Dual-mode parallelism real-time monitoring method and system based on injection molding machine

Abstract

The invention provides a dual-mold parallelism real-time monitoring method and system based on an injection molding machine, and the system comprises a sensor assembly which measures the relative distance data and dynamic data between a movable mold and a fixed mold in real time, and transmits the relative distance data and dynamic data to a data processing module; the data processing module filters, amplifies and calibrates the relative distance data, extracts the features of the dynamic data, and calculates and optimizes the parallelism deviation by combining the relative distance data and the dynamic features, and the control module generates an adjustment instruction according to the optimized parallelism deviation and drives the adjustment module to perform high-precision mold adjustment. And when the parallelism deviation exceeds a preset threshold value, the alarm and display module automatically triggers alarm, displays the deviation and the change trend in real time, and provides adjustment suggestions. Real-time monitoring and high-precision adjustment are combined, and automation, precision and stability of the injection molding production process are remarkably improved.

Description

Technical Field

[0001] The invention relates to the field of real-time monitoring of dual-mode parallelism, and in particular to a real-time monitoring method and system for dual-mode parallelism based on an injection molding machine. Background Art

[0002] With the continuous development of injection molding technology, precise control of the mold by the injection molding machine during the production process has become crucial, especially the parallelism control between the movable mold and the fixed mold. The deviation of the mold parallelism will directly affect the quality of the injection molded parts, production efficiency and the service life of the equipment. Therefore, how to monitor and automatically adjust the parallelism deviation of the mold in real time has become an important research direction. Traditional methods mainly rely on manual inspection and adjustment, but this is not only inefficient, but also prone to errors or delays due to human factors. Traditional inspection methods such as visual inspection or simple displacement sensors cannot provide sufficient real-time and high precision, and it is difficult to meet the needs of modern precision injection molding processes.

[0003] In order to solve this problem, many systems have begun to improve the detection accuracy of mold parallelism by integrating advanced sensing technology. Commonly used sensors include laser ranging sensors, vibration sensors, and temperature and pressure sensors, combined with efficient data processing modules for dynamic monitoring. The data collected by the sensors can reflect the changes in mold parallelism in real time, provide early warnings and automatic adjustments, thereby ensuring that the parallelism of the mold is always in the best state.

[0004] However, although the existing technology uses sensor monitoring, most systems still rely on manual adjustment, resulting in poor real-time performance and difficulty in timely detecting and adjusting parallelism deviations. In addition, the capabilities of dynamic data feature extraction and comprehensive optimization calculations are still insufficient, and it is impossible to fully utilize the interrelationships of multidimensional data such as vibration, temperature, and pressure to accurately optimize the parallelism deviation, resulting in poor stability and accuracy of the adjustment effect. Summary of the invention

[0005] In order to solve the technical problems mentioned in the current background technology, the present invention proposes a real-time monitoring method and system for dual-mold parallelism based on an injection molding machine.

[0006] To this end, the technical solution adopted in the present invention is as follows: A dual-mode parallelism real-time monitoring system based on an injection molding machine, characterized in that the system comprises: M1, sensor component module, which measures the relative distance data and dynamic data between the movable mold and the fixed mold in real time through the sensor component, and transmits the relative distance data and dynamic data to the data processing module in real time; M2, a data processing module, performs data processing on the relative distance data, calculates the parallelism deviation between the movable mold and the fixed mold according to the processed relative distance data, extracts features from the dynamic data, generates dynamic features, optimizes and calculates the parallelism deviation in combination with the dynamic features, generates an optimized parallelism deviation, and transmits the dynamic features and the optimized parallelism deviation to the control module; M3, a control module, performs adjustment judgment according to the comparison between the optimized parallelism deviation and the preset parallelism deviation threshold, and generates and outputs an adjustment instruction in combination with the dynamic characteristics; M4, alarm and display module, when the optimized parallelism deviation is greater than the preset parallelism deviation threshold, triggers an alarm signal, displays the parallelism deviation and change trend in real time, and provides adjustment suggestions according to the adjustment instructions output by the control module; M5, an adjustment module, adjusts the positions of the movable mold and the fixed mold according to the adjustment instructions output by the control module to eliminate the parallelism deviation, and feeds back the adjustment results to the control module and the data processing module for optimization.

[0007] Furthermore, the sensor assembly is a laser distance sensor, a vibration sensor, and a temperature and pressure sensor, and the relative distance data is obtained by real-time measurement by the laser distance sensor assembly, and the formula is: in, is the relative distance data between the moving mold and the fixed mold, c is the propagation speed of the laser, is the measured time difference, The vibration sensor detects the vibration data generated by the movable mold and the fixed mold during the working process. , The temperature and pressure sensor detects the temperature of the movable mold and the fixed mold when they are working and pressure data, the dynamic data including vibration data, temperature data and pressure data, The real-time relative distance data and dynamic data collected by the sensor component module are transmitted to the data processing module through the communication interface of the sensor.

[0008] Furthermore, the goal of the data processing is to eliminate noise and errors, and specific methods include filtering, signal amplification and calibration. The filtering removes inaccurate data points by weighted averaging the measured relative distance data. The signal amplification is to enhance the signal strength of weak signals through an amplification circuit. The calibration is to eliminate and correct the measurement errors caused by changes in the sensor and the environment through software compensation. The parallelism deviation is calculated by the relative distance data after data processing, and the formula is: in, It is the parallelism deviation between the movable mold and the fixed mold; is the preset standard distance, The preset standard distance A fixed value set according to the mold design requirements and the process parameters of the injection molding machine. This value is the standard distance between the movable mold and the fixed mold.

[0009] Furthermore, the dynamic features generated by the feature extraction include vibration features, temperature features and pressure features. The specific steps of optimizing the parallelism deviation calculation by the vibration features are: The root mean square value (RMS) of the vibration characteristics is extracted through time domain analysis. The formula is: in, is the sampling point of vibration data; is the number of sampling points, The vibration characteristics are converted from the time domain to the frequency domain through fast Fourier transform (FFT), the formula is: in, is a frequency domain signal; is the complex exponential kernel function of Fourier transform; f is the frequency, and the amplitude of each frequency component is extracted from the frequency domain signal , By calculating the spectrum amplitude , extract the frequency component with the largest amplitude and determine the main frequency of the vibration feature : Using RMS and dominant frequency , optimize the influence of vibration characteristics on parallelism deviation , The temperature characteristics are optimized to affect the parallelism deviation by calculating the temperature change rate TR and the temperature fluctuation amplitude TF. , The pressure characteristics are optimized by calculating the pressure change rate PR and the pressure fluctuation amplitude PF to optimize the influence of the pressure characteristics on the parallelism deviation. , The parallelism deviation is optimized by combining the vibration characteristics, temperature characteristics and pressure characteristics to obtain the optimized parallelism deviation, and the formula is: in, For the optimized parallelism deviation, , , , They are the weighted coefficients of the measured parallelism deviation, vibration characteristics, temperature characteristics and pressure characteristics, respectively, indicating the contribution of each characteristic to the final optimization result, and the weighted coefficients of the parallelism deviation, vibration characteristics, temperature characteristics and pressure characteristics are optimized through feedback of subsequent adjustment results.

[0010] Furthermore, the comparison between the optimized parallelism deviation and the preset parallelism deviation threshold is to compare the optimized parallelism deviation with the preset parallelism deviation threshold. For comparison, When the optimized parallelism deviation exceeds the preset parallelism deviation threshold, , then there is a problem with the parallelism of the mold, the control module triggers the adjustment program, generates adjustment instructions and outputs them to the adjustment module, When the optimized parallelism deviation is less than the preset parallelism deviation threshold, , continue to maintain the current state and conduct monitoring for the next cycle.

[0011] Furthermore, the adjustment instruction is that after the adjustment program is triggered, the control module adjusts the parallelism deviation based on the optimization. Calculate the adjustment amount, The adjustment amount is performed according to a PID control algorithm, in which the calculation of the adjustment amount includes proportional, integral and differential parts, and the formula is: in, is the adjustment amount; Kp is the proportional control coefficient; Ki is the integral control coefficient; Kd is the differential control coefficient; is the integral term, which is used to eliminate small deviations that exist for a long time; is a differential term, which is used to predict the changing trend of parallelism deviation. Relative distance measured by the sensor And the preset standard distance The control module determines the adjustment direction, specifically, , indicating that the relative distance between the movable mold and the fixed mold is greater than the preset standard distance, the control module will instruct the adjustment module to move the fixed mold and the movable mold to reduce the distance, , indicating that the relative distance between the movable mold and the fixed mold is less than the preset standard distance, the control module will instruct the adjustment module to move the fixed mold and the movable mold farther to increase the distance. According to the vibration characteristics, temperature characteristics and pressure characteristics, the control module determines the adjustment speed, and the formula is: in, To adjust the speed; is the proportional coefficient for adjusting the speed; , , is the weight coefficient of vibration, temperature and pressure characteristics, indicating the influence of each characteristic on the adjustment speed, and optimizing the weight coefficient of the vibration, temperature and pressure characteristics through feedback of subsequent adjustment results, The output of the adjustment instruction includes the output of the adjustment amount, the adjustment direction and the adjustment speed. The adjustment instructions are output in the form of digital signals through a programmable logic controller (PLC).

[0012] Furthermore, the alarm signal is generated based on the comparison between the optimized parallelism deviation and the preset parallelism deviation threshold. When the optimized parallelism deviation exceeds the preset parallelism deviation threshold, the alarm module triggers the alarm signal. The real-time display is that the alarm and display module displays real-time status information, including parallelism deviation, deviation change trend and adjustment suggestions. The parallelism deviation is the parallelism deviation value between the current movable mold and the fixed mold, which comes from the calculation result of the data processing module. The deviation change trend is a trend graph showing the change of the average value of the parallelism deviation over time. The calculation formula of the average value of the parallelism deviation is: in, is the average parallelism deviation, reflecting the long-term trend, is the number of data points in the time window, It is The parallelism deviation at a certain moment, The adjustment suggestion is to display the suggestions for adjusting the movable mold and the fixed mold according to the adjustment instruction of the control module, including adjusting the displacement direction and amplitude of the movable mold and the fixed mold.

[0013] Furthermore, the parallelism deviation is eliminated according to an adjustment instruction output by a control module, wherein the adjustment module includes a drive device and a feedback sensor. The driving device performs position adjustment of the movable mold and the fixed mold. The feedback sensor monitors the position of the movable mold and the fixed mold during the adjustment process and transmits feedback signals back to the control module to ensure the accuracy of the adjustment. The adjustment module controls the movement of the movable mold and the fixed mold by adjusting the force F, and ensures the stability of the adjustment by controlling the adjustment speed and acceleration. The adjustment module completes fine adjustment and eliminates parallelism deviation through a driving device and a feedback sensor, as well as control of adjustment speed and acceleration.

[0014] A method for real-time monitoring of dual-mode parallelism of an injection molding machine, characterized in that the method comprises the following steps: S1, measuring the relative distance and dynamic data between the movable mold and the fixed mold in real time through the sensor component, and outputting the relative distance data and dynamic data; S2, performing data processing on the relative distance data, wherein the data processing includes filtering, amplification and calibration, calculating the parallelism deviation between the movable mold and the fixed mold through the relative distance data after the data processing, performing feature extraction on the dynamic data to generate dynamic features, optimizing and calculating the parallelism deviation in combination with the dynamic features, and outputting the dynamic features and the optimized parallelism deviation; S3, making an adjustment judgment based on the comparison between the optimized parallelism deviation and the set parallelism deviation threshold, and generating and outputting an adjustment instruction in combination with the dynamic feature; S4. According to the comparison between the optimized parallelism deviation and the set threshold value, an alarm signal is triggered, and the parallelism deviation and change trend are displayed in real time, and adjustment suggestions are generated according to the adjustment instructions; S5. According to the adjustment instructions and adjustment suggestions, adjust the positions of the movable mold and the fixed mold to eliminate the parallelism deviation, and feed back the adjustment results.

[0015] Compared with the prior art, the advantages of the present invention are: 1. Multi-dimensional data optimization calculation: The present invention combines multi-dimensional dynamic data features such as vibration, temperature, and pressure to perform comprehensive optimization calculation of parallelism deviation. By weighted processing of each data feature, the final parallelism deviation result is optimized to ensure that the adjustment accuracy and stability are greatly improved. This can not only improve the accuracy of mold adjustment, but also improve the adaptability of the system to complex working conditions; 2. System integration and self-optimization capability: The various modules of the present invention are highly integrated, capable of real-time monitoring, adjustment and optimization of mold parallelism, and possess powerful self-learning and optimization capabilities. Through the feedback mechanism, the system can continuously adjust and optimize the weight coefficients of data features, so that the parallelism monitoring and adjustment effects can maintain optimal performance in different production environments and conditions; 3. Intelligent alarm and automatic feedback: When the parallelism deviation exceeds the preset threshold, the system not only sends out an alarm signal, but also displays the parallelism deviation and its changing trend in real time, and gives adjustment suggestions. Through this intelligent alarm and feedback mechanism, operators can quickly take effective measures. At the same time, the system automatically optimizes and adjusts the strategy based on historical data, thereby improving the intelligence and response speed of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0017] Figure 1 It is a flow chart of the dual-mode parallelism real-time monitoring system of the present invention; Figure 2 It is a flow chart of the data processing module of the present invention; Figure 3 It is a flow chart of the control module of the present invention. DETAILED DESCRIPTION

[0018] To achieve the above objectives, the present invention is implemented through the following technical solutions. The present invention provides a method and system for real-time monitoring of dual-mode parallelism based on an injection molding machine. Figure 1-3 The system comprises: M1, sensor component module, uses the sensor component to measure the relative distance data and dynamic data between the movable mold and the fixed mold in real time, and transmits the relative distance data and dynamic data to the data processing module in real time, The sensor components include laser distance sensors, vibration sensors, and temperature and pressure sensors. The laser distance sensors can maintain high accuracy over a long distance range while avoiding the mechanical errors caused by traditional displacement sensors. The measurement range of the laser distance sensor should cover the relative distance between the movable mold and the fixed mold, ensuring that even when the mold position is greatly offset, the change in its relative position can be accurately monitored. The working principle of the laser distance sensor is that the laser distance sensor calculates the distance by emitting a laser beam and receiving the signal reflected by the laser. By measuring the time difference of the laser beam going back and forth, the precise distance between the moving mold and the fixed mold can be obtained. The propagation speed of the laser is , the measured time difference is , then the measured distance It can be calculated by the following formula: in, It is the time it takes for light to propagate in the air. When the sensor is working, it can provide high-frequency data feedback to ensure that the system understands the relative position between the movable mold and the fixed mold in real time. Laser distance sensors are installed on both sides of the movable mold and the fixed mold to measure the relative distance between them. In the arrangement of the sensors, ensure that the measuring direction of the sensors is perpendicular to the contact surface of the molds in order to accurately capture the parallelism changes between the molds. The installation of the sensors needs to be customized according to the specific structure of the injection molding machine to minimize the interference of external factors on the measurement. The vibration sensor can detect the vibration data generated by the movable mold and the fixed mold during the working process. The vibration data is used to analyze the vibration intensity and frequency of the mold. The temperature and pressure sensors detect the working temperature of the movable mold and the fixed mold. and pressure data, The real-time measurement data and dynamic data collected by the sensor component are transmitted to the data processing module through the sensor's communication interface. In order to ensure the real-time and stability of the data, the transmission process needs to have high bandwidth and low latency.

[0019] M2, data processing module, processes the relative distance data, calculates the parallelism deviation between the movable mold and the fixed mold according to the processed relative distance data, extracts features from the dynamic data, generates dynamic features, optimizes and calculates the parallelism deviation in combination with the dynamic features, generates optimized parallelism deviation, and transmits the dynamic features and optimized parallelism deviation to the control module. The primary task of the data processing module is to receive the relative distance data and dynamic data from the sensor component module. The relative distance data and dynamic data are transmitted in the form of digital signals. The data acquisition module first needs to ensure the real-time and accuracy of the relative distance data. To achieve this, the data acquisition module needs to have high-speed signal processing capabilities and be able to receive sensor signals at a high frequency. The collected raw data often contains noise, interference and other inaccurate factors, so data processing is required. The core objectives of the data processing include eliminating noise and errors and improving the accuracy and stability of the data. The data processing method is specifically as follows: Filtering: The filter is used to remove high-frequency noise or low-frequency interference. In this embodiment, Kalman filtering technology is used. This technology is suitable for dynamic systems and can filter out inaccurate data points by weighted averaging the measured values. Signal amplification: For weak signals, the data acquisition module enhances the signal strength through the amplification circuit, making subsequent processing more accurate. Calibration: Changes in sensors and environment can cause measurement errors. The data processing module needs to be calibrated and corrected through software compensation.

[0020] The parallelism deviation calculation is to calculate the parallelism deviation between the movable mold and the fixed mold after the data processing is completed. The parallelism deviation refers to the parallelism error between the two surfaces of the mold during the mold closing process, which reflects whether the relative position of the movable mold and the fixed mold meets the production requirements. The calculation formula is: in, It is the parallelism deviation between the movable mold and the fixed mold; is the relative distance measured by the sensor; is the preset standard distance, Preset standard distance A fixed value set according to the mold design requirements and the process parameters of the injection molding machine. This value is the standard distance between the movable mold and the fixed mold. By analyzing and calculating the vibration features, temperature features and pressure features obtained by feature extraction, the influence of each dynamic feature on the parallelism deviation is obtained. The specific steps are as follows: The root mean square value (RMS) of the vibration characteristics is extracted through time domain analysis. The formula is: in, is the sampling point of vibration data; is the number of sampling points, The vibration characteristics are converted from the time domain to the frequency domain through fast Fourier transform (FFT), the formula is: in, is a frequency domain signal; is the complex exponential kernel function of Fourier transform; f is the frequency, and the amplitude of each frequency component is extracted from the frequency domain signal , By calculating the spectrum amplitude , extract the frequency component with the largest amplitude and determine the main frequency of the vibration feature : Using RMS and dominant frequency , optimize the influence of vibration characteristics on parallelism deviation , The temperature characteristics are optimized to affect the parallelism deviation by calculating the temperature change rate TR and the temperature fluctuation amplitude TF. , The pressure characteristics are optimized by calculating the pressure change rate PR and the pressure fluctuation amplitude PF to optimize the influence of the pressure characteristics on the parallelism deviation. , The parallelism deviation is optimized by combining vibration characteristics, temperature characteristics and pressure characteristics to obtain the optimized parallelism deviation. The formula is: in, For the optimized parallelism deviation, , , , They are the weighted coefficients of the measured parallelism deviation, vibration characteristics, temperature characteristics and pressure characteristics, respectively, indicating the contribution of each characteristic to the final optimization result, and the weighted coefficients of the parallelism deviation, vibration characteristics, temperature characteristics and pressure characteristics are optimized through feedback of subsequent adjustment results.

[0021] M3, the control module, makes adjustment judgments based on the comparison between the optimized parallelism deviation and the preset parallelism deviation threshold, and generates and outputs adjustment instructions in combination with dynamic characteristics. The control module is the brain of the entire system. It is responsible for judging whether adjustment is needed based on the parallelism deviation information provided by the data processing module, and issuing instructions to the adjustment module to ensure that the parallelism of the movable mold and the fixed mold is always maintained within the preset standard range. After receiving the parallelism deviation information from the data processing module, the control module will use the preset control strategy and control algorithm to determine the size and amplitude of the adjustment. The control module adopts a programmable logic controller (PLC). PLC has efficient and stable real-time control capabilities and is widely used in industrial automation systems. PLC obtains parallelism deviation data in real time and accurately controls the adjustment of the mold position to ensure that the parallelism of the mold meets the set requirements. The control module compares the parallelism deviation calculated in real time with the preset maximum allowable parallelism deviation threshold. The threshold is used to set a maximum allowable error range. When the parallelism deviation exceeds the threshold, If the mold is parallel to the same plane as the one in Figure 1, it is considered that there is a problem with the parallelism of the mold and adjustments must be made. when When the control module decides to trigger the adjustment program, generates adjustment instructions and outputs them to the adjustment module. Otherwise, the system continues to maintain the current state and conducts the next cycle of monitoring. The adjustment is determined as follows: after the adjustment procedure is triggered, the control module determines the parallelism deviation. The size of the adjustment determines the type and degree of adjustment. The control module will generate adjustment decisions based on the following conditions: Deviation size: The larger the parallelism deviation, the greater the adjustment range. The control module determines the adjustment amount through PID control. Adjustment speed: The control module will also take the adjustment speed into consideration to ensure the smoothness of the adjustment process and avoid over-adjustment and oscillation.

[0022] In the PID control algorithm, the calculation of the adjustment amount includes proportional, integral and differential parts, and the formula is: in, is the adjustment instruction, indicating the output control amount, , which is the parallelism deviation at the current moment, Kp is the proportional control coefficient, Ki is the integral control coefficient, Kd is the differential control coefficient, is an integral term used to eliminate small long-term deviations. is a differential term, which is used to predict the changing trend of parallelism deviation. Relative distance measured by the sensor And the preset standard distance The control module determines the adjustment direction, specifically, , indicating that the relative distance between the movable mold and the fixed mold is greater than the preset standard distance, the control module will instruct the adjustment module to move the fixed mold and the movable mold to reduce the distance, , indicating that the relative distance between the movable mold and the fixed mold is less than the preset standard distance, the control module will instruct the adjustment module to move the fixed mold and the movable mold farther to increase the distance. According to the vibration characteristics, temperature characteristics and pressure characteristics, the control module determines the adjustment speed. The formula is: in, To adjust the speed; is the proportional coefficient for adjusting the speed; , , is the weight coefficient of vibration, temperature and pressure characteristics, indicating the influence of each characteristic on the adjustment speed, and optimizing the weight coefficient of the vibration, temperature and pressure characteristics through feedback of subsequent adjustment results, The output of the adjustment instruction includes the adjustment amount, adjustment direction and adjustment speed. The output of the adjustment instruction is output to the adjustment module through the interface between the control module and the adjustment module. At this time, the control module will output the adjustment amount, adjustment direction and adjustment speed.

[0023] The output format of the control instruction is carried out through digital signals, and the instruction is output through the PLC.

[0024] M4, alarm and display module, when the optimized parallelism deviation is greater than the preset parallelism deviation threshold, an alarm signal is triggered, and the parallelism deviation and change trend are displayed in real time. Adjustment suggestions are provided according to the adjustment instructions output by the control module. The alarm and display module is an important part of the system. It is responsible for issuing an alarm signal when the mold parallelism deviation exceeds the set threshold, and displays the parallelism deviation and its changing trend in real time. By providing intuitive feedback and adjustment suggestions, it helps operators to find problems in time and take measures to ensure the accuracy and stability of the injection molding process. The core functions of the alarm and display module are alarm signal triggering, real-time data display and adjustment suggestion prompts. The alarm signal is triggered when the optimized parallelism deviation exceeds the set threshold. The alarm module triggers the alarm signal to notify the operator to take action and trigger different alarm levels according to the size of the parallelism deviation. The alarm levels are divided into low-level alarm, medium-level alarm and high-level alarm. According to the alarm level and the set response method, the module uses different types of alarm signals. The real-time display is that the module displays real-time status information through a touch screen display, including the current parallelism deviation, deviation change trend and adjustment suggestions, specifically, Current parallelism deviation, showing the parallelism deviation value of the current mold , which is the result of calculation from the data processing module; Deviation change trend, showing the trend of parallelism deviation over time, helping operators determine whether the mold is stable; The adjustment suggestion prompt is to display the mold adjustment suggestion according to the adjustment instruction of the control module, including the adjustment amount, adjustment direction and adjustment speed of the movable mold and the fixed mold.

[0025] Through the real-time data, the alarm and display module can draw a trend chart of the parallelism deviation, reflecting the change of the deviation in different time periods, helping operators to judge whether the system is stable. The calculation formula of the trend chart is: in, is the average value of parallelism deviation within a certain time window, reflecting the long-term trend. is the number of data points in the time window, It is The parallelism deviation at a certain moment, This trend analysis helps operators identify potential issues with mold parallelism and make adjustments in advance to prevent problems during production.

[0026] The alarm and display module is responsible for real-time monitoring of parallelism deviation, triggering alarm signals and displaying relevant data. Through close cooperation with the data processing module, control module and adjustment module, the alarm and display module can ensure that the status of the mold is always within the controllable range and promptly remind operators to make adjustments, thereby ensuring the accuracy and stability of the production process. Through the design of trend charts, adjustment suggestions and alarm levels, the module can provide more intelligent and accurate feedback.

[0027] M5, adjustment module, adjusts the position of the movable mold and the fixed mold according to the adjustment instructions output by the control module, eliminates the parallelism deviation, and feeds back the adjustment results to the control module and the data processing module for optimization. The main function of the adjustment module is to perform precise adjustments to the movable mold and the fixed mold according to the adjustment instructions output by the control module, so as to ensure that the parallelism of the mold is restored to the preset range. The adjustment module includes electric and pneumatic drive devices. This module works closely with the control module, data processing module, alarm and display module to ensure efficient and precise operation of the system.

[0028] First, the adjustment module receives the adjustment instruction output by the control module, including the adjustment amount, adjustment direction and adjustment speed. After receiving the adjustment instruction, the adjustment module immediately performs the adjustment operation and finely adjusts the movable mold and the fixed mold through the electric and pneumatic drive devices. The adjustment module includes a drive device and a feedback sensor, specifically, The drive device performs position adjustment through electric motors and pneumatic devices; Feedback sensor monitors the mold position during the adjustment process and transmits feedback signals back to the control module to ensure the accuracy of the adjustment.

[0029] The adjustment module controls the movement of the mold by adjusting the force F, and achieves precise mold position adjustment through appropriate adjustment force. In order to ensure the stability and accuracy of the mold during the adjustment process, the adjustment module also needs to control the adjustment speed and acceleration , through the system control algorithm to optimize, to ensure that there will be no over-adjustment or oscillation during the adjustment process, the control formula is: in, is the adjustment speed of the mold, is the initial velocity, is the acceleration, given by the applied adjustment force And mold quality Decide, is the time required for adjustment.

[0030] The adjustment module ensures that the parallelism of the movable mold and the fixed mold is accurately adjusted through precise drive control and adjustment amount calculation. This module works closely with other system modules such as the control module, data processing module, alarm and display module to ensure the coordination and real-time performance of the entire system. By adopting electric and pneumatic drive devices and precise speed and acceleration control, the adjustment module can complete fine adjustment in the shortest time, eliminate parallelism deviation, and ensure the accuracy and stability of the production process.

[0031] A method for real-time monitoring of dual-mode parallelism of an injection molding machine, characterized in that the method comprises the following steps: S1, measuring the relative distance and dynamic data between the movable mold and the fixed mold in real time through the sensor component, and outputting the relative distance data and dynamic data; S2, performing data processing on the relative distance data, wherein the data processing includes filtering, amplification and calibration, calculating the parallelism deviation between the movable mold and the fixed mold through the relative distance data after the data processing, performing feature extraction on the dynamic data to generate dynamic features, optimizing and calculating the parallelism deviation in combination with the dynamic features, and outputting the dynamic features and the optimized parallelism deviation; S3, making an adjustment judgment based on the comparison between the optimized parallelism deviation and the set parallelism deviation threshold, and generating and outputting an adjustment instruction in combination with the dynamic feature; S4. According to the comparison between the optimized parallelism deviation and the set threshold value, an alarm signal is triggered, and the parallelism deviation and change trend are displayed in real time, and adjustment suggestions are generated according to the adjustment instructions; S5. According to the adjustment instructions and adjustment suggestions, adjust the positions of the movable mold and the fixed mold to eliminate the parallelism deviation, and feed back the adjustment results.

[0032] In summary, the present invention provides a real-time monitoring method and system for dual-mode parallelism based on an injection molding machine, which aims to improve the ability to accurately monitor and adjust the parallelism of molds during the injection molding production process through advanced sensing technology and automated control. The system integrates laser ranging, vibration, temperature and pressure sensors to achieve real-time monitoring of the parallelism deviation between the movable mold and the fixed mold. Through the data processing module, the system optimizes and calculates the collected dynamic data to generate accurate parallelism deviations, and issues adjustment instructions based on the data to drive the adjustment module to perform high-precision adjustments.

[0033] In addition, when the parallelism deviation exceeds the preset threshold, the system will automatically trigger an alarm and display the parallelism deviation and its changing trend in real time, providing adjustment suggestions for the operator to ensure that timely measures are taken. The invention combines the characteristics of real-time monitoring, high-precision adjustment, intelligent alarm and multi-dimensional data optimization, which not only improves production efficiency, but also reduces human errors, ensuring that the parallelism of the mold during the injection molding process is always in the best state. The application of the present invention can significantly improve the automation level and production stability of the injection molding machine, and meet the needs of modern high-precision manufacturing.

[0034] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A real-time monitoring system for dual-mode parallelism based on an injection molding machine, characterized in that: The system includes: M1, sensor component module, which measures the relative distance data and dynamic data between the movable mold and the fixed mold in real time through the sensor component, and transmits the relative distance data and dynamic data to the data processing module in real time; M2, a data processing module, performs data processing on the relative distance data, calculates the parallelism deviation between the movable mold and the fixed mold according to the processed relative distance data, extracts features from the dynamic data, generates dynamic features, optimizes and calculates the parallelism deviation in combination with the dynamic features, generates an optimized parallelism deviation, and transmits the dynamic features and the optimized parallelism deviation to the control module; M3, a control module, performs adjustment judgment according to the comparison between the optimized parallelism deviation and the preset parallelism deviation threshold, and generates and outputs an adjustment instruction in combination with the dynamic characteristics; M4, alarm and display module, when the optimized parallelism deviation is greater than the preset parallelism deviation threshold, triggers an alarm signal, displays the parallelism deviation and change trend in real time, and provides adjustment suggestions according to the adjustment instructions output by the control module; M5, an adjustment module, adjusts the positions of the movable mold and the fixed mold according to the adjustment instructions output by the control module to eliminate the parallelism deviation, and feeds back the adjustment results to the control module and the data processing module for optimization.

2. A dual-mode parallelism real-time monitoring system based on an injection molding machine according to claim 1, characterized in that: The sensor components are laser distance measuring sensors, vibration sensors, and temperature and pressure sensors. The relative distance data is obtained by real-time measurement by the laser distance measuring sensor components. The formula is: in, is the relative distance data between the moving mold and the fixed mold, c is the propagation speed of the laser, is the measured time difference, The vibration sensor detects the vibration data generated by the movable mold and the fixed mold during the working process. , The temperature and pressure sensor detects the temperature of the movable mold and the fixed mold when they are working and pressure data, the dynamic data including vibration data, temperature data and pressure data, The real-time relative distance data and dynamic data collected by the sensor component module are transmitted to the data processing module through the communication interface of the sensor.

3. A dual-mode parallelism real-time monitoring system based on an injection molding machine according to claim 2, characterized in that: The goal of data processing is to eliminate noise and errors. Specific methods include filtering, signal amplification and calibration. The filtering removes inaccurate data points by weighted averaging the measured relative distance data. The signal amplification is to enhance the signal strength of weak signals through an amplification circuit. The calibration is to eliminate and correct the measurement errors caused by changes in the sensor and the environment through software compensation. The parallelism deviation is calculated by the relative distance data after data processing, and the formula is: in, It is the parallelism deviation between the movable mold and the fixed mold; is the preset standard distance, The preset standard distance A fixed value set according to the mold design requirements and the process parameters of the injection molding machine. This value is the standard distance between the movable mold and the fixed mold.

4. A dual-mode parallelism real-time monitoring system based on an injection molding machine according to claim 3, characterized in that: The dynamic features generated by the feature extraction include vibration features, temperature features and pressure features. The specific steps of optimizing the parallelism deviation calculation using the vibration features are as follows: The root mean square value (RMS) of the vibration characteristics is extracted through time domain analysis. The formula is: in, is the sampling point of the vibration data; is the number of sampling points, The vibration characteristics are converted from the time domain to the frequency domain through fast Fourier transform (FFT), the formula is: in, is a frequency domain signal; is the complex exponential kernel function of Fourier transform; f is the frequency, and the amplitude of each frequency component is extracted from the frequency domain signal , By calculating the spectrum amplitude , extract the frequency component with the largest amplitude and determine the main frequency of the vibration feature : Using RMS and dominant frequency , optimize the influence of vibration characteristics on parallelism deviation , The temperature characteristics are optimized to affect the parallelism deviation by calculating the temperature change rate TR and the temperature fluctuation amplitude TF. , The pressure characteristics are optimized by calculating the pressure change rate PR and the pressure fluctuation amplitude PF to optimize the influence of the pressure characteristics on the parallelism deviation. , The parallelism deviation is optimized by combining the vibration characteristics, temperature characteristics and pressure characteristics to obtain the optimized parallelism deviation, and the formula is: in, For the optimized parallelism deviation, , , , They are the weighted coefficients of the measured parallelism deviation, vibration characteristics, temperature characteristics and pressure characteristics, respectively, indicating the contribution of each characteristic to the final optimization result, and the weighted coefficients of the parallelism deviation, vibration characteristics, temperature characteristics and pressure characteristics are optimized through feedback of subsequent adjustment results.

5. A dual-mode parallelism real-time monitoring system based on an injection molding machine according to claim 4, characterized in that: The comparison between the optimized parallelism deviation and the preset parallelism deviation threshold is: comparing the optimized parallelism deviation with the preset parallelism deviation threshold For comparison, When the optimized parallelism deviation exceeds the preset parallelism deviation threshold, , then there is a problem with the parallelism of the mold, the control module triggers the adjustment program, generates adjustment instructions and outputs them to the adjustment module, When the optimized parallelism deviation is less than the preset parallelism deviation threshold, , continue to maintain the current state and conduct monitoring for the next cycle.

6. A dual-mold parallelism real-time monitoring system based on an injection molding machine according to claim 5, characterized in that: The adjustment instruction is that after the adjustment program is triggered, the control module is based on the optimized parallelism deviation Calculate the adjustment amount, The adjustment amount is performed according to a PID control algorithm, in which the calculation of the adjustment amount includes proportional, integral and differential parts, and the formula is: in, is the adjustment amount; Kp is the proportional control coefficient; Ki is the integral control coefficient; Kd is the differential control coefficient; is the integral term, which is used to eliminate small deviations that exist for a long time; is a differential term, which is used to predict the changing trend of parallelism deviation. Relative distance measured by the sensor And the preset standard distance The control module determines the adjustment direction, specifically, , indicating that the relative distance between the movable mold and the fixed mold is greater than the preset standard distance, the control module will instruct the adjustment module to move the fixed mold and the movable mold to reduce the distance, , indicating that the relative distance between the movable mold and the fixed mold is less than the preset standard distance, the control module will instruct the adjustment module to move the fixed mold and the movable mold farther to increase the distance. According to the vibration characteristics, temperature characteristics and pressure characteristics, the control module determines the adjustment speed, and the formula is: in, To adjust the speed; is the proportional coefficient for adjusting the speed; , , is the weight coefficient of vibration, temperature and pressure characteristics, indicating the influence of each characteristic on the adjustment speed, and optimizing the weight coefficient of the vibration, temperature and pressure characteristics through feedback of subsequent adjustment results, The output of the adjustment instruction includes the output of the adjustment amount, the adjustment direction and the adjustment speed. The adjustment instructions are output in the form of digital signals through a programmable logic controller (PLC).

7. A dual-mode parallelism real-time monitoring system based on an injection molding machine according to claim 1 or 6, characterized in that: The alarm signal is generated based on the comparison between the optimized parallelism deviation and the preset parallelism deviation threshold. When the optimized parallelism deviation exceeds the preset parallelism deviation threshold, the alarm module triggers the alarm signal. The real-time display is that the alarm and display module displays real-time status information, including parallelism deviation, deviation change trend and adjustment suggestions. The parallelism deviation is the parallelism deviation value between the current movable mold and the fixed mold, which comes from the calculation result of the data processing module. The deviation change trend is a trend graph showing the change of the average value of the parallelism deviation over time. The calculation formula of the average value of the parallelism deviation is: in, is the average parallelism deviation, reflecting the long-term trend, is the number of data points in the time window, It is The parallelism deviation at a certain moment, The adjustment suggestion is to display the suggestions for adjusting the movable mold and the fixed mold according to the adjustment instruction of the control module, including adjusting the displacement direction and amplitude of the movable mold and the fixed mold.

8. The real-time monitoring system for dual-mode parallelism of an injection molding machine according to claim 6, characterized in that: The parallelism deviation is eliminated according to the adjustment instruction output by the control module, and the adjustment module includes a driving device and a feedback sensor. The driving device performs position adjustment of the movable mold and the fixed mold. The feedback sensor monitors the position of the movable mold and the fixed mold during the adjustment process and transmits feedback signals back to the control module to ensure the accuracy of the adjustment. The adjustment module controls the movement of the movable mold and the fixed mold by adjusting the force F, and ensures the stability of the adjustment by controlling the adjustment speed and acceleration. The adjustment module completes fine adjustment and eliminates parallelism deviation through a driving device and a feedback sensor, as well as control of adjustment speed and acceleration.

9. A real-time monitoring method for dual-mode parallelism of an injection molding machine, characterized in that: The method comprises the following steps: S1, measuring the relative distance and dynamic data between the movable mold and the fixed mold in real time through the sensor component, and outputting the relative distance data and dynamic data; S2, performing data processing on the relative distance data, wherein the data processing includes filtering, amplification and calibration, calculating the parallelism deviation between the movable mold and the fixed mold through the relative distance data after the data processing, performing feature extraction on the dynamic data to generate dynamic features, optimizing and calculating the parallelism deviation in combination with the dynamic features, and outputting the dynamic features and the optimized parallelism deviation; S3, making an adjustment judgment based on the comparison between the optimized parallelism deviation and the set parallelism deviation threshold, and generating and outputting an adjustment instruction in combination with the dynamic feature; S4. According to the comparison between the optimized parallelism deviation and the set threshold value, an alarm signal is triggered, and the parallelism deviation and change trend are displayed in real time, and adjustment suggestions are generated according to the adjustment instructions; S5. According to the adjustment instructions and adjustment suggestions, adjust the positions of the movable mold and the fixed mold to eliminate the parallelism deviation, and feed back the adjustment results.

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