A method and system for controlling the injection molding process of a plastic product mold

Abstract

The application discloses a kind of plastic product mold injection molding process control method and system, it is related to the field of plastic product mold injection molding process control, for more accurate, more stable injection molding process control is implemented, including: in the melt runner of injection molding machine, utilize high-frequency dynamic spectroscopy sensor, real-time acquisition silica gel melt spectrum data, to reflect the viscosity characteristics of silica gel melt;According to spectrum data and injection molding machine operating parameter, predict the viscosity variation trend and amplitude of silica gel melt in future preset period;According to viscosity variation prediction result, adjust at least one in the heating cylinder power of injection molding machine, screw injection speed and pressure maintaining pressure curve, to eliminate the flow instability of silica gel melt.

Description

Technical Field

[0001] This invention relates to the field of injection molding process control for plastic product molds, and more particularly to a method and system for controlling the injection molding process for plastic product molds. Background Technology

[0002] Traditional injection molding methods for plastic products struggle to detect and respond in real time to changes in melt viscosity caused by temperature fluctuations and increased shear heat. This leads to defects such as shrinkage, bubbles, and dimensional deviations, impacting product yield and production efficiency. For example, on a silicone wristband production line, when the system loads process parameter formula files, data management errors may result in the incorrect loading of older formula files that are incompatible with the current batch of silicone raw materials. This mismatch causes the pressure-time curve data during multiple holding stages to not perfectly match the optimal filling requirements of the new raw material, resulting in a slightly lower pressure in the later stages of holding than the optimal level required for complete filling and elimination of shrinkage. This deviation is not significant enough to immediately trigger a system alarm, making the problem difficult for the automation system to detect in its early stages. Due to insufficient pressure in the later stages of holding, some areas of the molten plastic within the mold cavity are not adequately replenished during cooling and shrinkage, leading to a slight "shrinkage" defect in the product.

[0003] Faced with minor shrinkage issues, production line operators might manually fine-tune the "target injection pressure setpoint" based on experience, attempting to compensate for the defect by increasing the injection pressure. This empirical adjustment may seem to improve shrinkage in the short term, but the operator fails to realize that the root cause lies in the mismatch between the formulation file and the raw materials. Their operation is essentially correcting an error, inadvertently introducing new uncertainties. When the operator manually increases the "target injection pressure setpoint," the shear heat generated by the melt during injection increases. The PID control logic responsible for automatic temperature regulation within the system attempts to reduce the power output of the heating coils in the heating cylinder to maintain the set melt temperature. However, this manual pressure setting creates a "tug-of-war" effect with the system's automatic temperature regulation logic, causing small oscillations in the heating control logic. This results in the actual melt temperature frequently and slightly deviating from and recovering from the setpoint, rather than remaining stably at the target temperature. This melt temperature instability caused by heating control oscillations directly affects the viscosity of the plastic melt, making the actual injection flow rate unstable and unable to accurately follow the preset curve, further deteriorating the stability of the entire injection molding process. Summary of the Invention

[0004] This application discloses a method and system for controlling the injection molding process of plastic product molds, which aims to solve the technical problems of traditional injection molding methods, such as the difficulty in real-time and accurate sensing and response to changes in melt viscosity, resulting in high product defect rates, low production efficiency, and new uncertainties introduced by human intervention.

[0005] In a first aspect, in order to solve the above-mentioned technical problems, the present invention provides a method for controlling the injection molding process of plastic product molds, comprising: in the melt flow channel of the injection molding machine, using a high-frequency dynamic spectral sensor to collect spectral data of silicone melt in real time, so as to reflect the viscosity characteristics of silicone melt; Based on spectral data and injection molding machine operating parameters, predict the viscosity change trend and magnitude of silicone melt in the future preset time period; Based on the viscosity change prediction results, adjust at least one of the following parameters of the injection molding machine: heating cylinder power, screw injection speed, and holding pressure curve, to eliminate the flow instability of the silicone melt.

[0006] This technical solution enables real-time sensing and prediction of changes in the viscosity of silicone melt, and allows for dynamic adjustment of injection molding machine parameters. This effectively eliminates melt flow instability, solves product defects caused by viscosity changes in traditional methods, and improves product quality and production efficiency.

[0007] Furthermore, in some implementations, based on spectral data and injection molding machine operating parameters, the viscosity change trend and magnitude of the silicone melt over a predetermined period are predicted, including: Identify atypical spectral signals in spectral data and mark them as unexplained spectral events; On the injection molding machine's mold exit or cooling conveyor belt, defects on the surface of plastic products are detected in real time, and the defect type, location, severity, and corresponding product production timestamp are recorded. When an unexplained spectral event occurs, if a specific type of defect is continuously detected in the subsequent product manufacturing cycle, an association is established between the atypical spectral signal and the specific defect type. Based on the correlation, a rheological influence factor is assigned to atypical spectral signals to quantify the impact of microstructural changes on the flow resistance of silicone melt in specific regions within the mold cavity, thereby predicting the viscosity change trend and magnitude of silicone melt.

[0008] This technical solution enables the establishment of a more accurate viscosity prediction model by correlating atypical spectral signals with actual product defects. This effectively solves the problem that traditional prediction models cannot identify the impact of microstructural changes on macroscopic flow behavior, thereby improving the accuracy of viscosity prediction.

[0009] Based on this, this application also proposes that the method further includes: For multiple types of regions, acquire melt temperature and pressure data for each type of region; the multiple types of regions include a first type region and a second type region; the first type region is the target area of ​​each mold cavity, near the runner, gate, or cooling system; the second type region is the complex structural area of ​​the mold. Adjust the local cooling rate or local holding pressure of the type region based on the melt temperature and pressure data of the type region.

[0010] This technical solution enables precise control over different areas inside the mold, solving the problem that traditional injection molding methods cannot optimize and adjust local areas, thereby further improving product quality.

[0011] Furthermore, when the type region is a first-type region, the local cooling rate or local holding pressure of the type region is adjusted based on the melt temperature and pressure data of the type region, including: Based on the melt temperature and pressure data of each mold cavity, monitor the filling and solidification state of the melt inside each mold cavity; Based on the filling and solidification state of the melt inside each mold cavity, the inconsistencies in the melt filling and solidification process between each mold cavity are identified; Based on the inconsistency of melt filling and solidification processes between different mold cavities, differentiated compensation instructions for each mold cavity are generated. Adjust the local cooling rate or local holding pressure of each mold cavity according to the differentiated compensation command.

[0012] This technical solution can solve the problem of inconsistent filling and solidification in multi-cavity injection molding, enabling independent optimization and control of each cavity, thereby improving the overall pass rate of multi-cavity products.

[0013] Based on the above, this application further proposes, when the type region is a second type region, adjusting the local cooling rate or local holding pressure of the type region according to the melt temperature and pressure data of the type region, including: Based on the local melt temperature and pressure data of complex structural regions, monitor the melt filling state and solidification rate of complex structural regions; Based on the melt filling state and solidification rate of complex structural regions, identify melt flow instabilities in complex structural regions; Based on the melt flow instability in complex structural regions, local compensation commands for complex structural regions are generated. According to the local compensation command, adjust the local heating power, local injection speed, or local holding pressure in complex structural areas.

[0014] This technical solution enables precise identification and compensation for melt flow instability in complex structural regions, effectively addressing the problem of defects in complex structural products and improving the molding quality of complex products.

[0015] As a technical improvement, this application also discloses that the high-frequency dynamic spectrum sensor is equipped with a self-cleaning device, which sprays high-pressure gas onto the surface of the sensor probe after each injection molding cycle to remove the silicone melt deposits on the probe surface; the surface of the sensor probe is coated with a wear-resistant and non-stick coating to reduce the deposition of degradation products and wear. The high-frequency dynamic spectrum sensor also includes a reference signal transmitter and a reference signal receiver, with the reference signal transmitter periodically transmitting a reference signal to the reference signal receiver; The reference signal receiver is used to receive the reference signal and generate reference signal strength data. When the reference signal strength data is lower than a preset threshold, the self-cleaning device is triggered to perform cleaning.

[0016] This technical solution effectively addresses the issues of surface contamination and wear on sensor probes, ensuring the long-term stability and accuracy of spectral data acquisition, thereby improving the reliability of the entire control system.

[0017] To enhance functionality, this application also discloses determining spectral data based on the product of mapping weights and the original spectral data; By combining spectral data and injection molding machine operating parameters, the viscosity change trend and magnitude of silicone melt in the future preset time period are predicted.

[0018] Optionally, determine the mapping weights, including: Monitor the instantaneous values ​​and rates of change of the operating parameters of the injection molding machine; When the operating parameters of the injection molding machine change, the instantaneous viscosity of the silicone melt is calculated and compared with historical data to identify the initial response to the viscosity change; Determine the mapping weights based on the initial response.

[0019] Optionally, based on the initial response, determine the mapping weights, including: If the initial response is greater than the response threshold, the initial weight is increased by a preset step size based on the initial weight to obtain the mapped weight.

[0020] Secondly, this application also discloses a control system for the injection molding process of plastic product molds, the system comprising: The spectral data acquisition module is used to acquire the spectral data of the silicone melt in real time in the melt flow channel of the injection molding machine using a high-frequency dynamic spectral sensor, so as to reflect the viscosity characteristics of the silicone melt. The viscosity change prediction module is used to predict the viscosity change trend and magnitude of silicone melt in the future within a preset time period based on spectral data and injection molding machine operating parameters. The parameter adjustment module is used to adjust at least one of the following parameters of the injection molding machine based on the viscosity change prediction results: heating cylinder power, screw injection speed, and holding pressure curve, in order to eliminate the flow instability of the silicone melt.

[0021] This technical solution provides a system that integrates spectral data acquisition, viscosity prediction, and parameter adjustment, enabling automated and intelligent control of the injection molding process and effectively solving the problem of traditional injection molding systems lacking real-time feedback and adaptive adjustment capabilities.

[0022] Beneficial Effects: The injection molding process control method for plastic product molds disclosed in this application accurately reflects the viscosity characteristics of silicone melt by using a high-frequency dynamic spectral sensor to collect spectral data of silicone melt in real time within the melt flow channel of the injection molding machine. Based on this, and combining the spectral data with the operating parameters of the injection molding machine, the viscosity change trend and magnitude of the silicone melt over a preset period of time can be predicted. According to the prediction results, the system can intelligently adjust at least one of the following: the heating cylinder power, screw injection speed, and holding pressure curve of the injection molding machine, thereby effectively eliminating the flow instability of the silicone melt.

[0023] This method solves the problem in existing technologies where changes in melt viscosity are difficult to detect and respond to in real time, leading to various defects in products such as shrinkage, bubbles, or dimensional deviations. For example, regarding the insufficient holding pressure and slight shrinkage defects caused by mismatched formulation documents on silicone wristband production lines in the background technology, this application uses real-time spectral data to detect changes in melt viscosity, identifying this "gray area" viscosity anomaly and predicting its impact on product quality. Compared to operators manually fine-tuning parameters based on experience, the method in this application can automatically adjust injection molding machine parameters based on data-driven prediction results, avoiding new uncertainties and "tug-of-war" effects that may be introduced by manual intervention, thereby effectively maintaining the stability of melt temperature and injection flow rate. Through this technical solution, the pass rate and production efficiency of plastic products can be significantly improved, overcoming the limitations of traditional injection molding methods in dealing with complex viscosity changes, and achieving more precise and stable injection molding process control. Attached Figure Description

[0024] Figure 1 This is a schematic flowchart of a method for controlling the injection molding process of plastic products according to an embodiment of the present invention; Figure 2 This is a schematic diagram of another method for controlling the injection molding process of plastic products provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of a plastic product mold injection process control system provided in an embodiment of the present invention. Detailed Implementation

[0025] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0026] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0027] To address the aforementioned technical problems, this application proposes a method for controlling the injection molding process of plastic products. This method utilizes a high-frequency dynamic spectral sensor to collect real-time spectral data of the silicone melt in the melt flow channel of the injection molding machine, reflecting the viscosity characteristics of the silicone melt. Furthermore, based on the spectral data and the operating parameters of the injection molding machine, the viscosity change trend and magnitude of the silicone melt over a predetermined period are predicted. Finally, based on the viscosity change prediction results, at least one of the following parameters of the injection molding machine—heating cylinder power, screw injection speed, and holding pressure curve—is adjusted to eliminate the flow instability of the silicone melt, significantly improving the quality and production efficiency of the plastic products.

[0028] This application obtains cleaning execution parameters and batch formula switching information during the cleaning process, and dynamically adjusts the initial risk value based on this information to obtain a variable cleaning efficiency risk coefficient. This allows for the assessment of the total risk of different production sequences and the determination of the target production sequence. This effectively solves the problems of incomplete cleaning, difficulty in tracing quality risks, and low production efficiency caused by frequent formula switching in existing technologies. Through refined management of cleaning efficiency risks, this application optimizes production planning, reduces operating costs, and improves product quality stability and traceability.

[0029] To better understand this application, some key terms involved need to be explained.

[0030] A "high-frequency dynamic spectral sensor" is a device capable of acquiring spectral information of a substance in real time and continuously. Its working principle is typically based on the absorption, reflection, or scattering characteristics of a substance to light of specific wavelengths. By analyzing this spectral data, the physical or chemical properties of the substance can be inferred, such as the viscosity characteristics of silicone melt. In the injection molding process, the viscosity of silicone melt is one of the key parameters affecting product quality; its changes directly relate to mold cavity filling, pressure holding effect, and the dimensional accuracy and surface quality of the final product.

[0031] "Injection molding machine operating parameters" refer to various process parameters that can be monitored and controlled during the operation of the injection molding machine, such as heating cylinder temperature, injection pressure, injection speed, holding pressure, and cooling time. The setting and changes of these parameters directly affect the rheological behavior of the silicone melt and the molding quality of the final product.

[0032] Reference Figure 1 This invention provides a method for controlling the injection molding process of plastic product molds, comprising the following steps: S1, in the melt flow channel of the injection molding machine, a high-frequency dynamic spectral sensor is used to collect the spectral data of the silicone melt in real time to reflect the viscosity characteristics of the silicone melt.

[0033] Specifically, a high-frequency dynamic spectral sensor can be installed inside the melt flow channel of an injection molding machine, such as at the screw tip or near the nozzle, to directly contact the silicone melt. The sensor emits a beam of light of a specific wavelength that penetrates the melt and receives the light signals after absorption, scattering, or reflection by the melt. These light signals are converted into electrical signals and further processed to form spectral data. The spectral data can reflect information such as the molecular structure, degree of polymerization, degree of cross-linking, and the presence of impurities within the silicone melt; this information is closely related to the viscosity characteristics of the silicone melt. For example, the viscosity changes of the silicone melt can be monitored in real time by analyzing changes in the intensity or position of specific absorption peaks in the spectral data.

[0034] S2. Based on spectral data and injection molding machine operating parameters, predict the viscosity change trend and magnitude of the silicone melt in the future preset time period.

[0035] As one possible approach, the viscosity change trend and magnitude of the silicone melt can be predicted over a predetermined period of time based on spectral data, injection molding machine operating parameters, and a first mapping relationship. It should be noted that the first mapping relationship includes the correspondence between different spectral data, different injection molding machine operating parameters, and different viscosities.

[0036] For example, the viscosity of the silicone melt at 10:06 can be predicted based on the spectral data at 10:01, the injection molding machine operating parameters, and the first mapping relationship; the viscosity of the silicone melt at 10:07 can be predicted based on the spectral data at 10:01, the injection molding machine operating parameters, and the first mapping relationship; and so on, the viscosity of the silicone melt from 10:06 to 10:11 can be predicted based on the spectral data from 10:00 to 10:05, the injection molding machine operating parameters, and the first mapping relationship, so as to predict the viscosity change trend and magnitude of the silicone melt in the future preset time period.

[0037] S3. Based on the viscosity change prediction results, adjust at least one of the following parameters of the injection molding machine: heating cylinder power, screw injection speed, and holding pressure curve, to eliminate the flow instability of the silicone melt.

[0038] For example, if an increase in silicone melt viscosity is predicted, the following measures can be taken to maintain stable flowability: increase the heating cylinder power to raise the melt temperature and reduce viscosity; or increase the screw injection speed to overcome higher flow resistance; or adjust the holding pressure profile to apply higher pressure during the holding phase to ensure full filling of the mold cavity. Conversely, if a decrease in viscosity is predicted, the heating cylinder power, screw injection speed, or holding pressure can be reduced accordingly to avoid overfilling or flash issues. In this way, the injection molding process can be adjusted dynamically in real time, effectively eliminating the flow instability of the silicone melt and thus ensuring product quality.

[0039] The step size for each adjustment can be preset.

[0040] The injection molding process control method for plastic product molds disclosed in this application achieves real-time and accurate sensing of the viscosity characteristics of silicone melt by introducing a high-frequency dynamic spectral sensor. This contrasts sharply with traditional defect detection methods that rely on experience-based judgment or lagging sensors (such as pressure sensors). Traditional methods often only allow for reactive adjustments after defects have occurred, while this application can predict viscosity changes in advance, thus enabling proactive intervention before problems arise.

[0041] Specifically, the core innovation of this application lies in combining real-time spectral data with injection molding machine operating parameters to construct an advanced viscosity change prediction model. This model can predict the viscosity change trend and magnitude of the silicone melt over a preset period, transforming the control of the injection molding process from "passive response" to "active prediction." For example, when traditional injection molding systems face insufficient holding pressure due to mismatched formulation files, adjustments can only be made manually based on experience after shrinkage defects appear in the product, and such adjustments may introduce new uncertainties. This application, however, can perceive changes in the melt's microstructure in real time through spectral data, predict viscosity anomalies by combining operating parameters, and adjust the heating cylinder power, screw injection speed, or holding pressure in advance, thereby avoiding defects such as shrinkage.

[0042] Furthermore, this application effectively eliminates the flow instability of silicone melt by dynamically adjusting the operating parameters of the injection molding machine. For example, when the operator manually increases the target injection pressure in the conventional method, leading to an increase in shear heat and consequently causing heating control oscillations and melt temperature instability, the predictive control method of this application can precisely adjust the heating cylinder power, screw injection speed, and holding pressure based on the predicted viscosity changes, thereby stabilizing the melt temperature and flow rate and avoiding the negative impact of the "saw effect" in the conventional method.

[0043] In summary, this application achieves real-time and predictive control of the injection molding process by introducing a high-frequency dynamic spectral sensor and an advanced viscosity prediction model, which significantly improves the quality and production efficiency of plastic products and effectively solves the problems of inaccurate melt viscosity control and unavoidable defects in traditional injection molding methods. It has significant technological advancements and practical value.

[0044] In some embodiments described above, a method is proposed to acquire spectral data of silicone melt in real time using a high-frequency dynamic spectral sensor in the melt flow channel of an injection molding machine, and to predict the viscosity change trend and magnitude of the silicone melt over a predetermined period based on the spectral data and the operating parameters of the injection molding machine. However, in actual injection molding, changes in the microstructure of the silicone melt may lead to the appearance of some atypical spectral signals. These signals may not be directly related to conventional injection molding machine operating parameters, making it difficult to accurately capture these potential flow instabilities when relying solely on conventional spectral data and operating parameters for prediction, which may lead to defects in plastic products.

[0045] In response, one possible design is as follows: Figure 2 As shown, in order to predict the viscosity change trend and magnitude of silicone melt over a future preset period based on spectral data and injection molding machine operating parameters, this application may further include the following steps: S101. Identify atypical spectral signals in the spectral data and mark them as unexplained spectral events.

[0046] Specifically, identifying atypical spectral signals in spectral data means detecting spectral features with deviations greater than a deviation threshold by comparing them with a preset normal spectral baseline.

[0047] These signals may indicate changes in the microstructure of the silicone melt, such as the degree of polymerization, crosslinking, or degradation. Marked as unexplained spectral events, these signals cannot be directly attributed to known process parameter fluctuations or batch-to-batch material variations in the initial stages.

[0048] S102. Detect defects on the surface of plastic products in real time at the injection molding machine's mold exit or on the cooling conveyor belt, and record the defect type, location, severity, and corresponding product production timestamp.

[0049] Real-time detection of surface defects in plastic products at the injection molding machine's exit or cooling conveyor belt can be achieved using machine vision systems, automated optical inspection (AOI) equipment, or other non-contact sensors. These devices can accurately capture various surface defects in plastic products, such as short shots, flash, shrinkage marks, warping, bubbles, black spots, or uneven surface gloss. Recording the defect type, location, severity, and corresponding product manufacturing timestamp is crucial for establishing a precise correlation between defects and spectral events during the production process, enabling subsequent data analysis and correlation.

[0050] S103. When an unexplained spectral event occurs, if a specific type of defect is continuously detected in the subsequent product manufacturing cycle, an association is established between the atypical spectral signal and the specific defect type.

[0051] Specifically, the continuous detection of a specific type of defect can be understood as the occurrence frequency or severity of a certain defect exceeding a preset threshold in a series of products or products within a certain time window.

[0052] S104. Based on the correlation, assign rheological influence factors to atypical spectral signals to quantify the influence of microstructural changes on the flow resistance of silicone melt in a specific region of the mold cavity, thereby predicting the viscosity change trend and magnitude of silicone melt.

[0053] Specifically, assigning rheological influence factors to atypical spectral signals means giving these previously "unexplained" spectral signals a quantifiable physical meaning. The rheological influence factor is a numerical value.

[0054] For example, multiple initial predicted values ​​of the viscosity of the silicone melt can be multiplied by a rheological influence factor to obtain the predicted viscosity value of the silicone melt.

[0055] Understandably, this factor can quantify the impact of microstructural changes on the flow resistance of silicone melt in specific regions within the mold cavity. For example, an atypical spectral signal might indicate an increase in the local cross-linking degree of the silicone melt, leading to a significant increase in its flow resistance in specific narrow regions within the mold cavity. In this way, all factors that may affect viscosity can be considered more comprehensively and accurately, thereby predicting the viscosity variation trend and magnitude of the silicone melt.

[0056] This application's solution effectively overcomes the blind spots of traditional viscosity prediction methods by establishing a correlation between atypical spectral signals and actual product defects. Traditional methods mainly focus on the direct mapping between known parameters and viscosity, but their predictive ability is limited for spectral anomalies caused by changes in the material's microstructure that are not easily explained directly. This solution monitors product defects in real time and correlates them with corresponding atypical spectral events over time, enabling the system to "learn" the true physical meaning of these anomalous signals. Once this correlation is established, previously "unexplained" spectral events gain a clear rheological explanation, indicating that the flow resistance of the silicone melt will change in specific regions within the mold cavity. By assigning rheological influence factors to these atypical spectral signals, the prediction model can quantify the impact of these microstructural changes, thus considering more comprehensive factors in viscosity prediction and significantly improving the accuracy and foresight of the prediction.

[0057] Through the above technical solution, this application can significantly improve the accuracy and robustness of silicone melt viscosity prediction, especially when facing complex rheological behavior caused by changes in material microstructure. This solution enables the system to identify and utilize previously overlooked atypical spectral signals as early warning indicators, thereby predicting potential flow instabilities before product defects actually occur. This not only helps to adjust injection molding machine operating parameters in a timely manner to eliminate flow instabilities, but also effectively reduces scrap rates, improves the quality stability of plastic products, and lowers production costs. Furthermore, by establishing a correlation between atypical spectral signals and specific defect types, this solution also provides process engineers with deeper insights, helping to optimize material formulations and process parameters, and achieve more refined injection molding process control.

[0058] In some embodiments described above, this application proposes eliminating the flow instability of silicone melt by predicting the viscosity change trend and magnitude of the silicone melt and adjusting at least one of the heating cylinder power, screw injection speed, and holding pressure curve of the injection molding machine. However, in actual injection molding, even if the overall melt viscosity is controlled, local differences in temperature and pressure distribution may still exist in different regions inside the mold, especially in multi-cavity molds or molds with complex structures. These local differences may lead to uneven melt filling, inconsistent solidification rates, or local defects, which are difficult to effectively solve by simply adjusting global parameters. Therefore, this application further proposes a method for adjusting local parameters for multiple types of regions inside the mold to more precisely control the injection molding process, thereby improving the quality and consistency of plastic products.

[0059] In one possible design, the method also includes: S201. For multiple types of regions, obtain the melt temperature and pressure data of the regions.

[0060] The multiple types of regions include a first type region and a second type region; the first type region is the target area of ​​each mold cavity, near the runner, gate, or cooling system; the second type region is the complex structural area of ​​the mold.

[0061] The first type of area can be understood as the area that has the most critical impact on the quality of plastic products. The second type of area specifically refers to areas in the mold with complex geometry, tortuous melt flow paths, or significant changes in wall thickness, such as thin-walled structures, sharp angles, deep cavities, or areas with fine features. These areas are usually more prone to problems such as melt flow instability, incomplete filling, or uneven cooling.

[0062] Specifically, this can be achieved by arranging miniature temperature sensors (such as thermocouples) and pressure sensors (such as piezoelectric sensors) in these multiple types of regions to obtain melt temperature and pressure data for each type of region.

[0063] S202. Adjust the local cooling rate or local holding pressure of the type region according to the melt temperature and pressure data of the type region.

[0064] The local cooling rate can be adjusted by controlling the flow rate and temperature of the independent cooling loop in that area, or by using local heating / cooling elements (such as local heaters in a hot runner system or local cooling units integrated into the mold). Adjustment of the local holding pressure may involve fine control of the holding time, holding curve, or pressure applied by a local hydraulic / pneumatic mechanism in that area.

[0065] It should be noted that the adjustment methods for the first type of region and the second type of region are different. Please refer to the following sections for details, which will not be elaborated here.

[0066] This application's solution deploys sensors in key areas within the mold (including first-type and second-type areas) to acquire real-time local melt temperature and pressure data. This allows for the identification of local flow instabilities or thermodynamic non-uniformities that are difficult to detect using traditional global control methods. For example, when the temperature or pressure in the gate area of ​​a mold cavity is too low, it may lead to premature melt solidification or insufficient filling. When local overheating or pressure fluctuations occur in complex structural areas, it may cause product warping, shrinkage marks, or dimensional deviations. By analyzing this local data, the system can generate targeted, differentiated compensation commands and precisely adjust the local cooling rate or local holding pressure in the corresponding areas. This refined local intervention mechanism allows the control of the injection molding process to extend from the macroscopic to the microscopic level, ensuring that the filling, holding, and cooling processes of the melt within the mold cavity reach optimal conditions in each area.

[0067] Through the above technical solutions, the control precision and adaptability of the injection molding process have been significantly improved. This solution effectively addresses the inconsistencies in melt flow and solidification between cavities in multi-cavity molds and between different areas in complex mold structures. By precisely adjusting local cooling rates or local holding pressures, defects such as short shots, shrinkage marks, warpage, and flash in plastic products in critical areas (such as near the gate) and complex areas (such as thin walls and sharp angles) can be significantly reduced, thereby improving the consistency of product dimensional accuracy, appearance quality, and mechanical properties. Furthermore, this local optimization also helps shorten the overall molding cycle, reduce scrap rates, and improve production efficiency and economic benefits.

[0068] Specifically, when the type region is a first type region, the steps described above for adjusting the local cooling rate or local holding pressure of the type region based on the melt temperature and pressure data of the type region include: S301. Based on the melt temperature and pressure data of each mold cavity, monitor the filling and solidification state of the melt inside each mold cavity.

[0069] Specifically, the aforementioned "monitoring the filling and solidification state of the melt inside each mold cavity based on the melt temperature and pressure data of each cavity" refers to deploying miniature temperature and pressure sensors in key areas of each mold cavity, such as near the runner, gate, or cooling system. These sensors collect temperature and pressure data of the melt inside each mold cavity in real time, and through data analysis, evaluate key state parameters such as the flow front, filling speed, pressure distribution, and solidification start and end times of the melt within the mold cavity.

[0070] Among them, melt temperature data can reflect the thermal history and cooling rate of the melt, while pressure data can indicate the filling resistance, pressure holding effect, and whether underfilling or overfilling has occurred.

[0071] S302. Based on the filling and solidification state of the melt inside each mold cavity, identify the inconsistencies in the melt filling and solidification process between each mold cavity.

[0072] The phrase "identifying inconsistencies in the melt filling and solidification processes between different mold cavities" can be understood as comparing the real-time monitored filling and solidification state data of each mold cavity with preset ideal conditions or data from historical best production batches. Through statistical analysis, pattern recognition, or machine learning algorithms, significant differences are detected between mold cavities in terms of filling time, solidification rate, final pressure peak, or temperature profile. For example, if the filling time of one mold cavity is significantly longer than that of other mold cavities, or its solidification rate is too fast / too slow, it can be identified as inconsistency.

[0073] S303. Based on the inconsistency of melt filling and solidification processes between mold cavities, generate differentiated compensation instructions for each mold cavity.

[0074] For example, if a cavity is underfilled, its local holding pressure may need to be increased; if a cavity cools too quickly, causing stress concentration, its local cooling rate may need to be reduced. These instructions can be independent adjustments for individual cavities or coordinated adjustments for a group of cavities, with the aim of making the filling and solidification processes of all cavities more consistent.

[0075] S304. Adjust the local cooling rate or local holding pressure of each mold cavity according to the differential compensation instruction.

[0076] Specifically, based on the generated differential compensation instructions, the flow rate, temperature, or holding pressure of the cooling medium in each mold cavity can be precisely adjusted through the independent cooling circuit control unit or local pressure holding valve integrated inside the mold.

[0077] For example, the local cooling rate can be changed by controlling the flow rate or temperature of the coolant, or local pressure can be applied or released by an independent hydraulic / pneumatic system.

[0078] Through the above technical solution, this application enables independent and precise control of each cavity in a multi-cavity mold or complex mold. This solution significantly improves the stability of the injection molding process and the consistency of product quality, effectively avoiding increased scrap rates due to differences between cavities. Specifically, by real-time monitoring and identification of inconsistencies, filling and solidification deviations in each cavity can be corrected promptly, thereby reducing the occurrence of common defects such as shrinkage marks, warpage, and dimensional instability. Its advantages are particularly evident when producing plastic products with high precision and high consistency requirements. Furthermore, this differential compensation mechanism also helps optimize mold lifespan and reduces production costs by decreasing defective products.

[0079] In some preferred embodiments, a specific example is given below. Assume a four-cavity mold for producing small silicone seals. During injection molding, miniature temperature and pressure sensors are installed near the gate and cooling channel outlet of each cavity to collect melt temperature and pressure data in real time. System monitoring reveals that cavity 1 has a slightly longer filling time than other cavities, and its holding pressure drops more rapidly during the later stages of solidification, which may cause slight shrinkage marks on the seals produced in this cavity. Simultaneously, cavity 3 has a slightly higher cooling rate than other cavities, potentially leading to higher internal stress in its product. Based on this monitoring data, the system identifies inconsistencies in the filling and solidification processes of cavities 1 and 3 compared to the other cavities. Therefore, the system generates differentiated compensation instructions: for cavity 1, the instruction may require appropriately extending the holding time or slightly increasing the local holding pressure during the holding phase; for cavity 3, the instruction may require slightly reducing the coolant flow rate in its cooling channel to slow down the local cooling rate. These instructions are precisely executed through independent cooling circuit control valves and local holding pressure actuators. Through this differentiated adjustment, the shrinkage problem of mold cavity 1 was improved, and the internal stress of mold cavity 3 was effectively controlled, ultimately ensuring that the silicone sealing rings produced by the four mold cavities achieved a high degree of consistency in dimensional accuracy, surface quality, and physical properties.

[0080] Specifically, when the type region is the second type region, the local cooling rate or local holding pressure of the type region is adjusted based on the melt temperature and pressure data of the type region, including: S401. Based on the local melt temperature and pressure data of the complex structure region, monitor the melt filling state and solidification rate of the complex structure region.

[0081] Specifically, "monitoring the melt filling state and solidification rate in complex structural regions based on local melt temperature and pressure data" refers to acquiring real-time temperature and pressure information of the silicone melt within or near the complex structural region using miniature temperature and pressure sensors. This data is used to dynamically track the melt flow front, filling velocity, and subsequent solidification process within the complex geometry. The aim is to obtain refined real-time data on melt behavior within complex regions, providing a foundation for subsequent flow instability identification.

[0082] S402. Identify melt flow instabilities in complex structural regions based on the melt filling state and solidification rate.

[0083] "Identifying melt flow instability in complex structural regions based on melt filling state and solidification rate" refers to determining whether there is abnormal flow behavior of the melt in complex structural regions based on the monitored melt filling state and solidification rate data, using a preset analysis model or algorithm.

[0084] For example, it can identify phenomena such as localized inadequate filling, weld line formation, degradation risks due to excessively high shear rates, or uneven solidification. Its purpose is to accurately diagnose flow anomalies that may lead to product defects within complex areas.

[0085] S403. Based on the melt flow instability in complex structural regions, generate local compensation commands for the complex structural regions.

[0086] Specifically, "generating local compensation commands for complex structural regions based on melt flow instability" means that once melt flow instability is identified, the system automatically generates a set of targeted control commands based on the type, degree, and location of the instability. These commands aim to correct or alleviate the identified flow problems. Their purpose is to provide specific and quantitative guidance for subsequent injection molding parameter adjustments.

[0087] S404. Adjust the local heating power, local injection speed, or local holding pressure in complex structural areas according to the local compensation command.

[0088] Specifically, "adjusting the local heating power, local injection speed, or local holding pressure of complex structural areas according to local compensation instructions" means applying the generated local compensation instructions to the corresponding control unit of the injection molding machine to finely adjust the local injection parameters of complex structural areas.

[0089] For example, the mold temperature can be changed by using local heating elements, the speed at which the melt enters complex areas can be controlled by adjusting the screw injection speed, or the holding pressure in that area can be adjusted by using a local holding pressure system. The aim is to effectively eliminate melt flow instability in complex structural areas through multi-dimensional parameter adjustments, thereby ensuring product quality.

[0090] Through the above technical solution, this application can provide more refined and effective injection molding process control for complex structural areas of the mold. Compared with the general method of only adjusting the local cooling rate or local holding pressure, this solution can more accurately identify the melt flow instability unique to complex areas by deeply monitoring the melt filling state and solidification rate. More importantly, by generating local compensation commands and flexibly adjusting local heating power, local injection speed, or local holding pressure, this solution provides more dimensions of control, enabling more precise intervention and correction of melt behavior in complex areas. As a result, common injection molding defects in complex structural areas, such as short shots, weld lines, bubbles, or stress concentrations, can be significantly reduced, thereby greatly improving the overall quality and production qualification rate of plastic products, especially for high-precision plastic products with complex geometries.

[0091] In some embodiments, the high-frequency dynamic spectral sensor is equipped with a self-cleaning device that sprays high-pressure gas onto the surface of the sensor probe after each injection molding cycle to remove silicone melt deposits from the probe surface; the surface of the sensor probe is coated with a wear-resistant and non-stick coating to reduce degradation product deposition and wear. The high-frequency dynamic spectrum sensor also includes a reference signal transmitter and a reference signal receiver, with the reference signal transmitter periodically transmitting a reference signal to the reference signal receiver; The reference signal receiver is used to receive the reference signal and generate reference signal strength data. When the reference signal strength data is lower than a preset threshold, the self-cleaning device is triggered to perform cleaning.

[0092] Specifically, a self-cleaning device can be understood as a mechanical or pneumatic system integrated near the sensor probe. Its main function is to physically remove contaminants from the probe surface during non-working phases of the injection molding process, such as after each injection cycle. High-pressure gas jetting is a highly efficient and non-contact cleaning method that effectively removes the thin film or particulate deposits formed by the silicone melt on the probe surface, preventing their accumulation from affecting the transmission or reflection of spectral signals. The wear-resistant and non-stick coating applied to the sensor probe surface aims to fundamentally reduce the adhesion between the silicone melt and the probe surface, and improve the probe's resistance to high temperatures, high pressures, and melt flow erosion. This coating can be a fluoropolymer coating, a ceramic coating, or a composite material coating; its selection should comprehensively consider its chemical inertness, high-temperature resistance, wear resistance, and non-adhesion to the silicone melt. By reducing degradation product deposition and wear, the lifespan of the sensor probe can be significantly extended, and its optical performance can be maintained in a long-term stable manner.

[0093] In practical applications, the reference signal transmitter and receiver form an independent monitoring loop to assess the cleanliness of the sensor probe surface. The reference signal transmitter can be an LED or laser diode, emitting a light signal of a specific wavelength; the reference signal receiver is a photodetector that receives the signal and converts it into an electrical signal, thereby generating reference signal strength data. When there are deposits on the probe surface, the transmission of the reference signal is hindered, resulting in a decrease in the received signal strength. By setting a preset threshold, when the received reference signal strength data falls below the threshold, it indicates that there may be contamination on the probe surface that affects the measurement. At this point, the self-cleaning device is automatically triggered to clean the probe, thus achieving real-time monitoring and on-demand cleaning of the sensor probe's cleanliness.

[0094] In some embodiments described above, this application proposes a method for predicting the viscosity variation trend and magnitude of silicone melt based on spectral data and injection molding machine operating parameters. However, in actual injection molding processes, the operating parameters of the injection molding machine, such as melt temperature, injection pressure, or screw speed, may change instantaneously due to external disturbances or internal control requirements. The rheological behavior of silicone melt exhibits rapid responsiveness to these parameter changes. If the impact of such dynamic changes on viscosity is not captured and quantified accurately and in a timely manner, the accuracy of viscosity prediction may decrease, thereby affecting the timeliness and effectiveness of injection molding process control. Therefore, this application further proposes an optimized prediction method aimed at improving the response speed and prediction accuracy of the viscosity prediction model to changes in injection molding machine operating parameters, thereby more effectively eliminating the flow instability of silicone melt.

[0095] Based on spectral data and injection molding machine operating parameters, the above predictions of the viscosity change trend and magnitude of silicone melt over a future preset period include: S501. Determine the spectral data based on the product of the mapping weights and the original spectral data.

[0096] As one possible approach, it is possible to monitor the instantaneous values ​​and rates of change of the injection molding machine's operating parameters; When the operating parameters of the injection molding machine change, the instantaneous viscosity of the silicone melt is calculated and compared with historical data to identify the initial response to the viscosity change; based on the initial response, the mapping weight is determined.

[0097] For example, if the initial response is greater than the response threshold, the initial weight is increased by a preset step size based on the initial weight to obtain the mapped weight.

[0098] Specifically, once the instantaneous value or rate of change of any key operating parameter is detected to exceed the preset range, the system will immediately use the current spectral data and operating parameters to calculate the instantaneous viscosity of the silica melt. Subsequently, this instantaneous viscosity is compared with historical viscosity data accumulated under similar operating parameters to quickly identify the starting point, direction, and initial magnitude of the viscosity change caused by the parameter change, i.e., the initial response of the viscosity change.

[0099] S502. Combining spectral data and injection molding machine operating parameters, predict the viscosity change trend and magnitude of silicone melt in the future preset time period.

[0100] The solution proposed in this application effectively solves the problems of response lag and insufficient prediction accuracy that may exist in traditional prediction methods when facing dynamic changes in the injection molding process by introducing the monitoring of instantaneous values ​​and rates of change of injection molding machine operating parameters, and dynamically adjusting the mapping weight between spectral characteristics and viscosity based on this.

[0101] like Figure 3As shown in the figure, this embodiment of the invention also provides a control system for the injection molding process of plastic product molds. The system includes: The spectral data acquisition module is used to acquire the spectral data of the silicone melt in real time in the melt flow channel of the injection molding machine using a high-frequency dynamic spectral sensor, so as to reflect the viscosity characteristics of the silicone melt. The viscosity change prediction module is used to predict the viscosity change trend and magnitude of silicone melt in the future within a preset time period based on spectral data and injection molding machine operating parameters. The parameter adjustment module is used to adjust at least one of the following parameters of the injection molding machine based on the viscosity change prediction results: heating cylinder power, screw injection speed, and holding pressure curve, in order to eliminate the flow instability of the silicone melt.

[0102] This application also provides a computer-readable storage medium. All or part of the processes in the above method embodiments can be executed by a computer program instructing related hardware. This program can be stored in the computer-readable storage medium, and when executed, it can include the processes of the above method embodiments. The computer-readable storage medium can be an internal storage unit of the task execution device (including a data sending end and / or a data receiving end) of any of the foregoing embodiments, such as the hard disk or memory of the task execution device. The computer-readable storage medium can also be an external storage device of the terminal device, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the terminal device. Further, the computer-readable storage medium can include both the internal storage unit of the task execution device and an external storage device. The computer-readable storage medium is used to store the computer program and other programs and data required by the task execution device. The computer-readable storage medium can also be used to temporarily store data that has been output or will be output.

[0103] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0104] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, essentially, or the parts that contribute to the prior art, or all or part of the technical solutions, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0105] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions within the technical scope disclosed in this application should be covered within the scope of protection of this application.

Claims

1. A method for controlling the injection molding process of plastic product molds, characterized in that, include: In the melt flow channel of the injection molding machine, a high-frequency dynamic spectral sensor is used to collect the spectral data of the silicone melt in real time to reflect the viscosity characteristics of the silicone melt. Based on the spectral data and the operating parameters of the injection molding machine, the viscosity change trend and magnitude of the silicone melt are predicted over a future preset period. Based on the viscosity change prediction results, adjust at least one of the following: heating cylinder power, screw injection speed, and holding pressure curve of the injection molding machine, in order to eliminate the flow instability of the silicone melt; For multiple types of regions, melt temperature and pressure data of the regions are acquired; the multiple types of regions include a first type region and a second type region; the first type region is the target area of ​​each mold cavity, near the runner, gate or cooling system; the second type region is the complex structural area of ​​the mold. Based on the melt temperature and pressure data of the type of region, adjust the local cooling rate or local holding pressure of the type of region.

2. The method for controlling the injection molding process of plastic product molds according to claim 1, characterized in that, The step of predicting the viscosity change trend and magnitude of the silicone melt over a future preset period based on the spectral data and injection molding machine operating parameters includes: Identify atypical spectral signals in the spectral data and mark them as unexplained spectral events; On the injection molding machine's mold exit or cooling conveyor belt, defects on the surface of plastic products are detected in real time, and the defect type, location, severity, and corresponding product production timestamp are recorded. If a specific type of defect is continuously detected during the subsequent product manufacturing cycle after the unexplained spectral event occurs, an association is established between the atypical spectral signal and the specific defect type. Based on the correlation, the viscosity prediction logic of the silicone melt is adjusted, and a rheological influence factor is assigned to the atypical spectral signal to quantify the influence of microstructural changes on the flow resistance of the silicone melt in a specific region within the mold cavity, thereby predicting the viscosity change trend and magnitude of the silicone melt.

3. The method for controlling the injection molding process of plastic product molds according to claim 1, characterized in that, When the type region is the first type region, adjusting the local cooling rate or local holding pressure of the type region based on the melt temperature and pressure data of the type region includes: Based on the melt temperature and pressure data of each mold cavity, monitor the filling and solidification state of the melt inside each mold cavity; Based on the filling and solidification state of the melt inside each mold cavity, inconsistencies in the melt filling and solidification processes between each mold cavity are identified; Based on the inconsistency in the melt filling and solidification processes between the mold cavities, differentiated compensation instructions for each mold cavity are generated; According to the differentiated compensation command, the local cooling rate or local holding pressure of each mold cavity is adjusted.

4. The method for controlling the injection molding process of plastic product molds according to claim 1, characterized in that, When the type region is the second type region, adjusting the local cooling rate or local holding pressure of the type region based on the melt temperature and pressure data of the type region includes: Based on the local melt temperature and pressure data of the complex structure region, monitor the melt filling state and solidification rate of the complex structure region; Based on the melt filling state and solidification rate of the complex structural region, the melt flow instability of the complex structural region is identified. Based on the melt flow instability of the complex structure region, a local compensation command for the complex structure region is generated; According to the local compensation command, adjust the local heating power, local injection speed, or local holding pressure of the complex structure region.

5. The method for controlling the injection molding process of plastic product molds according to claim 1, characterized in that, The high-frequency dynamic spectral sensor is equipped with a self-cleaning device. After each injection molding cycle, the self-cleaning device sprays high-pressure gas onto the surface of the sensor probe to remove the silicone melt deposits on the probe surface. The surface of the sensor probe is coated with a wear-resistant and non-stick coating to reduce the deposition of degradation products and wear. The high-frequency dynamic spectral sensor also includes a reference signal transmitter and a reference signal receiver, wherein the reference signal transmitter periodically transmits a reference signal to the reference signal receiver; The reference signal receiver is used to receive the reference signal and generate reference signal strength data, and trigger the self-cleaning device to clean when the reference signal strength data is lower than a preset threshold.

6. The method for controlling the injection molding process of plastic product molds according to claim 1, characterized in that, The step of predicting the viscosity change trend and magnitude of the silicone melt over a future preset period based on the spectral data and injection molding machine operating parameters includes: The spectral data is determined by multiplying the mapping weights and the original spectral data. By combining the spectral data and the operating parameters of the injection molding machine, the viscosity change trend and magnitude of the silicone melt in the future preset time period are predicted.

7. The method for controlling the injection molding process of plastic product molds according to claim 6, characterized in that, Determine the mapping weights, including: Monitor the instantaneous values ​​and rate of change of the operating parameters of the injection molding machine; When the operating parameters of the injection molding machine change, the instantaneous viscosity of the silicone melt is calculated and compared with historical data to identify the initial response to the viscosity change; The mapping weights are determined based on the initial response.

8. The method for controlling the injection molding process of plastic product molds according to claim 7, characterized in that, Determining the mapping weights based on the initial response includes: If the initial response is greater than the response threshold, the initial weight is increased by a preset step size based on the initial weight to obtain the mapped weight.

9. A control system for the injection molding process of plastic product molds, characterized in that, The system, applied to the method as described in any one of claims 1-8, comprises: The spectral data acquisition module is used to acquire spectral data of silicone melt in real time in the melt flow channel of the injection molding machine using a high-frequency dynamic spectral sensor, so as to reflect the viscosity characteristics of the silicone melt. The viscosity change prediction module is used to predict the viscosity change trend and magnitude of the silicone melt in the future preset time period based on the spectral data and the injection molding machine operating parameters. The parameter adjustment module is used to adjust at least one of the following parameters of the injection molding machine based on the viscosity change prediction results: heating cylinder power, screw injection speed, and holding pressure curve, in order to eliminate the flow instability of the silicone melt.

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