Reclaimed material injection molding method based on mold temperature field self-adaptive control

Abstract

The invention discloses a reworked material injection molding method based on self-adaptive control of a mold temperature field, which is characterized by comprising the following steps: (1) reference modeling: acquiring reference data, and establishing a reference correlation model of reworked material physical properties and injection molding parameters; (2) initializing mold temperature field data: constructing a global temperature field acquisition link, and selecting effective temperature measurement points; (3) data real-time collection: collecting dynamic changes of the global temperature of the mold cavity in real time and preprocessing the dynamic changes, and calculating a preprocessed data set; (4) calculation of regulation and control parameters: performing data comparison on the data acquired in real time, and calculating a parameter regulation quantity; (5) linkage regulation and control are executed, specifically, the power and the cold water flow of the mold temperature controller are adjusted according to the parameter adjusting quantity, and the injection speed and the dwell pressure of the injection molding machine are adjusted; and (6) closed-loop optimization: judging whether the control coefficient needs to be optimized and corrected or not. The reclaimed material injection molding method has the beneficial effects that the product yield can be remarkably improved, and defects are reduced.

Description

Technical Field

[0001] This invention relates to the field of plastic molding technology, and in particular to a recycled material injection molding method based on adaptive control of mold temperature field. Background Technology

[0002] Plastic injection molding is one of the most widely used polymer processing methods today. With increasing global emphasis on sustainable development, the use of recycled polymers (such as recycled polypropylene, recycled polystyrene, and recycled polyolefin blends) in injection molding production has become an industry trend.

[0003] In existing technologies, mold temperature control for injection molding is usually achieved by using a mold temperature controller in conjunction with a cooling water circuit. The core is to determine a fixed target temperature during the trial molding stage and maintain a constant temperature through a PID controller. This method is suitable for injection molding of new materials with stable rheological properties. However, for recycled materials, due to the multiple thermal degradations of their molecular chains, their rheological properties are sensitive to temperature and exhibit large batch-to-batch fluctuations. Therefore, this type of technology has the following drawbacks: Limited sensing: Existing technologies typically use a single temperature measurement point or detect the cooling water outlet temperature to monitor the mold temperature, which cannot accurately and comprehensively capture the non-uniform temperature field distribution on the cavity surface during recycled material injection molding, thus creating local temperature control blind spots; Mismatched control strategies: Traditional constant temperature / PID rigid control often uses preset, fixed parameters, lacking the ability to dynamically adjust according to real-time process conditions. It cannot adapt to the fluctuations in rheological properties of recycled materials caused by batch differences, and it is difficult to respond to changes in physical properties for dynamic temperature control; Lack of parameter coordination: In existing injection molding processes, mold temperature control and other key molding parameters such as injection speed and holding pressure are often set independently in an open-loop state. There is a lack of effective linkage adjustment between the parameters, which cannot compensate for molding defects caused by fluctuations in the physical properties of recycled materials.

[0004] In summary, existing technologies struggle to address the fluctuations in rheological properties of recycled materials due to batch variations, hindering the real-time sensing and dynamic adaptive control of the mold cavity temperature field. Furthermore, they cannot link mold temperature control with molding parameters such as injection and holding pressure. Consequently, they fail to fundamentally resolve quality issues such as uneven filling and internal stress imbalance during recycled material injection molding, resulting in poor consistency and high defect rates in recycled material injection molded parts. Consequently, the large-scale and stable application of recycled materials remains significantly limited. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a recycled material injection molding method based on adaptive control of mold temperature field. This recycled material injection molding method can effectively adapt to the fluctuation of recycled material properties by sensing the mold temperature field in the whole domain, dynamically and adaptively controlling it, and linking and coordinating it with molding parameters, thereby significantly improving product yield, dimensional stability and production efficiency, and reducing defect rate.

[0006] To solve the above technical problems, the following technical solution is adopted: The recycled material injection molding method based on adaptive mold temperature field control is characterized by the following steps: (1) Benchmark modeling: Obtain benchmark data of recycled materials, establish benchmark correlation model between recycled material properties and injection molding parameters, and output preset process data; (2) Initialize mold temperature field data: Construct a global temperature field acquisition link consisting of all effective temperature measurement points in the mold cavity, verify all temperature measurement points, select effective temperature measurement points, and output the results; (3) Real-time data acquisition: Real-time acquisition of dynamic changes in the temperature of the entire mold cavity, preprocessing of the acquired raw temperature data, calculation and output of the preprocessed dataset of the temperature field of the entire cavity synchronized with the injection cycle. (4) Calculate control parameters: Compare the real-time collected data with the preset process data and the batch detection data of recycled materials respectively, calculate the comprehensive deviation, and dynamically calculate and output the temperature control parameter adjustment amount and molding parameter adjustment amount based on the comprehensive deviation amount; (5) Implement linkage control: Based on the adjustment amount of temperature control parameters and molding parameters, synchronously adjust the heating / cooling power and cooling water flow of the mold temperature controller, as well as the injection speed and holding pressure of the injection molding machine; (6) Closed-loop optimization: Input the real-time detection and production data after adjustment, determine whether the control coefficient needs to be optimized and corrected based on the magnitude of the deviation after adjustment, and store the data of the entire process.

[0007] This recycled material injection molding method can achieve real-time sensing and dynamic adaptive control of the mold cavity temperature field, and establish a linkage and coordination mechanism between mold temperature control and molding parameters such as injection and holding pressure. It can adapt to the fluctuation of rheological properties between batches of recycled materials, fundamentally improve the filling state and internal stress distribution of recycled material injection molded parts, enhance the dimensional stability, surface quality and mechanical properties of molded parts, reduce product defect rate, and realize the large-scale and stable resource utilization of recycled materials in injection molding.

[0008] In the preferred embodiment, the reference data for the recycled material in step (1) includes the physical property parameters of the reference batch of recycled material: reference flow activation energy. Reference melt viscosity And the baseline process parameters for standard trial molding: baseline mold temperature Reference injection speed Reference holding pressure After inputting the above data, the baseline melt viscosity is correlated using a linear fitting method. With reference mold temperature The fitting formula is ,in, , The reference fitting coefficients are used; during the reference molding process, the temperature values ​​T1, T2, ..., at each effective temperature measurement point in the mold cavity are collected in real time. The uniformity threshold is calculated using the temperature field standard deviation method, and the formula is as follows: ,in The average temperature at each temperature measuring point in the cavity during the baseline molding trial is given by the formula: And calculate the viscosity deviation threshold. The specific formula is as follows ,in This is the deviation coefficient, with a value range of 0.05-0.10; the final output preset process data includes a set of baseline control parameters: , , And the core deviation threshold: , and the baseline fit coefficients: , The preset process data serves as the benchmark for deviation calculation in step (4) and parameter adjustment in step (5).

[0009] The above viscosity deviation threshold The calculations are based on the property fluctuation characteristics of recycled materials and the injection molding process window. Set as the reference melt viscosity ±5% to ±10% (the specific value can be adjusted according to the type of recycled material, such as ±8% for PP recycled material and ±6% for PS recycled material).

[0010] In a further preferred embodiment, the construction of the global temperature field acquisition link consisting of various effective temperature measurement points within the mold cavity in step (2) involves placing various temperature measurement elements in various temperature-sensitive areas within the mold cavity; the method of verifying all temperature measurement points and selecting effective temperature measurement points is achieved by reading the real-time verification temperature values ​​of each temperature measurement point. Hardware calibration values Temperature measurement points with acquisition errors > ±0.5℃ are excluded. The verification formula is as follows: .

[0011] The temperature-sensitive areas mentioned above include locations prone to uneven temperature, such as gates, cavity edges, and areas where wall thickness changes abruptly. A total of 8-12 points are set up, and the coordinates of the distribution points directly correspond to the actual positions of the mold cavity.

[0012] In a further preferred embodiment, step (3) involves real-time acquisition of the dynamic changes in the temperature across the entire mold cavity. Specifically, this means uploading the real-time temperature data of each effective temperature measurement point during each injection cycle. The method for preprocessing the collected raw temperature data is to first remove outliers: based on the reference mold temperature in step (1). Set the temperature anomaly range as follows: Abnormal temperature values ​​outside the specified range are removed; then, a global statistical calculation is performed to statistically analyze the valid temperature data after removing outliers and calculate the average temperature across the entire cavity. and temperature field uniformity index The calculation formula is: , The average temperature across the entire region Indicators reflecting the overall temperature level of the mold cavity and the uniformity of the temperature field It reflects the degree of temperature fluctuation across the entire cavity; the output preprocessed dataset includes the real-time temperature of each valid temperature measurement point. Average temperature across the entire cavity Temperature field uniformity index and corresponding collection timestamp .

[0013] The timestamps of real-time temperature acquisition at each of the above valid temperature measurement points The industrial controller automatically records and marks the acquisition time of each set of temperature data, facilitating subsequent data traceability and time-series analysis. The injection cycle synchronization signal is issued by the injection molding machine's main control system to ensure precise synchronization between temperature acquisition and the injection production process (injection, holding pressure, cooling, demolding), avoiding invalid data caused by the disconnect between acquisition time and production cycle.

[0014] In a further preferred embodiment, in step (4), the data collected in real time is the preprocessed dataset of the cavity global temperature field from step (3); the batch detection data of recycled material includes the flow activation energy deviation between the current batch of recycled material and the reference batch of recycled material. and viscosity deviation ,in Activation energy of the current batch of recycled material Compared with the reference flow activation energy The comparison yields the following formula: , Based on the melt viscosity measurement value of the current batch of recycled material Compared with reference viscosity The comparison yields the following formula: The method for calculating the overall deviation is to include the temperature field uniformity deviation. Viscosity deviation As an input, a weighting coefficient is introduced. , The overall deviation is calculated using the following formula: The method for calculating the temperature control parameter adjustment and molding parameter adjustment is to first use a modified PID simplified algorithm, according to the PID proportional coefficient. Integral coefficient Through formula The controlled output quantity is obtained by solving. This output directly corresponds to the adjustment range of temperature control and molding parameters; further calculations are then performed to obtain the percentage adjustment of the mold temperature controller power. Cooling water flow rate adjustment value Temperature control parameters, including the amount of adjustment, and injection speed adjustment value. Pressure holding pressure adjustment value The formula for calculating the percentage adjustment of molding parameters, including the percentage adjustment of mold temperature controller power, is as follows: ,in, The power regulation coefficient of the mold temperature controller represents the maximum power regulation percentage corresponding to the full scale of the PID output; the formula for calculating the cooling water flow rate regulation value is... ,in, The cooling flow rate adjustment coefficient represents the maximum flow rate adjustment range corresponding to the full scale of the PID output; the formula for calculating the injection speed adjustment value is... ,in, The injection speed adjustment coefficient represents the maximum injection speed adjustment range corresponding to the full scale of the PID output; the formula for calculating the holding pressure adjustment value is... ,in The pressure holding adjustment coefficient represents the maximum pressure holding adjustment range corresponding to the full scale of the PID output.

[0015] The above (Unit: MPa) can be calibrated according to different equipment models, process windows and recycled material types.

[0016] The above-mentioned flow activation energy deviation and viscosity deviation Flow activation energy deviation can be quickly obtained and uploaded through conventional detection methods. and viscosity deviation This reflects the degree of fluctuation in the physical properties of the current batch of recycled material deviating from the baseline, as well as the temperature field uniformity deviation. It reflects the degree to which the temperature field fluctuation deviates from the baseline; the temperature control parameter adjustment amount is used to adjust the temperature field distribution in the mold cavity; the molding parameter adjustment amount is used to synchronously match the changes in the temperature field and offset the influence of the fluctuation of the physical properties of recycled materials.

[0017] The above weighting coefficients , Adjustments are made according to the type of recycled material to adapt to the characteristics of different recycled materials.

[0018] In a further preferred embodiment, the method of adjusting the heating / cooling power and cooling water flow of the mold temperature controller, as well as the injection speed and holding pressure of the injection molding machine in step (5), is to first use a parameter mapping analysis algorithm to convert the temperature control parameter adjustment amount and molding parameter adjustment amount output in step (4) from digital into standard input commands that can be directly recognized by the corresponding equipment. The conversion formula is: Then, based on the converted instructions, adjust the heating / cooling power and cooling water flow of the mold temperature controller, as well as the injection speed and holding pressure of the injection molding machine.

[0019] The above input data comes from the previous calculation results and the real-time operating status of the equipment, ensuring the pertinence and safety of the adjustment instructions: the temperature control / molding parameter adjustment amount comes from the output result of step (4), which clarifies the specific adjustment range of various parameters, and no additional processing is required; the real-time operating parameters of the injection molding machine and the mold temperature controller are uploaded in real time by the main control system of the corresponding equipment, including the current mold temperature controller power, cooling water flow rate, injection speed of the injection molding machine, holding pressure, etc., to determine whether the adjustment instructions are within the safe operating range of the equipment, and to avoid equipment failure or production abnormality due to excessive adjustment range. Adjusting the heating / cooling power and cooling water flow rate of the mold temperature controller is used to adjust the temperature field distribution of the mold cavity, so that the temperature field of the mold cavity gradually returns to the reference uniformity range and the temperature fluctuation range is reduced; adjusting the injection speed and holding pressure of the injection molding machine can accurately match the temperature field change, realize the coordinated matching of the two types of parameters, and ensure that the melt filling is uniform and the internal stress distribution is balanced during the injection process.

[0020] The above The input instruction value for the execution module. The command mapping coefficients are adapted to the hardware parameters of different brands of injection molding machines and mold temperature controllers, ensuring communication compatibility between adjustment commands and execution devices.

[0021] In a further preferred embodiment, the method for determining whether the control coefficient needs to be optimized in step (6) is to use a deviation feedback correction algorithm to compare the deviation after parameter adjustment. , ) and the core deviation threshold preset in step (1) , Since both positive and negative values ​​indicate deviation from the benchmark and reflect the degree of deviation, the absolute value is used for judgment. or If the deviation is still greater than the preset threshold, then the PID control coefficient ( , The correction method is as follows: 6-1. If and ,but ; 6-2. If only ,but ; 6-3. If only ,but ; in, This is a correction factor with a value range of 0.05-01. For the deviation weighting coefficient, and Meanwhile, the PID control coefficient ( , The correction range is controlled within ±10%; if the deviation is within the preset threshold range, the current PID coefficient is maintained unchanged to complete this optimization; finally, the entire process data is recalculated according to the injection molding cycle. The data is categorized and archived, including temperature field sensing data, algorithm calculation data, parameter adjustment instructions, equipment operation data, and product quality data.

[0022] After parameter adjustment in step (5), the cavity temperature field remeasurement data ( '、 The system is used to compare the changes in the temperature field before and after adjustment, verifying the effect of temperature control. Real-time operating parameters for injection molding are uploaded by the main control systems of the injection molding machine and mold temperature controller, recording the equipment's operating status after adjustment. Online initial inspection data (dimensional deviations, surface defect levels) is obtained from existing online inspection equipment (dimensional measuring instruments, vision inspection equipment), used to determine the improvement effect of parameter adjustment on the quality of molded parts, directly reflecting the actual effect of adaptive control. The stored full-process data can be retained long-term, facilitating subsequent traceability, analysis, and process optimization. The PID control coefficients (…) , The correction range is controlled within ±10% to avoid system oscillation.

[0023] In a further preferred embodiment, in step (1) above, after outputting the data, a linear fitting coordinate system curve of recycled material viscosity-reference mold temperature is generated based on the fitting formula; in step (2) above, a two-dimensional coordinate system distribution map of temperature measurement points in the mold cavity is generated based on the specific location of each effective temperature measurement point in the mold cavity; in step (3) above, after outputting the preprocessed dataset of the temperature field of the entire cavity, a time-series coordinate system curve of temperature at each point in the mold cavity is generated based on the dataset; in step (4) above, a deviation-adjustment correlation coordinate system curve is generated based on the data of temperature control parameter adjustment and molding parameter adjustment; in step (5) above, a time-temperature control / molding parameter time-series control curve is generated based on the real-time data of parameter adjustment; in step (6) above, a two-dimensional thermodynamic coordinate system map of temperature field uniformity in the mold cavity is generated based on the temperature field retest data after comparison parameter adjustment.

[0024] In a further preferred embodiment, a mold temperature field adaptive control system for the above-mentioned recycled material injection molding method includes a temperature field sensing module, a data processing and control module, a temperature control execution module, a molding parameter execution module, and a human-machine interaction storage module. The signal output terminals of the temperature field sensing module and the human-machine interaction storage module are electrically connected to the corresponding signal input terminals of the data processing and control module. The signal input terminals of the temperature control execution module, the molding parameter execution module, and the human-machine interaction storage module are electrically connected to the corresponding signal output terminals of the data processing and control module. The temperature field sensing module is composed of various temperature sensing elements and is used for… The system collects mold temperature data in real time; the data processing and control module is used to receive data, perform calculations for each step, and output control instructions; the temperature control execution module and the molding parameter execution module receive the standard input instructions in step (5), the temperature control execution module communicates with the main control system of the mold temperature controller, and adjusts the heating / cooling power and cooling water flow of the mold temperature controller according to the instructions; the molding parameter execution module communicates with the main control system of the injection molding machine, and adjusts the injection speed and holding pressure of the injection molding machine according to the instructions; the human-machine interaction storage module is used for parameter input, status display, and synchronous storage and retrieval of full-process data.

[0025] The aforementioned adaptive control system consists of a temperature field sensing module (collecting real-time temperature across the entire cavity), a data processing and control module (calculating and outputting control commands), a temperature control execution module (dynamically adjusting mold temperature parameters), a molding parameter execution module (linking and adjusting injection / holding pressure parameters), and a human-machine interaction storage module (parameter input, status display, and data retention). The modules can communicate and link together to form a closed-loop control adapted to the aforementioned recycled material injection molding method.

[0026] The aforementioned temperature field sensing module can employ multiple temperature sensors in conjunction with a temperature acquisition module. For example, the temperature sensor can be a Minebea MMTK-01 mold cavity temperature sensor (thermocouple type, front end diameter φ1mm, suitable for mold cavities with limited space), and the temperature acquisition module can be a barium rhenium technology Y52 2PT (PT1000). The system includes a hot runner temperature acquisition module; a data processing and control module that can use an industrial controller / PLC, such as a Delta DVP16ES200R / T PLC, Siemens S7-1200 series, or Beckhoff CX5140 embedded controller, with the core execution unit being an STM32F103VET6 microcontroller; a temperature control execution module that can use a mold temperature controller, such as an electric heating mold temperature controller with the STM32F103VET6 core from Zhongwei Automation; a molding parameter execution module that uses the main control system of the injection molding machine to control the injection servo motor and holding pressure proportional valve in the injection molding machine, such as the main control system of the injection molding machine controlling the injection servo motor (Mitsubishi HG-SR series servo motor) and holding pressure proportional valve (Atos DLHZO series proportional valve); and a human-machine interaction storage module that can use an industrial touch screen in conjunction with a data storage unit, such as an ADLINK OM-101 / OM-156 series industrial touch screen and a Siemens SIMATIC Memory Card.

[0027] Compared with the prior art, the present invention has the following beneficial effects: (1) A collaborative adaptive control mechanism for the physical properties of recycled materials and the temperature field of the mold is proposed, which breaks through the limitation of the existing technology that only designs temperature control strategies for fixed physical properties. It accurately correlates the batch physical property deviation of recycled materials with the temperature field fluctuation, and realizes dynamic parameter adaptation by improving the PID algorithm, thereby solving the industry pain point of the difficulty in adapting the batch fluctuation of recycled materials from the root. (2) The mold cavity temperature field sensing and point verification are integrated into one design, which abandons the limitation of the existing single temperature measurement point, optimizes the temperature measurement point layout by combining the temperature sensitivity characteristics of recycled material injection molding, and provides a simple verification mechanism to ensure data accuracy, realize the synchronous capture of temperature field and injection cycle, and fill the gap that the existing technology cannot fully grasp the temperature distribution of the cavity. (3) Construct a linkage and collaborative execution system for temperature control and molding parameters, break the design of temperature control and injection and pressure holding parameters being disconnected in the existing technology, and realize the millisecond-level synchronous linkage control of the two types of parameters through a unified instruction conversion mechanism, so as to solve the problem that single temperature control cannot completely improve the molding quality of recycled materials; (4) Design an integrated model of full-process data visualization and deviation feedback closed-loop optimization, link and archive control data, production data and quality data and visualize them, and provide a simple parameter correction mechanism to achieve continuous iteration of control accuracy, filling the technical gap of the existing recycled material injection molding temperature control process being invisible and difficult to optimize. Attached Figure Description

[0028] Figure 1 This is a flowchart illustrating the control process of the recycled material injection molding method in an embodiment of the present invention. Figure 2 This is a linear fitting coordinate system curve of the viscosity of recycled material versus reference mold temperature in an embodiment of the present invention; Figure 3 This is a two-dimensional coordinate system distribution diagram of the temperature measurement points in the mold cavity in an embodiment of the present invention; Figure 4 This is a time-series coordinate system curve of temperature at various points within the mold cavity in an embodiment of the present invention; Figure 5 This is a graph showing the correlation between deviation and adjustment in a coordinate system according to an embodiment of the present invention. Figure 6 This is a time-temperature control / molding parameter timing curve diagram in an embodiment of the present invention; Figure 7 This is a two-dimensional thermodynamic coordinate system diagram of the uniformity of the temperature field in the mold cavity in an embodiment of the present invention. Detailed Implementation

[0029] The present invention will be further described below with reference to the accompanying drawings and specific embodiments: This embodiment uses the injection molding of recycled polypropylene (PP) to produce small plastic shells as an application scenario. The preliminary preparations are as follows: Equipment preparation: 120-ton injection molding machine (standard configuration with adjustable injection / holding pressure parameters), K-type thermocouples (temperature range 0-200℃, error ±0.3℃, 10 in total), Siemens S7-200 PLC (industrial controller), constant temperature controller (TCU, temperature control range 0-120℃, power 3kW), cooling water circuit assembly, touch screen display (human-machine interaction), capillary rheometer (for measuring the physical properties of recycled materials); Material preparation: Select two batches of recycled PP material (baseline batch A and test batch B). Baseline batch A is recycled PP with stable performance and no obvious impurities, while test batch B is recycled PP from different batches (simulating the fluctuation of rheological properties of the batches). Mold preparation: Plastic shell mold (cavity size 100mm×80mm×2mm, gate diameter 2mm), 10 K-type thermocouples are arranged in the temperature sensitive areas (1 gate, 4 cavity edges, 3 wall thickness change points, 2 cavity centers), and the corresponding layout coordinates are pre-entered into the PLC; Software preparation: PLC programming software (for inputting the algorithm program of this invention), conventional industrial configuration software (for data visualization), and communication interface between the PLC and temperature measuring elements, mold temperature controller, and injection molding machine (using Modbus protocol) should be completed in advance.

[0030] Then, following the recycled material injection molding method, the process began as follows: Figure 1 The control process shown is implemented in steps, including the following steps: (1) Benchmark modeling: 1-a. Obtain the physical property parameters of the reference batch A: reference flow activation energy. Reference melt viscosity (Measured at 25℃); then, the reference process parameters for the standard mold are obtained through trial molding: reference mold temperature. Reference injection speed Reference holding pressure (Among them, the absence of shrinkage marks and warping in the molded parts after trial molding is considered as a qualified standard). 1-b. Establish a benchmark correlation model between the physical properties of recycled materials and injection molding parameters: Select 3 sets of trial molding auxiliary data ( , ; , ; , Substitute these values ​​into the linear fitting formula. Then, the solution is obtained by solving a system of simultaneous equations. The calculation process is as follows: 2200 = 55 + b; 2000 = 60 + b; 1800 = 65 + b; Solving for: =-40, b=4400, the final fitted model is: ; 1-c. Calculate the uniformity threshold using the temperature field standard deviation method. Substitute into the formula ,in The average temperature at each temperature measuring point in the cavity during the baseline molding trial is given by the formula: Finally, we obtained (This means that small fluctuations in the temperature field are permissible); then calculate the viscosity deviation threshold. The specific formula is as follows ,in Take 0.10 (i.e., ±10% of the reference viscosity) to obtain ; 1-d. The output preset process data includes a set of baseline control parameters: , , And the core deviation threshold: , and the baseline fit coefficients: =-40、 =4400; The preset process data serves as the benchmark for subsequent deviation calculations and parameter adjustments; 1-e, Finally, based on the fitting formula, generate as follows: Figure 2 The graph shown is a linear fit curve of the recycled material viscosity versus the reference mold temperature (X-axis is mold temperature, Y-axis is viscosity, slope of the fitted line is -40, intercept is 4400). This linear fit curve visually quantifies that for every 1°C increase in the reference mold temperature, the viscosity of the recycled PP melt decreases by approximately 40%. The rheological laws.

[0031] The purpose of this step is to solve the problems of no reference for batch fluctuations of recycled materials and no matching standard for temperature control and molding parameters by establishing a unified judgment benchmark. By linking the physical properties of recycled materials with injection molding process parameters, the core threshold for subsequent control is determined, providing a basis for subsequent adaptive adjustment.

[0032] The physical properties of the aforementioned benchmark batch of recycled material can be determined using conventional polymer material testing methods (capillary rheometer). A batch of recycled material with stable performance and no obvious impurities is selected as the benchmark batch. The benchmark process parameters for standard trial molding are obtained through the standard trial molding process of the existing injection molding production line. During the trial molding process, it is ensured that the molded parts are free of obvious defects (shrinkage marks, warpage, weld lines). The mold cavity size parameters are directly extracted from the core dimensions (cavity thickness, gate size, etc.) in the mold design drawings.

[0033] (2) Initialize mold temperature field data: 2-a. Arrange 10 temperature sensing elements (K-type thermocouples) in various temperature-sensitive areas within the mold cavity: gate (X10, Y10), 4 points on the cavity edge (X5, Y10; X95, Y10; X10, Y70; X95, Y70), 3 points at the abrupt change in wall thickness (X30, Y30; X70, Y30; X50, Y50), and 2 points at the cavity center (X50, Y20; X50, Y60). The coordinates of each of these points represent the location of the respective temperature sensing point. Figure 3 The corresponding distribution coordinates of the two-dimensional coordinate system distribution diagram of the temperature measurement points in the cavity of the mold. 2-b. Verify all temperature measurement points: calibration values ​​of 10 temperature sensing elements. (Unit: °C) The following are the temperatures in order: 60.0, 59.9, 60.1, 59.8, 60.2, 60.0, 59.9, 60.1, 60.0, 59.8; Then, communication between the PLC and the temperature measurement module is initiated. The specific communication link parameters (Modbus protocol) are: baud rate 9600, address code 1-10 (corresponding to 10 temperature measurement points), and real-time verification temperature values ​​of the 10 points are read. (Assume the fitting error range), which are as follows: 60.2, 60.0, 60.3, 59.7, 60.5, 60.1, 59.8, 60.2, 60.1, 59.7; According to the verification formula Verify each point individually (calculate the error at each point): Point 1 (gate): |60.2-60.0|=0.2℃≤ 0.5℃ (valid); Point 2 (edge): |60.0-59.9|=0.1℃≤ 0.5℃ (valid); Point 3 (edge): |60.3-60.1|=0.℃ ≤ 0.5℃ (valid); Point 4 (edge): |59.7-59.8|=0.1℃ ≤ 0.5℃ (valid); Point 5 (edge): |60.5-60.2|=0.3℃ ≤ 0.5℃ (valid); Point 6 (abrupt change in wall thickness): |60.1-60.0|=0.1℃ ≤ 0.5℃ (valid); Point 7 (abrupt change in wall thickness): |59.8-59.9|=0.1℃ ≤ 0.5℃ (valid); Point 8 (abrupt change in wall thickness): |60.2-60.1|=0.1℃≤ 0.5℃ (valid); Point 9 (center): |60.1-60.0|=0.1℃ ≤ 0.5℃ (valid); Point 10 (center): |59.7-59.8|=0.1℃ ≤ 0.5℃ (valid); 2-c. Next, debug the communication link and continuously collect data for 5 minutes to ensure no data loss or communication interruption occurs. Finally, output the results, which include all 10 valid points (without any exceptions). The real-time temperature field acquisition link is stable using the Modbus protocol, with a acquisition frequency of once per injection cycle t (approximately 20 seconds per cycle). Based on the specific location of each valid temperature measurement point in the mold cavity, generate data such as... Figure 3 The diagram shows the two-dimensional coordinate system distribution of temperature measurement points in the mold cavity. This diagram intuitively presents the actual layout of each temperature measurement point in the mold cavity and its corresponding communication address code, providing a spatial reference for rapid location of temperature measurement points, troubleshooting of abnormal points, and visual analysis of temperature field distribution.

[0034] This step is mainly used to ensure the acquisition accuracy and stability of the temperature field sensing module, troubleshoot any abnormalities in the temperature sensing element and communication link, determine the effective temperature measurement points in the mold cavity, and avoid deviations in subsequent temperature field judgment and control due to inaccurate data acquisition or unstable links, thus ensuring the reliability of the input data of the entire control system.

[0035] (3) Real-time data acquisition: 3-a. Collect the real-time temperature of each valid temperature measurement point. (i=1-10, unit: ℃, collected once per injection molding cycle), in the following order: 60.3, 60.1, 60.4, 59.6, 60.6, 60.2, 59.7, 60.3, 60.2, 59.6, where the collection timestamp is... The data was collected at 10:00:00 on January 27, 2026 (corresponding to the middle of the cooling stage of one injection molding cycle, which is the most representative time for data collection). 3-b. Preprocess the collected raw temperature data: 3-b-1. Outlier removal, based on the reference mold temperature set in step (1). The abnormal temperature range is set to Compared with the above 10 :all All in Within the specified range, there are no outliers; all values ​​are retained. 3-b-2, Calculate the global average temperature of the cavity Substitute into the formula The average temperature was obtained. ; Calculate the temperature field uniformity index Substitute into the formula First calculate each point (unit: ): Number 1: (60.3-60.1)²=0.04; Number 2: (60.1-60.1)²=0; Number 3: (60.4-60.1)²=0.09; Number 4: (59.6-60.1)²=0.25; Number 5: (60.6-60.1)²=0.25; Number 6: (60.2-60.1)²=0.01; Number 7: (59.7-60.1)²=0.16; Number 8: (60.3-60.1)²=0.04; Number 9: (60.2-60.1)²=0.01; Number 10: (59.6-60.1)²=0.25; Summing again: 0.04 + 0 + 0.09 + 0.25 + 0.25 + 0.01 + 0.16 + 0.04 + 0.01 + 0.25 = 1.1 Finally, the square root is calculated to obtain the temperature field uniformity index. ; 3-c. Output the preprocessed dataset, including the real-time temperature of each valid temperature measurement point. (60.3~59.6℃), average temperature across the entire cavity =60.1℃, temperature field uniformity index ≈0.33℃ and corresponding data collection timestamp =2026-01-27, 10:00:00; and generate based on the preprocessed dataset as follows Figure 4 The temperature time-series coordinate curves of various points in the mold cavity shown are intuitively displayed to show the dynamic change trend of temperature at various temperature measurement points in the mold cavity over time during the injection molding process. The X-axis represents time and the Y-axis represents temperature. The 10 curves are concentrated in the range of 59.6-60.6℃, and the trend is stable.

[0036] This step is used to synchronize the injection molding production cycle. By capturing the dynamic changes of the temperature across the entire mold cavity in real time, and preprocessing the collected raw temperature data, a basic dataset of the temperature field can be directly used for subsequent deviation calculations, providing reliable data support for accurately determining the temperature field distribution.

[0037] (4) Calculate the control parameters: 4-a. Test batch B property deviation (obtained by detection): Flow activation energy deviation ( (higher than the baseline batch), melt viscosity (Measured at 25℃), viscosity deviation (A positive deviation indicates that the viscosity is higher than the baseline, requiring cooling or adjustment of molding parameters); and preset parameters: weighting coefficient. , (Taking into account both physical properties and temperature field fluctuations), baseline PID parameters , ; 4-b. First calculate the temperature field uniformity deviation. (A negative deviation indicates that the temperature field uniformity is better than the reference), then calculate the overall deviation. ; 4-c. Calculate the control output. (The integral term is integrated over one injection cycle, and the integration time is...) , Consider it a constant value of 149.415), and substitute it into the formula. Among them, the proportional term Integral term Therefore, the output volume is adjusted. (Unitless, corresponding adjustment reference value); 4-d. Mapped to a specific adjustment value (fitting the equipment's adjustment range, linear mapping): 4-d-1. Calculate the temperature control parameter adjustment amount: Mold temperature controller power adjustment coefficient Here, we take 10% as the percentage of mold temperature controller power adjustment. (That is, power needs to be reduced and temperature lowered to suit high viscosity); Cooling flow rate adjustment coefficient Take here Cooling water flow rate adjustment value (That is, the flow rate needs to be increased to assist in cooling); 4-d-2, Calculate the adjustment amount of molding parameters: Injection speed adjustment coefficient Take here Injection speed adjustment value (That is, it is necessary to increase the speed to adapt to high viscosity melts); holding pressure adjustment coefficient Take here Pressure holding pressure adjustment value (That is, it is necessary to increase pressure and reduce shrinkage marks); 4-d-3, Output Results: Temperature Control Parameter Adjustment Amount: Mold Temperature Controller Power Adjustment Percentage (Power reduction) (Increase traffic); Molding parameter adjustment amount: Injection speed adjustment value (Speed ​​increase), pressure holding pressure adjustment value (Increase pressure); and based on the data of temperature control parameter adjustment and molding parameter adjustment, generate, for example Figure 5 The deviation-adjustment correlation coordinate system curve shown (X-axis is...) / (The Y-axis represents the adjustment amount, which is linearly positively correlated.) This curve visually presents the linear positive correlation between the viscosity deviation of recycled materials, the temperature field uniformity deviation, and the actual adjustment amount of each device.

[0038] This step is the core calculation step of the entire method. Its purpose is to combine the actual deviation of the batch physical property fluctuation of recycled materials with the temperature field of the mold cavity, and to accurately calculate the adjustment amount of temperature control parameters and molding parameters through calculation. This provides clear adjustment instructions for the subsequent execution module, realizes the precision and personalization of parameter adjustment, and adapts to the batch fluctuation characteristics of recycled materials.

[0039] (5) Execute linkage control: Real-time operating parameters of the equipment (before execution): Mold temperature controller power 50%, cooling water flow rate 10L / min, injection speed 50mm / s, holding pressure 80MPa; 5-a. Convert the temperature control parameter adjustment amount and molding parameter adjustment amount output in step (4) from digital to the standard input command that the corresponding equipment can directly recognize, according to the formula. The calculations include adapting the Siemens PLC to the device interface. =0.01, =0: Mold temperature controller power adjustment command (Digital instructions recognizable by the PLC, corresponding to a 6.0% adjustment range); Cooling water flow regulation instructions (Corresponding to a 3.0L / min adjustment volume); Injection speed adjustment command (Corresponds to an adjustment range of 3.0 mm / s); Pressure holding adjustment command (Corresponding to a 3.0 MPa adjustment range); 5-b. The mold temperature controller and injection molding machine operate synchronously (the PLC sends 4 instructions in milliseconds via the Modbus protocol): Receive , The instruction is to reduce the mold temperature controller power from 50% to 44.0% (50% - 6.0%), and increase the cooling water flow rate from 10L / min to 13.0L / min (10 + 3.0); Received , The instruction is to increase the injection speed of the injection molding machine from 50 mm / s to 53.0 mm / s (50 + 3.0) and the holding pressure from 80 MPa to 83.0 MPa (80 + 3.0). 5-c. Post-execution retest (collect temperature and equipment parameters once): Retest temperature field: , (Temperature decreased, uniformity slightly improved); Post-execution parameters: mold temperature controller power 44.0%, cooling water flow rate 13.0L / min, injection speed 53.0mm / s, holding pressure 83.0MPa (all executed normally according to instructions); Based on the real-time data of parameter adjustments, the following is generated: Figure 6 The time-temperature control / molding parameter timing curve shown (X-axis is time, Y-axis is parameter value, showing the synchronous adjustment and stabilization of parameters) intuitively demonstrates the synchronous adjustment process of mold temperature controller power, cooling water flow rate, injection speed, and holding pressure over time during injection molding production, as well as the final stable state of the parameters.

[0040] This step transforms the calculated adjustment values ​​into actual operational execution. The aim is to achieve synchronized adjustment of temperature control parameters with molding parameters such as injection and holding pressure, ensuring that adjustment commands are accurately implemented. By coordinating the adjustment of these two types of parameters, precise matching between the mold temperature field and the properties of recycled materials is achieved, ensuring the stability of the injection molding process and reducing molding defects.

[0041] (6) Closed-loop optimization: 6-a. Compare the deviation after parameter adjustment: measure the melt viscosity after execution. And measured in step (5) Therefore, after adjustment , ; 6-b. Deviation Comparison: After Execution , Therefore, the deviation has been reduced to within the threshold, and the current PID coefficients are maintained to complete this optimization. 6-c. Full-process data storage (archived according to the injection molding cycle and stored in the PLC data unit): Archived data classification: 1) Sensing data: Step (2) point verification data, Step (3) real-time temperature data, Step (5) retest temperature data; 2) Calculation data: Step (1) fitting coefficient, Step (4) deviation, Step (4) adjustment amount, PID coefficient (benchmark + correction); 3) Execution data: instruction data from step (5), and device parameters before and after execution; 4) Quality data: dimensional deviations of molded parts, surface defect levels; Based on the temperature field re-measurement data after parameter adjustment, the following is generated: Figure 7 The diagram shows the uniformity of the temperature field in the mold cavity using a two-dimensional thermodynamic coordinate system (based on the two-dimensional coordinate system of the cavity, with each point's temperature indicated by color as normal, undercooled, or overheated, where undercooled is red and overheated is blue). In this diagram, the temperature at each point is mapped to green (59.8-60.3℃), with no red / blue areas (no overheating / undercooling). This thermodynamic diagram provides an intuitive visualization of the temperature distribution in each area of ​​the mold cavity, allowing for rapid identification of areas with uneven temperature distribution.

[0042] This step is the final and optimization stage of the entire process. Its purpose is to verify the actual effect of parameter adjustment, optimize and correct control coefficients, and archive the entire process production and control data to provide data support for subsequent process iterations and batch optimizations, forming a complete closed loop of "collection-computation-execution-optimization" to continuously improve the stability of recycled material injection molding quality.

[0043] After implementing the method in this embodiment, the temperature of the entire mold cavity is stabilized within the process threshold of 59.8-60.3℃, with a temperature deviation of ≤0.2℃ and no overheating or undercooling areas, and the temperature field uniformity meets the standards. Multiple parameters can be synchronously controlled within 20 seconds, and the deviation and adjustment amount maintain a stable linear positive correlation. The control response rhythm is perfectly matched with the injection molding production cycle. During the process stabilization period, the fluctuation of each parameter is ≤±0.5%, and there are no abnormal fluctuations in continuous production. Frequent secondary control is not required, and the process stability is significantly improved. At the same time, the deviation-adjustment amount correlation curve, parameter time series curve, and temperature field thermogram can intuitively and accurately verify the control effect. The chart data are highly consistent with the actual on-site conditions, and the process compliance status can be quickly determined. Overall, the preset process control target has been achieved, and it has good feasibility for industrial on-site implementation.

Claims

1. A method of injection molding of recycled material based on adaptive control of the mold temperature field, characterized in that The method comprises the following steps: (1) Reference modeling: Obtain the reference data of the reclaimed material, establish a reference correlation model of the physical properties of the reclaimed material and the injection molding parameters, and output preset process data; (2) Initialize mold temperature field data: Construct a global temperature field acquisition link composed of each effective temperature measurement point in the mold cavity, check all temperature measurement points, select effective temperature measurement points, and output the results; (3) Real-time data acquisition: Real-time acquisition of dynamic changes of the global temperature of the mold cavity, preprocessing of the original temperature data collected, calculation and output of the global temperature field preprocessing data set of the cavity synchronized with the injection molding cycle; (4) Calculate the control parameters: Compare the real-time collected data with the preset process data and the reclaimed material batch detection data respectively, calculate the comprehensive deviation, and dynamically calculate and output the temperature control parameter adjustment amount and the molding parameter adjustment amount based on the comprehensive deviation; (5) Perform linkage control: According to the temperature control parameter adjustment amount and the molding parameter adjustment amount, synchronously adjust the heating / cooling power of the mold temperature machine and the cooling water flow, and adjust the injection speed and holding pressure of the injection molding machine; (6) Closed-loop optimization: Input the adjusted real-time detection and production data, judge whether the control coefficient needs to be optimized and corrected according to the adjusted deviation, and store the full-process data.

2. The mold temperature field adaptive control based reclaim injection molding method according to claim 1, wherein: The reference data of the reclaimed material in step (1) includes reference batch reclaimed material physical property parameters: reference flow activation energy , reference melt viscosity , and standard mold reference process parameters: reference mold temperature , reference injection speed , reference holding pressure ; after inputting the above data, the reference melt viscosity and the reference mold temperature are associated by a linear fitting method, and the fitting formula is , wherein , is a reference fitting coefficient; during the reference mold test, the temperature values T1, T2, …, of each effective temperature measurement point in the mold cavity are collected in real time, the uniformity threshold is calculated by a temperature field standard deviation method, and the formula is , wherein is the average value of the temperature of each temperature measurement point in the cavity during the reference mold test, and the formula is ; the viscosity deviation threshold is calculated, and the specific formula is , wherein is a deviation coefficient, and the value range is 0.05-0.10; the final output preset process data includes a reference control parameter set: , , , a core deviation threshold: , , and a reference fitting coefficient: , ; the preset process data serves as a reference for subsequent step (4) deviation calculation and step (5) parameter adjustment.

3. The mold temperature field self-adaptive control based reclaimed material injection molding method according to claim 2, characterized in that: The step (2) of constructing the global temperature field acquisition link composed of each effective temperature measurement point in the mold cavity is to arrange each temperature measurement element in each temperature sensitive area in the mold cavity; the method of checking all temperature measurement points and selecting effective temperature measurement points is to read the real-time check temperature measurement value of each temperature measurement point With the hardware calibration value , the temperature measurement points with an acquisition error greater than ±0.5℃ are removed, and the check formula is .

4. The mold temperature field adaptive control based reclaim injection molding method according to claim 3, characterized in that: The step (3) collects the dynamic change of the temperature of the whole cavity of the mold in real time, specifically, in each injection cycle, the real-time collection temperature of each effective temperature measurement point is uploaded in real time ; the pre-processing method of the collected original temperature data is to eliminate abnormal values first: based on the reference mold temperature in step (1) , the temperature abnormal range is set as , and the abnormal temperature values exceeding the range are eliminated; Then, the global statistical operation is performed again, the effective temperature data after removing the abnormal values is counted, and the global average temperature of the cavity is calculated and the temperature field uniformity index , the operation formula is 、 , wherein the global average temperature reflects the overall level of the cavity temperature, and the temperature field uniformity index reflects the fluctuation degree of the global temperature of the cavity; the output preprocessed data set includes the real-time temperature of each effective temperature measuring point, the global average temperature of the cavity, the temperature field uniformity index and the corresponding collection time stamp , , , .

5. The mold temperature field adaptive control based reclaim injection molding method according to claim 4, wherein: The data collected in real time in the step (4) is the global temperature field pretreatment data set from the cavity in the step (3); the reclaimed batch detection data includes the flow activation energy deviation of the current batch of reclaimed materials and the reference batch of reclaimed materials and the viscosity deviation , wherein the flow activation energy of the current batch of reclaimed materials is compared with the reference flow activation energy , and the formula is the melt viscosity measurement value of the current batch of reclaimed materials is compared with the reference viscosity , and the formula is ; the way to calculate the comprehensive deviation amount is to take the temperature field uniformity deviation and the viscosity deviation as input quantities, introduce weight coefficients , , and calculate the comprehensive deviation amount, and the formula is ; the way to calculate the temperature control parameter adjustment amount and the molding parameter adjustment amount is to first use the improved PID simple algorithm, to solve the control output according to the PID proportional coefficient and the integral coefficient by the formula , which directly corresponds to the adjustment range of the temperature control and molding parameters; and then to calculate the temperature control parameter adjustment amount including the mold temperature machine power adjustment percentage and the cooling water flow adjustment value , and the molding parameter adjustment amount including the injection speed adjustment value and the holding pressure adjustment value , wherein the calculation formula of the mold temperature machine power adjustment percentage is , wherein is the mold temperature machine power adjustment coefficient, representing the maximum power adjustment percentage corresponding to the full range of PID output; the calculation formula of the cooling water flow adjustment value is , wherein is the cooling flow adjustment coefficient, representing the maximum flow adjustment range corresponding to the full range of PID output; the calculation formula of the injection speed adjustment value is , wherein is the injection speed adjustment coefficient, representing the maximum injection speed adjustment range corresponding to the full range of PID output; and the calculation formula of the holding pressure adjustment value is , wherein is the holding pressure adjustment coefficient, representing the maximum holding pressure adjustment range corresponding to the full range of PID output.​ 6. The mold temperature field self-adaptive control based reclaimed material injection molding method according to claim 5, wherein: The way of adjusting the heating / cooling power of the mold temperature controller and the cooling water flow, and adjusting the injection speed and the holding pressure of the injection molding machine in the step (5) is that the temperature control parameter adjustment amount and the molding parameter adjustment amount output in the step (4) are converted from numbers to standard input instructions which can be directly recognized by the corresponding equipment by using a parameter mapping analysis algorithm , the conversion formula is ; then the heating / cooling power of the mold temperature controller and the cooling water flow, and the injection speed and the holding pressure of the injection molding machine are adjusted according to the converted instructions.

7. The mold temperature field adaptive control based reclaim injection molding method according to claim 6, wherein: The method for determining whether the control coefficient needs to be optimized in step (6) is to use a deviation feedback correction algorithm to compare the deviation after parameter adjustment. , ) and the core deviation threshold preset in step (1) , Since both positive and negative values ​​indicate deviation from the benchmark and reflect the degree of deviation, the absolute value is used for judgment. or If the deviation is still greater than the preset threshold, then the PID control coefficient ( , The correction method is as follows: 6-1. If and then ; 6-2. If only then ; 6-3. If only then ; wherein, is a correction coefficient with a value ranging from 0.05 to 0.1, is a deviation weight coefficient, and at the same time, the correction range of the PID control coefficient ( , ) is controlled within ±10%; if the deviation is within a preset threshold range, the current PID coefficient is maintained unchanged, and the current optimization is completed; finally, the whole process data is classified and archived according to the injection cycle , including temperature field sensing data, algorithm operation data, parameter adjustment instructions, equipment operation data and product quality data.

8. The mold temperature field adaptive control based reclaim injection molding method according to claim 7, wherein: In step (1) above, after outputting the data, a linear fitting coordinate system curve of the viscosity of the reclaimed material and the reference mold temperature is generated based on the fitting formula; in step (2) above, based on the specific positions of each effective temperature measurement point in the mold cavity, a two-dimensional coordinate system distribution map of the mold cavity temperature measurement points is generated; in step (3) above, after outputting the global temperature field preprocessing data set of the cavity, a temperature time sequence coordinate system curve of each point in the mold cavity is generated based on the data set; in step (4) above, based on the data of the temperature control parameter adjustment amount and the molding parameter adjustment amount, a deviation- adjustment amount correlation coordinate system curve is generated; in step (5) above, based on the real-time data of the parameter adjustment, a time-temperature control / molding parameter time sequence control curve is generated; in step (6) above, based on the temperature field re-measurement data after parameter adjustment, a two-dimensional thermal coordinate system diagram of the mold cavity temperature field uniformity is generated.

9. A mold temperature field adaptive control system for implementing the method of recycling material injection molding according to any one of claims 1-8, characterized in that: The temperature field sensing module, the data processing and control module, the temperature control execution module, the molding parameter execution module and the man-machine interaction storage module are included, the signal output ends of the temperature field sensing module and the man-machine interaction storage module are electrically connected with the corresponding signal input ends of the data processing and control module, the signal input ends of the temperature control execution module, the molding parameter execution module and the man-machine interaction storage module are electrically connected with the corresponding signal output ends of the data processing and control module; wherein the temperature field sensing module is used for collecting the temperature data of each effective temperature measuring point in the mold cavity in real time; the data processing and control module is used for receiving data, performing operation of each step and outputting control instructions; the temperature control execution module and the molding parameter execution module receive the standard input instructions in step (5) respectively, the temperature control execution module is in communication connection with the main control system of the mold temperature machine, and adjusts the heating / cooling power and the cooling water flow of the mold temperature machine according to the instructions; the molding parameter execution module is in communication connection with the main control system of the injection molding machine, and adjusts the injection speed and the holding pressure of the injection molding machine according to the instructions; the man-machine interaction storage module is used for parameter input, state display, synchronous storage and retrieval of full-process data.

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