the temperature to return to the normal range can be shortened from the original thirty seconds to about fifteen seconds. By establishing an association model between temperature and injection molding parameters, more precise control can be achieved. The model takes into account factors such as material thermal conductivity and mold structure characteristics and predicts the impact of parameter adjustment on temperature. For example, when producing a polycarbonate toy shell, the system can automatically calculate the optimal combination of cooling time and water temperature according to the material characteristics, improving the product dimensional accuracy by twenty percent.

[0029] Step S106: After the injection molding is completed, automatically detect the size, shape, and surface quality of the toy finished product to obtain the actual structural characteristic parameters of the toy finished product. Compare the actual structural characteristic parameters with the design parameters, calculate the structural deviation value. If the deviation value exceeds the preset threshold, feedback the deviation data to the process parameter optimization model to dynamically correct the initial setting range of the process parameters for the next production task.

[0030] Use an image acquisition device to perform a full - range scan on the finished injection - molded toy to obtain the surface image data of the finished toy, and extract the dimensional measurement values and shape contour data from the surface image data. Input the dimensional measurement values and shape contour data into a pre - established surface quality analysis module to analyze the surface defect distribution, calculate the surface smoothness and texture consistency indicators, and generate a set of actual structural characteristic parameters. Retrieve the standard design parameters of the finished toy from the product design database, construct a parameter comparison model, compare the set of actual structural characteristic parameters with the standard design parameters item by item, and calculate the deviation values of each parameter. According to the preset deviation threshold range, determine whether each deviation value exceeds the allowable range. If there are over - limit deviations, mark them as abnormal parameters, and generate a deviation data set with the abnormal parameters and their corresponding deviation values. Input the deviation data set into a process parameter optimization model, analyze the causes of the deviations, determine the types of injection - molding process parameters that need to be adjusted, and calculate the correction amounts of each process parameter. Adjust the process parameter setting values of the injection molding machine according to the correction amounts, update the initial setting range of the process parameters for the next batch of production tasks, and generate a new process parameter configuration file. Transmit the new process parameter configuration file to the injection molding machine control system to complete the optimization update of the process parameters, start the next batch of production tasks, and achieve the closed - loop optimization control of the injection molding process.

[0031] Specifically, the image acquisition device obtains the surface information of the toy in real time through a multi-angle scanning head at the end of the injection molding production line. For example, when using a high-resolution camera and a structured light scanner to scan a plastic car model, the contour dimensions, color distribution, and surface texture features of the outer surface of the car body can be obtained simultaneously. The surface quality analysis module first processes the scanned data. For the car model, the system will calculate indicators such as the length, width, and height dimensions of the car body, the surface concavity and convexity, and the seam flatness. For example, the smoothness of the car body surface can be measured by the uniformity of the laser reflection intensity, and the texture consistency can be quantified by the standard deviation of the image gray value. In the parameter comparison session, the system compares the measured data with the design standard. Taking the car model as an example, if the design requirement for the car body length is 150 millimeters and the measured value is 152 millimeters, exceeding the preset tolerance range of plus or minus 1 millimeter, the system will mark this deviation as abnormal. At the same time, it is detected that there is an obvious depression defect on the right side of the car body, and the surface roughness reaches 0.5 micrometers, far exceeding the design standard requirement of 0.2 micrometers. The process parameter optimization model will analyze the causes of these abnormalities. For the problem of oversized dimensions, it may be caused by too high a mold temperature resulting in a reduced plastic shrinkage rate. The system will recommend reducing the mold temperature by 5 degrees; for surface depression and excessive roughness, it may be due to insufficient injection pressure or too short a holding time. The system will accordingly increase the injection pressure and extend the holding time. These corrected parameters are integrated into a new process parameter configuration file to guide the production of the next batch. In the closed-loop control, the system continuously monitors the effects after adjustment. Assuming that in the first batch of products after adjustment, the car body size drops to 151 millimeters and the surface roughness improves to 0.3 micrometers, but still does not meet the target requirements, the system will further fine-tune the process parameters, such as continuing to reduce the mold temperature by 2 degrees and increasing the injection pressure. Through this continuous parameter optimization and dynamic adjustment, the production process gradually tends to the optimal state, ensuring continuous improvement of product quality. This automated quality control system significantly improves production efficiency, reduces the need for manual intervention, and makes product quality more stable and reliable. In practical applications, the system can also establish a feature database for different defect types to help quickly locate the causes of problems and provide more accurate process parameter correction suggestions.

[0032] Obtain the dimension measurement values and shape contour data based on the surface image data, compare the set of actual structural characteristic parameters with the standard design parameters item by item through the parameter comparison model to obtain the deviation values of each parameter, analyze the causes of the deviation using the process parameter optimization model, determine the types of injection molding process parameters that need to be adjusted, and obtain the correction amounts of each process parameter.

[0033] Use an image acquisition device to scan the finished toy to obtain surface image data, and extract dimensional measurement values and shape contour data. Input the extracted data into the surface quality analysis module to calculate surface smoothness and texture consistency indicators, and generate a set of actual structural characteristic parameters. Retrieve standard design parameters from the product design database, construct a parameter comparison model, compare the actual parameters with the standard parameters item by item, and calculate the deviation values for each item. According to the preset deviation threshold range, determine whether the deviation value exceeds the limit. If it exceeds the limit, mark it as an abnormal parameter and generate a set of deviation data. Input the set of deviation data into the process parameter optimization model, analyze the reasons for the deviation, determine the types of process parameters that need to be adjusted, and calculate the correction amounts for each process parameter. Adjust the process parameter setting values of the injection molding machine according to the correction amounts, generate a new process parameter configuration file. Transmit the configuration file to the injection molding machine control system, update the process parameter setting range, and start the next batch of production tasks.

[0034] Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. An optimization method for the injection molding process of intelligent toys, characterized in that, The method includes: Step S101: Obtain information such as the structural complexity, wall thickness distribution, size, and shape characteristics of the toy in the current production task. At the same time, obtain thermodynamic property parameters of the toy material, such as the heat distortion temperature, thermal conductivity, specific heat capacity, and melting temperature. Determine the initial setting ranges of the heating temperature value, cooling time value, and molding pressure value during the injection molding process through a pre-established correlation model between the toy structural characteristics, material thermodynamic properties, and process parameters; Step S102: According to the number of mold cavities and the cooling water channel layout, use the finite element analysis method to numerically simulate the mold temperature field during the injection molding process to obtain a temperature distribution map reflecting the temperature distribution state on the mold surface. For the temperature distribution map, judge the uniformity of its temperature distribution through an image processing algorithm. If there is a situation where the temperature fluctuation exceeds the preset threshold or the temperature distribution unevenness exceeds the preset ratio, automatically extract the position coordinates and temperature fluctuation amplitude values of the temperature anomaly area; Step S103: According to the position coordinates of the temperature anomaly area, judge whether it is located in a part with complex toy structure or large wall thickness change. If so, mark this area as a key temperature control area. At the same time, according to the temperature fluctuation amplitude value and the duration of temperature anomaly, judge whether the temperature anomaly is caused by uneven mold surface coating material or heater power. If so, generate corresponding mold repair or heater replacement instructions; Step S104: For the key temperature control area, calculate the optimized cooling time value and cooling water temperature setting value through the correlation model between the toy material thermodynamic property parameters, cooling time, and cooling water temperature, and send them to the injection molding machine control system. At the same time, fine-tune the molding pressure value according to the temperature fluctuation amplitude in the key area to reduce the temperature fluctuation in the key area; Step S105: During the injection molding process, collect temperature data at multiple positions on the mold surface in real time and transmit it to the central processing unit. After the central processing unit filters and denoises the collected temperature data, judge whether it exceeds the optimized temperature control range. If it exceeds, trigger the alarm mechanism and automatically adjust the operating parameters of the injection molding machine to minimize the time deviating from the temperature control range; Step S106: After the injection molding is completed, automatically detect the size, shape, and surface quality of the toy finished product, obtain the actual structural characteristic parameters of the toy finished product, compare the actual structural characteristic parameters with the design parameters, calculate the structural deviation value. If the deviation value exceeds the preset threshold, feedback the deviation data to the process parameter optimization model to dynamically correct the initial setting range of the process parameters for the next production task.

2. The method according to claim 1, characterized in that, The step S101 includes: Obtain the toy structure complexity, wall thickness distribution, size characteristics, and shape characteristic data of the current production task to form a toy structure feature set; Obtain the heat distortion temperature, thermal conductivity, specific heat capacity, and melting temperature data of the toy material to form a material thermodynamic property set; Input the toy structure feature set and the material thermodynamic property set into the pre-established correlation model to obtain the initial values of the process parameters; If the initial value of the process parameter exceeds the preset threshold, adjust the weight coefficient in the associated model and recalculate the initial value of the process parameter; Determine the setting ranges of the heating temperature, cooling time, and molding pressure according to the adjusted initial value of the process parameter; Optimize the setting ranges using a regression algorithm to obtain the optimal process parameter combination; Input the optimal process parameter combination into the injection molding control system to complete the process parameter configuration.

3. The method according to claim 1, characterized in that, The step S102 includes: Obtain the geometric data of the number of mold cavities and the cooling water channel layout as the input conditions for finite element analysis; Use the finite element analysis method to numerically simulate the mold temperature field to obtain the mold surface temperature distribution map; For the temperature distribution map, calculate the temperature uniformity value through an image processing algorithm and determine whether the temperature fluctuation exceeds the preset threshold; If the temperature fluctuation exceeds the preset threshold, further analyze whether the temperature distribution ratio exceeds the preset ratio; When the temperature distribution unevenness exceeds the preset ratio, automatically extract the position coordinates of the temperature abnormal area; Combine the position coordinates and the temperature distribution map to calculate the fluctuation amplitude value of the temperature abnormal area; Generate an analysis report of the temperature abnormal area based on the fluctuation amplitude value and the position coordinates.

4. The method according to claim 1, characterized in that, The step S103 includes: Obtain the position coordinate information of the temperature abnormal area, and match it with the pre-established coordinate database of the complex toy structure area and the wall thickness change area. If the match is successful, mark this area as the key temperature control area; For the key temperature control area, extract its temperature fluctuation amplitude and abnormal duration data, and conduct a comparative analysis in combination with the preset temperature fluctuation threshold and duration threshold. If it exceeds the threshold, enter the judgment link; According to the specific values of the temperature fluctuation amplitude and abnormal duration, use the pre-established mold surface coating material defect judgment model for analysis. If the model output is a coating material problem, generate a mold repair instruction; Use the pre-established heater power unevenness judgment model, and combine the specific values of the temperature fluctuation amplitude and abnormal duration for judgment. If the model output is a heater power unevenness problem, generate a heater replacement instruction; For the marked key temperature control area, obtain its historical temperature data, and use the time series analysis method to predict the future temperature change trend to obtain the temperature change prediction result; According to the temperature change prediction result, combine the pre-established key temperature control area optimization strategy model to obtain the temperature control optimization plan; Integrate the temperature control optimization plan with the mold repair instruction or heater replacement instruction to generate a complete temperature abnormal handling plan and output it.

5. The method according to claim 1, characterized in that The step S104 includes: Obtain the thermodynamic property parameters of the toy material, and calculate the optimized values of the cooling time and cooling water temperature based on the preset associated model; Transmit the optimized setting values of the cooling time and cooling water temperature to the injection molding machine control system to complete the parameter configuration; Monitor the temperature fluctuation amplitude of the key area and determine whether it exceeds the preset fluctuation threshold; If the temperature fluctuation amplitude exceeds the threshold, calculate the molding pressure adjustment value according to the fluctuation amplitude; Send the molding pressure adjustment value to the injection molding machine control system to update the molding pressure parameter; Continuously monitor the temperature of the key area and determine whether the temperature fluctuation tends to be stable; If the temperature fluctuation tends to be stable, end the adjustment process; otherwise, repeat the calculation and adjustment.

6. The method according to claim 1, wherein The step S105 includes: Obtain the temperature value at the preset acquisition points on the mold surface and transmit the collected temperature value to the central processing unit; The central processing unit performs denoising processing on the temperature value using a preset filter to obtain the filtered temperature value; According to the pre-established control limit, determine whether the filtered temperature value exceeds the control limit range; If the filtered temperature value exceeds the control limit range, trigger the alarm to send an alarm signal; Calculate the adjustment amount of the injection molding machine operation parameters according to the deviation degree of the temperature value; Automatically adjust the operation parameters of the injection molding machine according to the calculated adjustment amount; Use the optimization method to iteratively update the temperature control process and shorten the time when the temperature value deviates from the control limit range.

7. The method according to claim 1, wherein The step S106 includes: Use an image acquisition device to perform an omnidirectional scan of the injection-molded toy finished product, obtain the surface image data of the toy finished product, and extract the dimension measurement values and shape contour data from the surface image data; Input the dimension measurement values and shape contour data into the pre-established surface quality analysis module, analyze the surface defect distribution, calculate the surface smoothness and texture consistency indexes, and generate the actual structure characteristic parameter set; Retrieve the standard design parameters of the toy finished product from the product design database, construct a parameter comparison model, compare the actual structure characteristic parameter set with the standard design parameters item by item, and calculate the deviation values of each parameter; According to the preset deviation threshold range, determine whether each deviation value exceeds the allowable range. If there is an over-limit deviation, mark it as an abnormal parameter, and generate a deviation data set with the abnormal parameter and its corresponding deviation value; Input the deviation data set into the process parameter optimization model, analyze the generation of deviations, determine the types of injection molding process parameters that need to be adjusted, and calculate the correction amounts of each process parameter; Adjust the process parameter setting value of the injection molding machine according to the correction amount, update the initial setting range of the process parameters for the next batch of production tasks, and generate a new process parameter configuration file; Transmit the new process parameter configuration file to the injection molding machine control system, complete the optimization update of the process parameters, start the next batch of production tasks, and realize the closed-loop optimization control of the injection molding process; Obtain the dimension measurement values and shape contour data according to the surface image data, compare the actual structure characteristic parameter set with the standard design parameters item by item through the parameter comparison model to obtain the deviation values of each parameter, use the process parameter optimization model to analyze the deviations, determine the types of injection molding process parameters that need to be adjusted, and obtain the correction amounts of each process parameter.

8. The method according to claim 7, wherein The method includes: Use an image acquisition device to scan the toy finished product, obtain the surface image data, and extract the dimension measurement values and shape contour data; Input the extracted data into the surface quality analysis module, calculate the surface smoothness and texture consistency indexes, and generate the actual structure characteristic parameter set; Retrieve the standard design parameters from the product design database, construct a parameter comparison model, compare the actual parameters with the standard parameters item by item, and calculate the deviation values of each item; Judge whether the deviation value exceeds the limit according to the preset deviation threshold range. If it exceeds the limit, mark it as an abnormal parameter and generate a deviation data set; Input the deviation data set into the process parameter optimization model, analyze the deviation, determine the type of process parameters to be adjusted, and calculate the correction amount of each process parameter; Adjust the process parameter setting value of the injection molding machine according to the correction amount to generate a new process parameter configuration file; Transmit the configuration file to the injection molding machine control system, update the process parameter setting range, and start the next batch of production tasks.

Images