Time sequence controller for needle valve type hot runner injection molding system

Abstract

The invention discloses a time schedule controller for a needle valve type hot runner injection molding system. The time schedule controller is used for controlling a needle valve deployed in the hot runner injection molding system. The control method of the time schedule controller comprises the following steps: acquiring pressure data and temperature data in a mold at a preset sampling frequency; the time sequence distribution of the collected pressure data in the mold and the time sequence distribution of the collected temperature data in the mold are transmitted to a central control unit through a communication module; processing the time sequence distribution of the pressure data in the mold and the time sequence distribution of the temperature data in the mold to determine an optimal opening time point and an optimal closing time point of a needle valve; and sending a time control instruction to an actuator of the time schedule controller so as to control the state of the needle valve based on the optimal opening time point and the optimal closing time point of the needle valve. In this way, the opening and closing time of the needle valve is controlled more accurately, so that the product quality is ensured, and the production efficiency is improved.

Description

Technical Field

[0001] The present application relates to a timing controller, and more specifically, to a timing controller for a needle valve hot runner injection molding system. Background Art

[0002] In the modern injection molding industry, needle valve hot runner systems are widely used because they can significantly improve production efficiency and product quality. However, the existing needle valve control method has some obvious defects, which limit its application effect in complex processes and urgently need improvement and innovation.

[0003] First, traditional needle valve control methods usually rely on fixed time intervals or manual adjustments. Although this method is simple and easy to use, it lacks flexibility and adaptability. In the actual production process, the pressure and temperature in the mold will change with the changes in material properties, environmental conditions and process parameters. Fixed time intervals cannot accurately match these changes, which can easily lead to unstable product quality. For example, premature or late needle valve action can cause defects such as shrinkage, bubbles or overflow in the product, which seriously affects the appearance and performance of the product.

[0004] Secondly, manual adjustment of the needle valve is not only time-consuming and laborious, but also difficult to ensure the stability of each production. The operator's experience and skill level directly affect the effect of the adjustment, and the quality of products in different batches varies greatly. In addition, human errors in the manual adjustment process are also difficult to avoid, which increases production costs and management difficulties. Especially in multi-cavity injection molding systems, manual adjustment of multiple needle valves is even more difficult to achieve synchronization, which easily leads to inconsistent product quality between cavities.

[0005] Therefore, an optimized timing controller for needle valve hot runner injection molding system is expected. Summary of the invention

[0006] In order to solve the above technical problems, the present application is proposed. The embodiment of the present application provides a timing controller for a needle valve hot runner injection molding system, which infers the optimal opening time point of the needle valve and the optimal closing time point of the needle valve based on the timing distribution of the temperature data and the pressure data in the mold, so that the opening and closing time of the needle valve can be adaptively controlled based on the real-time working conditions to ensure product quality and improve production efficiency.

[0007] According to one aspect of the present application, a timing controller for a needle valve type hot runner injection molding system is provided, wherein the timing controller controls the needle valve deployed in the hot runner injection molding system in the following control method, wherein the control method comprises: collecting mold pressure data and mold temperature data at a predetermined sampling frequency; transmitting the collected mold pressure data and mold temperature data to a central control unit by using a communication module; processing the mold pressure data and mold temperature data in the central control unit to determine an optimal opening time point and an optimal closing time point of the needle valve, and generating a time control instruction based on the optimal opening time point and the optimal closing time point of the needle valve; and sending the time control instruction to an actuator of the timing controller to control the state of the needle valve based on the optimal opening time point and the optimal closing time point of the needle valve.

[0008] In the above-mentioned timing controller for the needle valve hot runner injection molding system, the communication module is used to transmit the collected timing distribution of the pressure data inside the mold and the timing distribution of the temperature data inside the mold to the central control unit, including: compressing the timing distribution of the pressure data inside the mold and the timing distribution of the temperature data inside the mold to obtain compressed data; adding FEC information to the compressed data to obtain anti-packet loss compressed data; and transmitting the anti-packet loss compressed data to the central control unit through the communication module.

[0009] In the above-mentioned timing controller for the needle valve hot runner injection molding system, the anti-packet loss compression data is transmitted to the central control unit through the communication module, including: real-time monitoring of the packet loss rate of the communication module to obtain a time queue of the packet loss rate; real-time monitoring of the signal strength value of the communication module to obtain a time queue of the signal strength value; based on the time queue of the packet loss rate and the time queue of the signal strength value, calculating the recommended data transmission rate; based on the recommended data transmission rate, generating a data transmission rate adjustment instruction to adjust the data transmission rate of the communication module.

[0010] In the above-mentioned timing controller for the needle valve hot runner injection molding system, the recommended data transmission rate is calculated based on the time queue of the packet loss rate and the time queue of the signal strength value, including: based on the time queue of the packet loss rate, determining the average packet loss rate and the packet loss fluctuation coefficient, the packet loss fluctuation coefficient is the standard deviation of the time queue of the packet loss rate; based on the time queue of the signal strength value, determining the average signal strength value and the signal strength fluctuation coefficient, the signal strength fluctuation coefficient is the standard deviation of the time queue of the signal strength value; and, based on the average packet loss rate, the packet loss fluctuation coefficient, the average signal strength value and the signal strength fluctuation coefficient, calculating the recommended data transmission rate.

[0011] In the above-mentioned timing controller for the needle valve hot runner injection molding system, the recommended data transmission rate is calculated based on the average packet loss rate, the packet loss fluctuation coefficient, the average signal strength value and the signal strength fluctuation coefficient, including: calculating the packet loss influence factor based on the average packet loss rate and the packet loss fluctuation coefficient; calculating the signal strength influence factor based on the average signal strength value and the signal strength fluctuation coefficient; determining the comprehensive influence factor based on the packet loss influence factor and the signal strength influence factor; and calculating the recommended data transmission rate based on the comprehensive influence factor and the basic data transmission rate.

[0012] In the above-mentioned timing controller for the needle valve type hot runner injection molding system, in the central control unit, the timing distribution of the pressure data in the mold and the timing distribution of the temperature data in the mold are processed to determine the optimal opening time point and the optimal closing time point of the needle valve, including: decompressing the anti-packet loss compressed data to obtain the timing distribution of the pressure data in the mold and the timing distribution of the temperature data in the mold; arranging the timing distribution of the pressure data in the mold and the timing distribution of the temperature data in the mold according to the time dimension as a timing input vector of the pressure in the mold and a timing input vector of the temperature in the mold respectively; and, based on the timing distribution characteristics of the timing input vector of the pressure in the mold and the timing input vector of the temperature in the mold, calculating the optimal opening time point and the optimal closing time point of the needle valve.

[0013] In the above-mentioned timing controller for the needle valve type hot runner injection molding system, the optimal opening time point of the needle valve and the optimal closing time point of the needle valve are calculated based on the timing distribution characteristics of the pressure timing input vector in the mold and the temperature timing input vector in the mold, including: extracting the temperature timing characteristics of the pressure timing input vector in the mold, the temperature timing characteristics include temperature mean, temperature variance, temperature peak, temperature timing change trend and temperature periodicity characteristics; extracting the pressure timing characteristics of the temperature timing input vector in the mold, the pressure timing characteristics include pressure mean, pressure variance, pressure peak, pressure timing change trend and pressure periodicity characteristics; splicing the temperature timing characteristics and the pressure timing characteristics to obtain a temperature-pressure comprehensive feature vector; and inputting the temperature-pressure comprehensive feature vector into a trained needle valve optimal state predictor based on a random forest model to obtain the optimal opening time point of the needle valve and the optimal closing time point of the needle valve.

[0014] Compared with the prior art, the timing controller for the needle valve type hot runner injection molding system provided in the present application infers the optimal opening time point and the optimal closing time point of the needle valve based on the timing distribution of the temperature data and the pressure data in the mold, so that the opening and closing time of the needle valve can be adaptively controlled based on the real-time working conditions to ensure product quality and improve production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] By describing the embodiments of the present application in more detail in conjunction with the accompanying drawings, the above and other purposes, features and advantages of the present application will become more apparent. The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0016] Figure 1 The figure illustrates a flow chart of a control method of a timing controller for a needle valve hot runner injection molding system according to an embodiment of the present application.

[0017] Figure 2 The figure illustrates a flow chart of using a communication module to transmit the collected time series distribution of the pressure data inside the mold and the time series distribution of the temperature data inside the mold to a central control unit according to an embodiment of the present application.

[0018] Figure 3 The figure illustrates a flow chart of transmitting the anti-packet loss compression data to the central control unit through the communication module according to an embodiment of the present application. DETAILED DESCRIPTION

[0019] Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described here.

[0020] Embodiment 1: Figure 1The figure shows a flow chart of a control method for a timing controller of a needle valve hot runner injection molding system according to the present application, which includes the steps of: S110, collecting mold pressure data and mold temperature data at a predetermined sampling frequency; S120, using a communication module to transmit the collected timing distribution of the mold pressure data and the timing distribution of the mold temperature data to a central control unit; S130, in the central control unit, processing the timing distribution of the mold pressure data and the timing distribution of the mold temperature data to determine the optimal opening time point of the needle valve and the optimal closing time point of the needle valve, and generating a time control instruction based on the optimal opening time point of the needle valve and the optimal closing time point of the needle valve; and, S140, the central control unit sends the time control instruction to the actuator of the timing controller to control the state of the needle valve based on the optimal opening time point of the needle valve and the optimal closing time point of the needle valve.

[0021] In step S110, the pressure data and temperature data in the mold are collected at a predetermined sampling frequency. Those skilled in the art should know that during the injection molding process, the pressure and temperature in the mold are key factors affecting product quality. In order to monitor the changes of these parameters in real time, the present invention uses a high-precision temperature sensor and a pressure sensor, and collects data at a predetermined sampling frequency.

[0022] First of all, the selection and installation of sensors are the basis of data collection. Temperature sensors and pressure sensors should be selected with high precision and high response speed to ensure the accuracy and real-time nature of the data. These sensors are installed in key locations of the mold, such as inside the mold cavity or near the needle valve, so that the pressure and temperature changes in the mold can be accurately captured. High-precision sensors can provide more accurate data, which is crucial for subsequent data processing and needle valve control.

[0023] The selection of sampling frequency is a key link in data acquisition. The selection of sampling frequency needs to be determined according to the specific injection molding process and mold characteristics. Generally speaking, the higher the sampling frequency, the better the resolution and real-time performance of the data, but it also increases the burden of data processing and the load of the communication network. Therefore, it is necessary to find a balance between data accuracy and system performance. For example, for a fast injection molding process, a higher sampling frequency (such as 10 times per second or higher) can be set to capture rapidly changing pressure and temperature data; while for a slow injection molding process, the sampling frequency can be appropriately reduced (such as 1 time per second or lower) to reduce the burden of data processing. By reasonably setting the sampling frequency, the real-time and accuracy of the data can be ensured, providing reliable data support for subsequent needle valve control.

[0024] Once the sensor is installed and the sampling frequency is set, the data collection process begins. The sensor collects the pressure and temperature data in the mold in real time at the set sampling frequency and stores this data in the local cache. To ensure the integrity of the data, the sensor is also equipped with a data checksum and error handling mechanism, which can detect and correct potential errors during the data collection process. The data checksum and error handling mechanism can effectively prevent data loss or corruption and ensure that the collected data is true and reliable. This step is very important for subsequent data transmission and processing, because any data error may lead to misjudgment of the control strategy, thereby affecting product quality.

[0025] In step S120, the communication module is used to transmit the collected temporal distribution of the mold pressure data and the mold temperature data to the central control unit. Accordingly, after the data collection is completed, the data needs to be transmitted to the central control unit for further processing. In order to ensure the efficiency and reliability of data transmission, the present invention adopts a communication module. The communication module provides high-speed and stable wireless data transmission capabilities, and can transmit the collected data to the central control unit in real time. The use of the communication module not only improves the speed of data transmission, but also ensures the stability of data transmission, avoiding the problems of complex wiring and difficult maintenance common in traditional wired transmission.

[0026] More specifically, the present invention adopts a communication module. The communication module provides high-speed and stable wireless data transmission capabilities, and can transmit the collected data to the central control unit in real time. The specific implementation process is as follows: First, the collected temporal distribution of the pressure data inside the mold and the temporal distribution of the temperature data inside the mold are compressed to obtain compressed data. Data compression can significantly reduce the time and bandwidth occupancy of data transmission, and improve the efficiency of data transmission. Commonly used compression algorithms include image compression algorithms such as JPEG and PNG and general compression algorithms such as ZIP and GZIP. Through data compression, a large amount of original data can be converted into smaller files, thereby speeding up data transmission and reducing network load. For example, assuming that the original data size is 1MB, it can be reduced to 500KB after compression, and the data transmission time can be reduced by half.

[0027] Next, forward error correction (FEC) information is added to the compressed data to obtain anti-packet loss compressed data. Forward error correction is a data transmission technology that can detect and correct errors during data transmission, thereby improving the reliability of data transmission. By adding FEC information, even if data packet loss occurs during network transmission, the receiving end can recover the lost data based on the FEC information to ensure data integrity. For example, assuming that 10% of the data packets are lost during the transmission process, these lost data packets can be recovered through FEC information to ensure the integrity of data transmission. Next, the anti-packet loss compressed data is transmitted to the central control unit through the communication module. The communication module provides high-speed and stable wireless data transmission capabilities, and can transmit compressed data to the central control unit in real time.

[0028] That is, if Figure 2 As shown, in the technical solution of the present application, the step S120 includes the steps of: S121, compressing the time series distribution of the pressure data inside the mold and the time series distribution of the temperature data inside the mold to obtain compressed data; S122, adding FEC information to the compressed data to obtain anti-packet loss compressed data; and, S123, transmitting the anti-packet loss compressed data to the central control unit through the communication module.

[0029] In particular, in order to further improve the reliability of data transmission, the present invention adopts a method of real-time monitoring of the packet loss rate and signal strength value of the communication module to dynamically adjust the data transmission rate. Figure 3 As shown, the step S123 includes the steps of: S1231, monitoring the packet loss rate of the communication module in real time to obtain a time queue of the packet loss rate; S1232, monitoring the signal strength value of the communication module in real time to obtain a time queue of the signal strength value; S1233, calculating the recommended data transmission rate based on the time queue of the packet loss rate and the time queue of the signal strength value; and, S1234, generating a data transmission rate adjustment instruction based on the recommended data transmission rate to adjust the data transmission rate of the communication module.

[0030] Specifically, in the step S1231, the packet loss rate of the communication module is monitored in real time to obtain a time queue of the packet loss rate. The packet loss rate refers to the proportion of data packets lost during network transmission to the total data packets. By monitoring the packet loss rate in real time, the stability of the current network transmission can be understood. For example, assuming that in a certain time period, the packet loss rate is 5%, and in another time period, the packet loss rate is 10%, these packet loss rate data can form a time queue for subsequent data transmission rate adjustment. At the same time, in the step S1232, the signal strength value of the communication module is monitored in real time to obtain a time queue of the signal strength value. The signal strength value reflects the signal strength received by the communication module, usually in dBm. By monitoring the signal strength value in real time, the quality of the current network transmission can be understood. For example, assuming that in a certain time period, the signal strength value is -70dBm, and in another time period, the signal strength value is -80dBm, these signal strength value data can form a time queue for subsequent data transmission rate adjustment.

[0031] More specifically, in the technical solution of the present application, when monitoring the packet loss rate of the communication module in real time, it is necessary to determine the packet loss count during the transmission of the compressed data without packet loss. Therefore, if the accuracy of determining whether packet loss occurs during data packet transmission can be improved, the accuracy of the determined packet loss rate can be improved. In the embodiment of the present application, the packet loss action occurring during data packet transmission can be regarded as data packet transmission jitter, so as to use the jitter counting mechanism to count packet loss.

[0032] Specifically, first, after successfully receiving the previous data packet, the receiving state of the current data packet is detected. If the receiving state of the current data packet is the same as that of the previous data packet, the packet loss count is not triggered. Conversely, if the receiving state of the current data packet is different from that of the previous data packet, a predetermined time is delayed by the delay state level, for example, a predetermined time that is less than the receiving time of a single data packet and greater than a certain fault tolerance time threshold, to determine whether the current data packet has been lost, and after the predetermined time, the receiving state of the current data packet and the receiving state of the previous data packet are continuously detected. At this time, if the receiving state of the current data packet is the same as that of the previous data packet, the packet loss count is not triggered, and if the receiving state of the current data packet is different from that of the previous data packet, the packet loss counter is increased by one. That is, when a change in the receiving state of a data packet is detected, it is considered to be a valid state jitter by pausing the predetermined time and then checking whether the receiving state still changes, otherwise it is considered to be a jitter abnormality and ignored.

[0033] In the step S1233, the recommended data transmission rate is calculated based on the time queue of the packet loss rate and the time queue of the signal strength value. In a specific example, the process of calculating the recommended data transmission rate based on the time queue of the packet loss rate and the time queue of the signal strength value includes: first, by analyzing the time queue of the packet loss rate, the average packet loss rate and the packet loss fluctuation coefficient are calculated. The average packet loss rate reflects the overall level of the packet loss rate, and the packet loss fluctuation coefficient reflects the degree of fluctuation of the packet loss rate. In a specific example, the packet loss fluctuation coefficient is the standard deviation of the time queue of the packet loss rate; then, by analyzing the time queue of the signal strength value, the average signal strength value and the signal strength fluctuation coefficient are calculated. The average signal strength value reflects the overall level of the signal strength, and the signal strength fluctuation coefficient reflects the degree of fluctuation of the signal strength. In a specific example, the signal strength fluctuation coefficient is the standard deviation of the time queue of the signal strength value.

[0034] Then, based on these parameters, the recommended data transmission rate is calculated. In one example, the process is: first, based on the average packet loss rate and the packet loss fluctuation coefficient, the packet loss impact factor is calculated; then, based on the average signal strength value and the signal strength fluctuation coefficient, the signal strength impact factor is calculated; then, based on the packet loss impact factor and the signal strength impact factor, a comprehensive impact factor is determined; finally, based on the comprehensive impact factor and the basic data transmission rate, the recommended data transmission rate is calculated.

[0035] The process is expressed by the formula: First, set APLR as the average packet loss rate, PLV as the packet loss rate fluctuation coefficient, ASS as the average signal strength value, and SSV as the signal strength fluctuation coefficient.

[0036] Then, the calculation process of the packet loss impact factor (PLIF) is: ;in, It is the weight coefficient used to adjust the impact of packet loss fluctuation coefficient.

[0037] Then, the calculation process of the signal strength impact factor (SSIF) is: ;in, is the weight coefficient, which is used to adjust the influence of the signal strength fluctuation coefficient.

[0038] Then, the calculation process of the comprehensive impact factor (CIF) is: ;in, is a weight coefficient used to balance the importance of packet loss factor and signal strength factor.

[0039] Then, the calculation process of the recommended data transfer rate (RDR) is: ;in, is the basic data transfer rate, is the maximum comprehensive impact factor, which is used to normalize CIF. In a specific example, the basic data transmission rate is a preset initial transmission rate, the comprehensive impact factor is calculated based on the average packet loss rate, the packet loss fluctuation coefficient, the average signal strength value and the signal strength fluctuation coefficient, and the maximum comprehensive impact factor is the maximum value of the comprehensive impact factor, which is used to normalize the comprehensive impact factor.

[0040] In particular, in another preferred embodiment of the present application, as described above, in the case of packet loss counting through a jitter counting mechanism, since there is a predetermined delay time when determining whether packet loss occurs, that is, the delay time, then when calculating the recommended data transmission rate, the delay time also needs to be taken into account. Based on the calculation method of the recommended data rate, relative to the comprehensive impact factor as a negative impact factor, the impact of the delay time on the recommended data transmission rate is a positive impact factor, that is, the recommended data transmission rate should be appropriately increased based on the delay time. In addition, due to the correspondence between the delay time and the reception time of a single data packet, the delay impact factor can be obtained based on the corresponding properties between the delay time and the reception time of a single data packet.

[0041] For example, the delay impact factor can be obtained by simply calculating the ratio between the delay time and the reception time of a single data packet. For example, the delay time is expressed as , the single data packet reception time is expressed as The delay factor is , then: .

[0042] Therefore, based on the delay impact factor, the comprehensive impact factor and the basic data transmission rate, the recommended data transmission rate can be calculated as follows: ;in, is the weighting coefficient.

[0043] In addition, in order to strengthen the degree of reverse correlation between positive and negative influencing factors, we can further and To optimize these two items, first, considering that the two have the same dimension, we first construct the negative difference relationship representation factor and , and then the negative difference relationship is expressed by factors and Perform a monotonic nonlinear correlation fit, that is: That is, due to In order to obtain the monotonic nonlinear correlation fit, it is necessary to emphasize the nonlinear monotonic relationship based on rank order rather than simple reverse value that is not affected by extreme values, so as to obtain the rank order fitting factor based on the alternating input of independent variables.

[0044] Then, respectively and The calculation formula for optimizing the recommended data transfer rate as a weighting factor is: .

[0045] Finally, based on the recommended data transmission rate, a data transmission rate adjustment instruction is generated to adjust the data transmission rate of the communication module. Specifically, the central control unit generates a data transmission rate adjustment instruction according to the calculated recommended data transmission rate, and sends the adjustment instruction to the communication module through the control interface. After receiving the adjustment instruction, the communication module adjusts the data transmission rate according to the instruction to ensure the efficiency and reliability of data transmission.

[0046] In this way, the present invention not only improves the efficiency and reliability of data transmission, but also can dynamically adapt to changes in the network environment to ensure the stability of data transmission. For example, in the case of a poor network environment, the packet loss rate can be reduced by reducing the data transmission rate to ensure the integrity of the data; in the case of a good network environment, the data transmission speed can be accelerated by increasing the data transmission rate to improve the response speed of the system. In addition, by real-time monitoring of the packet loss rate and signal strength value, the present invention can also promptly detect changes in the network environment and take measures in advance to avoid errors and delays in the data transmission process. For example, when a sudden increase in the packet loss rate is detected, the data transmission rate can be adjusted immediately to avoid data loss; when a sudden decrease in the signal strength value is detected, the transmission strategy can be adjusted in time to ensure the stability of data transmission.

[0047] In step 130, the central control unit processes the time series distribution of the pressure data inside the mold and the time series distribution of the temperature data inside the mold to determine the optimal opening time point and the optimal closing time point of the needle valve, and generates a time control instruction based on the optimal opening time point and the optimal closing time point of the needle valve.

[0048] In a specific implementation, after receiving the anti-packet loss compressed data, the anti-packet loss compressed data is decompressed to obtain the time series distribution of the pressure data inside the mold and the time series distribution of the temperature data inside the mold. Data decompression is the inverse process of data compression, and the original data can be restored by decompression. Commonly used decompression algorithms include image compression algorithms such as JPEG and PNG and general compression algorithms such as ZIP and GZIP. Through data decompression, the compressed data can be restored to the original time series distribution data to ensure the accuracy of subsequent processing. For example, assuming that the compressed data size is 500KB, it can be restored to the original 1MB data by decompression.

[0049] Next, the time series distribution of the mold pressure data and the time series distribution of the mold temperature data are arranged according to the time dimension into a mold pressure time series input vector and a mold temperature time series input vector. The time series input vector is a data sequence arranged in time order, which can intuitively reflect the change trend of the data over time.

[0050] For example, suppose the collected mold pressure data is , the temperature data is , these data can be arranged as pressure time series input vectors and temperature timing input vector .

[0051] Next, based on the time series distribution characteristics of the pressure time series input vector and the temperature time series input vector in the mold, the optimal opening and closing time points of the needle valve are calculated. Specifically, various time series distribution characteristics of the pressure time series input vector and the temperature time series input vector are first extracted, including mean, variance, peak value, time series change trend and periodic characteristics. These characteristics can fully reflect the characteristics of the data and help the model better understand and predict the optimal opening and closing time points of the needle valve.

[0052] In a specific example, the temperature time series characteristics of the pressure time series input vector in the mold are extracted, and the temperature time series characteristics include temperature mean, temperature variance, temperature peak, temperature time series change trend and temperature periodicity characteristics. The temperature mean reflects the average level of temperature data, the temperature variance reflects the degree of fluctuation of temperature data, the temperature peak reflects the maximum value of temperature data, the temperature time series change trend reflects the change trend of temperature data over time, and the temperature periodicity characteristics reflect the periodic change of temperature data. By extracting these features, the temperature change in the mold can be fully understood. For example, assuming that the temperature time series input vector is , we can calculate that the mean temperature is 72.2°C, the temperature variance is 3.76, the temperature peak is 75°C, the temperature time series change trend is a slight decrease, and the temperature periodicity characteristic is no obvious periodic change.

[0053] In a specific example, the pressure time series features of the temperature time series input vector in the mold are extracted. The pressure time series features include the pressure mean, pressure variance, pressure peak, pressure time series change trend and pressure periodicity features. The pressure mean reflects the average level of the pressure data, the pressure variance reflects the degree of fluctuation of the pressure data, the pressure peak reflects the maximum value of the pressure data, the pressure time series change trend reflects the change trend of the pressure data over time, and the pressure periodicity features reflect the periodic changes of the pressure data. By extracting these features, the pressure changes in the mold can be fully understood. For example, assuming that the pressure time series input vector is , we can calculate that the mean pressure is 12.8, the pressure variance is 3.76, the pressure peak is 15, the pressure time series change trend is a slight decrease, and the pressure periodicity characteristic is no obvious periodic change.

[0054] The temperature time series features and pressure time series features are combined to obtain the temperature-pressure comprehensive feature vector. The temperature-pressure comprehensive feature vector contains multiple features of temperature and pressure, which can fully reflect the state changes in the mold. For example, assuming that the temperature time series feature is , the pressure time series characteristics are , these two eigenvectors can be concatenated into a temperature-pressure comprehensive eigenvector .

[0055] The temperature-pressure integrated feature vector is input into the trained needle valve optimal state predictor based on the random forest model to obtain the optimal opening time point and the optimal closing time point of the needle valve. The random forest model is an integrated learning method that improves the accuracy and robustness of prediction by combining multiple decision trees. The trained random forest model can predict the optimal opening and closing time points of the needle valve based on the input feature vector. For example, assuming that the temperature-pressure integrated feature vector is , the feature vector can be input into the trained random forest model, and the model will output that the optimal opening time of the needle valve is 2.5 seconds and the optimal closing time is 4.0 seconds.

[0056] In step S140, the central control unit sends the time control instruction to the actuator of the timing controller to control the state of the needle valve based on the optimal opening time point of the needle valve and the optimal closing time point of the needle valve. That is, once the central control unit determines the optimal opening and closing time points of the needle valve, it is necessary to send these time control instructions to the actuator of the timing controller to control the state of the needle valve. The specific implementation steps are as follows: The central control unit generates a time control instruction. The time control instruction contains information about the optimal opening time point and the optimal closing time point of the needle valve. For example, assuming that the optimal opening time point of the needle valve is 2.5 seconds and the optimal closing time point is 4.0 seconds, the following time control instruction can be generated: Time Control Instruction={Open Time: 2.5 seconds,Close Time: 4.0 seconds}Time Control Instruction={Open Time: 2.5 seconds,Close Time: 4.0 seconds}.

[0057] The central control unit sends the time control instructions to the actuator of the timing controller through the communication interface. The communication interface can be a serial communication interface (such as UART, SPI, I2C), an Ethernet interface, or a wireless communication interface (such as Wi-Fi, Bluetooth). Selecting a suitable communication interface can ensure efficient transmission of the time control instructions. For example, assuming that a UART interface is used, the central control unit can send the time control instructions to the actuator through the UART interface.

[0058] After the actuator receives the time control instruction, it accurately controls the opening and closing of the needle valve according to the time point information in the instruction. The actuator is usually composed of a microcontroller, a drive circuit and an actuator. The microcontroller is responsible for parsing the time control instruction and generating the corresponding control signal; the drive circuit is responsible for amplifying the control signal and driving the actuator (such as a solenoid valve or a motor) to move; the actuator is responsible for the actual opening and closing of the needle valve. For example, assuming that the time control instruction received by the actuator is Time Control Instruction={Open Time: 2.5 seconds, Close Time: 4.0seconds}Time Control Instruction={Open Time: 2.5 seconds, Close Time: 4.0seconds}, the microcontroller will generate an opening signal at 2.5 seconds, the drive circuit will amplify the signal, the actuator (such as a solenoid valve) will move, and the needle valve will open; it will generate a closing signal at 4.0 seconds, the drive circuit will amplify the signal, the actuator will move, and the needle valve will close.

[0059] In summary, a timing controller for a needle valve hot runner injection molding system based on an embodiment of the present application is explained, which is used to control a needle valve deployed in a hot runner injection molding system. The control method of the timing controller includes: collecting pressure data and temperature data in the mold at a predetermined sampling frequency; using a communication module to transmit the collected timing distribution of the pressure data in the mold and the timing distribution of the temperature data in the mold to a central control unit; processing the timing distribution of the pressure data in the mold and the timing distribution of the temperature data in the mold to determine the optimal opening time point of the needle valve and the optimal closing time point of the needle valve; and sending a time control instruction to the actuator of the timing controller to control the state of the needle valve based on the optimal opening time point of the needle valve and the optimal closing time point of the needle valve. In this way, the opening and closing time of the needle valve can be controlled more accurately to ensure product quality and improve production efficiency.

[0060] The present invention also has the following advantages: Accurately monitor the state in the mold: By collecting the pressure and temperature data in the mold in real time and performing feature extraction, the state changes in the mold can be fully understood, providing accurate data support for needle valve control. For example, when the pressure in the mold suddenly increases, the opening time of the needle valve can be adjusted in time through real-time data collection to avoid defects such as shrinkage or bubbles in the product.

[0061] Dynamically adapt to process changes: The injection molding process and mold characteristics may vary in different production batches. Through real-time data collection and feature extraction, the needle valve control strategy can be dynamically adjusted to ensure that the product quality of each production batch remains consistent. For example, when the fluidity of the injection molding material changes, the opening and closing time points of the needle valve can be adjusted in time through real-time data collection to ensure the stability of product quality.

[0062] Improve data accuracy and real-time performance: Through high-precision sensors and high sampling frequency data acquisition, higher data resolution and real-time performance can be provided, enabling the central control unit to more accurately capture instantaneous changes in the mold. This is crucial for determining the optimal opening and closing time of the needle valve. For example, when the temperature in the mold suddenly rises, the high sampling frequency can quickly capture this change and adjust the needle valve control strategy in time to avoid product quality problems.

[0063] Optimize data processing and control strategies: By extracting multiple features and performing feature splicing, the state changes in the mold can be fully reflected, providing a scientific basis for needle valve control. For example, by extracting the mean, variance, peak, trend and periodic characteristics of pressure and temperature, the optimal opening and closing time points of the needle valve can be more accurately predicted, improving the accuracy and stability of control.

[0064] Achieve synchronous control of multiple mold cavities: In a multi-cavity injection molding system, it is a challenge to synchronously control the opening and closing time points of multiple needle valves. Through real-time data acquisition and feature extraction, the central control unit can independently process the data of each mold cavity and generate corresponding control instructions to achieve precise synchronous control of multiple needle valves. This not only improves production efficiency, but also ensures the consistency of product quality in each mold cavity.

[0065] Improve system flexibility and scalability: The timing controller of the present invention is not only suitable for single-cavity injection molding systems, but can also be flexibly applied to multi-cavity injection molding systems. By adjusting the installation position and number of sensors, as well as the setting of sampling frequency, it can adapt to injection molding processes of different scales and complexities. In addition, the processing capacity of the central control unit and the transmission capacity of the communication module can also be expanded and upgraded according to actual needs to ensure the efficient operation of the system in different application scenarios.

[0066] Reduce production costs and management difficulty: The traditional needle valve control method is not only time-consuming and labor-intensive, but also easily leads to unstable product quality, increasing production costs and management difficulties. Through automated control, the present invention reduces the number of manual interventions, reduces the risk of human errors, and improves production efficiency. At the same time, by real-time monitoring and adjusting the needle valve control strategy, product defects caused by improper control can be avoided, further improving product quality and customer satisfaction.

[0067] Improve system reliability and stability: The reliability of data acquisition and transmission directly affects the stability and control effect of the system. By using high-precision sensors and communication modules, the efficiency and reliability of data acquisition and transmission can be ensured. The high precision and high response speed of the sensor can provide accurate data support, while the high speed and stable characteristics of the communication module can ensure real-time data transmission. In addition, the application of data compression and forward error correction technology further improves the reliability of data transmission and ensures the stable operation of the system.

[0068] The basic principles of the present application are described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, effects, etc. mentioned in the present application are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. are required by each embodiment of the present application. In addition, the specific details disclosed above are only for the purpose of illustration and ease of understanding, not for limitation, and the above details do not limit the present application to being implemented by adopting the above specific details.

[0069] The block diagrams of the devices, apparatuses, equipment, and systems involved in this application are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagram. As will be appreciated by those skilled in the art, these devices, apparatuses, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including", "comprising", "having", etc. are open words, referring to "including but not limited to", and can be used interchangeably with them. The words "or" and "and" used here refer to the words "and / or" and can be used interchangeably with them, unless the context clearly indicates otherwise. The words "such as" used here refer to the phrase "such as but not limited to", and can be used interchangeably with them.

[0070] It should also be noted that in the apparatus, device and method of the present application, each component or each step can be decomposed and / or recombined. Such decomposition and / or recombination should be regarded as equivalent solutions of the present application.

[0071] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present application. Therefore, the present application is not intended to be limited to the aspects shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

[0072] The above description has been given for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present application to the forms disclosed herein. Although multiple example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions and sub-combinations thereof.

Claims

1. A timing controller for a needle valve hot runner injection molding system, characterized in that: The timing controller controls the needle valve deployed in the hot runner injection molding system in the following control method, the control method comprising: collecting mold pressure data and mold temperature data at a predetermined sampling frequency; using a communication module to transmit the collected mold pressure data and mold temperature data to a central control unit, wherein the data transmission rate of the communication module is adaptively adjusted based on a packet loss rate and a signal strength value; in the central control unit, the mold pressure data and the mold temperature data are processed to determine an optimal opening time point and an optimal closing time point of the needle valve, and a time control instruction is generated based on the optimal opening time point and the optimal closing time point of the needle valve; the central control unit sends the time control instruction to the actuator of the timing controller to control the state of the needle valve based on the optimal opening time point and the optimal closing time point of the needle valve.

2. The timing controller for a needle valve hot runner injection molding system according to claim 1, characterized in that: The communication module is used to transmit the collected timing distribution of the pressure data inside the mold and the timing distribution of the temperature data inside the mold to the central control unit, including: compressing the timing distribution of the pressure data inside the mold and the timing distribution of the temperature data inside the mold to obtain compressed data; adding FEC information to the compressed data to obtain anti-packet loss compressed data; and transmitting the anti-packet loss compressed data to the central control unit through the communication module.

3. The timing controller for a needle valve hot runner injection molding system according to claim 2, characterized in that: The anti-packet loss compressed data is transmitted to the central control unit through the communication module, including: real-time monitoring of the packet loss rate of the communication module to obtain a time queue of the packet loss rate; real-time monitoring of the signal strength value of the communication module to obtain a time queue of the signal strength value; based on the time queue of the packet loss rate and the time queue of the signal strength value, calculating the recommended data transmission rate; based on the recommended data transmission rate, generating a data transmission rate adjustment instruction to adjust the data transmission rate of the communication module.

4. The timing controller for a needle valve hot runner injection molding system according to claim 3, characterized in that: The packet loss rate of the communication module is monitored in real time to obtain a time queue of the packet loss rate, including: detecting the receiving state of the current data packet after successfully receiving the previous data packet, if the receiving state of the current data packet is the same as the receiving state of the previous data packet, the packet loss count is not triggered; if the receiving state of the current data packet is different from the receiving state of the previous data packet, delaying the predetermined time by the delay state level to determine whether the current data packet is lost.

5. The timing controller for a needle valve hot runner injection molding system according to claim 4, characterized in that: If the receiving state of the current data packet is different from the receiving state of the previous data packet, a delay state level is used to delay a predetermined time to determine whether the current data packet is lost, including: after the predetermined time, continue to detect the receiving state of the current data packet and the receiving state of the previous data packet, if the receiving state of the current data packet is the same as the receiving state of the previous data packet, the packet loss count is not triggered, and if the receiving state of the current data packet is different from the receiving state of the previous data packet, the packet loss counter is increased by one.

6. The timing controller for a needle valve hot runner injection molding system according to claim 5, characterized in that: The recommended data transmission rate is calculated based on the time queue of the packet loss rate and the time queue of the signal strength value, including: determining the average packet loss rate and the packet loss fluctuation coefficient based on the time queue of the packet loss rate, wherein the packet loss fluctuation coefficient is the standard deviation of the time queue of the packet loss rate; determining the average signal strength value and the signal strength fluctuation coefficient based on the time queue of the signal strength value, wherein the signal strength fluctuation coefficient is the standard deviation of the time queue of the signal strength value; and calculating the recommended data transmission rate based on the average packet loss rate, the packet loss fluctuation coefficient, the average signal strength value and the signal strength fluctuation coefficient.

7. The timing controller for a needle valve hot runner injection molding system according to claim 6, characterized in that: The recommended data transmission rate is calculated based on the average packet loss rate, the packet loss fluctuation coefficient, the average signal strength value and the signal strength fluctuation coefficient, including: calculating the packet loss influence factor based on the average packet loss rate and the packet loss fluctuation coefficient; calculating the signal strength influence factor based on the average signal strength value and the signal strength fluctuation coefficient; determining a comprehensive influence factor based on the packet loss influence factor and the signal strength influence factor; and calculating the recommended data transmission rate based on the comprehensive influence factor and the basic data transmission rate.

8. The timing controller for a needle valve hot runner injection molding system according to claim 7, characterized in that: The recommended data transmission rate is calculated based on the comprehensive impact factor and the basic data transmission rate, including: determining the delay impact factor based on the scheduled time and the single data packet reception time; and calculating the recommended data transmission rate based on the comprehensive impact factor, the basic data transmission rate and the delay impact factor.

9. The timing controller for a needle valve hot runner injection molding system according to claim 2, wherein: In the central control unit, the timing distribution of the pressure data inside the mold and the timing distribution of the temperature data inside the mold are processed to determine the optimal opening time point of the needle valve and the optimal closing time point of the needle valve, including: decompressing the anti-packet loss compressed data to obtain the timing distribution of the pressure data inside the mold and the timing distribution of the temperature data inside the mold; and arranging the timing distribution of the pressure data inside the mold and the timing distribution of the temperature data inside the mold according to the time dimension as a timing input vector of the pressure inside the mold and a timing input vector of the temperature inside the mold, respectively; and calculating the optimal opening time point of the needle valve and the optimal closing time point of the needle valve based on the timing distribution characteristics of the timing input vector of the pressure inside the mold and the timing input vector of the temperature inside the mold.

10. The timing controller for a needle valve hot runner injection molding system according to claim 9, wherein: Based on the timing distribution characteristics of the pressure timing input vector in the mold and the temperature timing input vector in the mold, the optimal opening time point of the needle valve and the optimal closing time point of the needle valve are calculated, including: extracting the temperature timing characteristics of the pressure timing input vector in the mold, the temperature timing characteristics include temperature mean, temperature variance, temperature peak, temperature timing change trend and temperature periodicity characteristics; extracting the pressure timing characteristics of the temperature timing input vector in the mold, the pressure timing characteristics include pressure mean, pressure variance, pressure peak, pressure timing change trend and pressure periodicity characteristics; splicing the temperature timing characteristics and the pressure timing characteristics to obtain a temperature-pressure comprehensive feature vector; inputting the temperature-pressure comprehensive feature vector into a trained needle valve optimal state predictor based on a random forest model to obtain the optimal opening time point of the needle valve and the optimal closing time point of the needle valve.

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