Energy-saving-oriented blow molding process intelligent grading pressure regulation and control system

Abstract

The invention discloses an intelligent grading pressure regulation and control system and method for an energy-saving blow molding process. The system comprises a data sensing module, a main controller and a pressure execution module. The main controller is preset with a graded pressure-temperature-stage mapping model, and the model is calibrated based on specific products and materials and comprises logics for automatically recognizing pre-inflation, main inflation and pressure maintaining cooling stages based on cavity pressure changes and pressure regulation and control strategies with key temperature as input in all the stages. The method comprises the steps of collecting temperature and pressure in real time; identifying a current forming stage; calculating an optimal pressure set value based on the current stage strategy and the real-time temperature; and driving the pressure valve group to execute. According to the method, the blowing pressure is intelligently matched with the actual requirements of all stages of the forming process, the problems that traditional constant pressure or sequential control is high in energy consumption and poor in adaptability are solved, remarkable energy saving is achieved, and the product quality consistency is improved.

Description

Technical Field

[0001] This invention relates to the field of plastic processing machinery control technology, and in particular to an intelligent graded pressure control system for energy-saving blow molding processes. Background Technology

[0002] Blow molding is an important method for manufacturing hollow plastic products. The basic process involves placing an extruded thermoplastic preform into a mold, introducing compressed air into the preform to inflate it, and then allowing it to cool and solidify against the inner wall of the mold. During this process, controlling the inflating pressure is crucial, directly affecting the uniformity of the product's wall thickness, mechanical strength, surface quality, and production energy consumption.

[0003] Currently, most blow molding machines employ simple pressure control methods: one is constant pressure control, which uses a single pressure value throughout the entire blow-holding process; the other is time-sequenced control, which switches the pressure at preset time points using time relays or PLCs. These traditional methods have significant drawbacks: High energy consumption: To meet the most demanding molding time (usually the main blow-up), the constant pressure value is often set too high, resulting in a large amount of compressed air wasted during the pre-blow-up and long pressure holding and cooling stages.

[0004] Poor adaptability: It cannot respond to fluctuations in processing conditions. For example, when the preform temperature changes due to environmental factors or uneven heating, a fixed pressure mode may lead to product defects (such as blow-out or incomplete molding).

[0005] Unstable quality: Simple timing control cannot accurately match the actual deformation and cooling dynamics of the blank, resulting in an unsatisfactory wall thickness distribution and low repeatability of the product. Summary of the Invention

[0006] The present invention aims to overcome the shortcomings of the prior art and solve the problems of high energy consumption, product quality sensitivity to process fluctuations, and low control precision caused by traditional blow molding pressure control methods.

[0007] To achieve the above objectives, this invention provides an intelligent graded pressure control system for energy-saving blow molding processes. Its core idea is to divide the blow molding process into different stages based on its physical state, and intelligently and dynamically calculate and apply the optimal blowing pressure for each stage based on real-time sensing of the preform temperature (which determines material viscosity and deformation resistance) and cavity pressure (reflecting the molding state), achieving precise matching between pressure and process requirements.

[0008] The system specifically includes the following modules: Data sensing module: It consists of cavity pressure sensors deployed at key cavity locations of the mold and an array of infrared temperature sensors deployed at different axial heights of the blank, used to collect blank temperature parameters and mold cavity pressure parameters in real time.

[0009] Main Controller: A high-performance industrial PLC or embedded controller is used as the main controller. It has a pre-built graded pressure-temperature-stage mapping model. This model is constructed based on process data calibration, theoretical analysis, or simulation of specific products and materials, and stores: (a) stage identification logic that automatically divides the pre-blowing, main blowing, and pressure holding cooling stages based on the changing characteristics of the cavity pressure parameters; (b) pressure control strategies corresponding to each stage, with key temperature variables as input and pressure setpoints as output. The main controller receives signals from the data sensing module, identifies the current forming stage according to the stage identification logic, and dynamically calculates and outputs the optimal pressure setpoint for the current stage based on the real-time preform temperature distribution and the pressure control strategy corresponding to the current stage.

[0010] Pressure execution module: includes an intelligent graded pressure regulating valve group that is signal-connected to the main controller. The valve group is connected to at least two gas source pipelines with different pressures and is used to receive the optimal pressure setpoint command and switch and regulate the inflation gas pressure of the input mold.

[0011] Preferably, the stage identification logic includes: using the mold closing signal as the start of the pre-inflation stage; using the first time the rate of change of the cavity pressure sensor signal exceeds a first set slope threshold as the basis for switching to the main inflation stage; and using the fact that after the cavity pressure sensor signal reaches its peak value, its drop value exceeds a set absolute pressure threshold ΔP as the basis for switching to the pressure holding and cooling stage.

[0012] Preferably, the graded pressure-temperature-stage mapping model divides the blow molding process into a pre-blowing stage, a main blowwing stage, and a pressure holding and cooling stage, and defines an independent pressure control strategy for each stage.

[0013] Preferably, the pressure control strategy in the pre-expansion stage is as follows: the pressure setpoint P_set is set to have a compensatory relationship with the real-time temperature T_top on the top of the blank, specifically calculated by the function P_set = P_base1 + C1 * (T_top - T_ref1), where P_base1, C1, and T_ref1 are parameters calibrated for specific materials and products.

[0014] Preferably, the pressure control strategy in the main inflation stage is as follows: the pressure setpoint P_set is set to have a compensatory relationship with the lowest temperature T_min of the entire preform, specifically calculated by the function P_set = P_base2 + C2 * (T_ref2 - T_min), where P_base2, C2, and T_ref2 are parameters calibrated for specific materials and products; and in this stage, the actual output pressure is controlled in a closed loop based on the cavity pressure feedback.

[0015] Preferably, the pressure control strategy for the pressure holding and cooling stage is as follows: the pressure setpoint P_set starts from the initial value P_hold of the stage switching and decreases according to a preset linear decay slope, exponential decay law, or feedback function based on the real-time billet temperature until a minimum pressure limit is reached.

[0016] Preferably, the intelligent graded pressure regulating valve group includes a pilot-operated proportional pressure regulating valve and multiple high-speed switching valves, which can achieve rapid switching and stepless fine adjustment between multiple pressure levels through combined control.

[0017] Preferably, the system further includes a human-machine interface for displaying real-time process curves, setting model parameters, and allowing operators to perform manual intervention or mode selection.

[0018] This invention also provides an energy-saving pressure control method for blow molding based on this system, comprising the following steps: S1: Real-time synchronous acquisition of the axial temperature distribution T(t) of the billet and the pressure P_c(t) of the mold cavity; S2: The main controller identifies the current molding stage based on the change characteristics of the cavity pressure P_c(t) and a preset stage identification logic. The stage identification logic includes starting the pre-blowing stage with the mold closing signal, switching to the main blowing stage when the rate of change of P_c(t) exceeds the slope threshold, and switching to the pressure holding and cooling stage when P_c(t) drops from the peak value by more than ΔP. S3: Invoke the pressure control strategy corresponding to the current stage, and calculate the optimal set pressure value P_set(t) at this moment in combination with the real-time temperature T(t); the strategy includes calculation based on the upper temperature in the pre-expansion stage, calculation based on the lowest temperature in the main expansion stage, and calculation based on the decreasing rule in the pressure holding and cooling stage. S4: Send the P_set(t) command to the intelligent graded pressure regulating valve group to drive it to adjust the blowing pressure output to the mold; S5: Continuously monitor P_c(t) and T(t) during the stage, and repeat steps S2-S4 until the product cools and solidifies, completing one molding cycle.

[0019] Compared with existing technologies, the intelligent graded pressure control system for energy-saving blow molding processes disclosed in this application has the following advantages: 1. Significant energy-saving effect: By changing the traditional "full-process high pressure" mode to the "on-demand pressure supply" intelligent hierarchical mode, the amount of compressed air consumed in the low pressure demand stage (especially the long-term pressure holding and cooling stage) is greatly reduced, thereby effectively reducing the energy consumption of the air compressor unit.

[0020] 2. Improve product quality and consistency: Based on pressure compensation control of real-time parison temperature, the fluctuation of material properties caused by uneven heating or environmental changes is offset, making the molding conditions closer to the ideal state for each molding, thereby improving the uniformity of wall thickness and the consistency of mechanical properties of the product.

[0021] 3. Enhanced process intelligence and adaptability: The system can automatically identify the molding stage and dynamically adjust the pressure based on real-time sensor data, reducing reliance on operator experience and improving the level of production automation. Attached Figure Description

[0022] Figure 1 This is a schematic diagram of the overall architecture of the system of the present invention.

[0023] Figure 2 This is a schematic diagram of the workflow of the intelligent graded pressure control method of the present invention.

[0024] Figure 3 This is a schematic diagram comparing the pressure-time curves of traditional constant pressure control and the intelligent graded pressure control of this invention within a molding cycle.

[0025] Reference numerals: 1. Data sensing module; 11. Cavity pressure sensor; 12. Infrared temperature sensor array; 2. Intelligent decision-making module (main controller); 21. Graded pressure-temperature-stage mapping model; 3. Pressure execution module; 31. Intelligent graded pressure regulating valve group; 311. Proportional pressure regulating valve; 312. High-speed switching valve; 32. High-pressure air source; 33. Medium-pressure air source; 4. Human-machine interface; 5. Blow molding die; 6. Preform. Detailed Implementation

[0026] The present invention will be described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention. Those skilled in the art, based on their understanding of the core ideas of the present invention, can adjust specific parameters for different products and materials. Example

[0027] This embodiment uses a blow molding machine for producing a certain type of HDPE plastic bucket as an example to illustrate the specific implementation of this system.

[0028] like Figure 1 As shown, the system in this embodiment is installed on a horizontal extrusion blow molding machine.

[0029] Hardware connection and installation: A cavity pressure sensor (11) is installed at the bottom of the cavity of the blow mold (5).

[0030] Near the mold parting line, an infrared temperature sensor array (12) is installed, containing 3 probes, which are respectively aimed at the upper, middle and lower parts of the blank (6).

[0031] The main controller (2) is an industrial PLC that supports advanced algorithm programming. Various sensor signals are connected to the PLC.

[0032] The intelligent graded pressure regulating valve assembly (31) consists of a pilot-operated electric proportional valve (311) and two two-position three-way high-speed solenoid valves (312), according to... Figure 1 The diagram shows the connection between the high and low pressure air sources and the inflation pipeline.

[0033] The human-machine interface (4) is a touch screen that communicates with the PLC.

[0034] Software, model building, and parameter calibration methods: Implementing a graded pressure-temperature-stage mapping model in a PLC (21) and such Figure 2 The workflow is shown. It should be noted that the construction and implementation of the graded pressure-temperature-stage mapping model (21) described in this system follows the following general principles: Stage division and identification: Based on the physical characteristics of the cavity pressure change curve (such as the mold closing zero point, the inflection point of rapid increase in the rate of change, and the peak value drop point), the pre-expansion, main expansion and pressure holding cooling stages are automatically divided.

[0035] Strategy Definition: For each stage, establish a mathematical relationship or data mapping with key temperature variables (such as the upper temperature of concern in the pre-inflation stage and the overall minimum temperature of concern in the main inflation stage) as inputs and the optimal pressure setpoint of that stage as the output. This relationship can be obtained through experimental data calibration, theoretical analysis, or simulation for specific products and materials, and its specific form can be a linear formula, a nonlinear fitting function, a piecewise function, or a lookup table method.

[0036] Model integration: The stage identification logic is linked with the stress calculation strategy of each stage to form a complete closed-loop control model.

[0037] This embodiment demonstrates a specific implementation method, and the calibration method for its model parameters is as follows: Model parameter calibration process for HDPE plastic drums: S01: Pre-blowing stage parameter calibration: Under fixed mold closing, extrusion speed, and other conditions, the pre-blowing pressure setting value P_pre and the corresponding parison top temperature T_top are systematically changed. By measuring the wall thickness uniformity of the top of the product or observing whether local blow-through occurs, the "optimal pressure range" that achieves both good preforming and energy saving under different T_top values ​​is determined. Linear regression analysis is performed on a large amount of experimental data to obtain the following relationship: P_pre_optimal = P_base1 + C1 * (T_top - T_ref1). Where P_base1 is the base pressure, C1 is the temperature compensation coefficient, and T_ref1 is the reference temperature. In this embodiment, the calibration results are: P_base1 = 0.4 MPa, C1 = 0.002 MPa / ℃, T_ref1 = 190°C.

[0038] S02: Main Inflation Stage Parameter Calibration: Similarly, the main inflation pressure P_main and the lowest recorded temperature T_min of the entire preform are systematically changed. The relationship between P_main and T_min is determined by evaluating whether the product is fully formed (without wrinkles) and whether it bursts. Data analysis yields: P_main_optimal = P_base2 + C2 * (T_ref2 - T_min). In this embodiment, the calibration results are: P_base2 = 0.8 MPa, C2 = 0.01 MPa / ℃, T_ref2 = 200°C. Simultaneously, by analyzing normal production data, the slope threshold Slope_th for the rapid rise of the cavity pressure is determined to be 10 kPa / ms.

[0039] S03: Stage Switching Threshold Calibration: Analyze the cavity pressure curves of a large number of normal production cycles, and statistically determine the typical decrease value ΔP when the pressure enters a stable decreasing stage from the peak value P_max at the end of the main blowing stage (when the parison is basically attached to the mold and the pressure reaches its peak). In this embodiment, ΔP is determined to be 0.05 MPa.

[0040] S04: Pressure Holding and Cooling Rules Determined: Based on the cooling and shrinkage characteristics of the product, to balance the shaping effect and energy saving, the pressure holding pressure is set to decrease linearly with a slope R, starting from the pressure value P_hold at the switching moment. Experiments determined the maximum energy-saving R value while ensuring no deformation; in this example, R = 0.01 MPa / s. The minimum pressure limit P_min is set to 0.5 MPa.

[0041] S05: Model Integration: The functional relationships, parameters (P_base1, C1, T_ref1, P_base2, C2, T_ref2, Slope_th, ΔP, R, P_min) and logical rules obtained from the above calibration are programmed and stored in the main controller to form a dedicated graded pressure-temperature-stage mapping model for the production of this HDPE barrel.

[0042] System working process: After the mold is closed, the system starts. The temperature array (12) scans to obtain the temperature distribution T(t) of the blank. The PLC recognizes that it has entered the pre-blowing stage, calculates P_set according to the calibration formula based on the real-time T_top, and controls the valve group to output the corresponding pressure for pre-blowing.

[0043] When the rate of change of the cavity pressure P_c(t) first exceeds Slope_th (10 kPa / ms), the PLC recognizes that it has entered the main inflation stage. It calculates P_main_optimal based on the real-time T_min and uses it as the set value. It controls the valve group to switch to the high-pressure source for rapid inflation and starts PID closed-loop control to stabilize the cavity pressure.

[0044] When the cavity pressure P_c(t) reaches its peak value P_max and continues to decrease beyond ΔP (0.05 MPa), the PLC recognizes that it has entered the pressure holding and cooling stage. It controls the pressure to decrease linearly from the current value P_hold at a slope R (0.01 MPa / s) until it reaches P_min (0.5 MPa) or the cycle ends.

[0045] Effect Analysis: like Figure 3 As shown, traditional constant pressure control requires maintaining a high pressure throughout the entire process. However, the system of this invention dynamically adjusts the pressure according to the process status, significantly reducing the area under the pressure-time curve (representing total gas consumption), thereby achieving energy savings. Simultaneously, real-time temperature compensation enhances process adaptability.

[0046] Generalization notes: The above embodiments are merely specific examples of implementing the present invention. The present invention provides an intelligent pressure control architecture based on real-time sensor feedback and a pre-set mapping model. For different plastic materials (such as PP and PET) and different product geometries, those skilled in the art can, based on the model construction principles and parameter calibration methods disclosed in the present invention (as described in the embodiments), recalibrate using experimental data for the specific product and material to obtain the corresponding stage identification parameters, temperature compensation coefficients, and pressure reference values, thereby implementing the present invention. The functional relationship in the pressure control strategy is not limited to linearity and can be implemented using nonlinear fitting or table lookup methods based on the characteristics of the calibration data; the pressure holding attenuation rule can also employ exponential attenuation, step attenuation, or other strategies, all of which are within the scope of the present invention.

[0047] The well-known structures and characteristics of this embodiment are not described in detail here. It should be noted that those skilled in the art can make various modifications and improvements without departing from the structure of this invention, and these should also be considered within the scope of protection of this invention. These modifications will not affect the effectiveness of the invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. A smart graded pressure control system for energy-saving blow molding processes, characterized in that, include: Data sensing module: used to collect process parameters in real time during blow molding. The process parameters include at least the mold cavity pressure parameters measured by cavity pressure sensors deployed at key cavity positions of the mold, and the preform temperature parameters measured by an array of infrared temperature sensors deployed at different axial height positions of the preform. Main controller: It has a pre-built graded pressure-temperature-stage mapping model; the graded pressure-temperature-stage mapping model is constructed based on the process data calibration, theoretical analysis or simulation of specific products and materials, and stores: (a) stage identification logic that automatically divides the pre-blowing, main blowing and pressure holding cooling stages based on the change characteristics of the cavity pressure parameters; (b) pressure control strategy corresponding to each stage, with key temperature variables as input and pressure setpoint as output; the main controller is used to receive the signal from the data sensing module, identify the current forming stage according to the stage identification logic, and dynamically calculate and output the optimal pressure setpoint for the current stage based on the real-time blank temperature distribution and the pressure control strategy corresponding to the current stage; Pressure execution module: includes an intelligent graded pressure regulating valve group that is signal-connected to the main controller. The valve group is connected to at least two gas source pipelines with different pressures and is used to receive the optimal pressure setpoint command and switch and regulate the inflation gas pressure of the input mold.

2. The system according to claim 1, characterized in that, The stage identification logic includes: using the mold closing signal as the start of the pre-inflation stage; using the first time the rate of change of the cavity pressure sensor signal exceeds a first set slope threshold as the basis for switching to the main inflation stage; and using the fact that after the cavity pressure sensor signal reaches its peak value, its drop value exceeds a set absolute pressure threshold ΔP as the basis for switching to the pressure holding and cooling stage.

3. The system according to claim 1 or 2, characterized in that, The graded pressure-temperature-stage mapping model divides the blow molding process into a pre-blowing stage, a main blow-blowing stage, and a holding and cooling stage, and defines an independent pressure control strategy for each stage.

4. The system according to claim 3, characterized in that, The pressure control strategy for the pre-expansion stage is as follows: the pressure setpoint P_set is set to have a compensatory relationship with the real-time temperature T_top on the top of the blank. Specifically, it is calculated by the function P_set = P_base1 + C1 * (T_top - T_ref1), where P_base1, C1, and T_ref1 are parameters calibrated for specific materials and products.

5. The system according to claim 3, characterized in that, The pressure control strategy for the main inflation stage is as follows: the pressure setpoint P_set is set to compensate for the minimum temperature T_min of the entire preform, specifically calculated by the function P_set = P_base2 + C2 * (T_ref2 - T_min), where P_base2, C2, and T_ref2 are parameters calibrated for specific materials and products; and during this stage, the actual output pressure is controlled in a closed loop based on the cavity pressure feedback.

6. The system according to claim 3, characterized in that, The pressure control strategy for the pressure holding and cooling stage is as follows: the pressure setpoint P_set starts from the initial value P_hold of the stage switching and decreases according to the preset linear decay slope, exponential decay law or feedback function based on the real-time billet temperature until a minimum pressure limit is reached.

7. The system according to claim 1, characterized in that, The intelligent graded pressure regulating valve group includes a pilot-operated proportional pressure regulating valve and multiple high-speed switching valves, which can achieve rapid switching and stepless fine adjustment between multiple pressure levels through combined control.

8. The system according to claim 1, characterized in that, The system also includes a human-machine interface for displaying real-time process curves, setting model parameters, and allowing operators to manually intervene or select modes.

9. A blow molding energy-saving pressure control method based on the system described in any one of claims 1-8, characterized in that, Includes the following steps: S1: Real-time synchronous acquisition of the axial temperature distribution T(t) of the billet and the pressure P_c(t) of the mold cavity; S2: The main controller identifies the current molding stage based on the change characteristics of the cavity pressure P_c(t) and a preset stage identification logic. The stage identification logic includes starting the pre-blowing stage with the mold closing signal, switching to the main blowing stage when the rate of change of P_c(t) exceeds the slope threshold, and switching to the pressure holding and cooling stage when P_c(t) drops from the peak value by more than ΔP. S3: Invoke the pressure control strategy corresponding to the current stage, and calculate the optimal set pressure value P_set(t) at this moment in combination with the real-time temperature T(t); the strategy includes calculation based on the upper temperature in the pre-expansion stage, calculation based on the lowest temperature in the main expansion stage, and calculation based on the decreasing rule in the pressure holding and cooling stage. S4: Send the P_set(t) command to the intelligent graded pressure regulating valve group to drive it to adjust the blowing pressure output to the mold; S5: Continuously monitor P_c(t) and T(t) during the stage, and repeat steps S2-S4 until the product cools and solidifies, completing one molding cycle.

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