Plastic tableware automatic production control system based on PLC

Abstract

The invention relates to the technical field of injection molding production automatic control, in particular to a plastic tableware automatic production control system based on a PLC. The system comprises a process state data acquisition module, a load characteristic dynamic mapping module, a constraint conflict prediction module, a decoupling control architecture generation module and a closed loop feedback execution module. The system solves the nonlinear load impedance characteristic by acquiring kinematics of a driving part and medium data of a processing cavity; the core of the method is to construct a load and motion characteristic decoupling model, identify melt viscosity drift in real time and generate a self-adaptive feedforward control instruction so as to predict inertial state impact of an execution tail end; according to the method, a control mode based on machine action is converted into a control mode based on a melt state, calculation errors caused by water gap material proportion fluctuation or raw material thermal degradation are effectively eliminated, and the stability of the physical state of a processing medium is ensured in a time-varying load environment.

Description

Technical Field

[0001] This invention relates to the field of automated control technology for injection molding production, specifically to a PLC-based automated production control system for plastic tableware. Background Technology

[0002] In the injection molding production environment of plastic tableware, the rheological properties of the processing medium directly affect the molding quality. Due to the random fluctuation of the mixing ratio of sprue in the raw materials, the melt viscosity will drift nonlinearly, which will place extremely high demands on the stability of the production process.

[0003] To address the aforementioned issues, existing control schemes generally employ fixed-parameter logic based on PLCs or conventional PID regulation. These schemes utilize a preset sequence of mechanical actions for open-loop control, providing a certain level of processing capability under standard operating conditions. However, due to the lack of real-time sensing capability of the melt's micro-rheological state in traditional control systems, they cannot identify deviations in physical properties caused by raw material thermal degradation and component differences. Consequently, during high-speed thin-wall injection molding, the inherent contradiction between the high filling rate and the peak pressure constraint of the mold cavity is difficult to reconcile. This control method, based on machine actions rather than the melt state, makes it difficult to control the inertial impact at the end of the filling process, easily leading to quality defects such as underfilling or flash, and making it difficult to support the demand for high-precision, consistent production.

[0004] Therefore, how to achieve real-time decoupling of rheological properties and kinematic characteristics, and establish a closed-loop control architecture from sensing to compensation, has become a technical problem that urgently needs to be solved to improve the accuracy of automated control in injection molding production. Summary of the Invention

[0005] To solve the above-mentioned technical problems, the present invention provides a PLC-based automated production control system for plastic tableware. Specifically, the technical solution of the present invention includes:

[0006] Process status data acquisition module: used to collect kinematic data of the drive components of the production equipment and media status response data in the processing chamber, and to construct a time-varying dataset of the production process;

[0007] Load characteristic dynamic mapping module: used to calculate the nonlinear load impedance characteristics of the processing medium based on the kinematic data of the drive component, and establish a dynamic correlation between the drive action and the behavior of the medium;

[0008] Constraint Conflict Prediction Module: Used to identify quantitative conflicts between driving energy and process execution rate and terminal state constraint boundaries, and predict inertial state impact at the end of execution;

[0009] Decoupled control architecture generation module: used to build a decoupled model of load characteristics and kinematic features, and generate adaptive feedforward control commands for time-varying loads;

[0010] Closed-loop feedback execution module: used to adjust the process execution parameters in real time within the PLC controller according to the adaptive feedforward control instructions, and lock the physical state of the processing medium.

[0011] Preferably, the modules are interconnected using the following method:

[0012] S1. Collect instantaneous drive torque data, propulsion speed data, and chamber pressure response curve data during the production cycle to construct the time-varying dataset of the production process;

[0013] S2. Perform time-series analysis on the instantaneous driving torque data, map the changing trend of the flow resistance of the processing medium wavefront, and generate a load state drift matrix.

[0014] S3. Input the load state drift matrix into the constraint conflict prediction module, calculate the drive increment required to maintain the current execution rate, and predict the inertial state impact value caused by it.

[0015] S4. Based on the decoupling model, calculate the dynamic transfer function between the change in driving torque and the chamber pressure response, and generate an adaptive feedforward control command that includes pressure compensation value and state switching point correction value.

[0016] S5. The adaptive feedforward control command is sent to the PLC controller to dynamically adjust the action execution logic within the current production cycle, thereby achieving closed-loop locking of the physical state of the processing medium.

[0017] Preferably, S1 specifically includes:

[0018] Sensor nodes are deployed at key locations in the actuators and processing chambers of the production equipment to collect the screw's rotational torque and angular velocity in real time during the sampling period. axial displacement value and medium pressure value inside the mold cavity;

[0019] The rotational torque value, the axial displacement value, and the medium pressure value of each sensor node at the same timestamp are constructed into a multi-dimensional data matrix.

[0020] The multidimensional data matrix is ​​subjected to time alignment, high-frequency noise filtering and normalization to obtain a standardized process rheological data matrix, which is then combined in the order of the production cycle to form the time-varying dataset of the production process.

[0021] Preferably, S2 specifically includes:

[0022] S21. Based on a preset standard medium rheological model, construct a benchmark driving torque time series;

[0023] S22. Extract the real-time instantaneous drive torque sequence of the current cycle from the time-varying dataset of the production process, and compare it with the reference drive torque time sequence;

[0024] S23. Calculate the deviation between the real-time sequence and the reference sequence at each time step, and identify the critical point drift of the non-Newtonian fluid shear thinning effect caused by fluctuations in raw material composition.

[0025] S24. Map the deviation value and critical point drift data to a medium viscosity fluctuation index to generate the load state drift matrix, which is used to characterize the real-time change of the processing medium's sensitivity to shear rate.

[0026] Preferably, S3 specifically includes:

[0027] S31. Set the target holding threshold for filling rate and the safe peak threshold for cavity pressure;

[0028] S32. Based on the viscosity fluctuation index of the medium, calculate the theoretical driving pressure required to overcome the current flow resistance;

[0029] S33. Based on the theoretical driving pressure value, simulate the fluid kinetic energy release process at the filling end and calculate the inertial state impact value;

[0030] S34. If the inertial state impact value causes the cavity pressure to exceed the safety peak threshold, then it is determined that the quantization conflict exists, and a conflict intensity coefficient is generated.

[0031] S35. If the inertial state impact value does not exceed the safety peak threshold, it is determined to be a non-conflict state, and the original parameter maintenance command is output.

[0032] Preferably, S4 specifically includes:

[0033] S41. Introduce a rheology-kinematics decoupling algorithm to establish an energy balance equation between the mechanical input power of the screw and the power consumption of the medium filling.

[0034] S42. Based on the conflict intensity coefficient, the energy balance equation is modified to solve for the optimal pressure-holding switching position under the premise of satisfying the target maintenance threshold.

[0035] S43. Based on the dynamic transfer function of the driving torque change and the chamber pressure response, calculate the pressure compensation curve for different viscosity states.

[0036] S44. Integrate the optimal pressure holding switching position with the pressure compensation curve to generate the adaptive feedforward control command to achieve the same filling mode under different viscosities.

[0037] Preferably, S5 specifically includes:

[0038] S51, the PLC controller receives the adaptive feedforward control command and parses out the millisecond-level pressure adjustment sequence and position switching signal;

[0039] S52. During the injection stage, in response to the detection of an increase in the viscosity of the medium, the output force of the hydraulic or electric actuator is increased according to the pressure adjustment sequence to maintain the filling rate.

[0040] S53. At the end of filling, in response to the position switching signal, the pressure holding switching action is triggered earlier or later to reduce the inertial state impact value.

[0041] S54. Real-time monitoring of mold cavity pressure feedback. If the deviation between the monitored value and the predicted value exceeds the preset safety tolerance, the emergency state locking logic is triggered to forcibly reduce the injection speed to prevent state overflow.

[0042] Preferably, the system is for injection molding production of plastic tableware, and the construction of the time-varying dataset of the production process in S1 further includes:

[0043] Identify the mixing ratio characteristics of sprue in the raw materials, and associate the mixing ratio characteristics as external perturbation variable labels with the corresponding process rheological data matrix;

[0044] When processing, the load characteristic dynamic mapping module calls the external disturbance variable label to perform weighted correction on the critical point drift of the non-Newtonian fluid shear thinning effect, so as to eliminate the calculation error introduced by the difference in the degree of thermal degradation of raw materials.

[0045] Preferably, the system is further configured with energy efficiency synergistic optimization logic:

[0046] When generating the adaptive feedforward control command in S4, the minimum clamping force requirement that satisfies the molding quality constraint is calculated.

[0047] The minimum clamping force requirement is fed back to the injection molding machine's clamping system to dynamically reduce clamping pressure, achieving non-linear synergistic optimization of quality assurance and energy consumption reduction.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] 1. This system can identify the nonlinear drift of melt viscosity caused by fluctuations in the proportion of sprue material in real time by collecting kinematic data of the driving components and establishing a dynamic mapping of load characteristics. This mechanism breaks through the lack of perception of the microscopic physical properties of the processing medium in traditional control schemes, enabling the system to have a function similar to a virtual viscometer. By introducing external disturbance variable labels and performing weighted correction, the calculation error introduced by the thermal degradation of raw materials is eliminated, providing a high-fidelity data foundation for realizing the transformation from control based on machine action to control based on melt state.

[0050] 2. To address the challenge of simultaneously managing underfill and flash mentioned in the background technology, this system employs a constraint conflict prediction module to quantitatively predict inertial state impact before the end of the filling process. Combined with adaptive feedforward control commands, the system can maintain a high filling rate to prevent underfill while precisely reducing the risk of flash caused by pressure overshoot by triggering the pressure holding switching action earlier or later. This mechanism of identifying potential risks before physical action occurs significantly improves the consistency of product quality under complex injection molding conditions.

[0051] 3. By leveraging the rheology-kinematics decoupling algorithm and dynamic transfer function, this system constructs a complete closed-loop architecture from sensing, prediction to compensation. This enables the PLC controller to no longer rigidly execute preset fixed parameter logic, but instead dynamically adjust the pressure compensation curve and position switching point within milliseconds within the current production cycle in response to flow resistance fluctuations caused by changes in raw material composition. This ability to self-correct within the same cycle ensures that the same filling mode can be achieved under different viscosity conditions, greatly enhancing the system's adaptability to raw material fluctuations.

[0052] 4. The energy efficiency synergistic optimization logic configured in this system can reverse-calculate the minimum clamping force requirement to meet molding quality constraints based on the predicted peak pressure of the mold cavity, and feed it back to the mold closing system to dynamically adjust the pressure. Through this precise on-demand allocation logic, not only is the power consumption of the injection molding machine in the high-pressure mold closing stage greatly reduced, but the mechanical fatigue and wear of the mold and mechanical structure are also reduced simultaneously. While ensuring high-precision production, it achieves the synergistic benefit of energy saving and consumption reduction and extending equipment life. Attached Figure Description

[0053] The present invention will be further explained below with reference to the accompanying drawings and embodiments:

[0054] Figure 1 This is a structural diagram of the system of the present invention. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0056] Example 1:

[0057] Please see Figure 1 A PLC-based automated production control system for plastic tableware includes:

[0058] Process status data acquisition module: used to collect kinematic data of the drive components of the production equipment and media status response data in the processing chamber, and to construct a time-varying dataset of the production process;

[0059] Load characteristic dynamic mapping module: used to calculate the nonlinear load impedance characteristics of the processing medium based on the kinematic data of the driving components, and to establish a dynamic relationship between the driving action and the behavior of the medium;

[0060] Constraint Conflict Prediction Module: Used to identify quantitative conflicts between driving energy and process execution rate and terminal state constraint boundaries, and predict inertial state impact at the end of execution;

[0061] Decoupled control architecture generation module: used to build a decoupled model of load characteristics and kinematic features, and generate adaptive feedforward control commands for time-varying loads;

[0062] Closed-loop feedback execution module: used to adjust process execution parameters in real time within the PLC controller based on adaptive feedforward control instructions, and to lock the physical state of the processing medium.

[0063] PLC-based automated production control system for plastic tableware: In the existing plastic tableware injection molding production, the nonlinear drift of melt viscosity caused by the fluctuation of the mixing ratio of sprue material leads to the difficulty in simultaneously controlling underfill and flash. This system constructs a rheological-kinematic decoupled closed-loop architecture.

[0064] The process status data acquisition module is configured to synchronously acquire kinematic data of the injection molding machine screw drive components and pressure response data of the plastic melt processing medium in the mold cavity in real time. Through high-frequency sampling, discrete mechanical actions are transformed into continuous time-varying datasets of the production process. The load characteristic dynamic mapping module, based on the instantaneous torque change of the screw, uses rheological principles to inversely calculate the nonlinear load impedance characteristics of the melt under high temperature and high pressure, thereby establishing a dynamic correlation between machine drive actions and melt flow behavior, achieving virtual perception of the melt rheological state. The constraint conflict prediction module is used to identify the driving energy required to maintain the filling rate, process execution rate, mold clamping force, and discharge... The quantitative conflict between the limit state constraint boundaries of the gas; it can calculate in advance whether a destructive inertial state impact will occur at the filling end if the set parameters are forcibly executed; the decoupled control architecture generation module constructs a decoupled model of load characteristics and kinematic features based on the above prediction results; this model no longer relies solely on PID regulation, but generates adaptive feedforward control instructions containing feedforward compensation to ensure that the system adopts different control forces under different viscosities; the closed-loop feedback execution module is directly embedded in the PLC controller bottom layer, and adjusts the process execution parameters of hydraulic valves or servo motors in real time according to adaptive instructions, thereby locking the physical state of the processing medium and ensuring the consistency of product quality;

[0065] Through the synergistic effect of the above modules, the limitations of parameter freezing logic in traditional injection molding PLC control are broken; the system can sense the micro-rheological differences caused by batch changes in raw materials and transform them into macro-level machine compensation actions; this not only solves the inherent contradiction between high filling rate and low cavity pressure peak in high-speed thin-wall injection molding, but also realizes the transformation from open-loop control based on machine actions to closed-loop control based on melt state.

[0066] Example 2:

[0067] The modules are interconnected using the following method:

[0068] S1. Collect instantaneous drive torque data, propulsion speed data, and chamber pressure response curve data during the production cycle to construct a time-varying dataset of the production process;

[0069] S2. Perform time-series analysis on the instantaneous torque data of the drive, map the trend of the flow resistance change of the processing medium wavefront, and generate a load state drift matrix.

[0070] S3. Input the load state drift matrix into the constraint conflict prediction module, calculate the drive increment required to maintain the current execution rate, and predict the inertial state impact value caused by it.

[0071] S4. Based on the decoupling model, calculate the dynamic transfer function between the change in driving torque and the chamber pressure response, and generate an adaptive feedforward control command that includes pressure compensation value and state switching point correction value.

[0072] S5. The adaptive feedforward control command is sent to the PLC controller to dynamically adjust the action execution logic within the current production cycle, thereby achieving closed-loop locking of the physical state of the processing medium.

[0073] During the injection molding cycle, the system synchronously collects instantaneous drive torque data, feed speed data, and chamber pressure response curve data of the screw. These data are timestamped and aligned to form a holographic dataset describing the current production state. The system performs time-series analysis on the collected instantaneous drive torque data. Since non-Newtonian fluids have different resistance characteristics at different shear rates, by analyzing the torque waveform, the system can map the trend of flow resistance change of the melt wavefront in the mold, and thus generate a load state drift matrix. This matrix intuitively quantifies the degree of deviation of the current material viscosity from the standard process.

[0074] The load state drift matrix is ​​input into the prediction module; the system calculates how much additional thrust, i.e., drive increment, is needed from the screw to maintain the original injection speed when the viscosity increases; then, the system uses the momentum theorem to predict whether this huge thrust will generate an inertial state impact value exceeding the mold's capacity, i.e., pressure overshoot, at the moment of filling completion; based on the decoupling model, the system calculates the dynamic transfer function between the change in drive torque and the chamber pressure response; this means the system knows how much thrust is needed for each increase in viscosity. How much will the mold cavity pressure increase with the torque? Based on this, the system generates adaptive feedforward control instructions containing specific pressure compensation values ​​for the first half of acceleration and state switching point correction values ​​for the second half of early deceleration; these instructions are then sent to the PLC; the PLC no longer rigidly executes the preset program, but dynamically adjusts the action logic within the current cycle, for example, in Pressure boosting and cutoff are completed within seconds; simultaneously, the load state drift matrix is ​​used to quantify the rheological deviation of the current production cycle relative to the reference cycle, which is the mapping result of the process rheological data matrix after time series analysis; thereby achieving closed-loop locking of the physical state of the processing medium;

[0075] The methodological innovation lies in establishing a real-time chain of perception-prediction-compensation; the system can anticipate risks before quality problems occur, even before filling is finished; the system can complete self-correction within the same module; this mechanism makes the production process extremely robust and can adaptively handle unstable factors caused by the mixing of sprue materials; the system can complete self-correction within the same production cycle.

[0076] Example 3:

[0077] S1 specifically includes:

[0078] Sensor nodes are deployed at key locations in the actuators and processing chambers of the production equipment to collect the screw's rotational torque and angular velocity in real time during the sampling period. axial displacement value and medium pressure value inside the mold cavity;

[0079] The rotational torque value, axial displacement value and medium pressure value of each sensor node at the same timestamp are constructed into a multi-dimensional data matrix.

[0080] The multidimensional data matrix is ​​time-aligned, high-frequency noise filtered, and normalized to obtain a standardized process rheological data matrix, which is then combined in the order of the production cycle to form a time-varying dataset of the production process.

[0081] At the hardware level, this system deploys sensor nodes at the servo drive end of the injection molding machine and at key locations in the mold cavity, such as the gate and flow end; during a preset sampling period, for example... Internally, the following key physical quantities are collected in real time: screw rotation torque value. Used to indirectly characterize the plasticizing quality and viscosity of the melt; screw rotation angular velocity. Used for calculating energy input in conjunction with torque; axial displacement value. Used to accurately calculate the current injection position and speed; media pressure value. Used to directly provide feedback on the filling status within the mold cavity; at the software level, the system synchronizes the timestamps of each sensor node. The following data is constructed into a multidimensional data matrix. Due to transmission delays and noise in the sensor signals, the system performs the following processing on the matrix: Time alignment: Based on the PLC's master clock signal, timing misalignment caused by communication delays is eliminated; High-frequency noise filtering: Kalman filtering algorithm is used to remove signal glitches caused by mechanical vibration, and random measurement noise in sensor acquisition is predicted and corrected in real time by establishing the state-space equations of screw displacement and velocity; Normalization processing: Data of different dimensions are mapped to... The process rheological data matrix is ​​obtained by dividing the data into intervals. Finally, these matrices are combined in the order of the production cycle to form a time-varying dataset of the production process, providing high-fidelity input for subsequent algorithms. By fusing and standardizing multi-source heterogeneous data, the bias and noise interference of single sensor data are eliminated. In particular, aligning the torque data at the machine end and the pressure data at the mold end in the same time domain provides a solid data foundation for revealing the deep causal relationship between equipment output and material response.

[0082] Example 4:

[0083] S2 specifically includes:

[0084] S21. Based on a preset standard medium rheological model, construct a benchmark driving torque time series;

[0085] S22. Extract the real-time instantaneous drive torque sequence of the current cycle from the time-varying dataset of the production process and compare it with the benchmark drive torque time series.

[0086] S23. Calculate the deviation between the real-time sequence and the reference sequence at each time step, and identify the critical point drift of the non-Newtonian fluid shear thinning effect caused by fluctuations in raw material composition.

[0087] S24. Map the deviation value and critical point drift data to a medium viscosity fluctuation index to generate a load state drift matrix, which is used to characterize the real-time change of the processing medium's sensitivity to shear rate.

[0088] The implementation logic of the virtual viscometer for identifying viscosity changes is as follows: The system pre-processes a trial molding process, using standard raw materials and preset rheological parameters based on the Power-Law fluid model. Under ideal process conditions, it records the torque changes throughout the entire process and constructs a benchmark driving torque time series. This represents the equipment output curve under perfect conditions; during production, the current real-time drive instantaneous torque sequence is extracted. The system then performs point-by-point comparisons with the reference sequence; the system calculates the deviation between the two at each time step.

[0089]

[0090] In particular, the focus is on the critical point drift caused by the non-Newtonian fluid shear thinning effect due to the breakage of molecular chains in the raw materials containing sprue; the critical point drift is extracted by identifying the real-time driving instantaneous torque sequence. The feature points are used for extraction, and the extraction operator is:

[0091]

[0092] in, The time of the characteristic abrupt change in the rate of torque change;

[0093] Critical point drift refers to the change in the position of the inflection point where the melt viscosity changes abruptly with shear rate. This embodiment utilizes this indicator to accurately capture changes in the rheological properties of materials. The system maps the deviation value and critical point drift data into a medium viscosity fluctuation index. The specific mapping calculation formula is as follows:

[0094]

[0095] in, The injection start time. To maintain the pressure before the switchover, This is the integral weighting coefficient for the deviation, in units of... , The critical point time offset weighting coefficient, in units of By aligning the units of the weighting coefficients, ensure As a dimensionless index, it characterizes viscosity fluctuations; and These are the critical point times for real-time and baseline, respectively;

[0096] The final result is a load state drift matrix; it cleverly utilizes torque as a proxy variable for viscosity; it does not require an expensive online rheometer, but can accurately capture the real-time changes in the melt's sensitivity to shear rate caused by recycled material, i.e., the changes in shear thinning behavior, simply by comparing the computing power inside the PLC, thus solving the technical bias of traditional methods that cannot detect micro-rheological fluctuations.

[0097] Example 5:

[0098] S3 specifically includes:

[0099] S31. Set the target holding threshold for filling rate and the safe peak threshold for cavity pressure;

[0100] S32. Based on the viscosity fluctuation index of the medium, calculate the theoretical driving pressure required to overcome the current flow resistance;

[0101] S33. Based on the theoretical driving pressure value, simulate the fluid kinetic energy release process at the filling end and calculate the inertial state impact value.

[0102] S34. If the inertial state impact value causes the cavity pressure to exceed the safe peak threshold, then a quantization conflict is determined to exist, and a conflict intensity coefficient is generated.

[0103] S35. If the inertial state impact value does not exceed the safety peak threshold, it is determined to be a non-conflict state, and the original parameter instruction is output.

[0104] The logic of conflict prediction is to find a balance between speed and stability: the system presets a target maintenance threshold for the filling rate. Minimum speed to ensure no material shortage and safe peak pressure threshold of the mold cavity The highest pressure required to prevent flash; based on the obtained medium viscosity fluctuation index. The system calculates the theoretical driving pressure that the hydraulic / electric system needs to provide to overcome the increased flow resistance. The computational logic follows:

[0105]

[0106] in, This is a preset pressure proportional conversion constant, and its unit is... This is used to characterize the system's pressure regulation sensitivity to viscosity fluctuations at a unit filling rate, ensuring the consistency of dimensions on both sides of the formula; based on The system simulates the kinetic energy release process of the fluid at the end of the filling process; when the screw suddenly stops at high speed, the inertia of the melt will be converted into a pressure surge, and the inertial state impact value generated by this process is calculated. The fluid kinetic energy release process at the filling end follows the momentum conversion theorem, and the formula for calculating its impact value is:

[0107]

[0108] in, The density of the melt. This represents the end-axis velocity of the screw at the instant of switching. This represents the propagation speed of the pressure wave in the molten medium. This is a preset momentum conversion correction coefficient;

[0109] If the predicted final pressure Exceeded If a quantization conflict is detected, the system will determine that a conflict exists; at this point, the system will generate a conflict intensity coefficient. This coefficient is obtained through the formula Calculations are performed to characterize the deviation of the predicted pressure value from the safety threshold. The larger the value, the more intense the conflict; if it does not exceed the threshold, the original parameters are maintained to avoid unnecessary algorithm intervention; the implicit process contradictions are made explicit and quantified; by predicting in advance the possible pressure overshoot consequences of increasing pressure to maintain speed, the system identifies potential fly-edge risks before physical actions occur, providing accurate decision-making basis for subsequent decoupling control.

[0110] Example 6:

[0111] S4 specifically includes:

[0112] S41. Introduce a rheology-kinematics decoupling algorithm to establish an energy balance equation between the mechanical input power of the screw and the power consumption of the medium filling.

[0113] S42. Combine the conflict intensity coefficient to modify the energy balance equation and solve for the optimal pressure holding switching position under the premise of satisfying the target maintenance threshold.

[0114] S43. Based on the dynamic transfer function of the change in driving torque and the chamber pressure response, calculate the pressure compensation curves for different viscosity states.

[0115] S44. Integrate the optimal pressure holding switching position and pressure compensation curve to generate adaptive feedforward control commands to achieve the same filling mode under different viscosities.

[0116] Decoupling control strategy: The system introduces a rheological-kinematic decoupling algorithm to establish a balance between the screw's mechanical input power and the power consumption of the medium filling process. The specific mathematical expression of its energy balance equation is as follows:

[0117]

[0118] in, For integration time; This is a viscosity-related filling power consumption function. The mechanical loss constant preset for the system;

[0119] Total mechanical input energy of the screw, per unit The data originates from feedback data from the servo motor. Instantaneous torque, unit It is derived from real-time data collection; : Screw angular velocity, unit It is derived from real-time data collection; The rheological power consumption required for medium filling, and viscosity Related; System mechanical and thermal losses; combined with the aforementioned conflict intensity coefficient. The energy balance equation needs to be modified; to avoid flash as viscosity increases, energy must be released in advance to counteract inertial impact; therefore, the optimal pressure holding switching position needs to be solved. The operator for calculating the pressure holding switching position is:

[0120]

[0121] in, The switching position set for the baseline process. The position-corrected damping factor has the following dimensions: This is used to linearly map the predicted residual energy of the pressure impact to the lead of the screw position; This represents the conflict intensity coefficient for the current cycle. Corrected pressure holding switching point location, unit ; The predicted inertial state impact value is derived from calculation; the dynamic transfer function is specifically established as the driving torque. With chamber pressure The complex frequency domain mapping model between them is approximated in this embodiment using a second-order lead-lag element, i.e. Based on the dynamic transfer function, the static gain and time constant of the second-order element are determined by the current period. Perform real-time mapping correction and calculate the pressure compensation curve for the current viscosity state. This curve indicates how much additional pressure the system should increase or decrease at each moment during the injection phase to maintain a constant wavefront velocity; integration and This generates the final adaptive feedforward control command, where... This is the state switching point correction value; the decoupling of rheological properties and motion control is achieved through a mathematical model; the formula not only considers the current energy input, but also introduces a conflict coefficient for negative feedback correction; by dynamically adjusting the spatial dimension of the switching point and the time dimension of the compensation pressure curve, the system achieves the same filling mode for different viscosities, that is, regardless of the fluctuation of the raw material viscosity, the microscopic behavior of the melt in the mold remains consistent.

[0122] Example 7:

[0123] S5 specifically includes:

[0124] The S51 PLC controller receives adaptive feedforward control instructions and parses out millisecond-level pressure adjustment sequence and position switching signals.

[0125] S52. During the injection stage, in response to the detection of increased medium viscosity, the output force of the hydraulic or electric actuator is increased according to the pressure adjustment sequence to maintain the filling rate.

[0126] S53. At the end of filling, in response to the position switching signal, the pressure holding switching action is triggered in advance or delayed to reduce the impact value of inertial state.

[0127] S54. Real-time monitoring of mold cavity pressure feedback. If the deviation between the monitored value and the predicted value exceeds the preset safety tolerance, the emergency state locking logic is triggered to forcibly reduce the injection speed to prevent state overflow.

[0128] The specific logic at the PLC execution level is as follows: The PLC controller receives adaptive feedforward control instructions generated by the upper-level algorithm and parses them into a specific time-axis action sequence, namely, millisecond-level pressure adjustment steps and position switching signals. During the injection filling stage, when the system detects an increase in medium viscosity and flow resistance, the PLC instructs the hydraulic valve or servo motor to linearly increase the output force according to the pressure adjustment steps. For example, the injection pressure is dynamically increased from 12MPa to 12.8MPa to forcibly maintain the set filling rate and prevent underfilling. At the end of filling the cavity, in response to the position switching signal, if excessive inertial impact is predicted, the PLC will trigger the pressure holding switching action earlier or later. By utilizing the early deceleration operation, the momentum of the melt itself is used to complete the final stage of filling, thereby reducing the inertial impact value and avoiding flash. The system also runs emergency monitoring logic simultaneously. If the deviation between the real-time monitored cavity pressure and the predicted value exceeds the preset safety tolerance, for example... This indicates a deviation in the model, immediately triggering an emergency state lockout logic that forcibly and significantly reduces the injection speed to prevent mold damage; it demonstrates the precise implementation of control commands in the physical world; by applying force midway to ensure no material shortage and by braking in advance to prevent flash, this refined control strategy perfectly resolves the core contradictions in high-speed thin-wall injection molding, achieving deterministic control over product quality.

[0129] Example 8:

[0130] The system is used for the injection molding production of plastic tableware. The time-varying dataset for building the production process in S1 also includes:

[0131] Identify the mixing ratio characteristics of sprue in the raw materials, and use the mixing ratio characteristics as labels for external perturbation variables, and associate them with the corresponding process rheological data matrix;

[0132] During processing, the load characteristic dynamic mapping module calls external disturbance variable labels to perform weighted correction on the critical point drift of the non-Newtonian fluid shear thinning effect, so as to eliminate the calculation error introduced by the difference in the degree of thermal degradation of raw materials.

[0133] During the feeding stage, the system identifies the mixing ratio of sprue in the raw materials through RFID tags or manual input, for example, 30% sprue. This ratio is used as an external perturbation variable tag and associated with the process rheological data matrix. Because the sprue undergoes multiple thermal processes, its molecular chain degradation varies. When calling the model, the load characteristic dynamic mapping module reads this tag and performs a weighted correction on the critical point drift of the non-Newtonian fluid shear thinning effect. This weighted correction refers to adjusting the sensitivity coefficient to viscosity changes in the algorithm based on the recycled material ratio. For example, a higher recycled material ratio results in a greater expected viscosity decrease, and the algorithm's baseline is adjusted accordingly. The specific calculation logic for the weighted correction is as follows:

[0134]

[0135] in, The mixing ratio label value of sprue material is entered via RFID or manually, and is a dimensionless parameter. The thermal degradation compensation sensitivity coefficient, determined experimentally, is used to eliminate viscosity shift errors caused by molecular chain degradation.

[0136] By introducing external prior knowledge about the recycling ratio, the calculation error caused by the difference in the degree of thermal degradation of raw materials is eliminated; this makes the system more accurate and robust when dealing with complex multi-source material mixing production.

[0137] Example 9:

[0138] The system is also equipped with energy efficiency synergistic optimization logic:

[0139] When generating adaptive feedforward control commands in S4, the minimum clamping force requirement that satisfies the molding quality constraints is calculated.

[0140] The minimum clamping force requirement is fed back to the injection molding machine's clamping system to dynamically reduce clamping pressure, achieving non-linear synergistic optimization of quality assurance and energy consumption reduction.

[0141] While generating control commands, the system, based on the current peak pressure prediction in the mold cavity, calculates in reverse the minimum clamping force required to meet the molding quality constraint, i.e., to prevent mold bulging; minimum clamping force requirement. The calculation mapping relationship is as follows:

[0142]

[0143] in, The predicted peak pressure at the end of the filling cavity. Let be the projected area of ​​the plastic tableware on the parting surface of the mold. This is a preset dimensionless safety factor;

[0144] Traditional processes, for safety reasons, often set a maximum clamping force, such as... However, this system only needs to predict the peak pressure of the current cycle. If the clamping force can resist it, then the output... The requirement value includes a safety margin; this requirement is fed back to the injection molding machine's clamping system to dynamically reduce clamping pressure in each cycle or batch; non-linear synergistic optimization of quality assurance and energy consumption reduction is achieved; through precise control, there is no longer a need to waste huge clamping energy for unforeseen fluctuations; this not only significantly reduces power consumption, but also reduces mechanical wear on molds and machines, extends equipment life, and generates unexpected synergistic benefits.

[0145] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A PLC-based automated production control system for plastic tableware, characterized in that, include: Process status data acquisition module: used to collect kinematic data of the drive components of the production equipment and media status response data in the processing chamber, and to construct a time-varying dataset of the production process; Load characteristic dynamic mapping module: used to calculate the nonlinear load impedance characteristics of the processing medium based on the kinematic data of the drive component, and establish a dynamic correlation between the drive action and the behavior of the medium; Constraint Conflict Prediction Module: Used to identify quantitative conflicts between driving energy and process execution rate and terminal state constraint boundaries, and predict inertial state impact at the end of execution; Decoupled control architecture generation module: used to build a decoupled model of load characteristics and kinematic features, and generate adaptive feedforward control commands for time-varying loads; Closed-loop feedback execution module: used to adjust the process execution parameters in real time within the PLC controller according to the adaptive feedforward control instructions, and lock the physical state of the processing medium.

2. The PLC-based automated production control system for plastic tableware according to claim 1, characterized in that, The modules are interconnected using the following method: S1. Collect instantaneous drive torque data, propulsion speed data, and chamber pressure response curve data during the production cycle to construct the time-varying dataset of the production process; S2. Perform time-series analysis on the instantaneous driving torque data, map the changing trend of the flow resistance of the processing medium wavefront, and generate a load state drift matrix. S3. Input the load state drift matrix into the constraint conflict prediction module, calculate the drive increment required to maintain the current execution rate, and predict the inertial state impact value caused by it. S4. Based on the decoupling model, calculate the dynamic transfer function between the change in driving torque and the chamber pressure response, and generate an adaptive feedforward control command that includes pressure compensation value and state switching point correction value. S5. The adaptive feedforward control command is sent to the PLC controller to dynamically adjust the action execution logic within the current production cycle, thereby achieving closed-loop locking of the physical state of the processing medium.

3. The PLC-based automated production control system for plastic tableware according to claim 2, characterized in that, S1 specifically includes: Sensor nodes are deployed at key locations in the actuators and processing chambers of the production equipment to collect the screw's rotational torque and angular velocity in real time during the sampling period. axial displacement value and medium pressure value inside the mold cavity; The rotational torque value, the axial displacement value, and the medium pressure value of each sensor node at the same timestamp are constructed into a multi-dimensional data matrix. The multidimensional data matrix is ​​subjected to time alignment, high-frequency noise filtering and normalization to obtain a standardized process rheological data matrix, which is then combined in the order of the production cycle to form the time-varying dataset of the production process.

4. The PLC-based automated production control system for plastic tableware according to claim 3, characterized in that, S2 specifically includes: S21. Based on a preset standard medium rheological model, construct a benchmark driving torque time series; S22. Extract the real-time instantaneous drive torque sequence of the current cycle from the time-varying dataset of the production process, and compare it with the reference drive torque time sequence; S23. Calculate the deviation between the real-time sequence and the reference sequence at each time step, and identify the critical point drift of the non-Newtonian fluid shear thinning effect caused by fluctuations in raw material composition. S24. Map the deviation value and critical point drift data to a medium viscosity fluctuation index to generate the load state drift matrix, which is used to characterize the real-time change of the processing medium's sensitivity to shear rate.

5. The PLC-based automated production control system for plastic tableware according to claim 4, characterized in that, S3 specifically includes: S31. Set the target holding threshold for filling rate and the safe peak threshold for cavity pressure; S32. Based on the viscosity fluctuation index of the medium, calculate the theoretical driving pressure required to overcome the current flow resistance; S33. Based on the theoretical driving pressure value, simulate the fluid kinetic energy release process at the filling end and calculate the inertial state impact value; S34. If the inertial state impact value causes the cavity pressure to exceed the safety peak threshold, then it is determined that the quantization conflict exists, and a conflict intensity coefficient is generated. S35. If the inertial state impact value does not exceed the safety peak threshold, it is determined to be a non-conflict state, and the original parameter maintenance command is output.

6. The PLC-based automated production control system for plastic tableware according to claim 5, characterized in that, S4 specifically includes: S41. Introduce a rheology-kinematics decoupling algorithm to establish an energy balance equation between the mechanical input power of the screw and the power consumption of the medium filling. S42. Based on the conflict intensity coefficient, the energy balance equation is modified to solve for the optimal pressure-holding switching position under the premise of satisfying the target maintenance threshold. S43. Based on the dynamic transfer function of the driving torque change and the chamber pressure response, calculate the pressure compensation curve for different viscosity states. S44. Integrate the optimal pressure holding switching position with the pressure compensation curve to generate the adaptive feedforward control command to achieve the same filling mode under different viscosities.

7. The PLC-based automated production control system for plastic tableware according to claim 6, characterized in that, S5 specifically includes: S51, the PLC controller receives the adaptive feedforward control command and parses out the millisecond-level pressure adjustment sequence and position switching signal; S52. During the injection stage, in response to the detection of an increase in the viscosity of the medium, the output force of the hydraulic or electric actuator is increased according to the pressure adjustment sequence to maintain the filling rate. S53. At the end of filling, in response to the position switching signal, the pressure holding switching action is triggered earlier or later to reduce the inertial state impact value. S54. Real-time monitoring of mold cavity pressure feedback. If the deviation between the monitored value and the predicted value exceeds the preset safety tolerance, the emergency state locking logic is triggered to forcibly reduce the injection speed to prevent state overflow.

8. The PLC-based automated production control system for plastic tableware according to claim 7, characterized in that, The system is used for injection molding production of plastic tableware, and the time-varying dataset for constructing the production process in S1 further includes: Identify the mixing ratio characteristics of sprue in the raw materials, and associate the mixing ratio characteristics as external perturbation variable labels with the corresponding process rheological data matrix; When processing, the load characteristic dynamic mapping module calls the external disturbance variable label to perform weighted correction on the critical point drift of the non-Newtonian fluid shear thinning effect, so as to eliminate the calculation error introduced by the difference in the degree of thermal degradation of raw materials.

9. The PLC-based automated production control system for plastic tableware according to claim 8, characterized in that, The system is also equipped with energy efficiency synergistic optimization logic: When generating the adaptive feedforward control command in S4, the minimum clamping force requirement that satisfies the molding quality constraint is calculated. The minimum clamping force requirement is fed back to the injection molding machine's clamping system to dynamically reduce clamping pressure, achieving non-linear synergistic optimization of quality assurance and energy consumption reduction.

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