An automated control system for a composite material forming apparatus

Abstract

The application discloses an automatic control system for a composite material forming device, comprising: a central controller; a sensing network in communication connection with the central controller, used for acquiring multi-source real-time data from multiple process stations of the device; a control network in communication connection with the central controller, used for issuing collaborative control instructions to multiple actuators of the device; and the central controller is configured to generate and coordinate the issuance of the control instructions based on the multi-source real-time data. The automatic control system for the composite material forming device provided by the embodiment of the application significantly improves the one-time pass rate and quality consistency of products through multi-station real-time data fusion and closed-loop collaborative control, and realizes accurate tracing of the whole production process. Meanwhile, the system gradually replaces manual experience with data driving, reduces operation dependence and change production difficulty, and provides a reliable data basis for continuous self-optimization of the process.

Description

Technical Field

[0001] This application relates to the technical field of cable connectors, and more particularly to an automated control system for composite material molding equipment. Background Technology

[0002] As a key structural component supporting electrical connections, the plastic housing of cable connectors directly affects the assembly accuracy of subsequent metal pins and the electrical performance of the final product. Currently, composite materials are mostly used for injection molding. However, in actual production, the molding process is often independent of subsequent assembly and pressing processes. There is a lack of closed-loop feedback and collaborative control based on real-time molding quality data (such as dimensional shrinkage and warpage), making it difficult to identify and compensate for molding defects in a timely manner in subsequent stages, thus affecting the overall production yield and product consistency. Summary of the Invention

[0003] This application aims to at least partially address one of the technical problems in the related art.

[0004] Therefore, the first objective of this application is to provide an automated control system for composite material molding equipment. Through multi-station real-time data fusion and closed-loop collaborative control, the system significantly improves the first-pass yield and quality consistency of products, and realizes accurate traceability of the entire production process. At the same time, the system gradually replaces manual experience with data-driven approaches, reduces operational dependence and the difficulty of changing production, and provides a reliable data foundation for continuous self-optimization of the process.

[0005] To achieve the above objectives, a first aspect of this application provides an automated control system for a composite material molding equipment, comprising: Central controller; The sensor network, which is communicatively connected to the central controller, is used to acquire multi-source real-time data from multiple process stations of the equipment. A control network, which is communicatively connected to the central controller, is used to issue coordinated control commands to multiple actuators of the equipment. The central controller is configured as follows: Based on the multi-source real-time data, the control commands are generated and coordinated and issued to form a collaborative control closed loop with data feedback between at least two process stations. The multi-source real-time data, control commands, and execution results are bound to the individual workpiece identification to form a full-process traceability data chain.

[0006] An automated control system for composite material molding equipment according to an embodiment of this application significantly improves the first-pass yield and quality consistency of products through multi-station real-time data fusion and closed-loop collaborative control, and realizes accurate traceability of the entire production process. At the same time, the system gradually replaces manual experience with data-driven approaches, reduces operational dependence and the difficulty of changing production, and provides a reliable data foundation for continuous self-optimization of the process.

[0007] In addition, the automated control system for composite material molding equipment proposed above in this application may also have the following additional technical features: In one embodiment of this application, the cooperative control closed loop includes at least one of the following: Assembly guidance closed loop: Based on 3D visual data from the assembly station, a collision-free path is planned through virtual assembly simulation, and the actions of the actuators at the assembly station are guided. Pressing control closed loop: Based on the force, displacement and workpiece deformation data from the pressing station, the pressing parameters of the pressing station are dynamically adjusted through multivariate fusion analysis; Sorting decision closed loop: Based on workpiece performance data from the testing unit and visual final inspection data from the sorting station, the sorting action of the sorting station is triggered.

[0008] In one embodiment of this application, the multivariate fusion analysis in the pressing control closed loop includes constructing and analyzing force data, displacement data and workpiece deformation data, and using the constraint that the workpiece deformation does not exceed the limit, and the optimization objective that the pressing force reaches a preset target value, to adjust the pressing speed and pressure in real time.

[0009] In one embodiment of this application, the pressing station integrates an in-situ electrical performance testing unit, including a test probe embedded in the pressing mold, for acquiring contact resistance and insulation resistance data immediately after the pressing action is completed. The workpiece performance data includes at least the contact resistance and insulation resistance data.

[0010] In one embodiment of this application, the system is configured to control a composite material molding apparatus, the apparatus comprising: frame; The loading tray, mold forming station, auxiliary parts assembly station, pin pressing station and sorting station are set on the frame. The conveyor lines are installed on the frame and connected to each workstation; The conveyor line includes a conveyor guide rail and a drive unit; A glue injection mechanism is installed at the mold forming station; A three-dimensional vision sensor is installed at the auxiliary parts assembly station; A pressing force sensor, a displacement sensor, and a workpiece deformation monitoring sensor are installed at the pin pressing station. Visual inspection sensors are installed at the sorting station; Clamping mechanisms installed at one or more workstations; The drive unit, the glue injection mechanism, each clamping mechanism, each sensor, and the actuator at each workstation constitute the controlled terminal and sensing node of the control network, and are connected to the central controller through the sensing network.

[0011] In one embodiment of this application, the mold forming station is further provided with a process monitoring unit for collecting data on injection pressure, resin flow rate and mold temperature. The central controller issues feedforward compensation commands to the glue injection mechanism or mold temperature control mechanism based on the mass deviation trend analyzed from the data.

[0012] In one embodiment of this application, the equipment further includes an auxiliary station disposed on the frame and connected to the conveyor line; The central controller also communicates with the auxiliary workstation.

[0013] In one embodiment of this application, the system further includes a material identification unit; the central controller automatically calls up the virtual assembly simulation model, pressing control parameter set and sorting decision threshold associated with the material identification based on the identified material identification information.

[0014] In one embodiment of this application, the central controller is further connected to a data management and optimization module, which is used to store and analyze the full-process traceability data chain, and to establish the correlation between process parameters, process data and final quality through machine learning models, and iteratively optimize the generation strategy of the collaborative control closed loop.

[0015] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0016] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a system block diagram of an automated control system for a composite material molding equipment according to this application; Figure 2 This is a schematic diagram of the structure of an automated control system for a composite material molding equipment according to this application; Figure 3This is a partial structural diagram of an automated control system for a composite material molding equipment according to this application.

[0017] As shown in the figure: 1. Frame; 2. Transfer mechanism; 3. Feeding tray; 4. Conveyor line; 5. Mold forming station; 401. Conveyor guide rail; 402. Drive unit; 403. Clamping mechanism; 404. Glue injection mechanism; 405. Sorting station; 406. Auxiliary station; 407. Auxiliary parts assembly station; 408. Pin pressing station. Detailed Implementation

[0018] Embodiments of this application are described in detail below. Examples of these embodiments are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application. Rather, embodiments of this application include all variations, modifications, and equivalents falling within the spirit and scope of the appended claims.

[0019] The following is in conjunction with the appendix Figures 1-3 This application describes an automated control system for a composite material molding equipment, comprising: Central controller; The sensor network, which communicates with the central controller, is used to acquire multi-source real-time data from multiple process stations of the equipment. A control network that communicates with the central controller is used to issue coordinated control commands to multiple actuators of the equipment. The central controller is configured as follows: Based on multi-source real-time data, control commands are generated and coordinated to form a collaborative control closed loop with data feedback between at least two process stations. By binding multi-source real-time data, control commands, and execution results with individual workpiece identification, a full-process traceability data chain is formed.

[0020] Specifically, each workpiece is assigned an identification code before being loaded. Driven by the drive unit 402, the conveyor line 4 delivers the workpieces to each station in sequence, and the clamping mechanism 403 completes positioning and fixing at the corresponding station.

[0021] Molding and process monitoring: At mold forming station 5, the glue injection mechanism 404 injects resin according to the set parameters. The system simultaneously monitors the glue injection pressure, flow rate and mold temperature, and binds this data to the workpiece identification code.

[0022] Visual guidance and assembly: After the workpiece arrives at the auxiliary parts assembly station 407, the 3D vision sensor acquires precise three-dimensional information of the workpiece. Based on this information, the control system performs virtual path simulation, and then sends action commands to the assembly execution mechanism at that station to complete the gripping and pressing of the part.

[0023] Multi-parameter sensing during pressing: The press performs the pressing action. The system synchronously collects pressing force, pressing displacement, and workpiece deformation data measured by strain gauges integrated within the mold in real time. The central controller fuses and analyzes these three sets of data to determine the pressing status in real time. For example, when the system analysis detects that the workpiece deformation is close to the material safety limit while the pressing force has not yet reached the standard, it will automatically fine-tune the pressing speed or pressure setting to ensure reliable electrical connection while protecting the integrity of the workpiece structure. After the pressing action is completed, the test probes built into the mold immediately perform contact resistance and other electrical performance tests on the formed electrical connector.

[0024] Comprehensive Judgment and Sorting: The workpiece arrives at sorting station 405. The system integrates all the data of the workpiece throughout the process, including the pressing curve, electrical performance results, and the visual final inspection results of this station, determines the grade, and controls the sorting actuator to send the workpiece to the corresponding exit.

[0025] Data traceability and optimization: The process parameters, sensor data and operation results of all the above links are automatically linked to the workpiece identification code to form a production record.

[0026] In one embodiment of this application, the cooperative control closed loop includes at least one of the following: Assembly guidance closed loop: Based on the 3D vision data from assembly station 407, a collision-free path is planned through virtual assembly simulation, and the actions of the actuators at assembly station 407 are guided. Pressing control closed loop: Based on the force, displacement and workpiece deformation data from the pressing station 408, the pressing parameters of the pressing station 408 are dynamically adjusted through multivariate fusion analysis; Sorting decision closed loop: Based on the workpiece performance data from the test unit and the visual final inspection data from the sorting station 405, the sorting action of the sorting station 405 is triggered.

[0027] Once the workpiece enters assembly station 407, the system acquires its 3D data using 3D vision and performs virtual assembly simulation. After confirming there are no collisions, the system guides the robotic arm to perform the actual assembly. Next, at pressing station 408, the system analyzes the collected pressing force, displacement, and workpiece deformation data, and adjusts the pressing parameters based on the analysis results, for example, ensuring the pressing force reaches the required value while preventing workpiece overload. Finally, in the sorting stage, the system integrates the electrical performance data from the preceding testing unit with the visual final inspection data from sorting station 405 to make a sorting decision and execute the corresponding sorting action.

[0028] In one embodiment of this application, the multivariate fusion analysis in the pressing control closed loop includes constructing and analyzing force data, displacement data, and workpiece deformation data. With the constraint that the workpiece deformation does not exceed the limit, and the optimization objective being to achieve a preset target value for the pressing force, the pressing speed and pressure are adjusted in real time. Specifically, when the servo press performs the pressing action, the pressure sensor and displacement encoder integrated on it collect the pressing force and pressing depth data in real time. At the same time, the micro strain gauges attached to specific positions in the mold cavity synchronously monitor the local deformation of the connector shell.

[0029] The central controller receives these three sets of synchronized data and continuously analyzes their dynamic relationship. Its control logic is as follows: the primary constraint is that the strain gauge data does not exceed the material's safe deformation threshold, and the core objective is to ensure that the pressing force reaches the process target value for forming reliable electrical contact.

[0030] For example, during the pressing process, if the system detects that the deformation data is rising too fast and approaching the safety limit, while the pressing force has not yet reached the standard, it will immediately issue an instruction to reduce the pressing speed of the servo press or perform a short-term pressure holding at the micron level. In this way, while protecting the integrity of the plastic shell structure, the pressing force will eventually be stabilized to reach the preset target value through dynamic adjustment.

[0031] Miniature strain gauges are attached to the inner wall of the pressing mold cavity at pressing station 408. This position corresponds to the plastic shell support wall directly below the pin to be pressed, or the weakest area in the plastic shell structure most prone to stress concentration under pressure.

[0032] The strain gauge is fixed with a high-temperature resistant adhesive and insulated from the mold. Its signal wires are led out through special channels on the mold and connected to an external signal conditioner and data acquisition module, and finally connected to the central controller.

[0033] In one embodiment of this application, the pressing station 408 integrates an in-situ electrical performance testing unit, including a test probe embedded in the pressing mold, for acquiring contact resistance and insulation resistance data immediately after the pressing action is completed. The workpiece performance data should include at least contact resistance and insulation resistance data.

[0034] In a specific embodiment of the in-situ electrical performance testing unit, both the upper and lower molds of the pressing mold are precision-machined with mounting holes, and gold-plated test probes with spring buffers are installed in the holes. After the mold is closed, the tips of the probes can reliably contact the tail end of the designated pin and the adjacent pin of the connector under test.

[0035] When the servo press completes the pressing and enters the pressure holding stage, the central controller immediately sends a trigger command to an integrated high-precision multi-functional electrical tester.

[0036] The tester connects to the test probes inside the mold via a shielded cable and automatically executes the following sequence: Contact resistance test: Apply a constant current to the two test probes of the same pin and measure the voltage drop to calculate the contact resistance value.

[0037] Insulation resistance test: A set DC high voltage is applied between two adjacent pins, and the leakage current between them is measured to calculate the megaohm-level insulation resistance value. The test results are uploaded to the central controller in real time through the communication interface.

[0038] In one embodiment of this application, the system is configured to control a composite material molding apparatus, the apparatus including: Rack 1; The following are set on the frame 1: feeding tray 3, mold forming station 5, auxiliary parts assembly station 407, pin pressing station 408, and sorting station 405. The conveyor line 4 is installed on the frame 1 and connected to each workstation; Conveyor line 4 includes a conveyor guide rail 401 and a drive unit 402; The glue injection mechanism 404 is installed at the mold forming station 5; A 3D vision sensor is installed at the auxiliary parts assembly station 407; A pressing force sensor, a displacement sensor, and a workpiece deformation monitoring sensor are installed at the pin pressing station 408. A vision inspection sensor is installed at sorting station 405; Clamping mechanism 403 installed at one or more workstations; Among them, the drive unit 402, the glue dispensing mechanism 404, each clamping mechanism 403, each sensor, and the actuator of each station constitute the controlled terminal and sensing node of the control network, and are connected to the central controller through the sensing network.

[0039] In actual use, the production line driven by this control system operates according to the following continuous process: After the operator places the cable connector assembly to be processed on the loading tray 3, the system process starts automatically. Driven by the drive unit 402, the conveyor line 4 sequentially delivers the workpieces to each processing station.

[0040] First, the workpiece arrives at the mold forming station 5 and is fixed by the clamping mechanism 403. Then, the glue injection mechanism 404 performs injection molding.

[0041] The system synchronously monitors and records parameters such as pressure and temperature during this process, and binds them to the workpiece's identity.

[0042] The workpiece is then transferred to auxiliary parts assembly station 407.

[0043] The 3D vision sensor at this workstation scans the workpiece. The control system simulates the assembly path in advance based on the scan data. After confirming its feasibility, it instructs the automatic assembly unit at the workstation to accurately place parts such as pins or sealing rings.

[0044] The assembled workpiece enters the critical pin pressing station 408.

[0045] When the servo press is pressing, the system synchronously monitors the pressure, displacement and deformation of the plastic shell through force sensors, displacement sensors and strain gauges.

[0046] The central controller analyzes the relationship between these three factors in real time and dynamically adjusts the pressing process to ensure that the plastic shell does not crack and the pressing force meets the standard.

[0047] Once the pressing action is complete, the test probes built into the mold automatically test the contact resistance and insulation resistance of the connector.

[0048] Finally, the workpiece arrives at sorting station 405.

[0049] The system integrates the pressing curve of the workpiece throughout the entire process, the resistance test results, and the appearance inspection results of the visual sensor at this station to make a comprehensive judgment on whether it is qualified or not, and controls the sorting mechanism to send the workpiece to the corresponding exit.

[0050] In one embodiment of this application, the mold forming station 5 is further provided with a process monitoring unit for collecting data on injection pressure, resin flow rate and mold temperature. Based on the quality deviation trend analyzed by the data, the central controller issues feedforward compensation commands to the dispensing mechanism 404 or the mold temperature control mechanism.

[0051] Specifically, the process monitoring and compensation function operates as an automated closed-loop system. During each injection molding cycle, the system collects real-time data on injection pressure, resin flow rate, and mold temperature using sensors integrated into the mold and injection piping.

[0052] The central controller continuously compares this real-time data with the established standard process model for the product, and uses algorithms to identify subtle, persistent deviation trends, such as the slow decay of the pressure curve or the directional drift of the mold temperature.

[0053] When the system determines that a certain deviation trend may lead to insufficient product weight or filling defects, it will proactively issue adjustment instructions to the relevant actuators before the start of the next production cycle. For example, if it predicts that the resin fluidity will decrease due to a temperature trend, the system will fine-tune the injection speed of the dispensing mechanism 404 or the set temperature of the mold temperature control mechanism in advance to compensate for the deviation.

[0054] In one embodiment of this application, the device further includes an auxiliary station 406 disposed on the frame 1 and connected to the conveyor line 4; The central controller also communicates with auxiliary workstation 406.

[0055] In the actual production process, auxiliary station 406 is a controlled branch node of the main production line, dynamically scheduled by the central controller based on real-time production status. The working process is as follows: When upstream of the main line, such as at sorting station 405, a workpiece is determined to require additional processing, the central controller will instruct conveyor line 4 to divert the workpiece to auxiliary station 406. This station will then perform specified operations such as cooling, re-inspection, or buffering according to a preset program.

[0056] For example, when used as a cooling station, it will time or monitor the workpiece until it reaches the process temperature; when used as a re-inspection station, it will start special equipment to perform depth measurement.

[0057] Upon completion of the task, auxiliary station 406 sends the result and a readiness signal back to the central controller. Based on the downstream main line's production capacity, the controller instructs the conveyor line to retrieve the workpiece and reintegrate it into the subsequent stages of the main process at the optimal time.

[0058] In one embodiment of this application, the system further includes a material identification unit; the central controller automatically calls up the virtual assembly simulation model, pressing control parameter set and sorting decision threshold associated with the material identification based on the identified material identification information.

[0059] In actual production, the workflow is seamless and automated: when a carrier carrying materials of a specific type of cable connector enters the conveyor line, the RFID reader at the entrance immediately and automatically identifies its identification code. Based on this identification code, the central controller retrieves the corresponding complete set of production parameters from the process database, including the virtual assembly simulation model specific to that model, pressing target parameters, and sorting judgment criteria, and automatically sends them to the control systems of assembly station 407, pressing station 408, and sorting station 405.

[0060] Subsequently, starting with the first workpiece in this batch, all processing, inspection and sorting processes are automatically executed based on this set of parameters customized for this model, enabling continuous production switching between different product models.

[0061] In one embodiment of this application, the central controller is also connected to a data management and optimization module, which is used to store and analyze the full-process traceability data chain, and to establish the correlation between process parameters, process data and final quality through machine learning models, and iteratively optimize the generation strategy of collaborative control closed loop.

[0062] Specifically, the module continuously verifies and iterates based on new data, and automatically generates optimization suggestions for existing collaborative control strategies. For example, for a specific model or batch, the module may suggest fine-tuning the holding time of the pressure-closing closed loop or the path planning parameters of the assembly guide.

[0063] Once confirmed, these optimized strategy parameters will be packaged into new process parameter packages and silently deployed to the corresponding production stations by the central controller.

[0064] This application provides a method for manufacturing a cable connector based on the above-described control system, comprising the following steps: Through sensor networks, real-time data from multiple sources from molding, assembly, pressing and testing stages are acquired synchronously. The central controller executes a collaborative control closed loop, which includes: assembly guidance based on 3D vision, dynamic control of the pressing process based on the fusion analysis of force data, displacement data and workpiece deformation data, and sorting decisions based on electrical performance and visual inspection. During the execution of the collaborative control closed loop, the process data, process data and result data of each link are bound to the individual workpiece identification in real time; Based on the bound historical data, the collaborative control logic for subsequent production is optimized through machine learning models.

[0065] In actual production, this cable connector manufacturing method is carried out continuously according to the following steps: First, while the workpiece is in transit, the system simultaneously collects data on glue injection during the molding process, 3D scanning data before assembly, force, displacement, and deformation data during the pressing process, and online electrical performance test data after pressing through a sensor network integrated at each workstation.

[0066] Secondly, the central controller drives the coordinated operation of three core closed loops based on these real-time data: at the assembly station, it simulates and guides the automatic assembly unit to complete collision-free assembly based on the 3D scanning data in real time; at the pressing station, it integrates and analyzes force and deformation data to dynamically adjust the pressing parameters, ensuring that the pressing force meets the standard while protecting the plastic shell; at the sorting station, it makes sorting judgments by combining the electrical performance results and visual final inspection data.

[0067] Throughout the entire process, the system automatically binds the process parameters, process curves, and test results generated at each stage to the unique identifier of the workpiece in real time, forming a complete data chain.

[0068] In summary, the automated control system for composite material molding equipment in this application significantly improves the first-pass yield and quality consistency of products through multi-station real-time data fusion and closed-loop collaborative control, and realizes accurate traceability of the entire production process. At the same time, the system gradually replaces manual experience with data-driven approaches, reduces operational dependence and the difficulty of changing production, and provides a reliable data foundation for continuous self-optimization of the process.

[0069] In the description of this specification, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0070] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0071] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. An automated control system for composite material molding equipment, characterized in that, include: Central controller; The sensor network, which is communicatively connected to the central controller, is used to acquire multi-source real-time data from multiple process stations of the equipment. A control network, which is communicatively connected to the central controller, is used to issue coordinated control commands to multiple actuators of the equipment. The central controller is configured as follows: Based on the multi-source real-time data, the control commands are generated and coordinated and issued to form a collaborative control closed loop with data feedback between at least two process stations. The multi-source real-time data, control commands, and execution results are bound to the individual workpiece identification to form a full-process traceability data chain.

2. The system according to claim 1, characterized in that, The collaborative control closed loop includes at least one of the following: Assembly guidance closed loop: Based on the three-dimensional visual data from the assembly station (407), a collision-free path is planned through virtual assembly simulation, and the actuator of the assembly station (407) is guided to move. Pressing control closed loop: Based on the force, displacement and workpiece deformation data from the pressing station (408), the pressing parameters of the pressing station (408) are dynamically adjusted through multivariate fusion analysis; Sorting decision closed loop: Based on the workpiece performance data from the test unit and the visual final inspection data from the sorting station (405), the sorting action of the sorting station (405) is triggered.

3. The system according to claim 2, characterized in that, In the closed-loop control of the pressing process, the multivariate fusion analysis includes constructing and analyzing force data, displacement data, and workpiece deformation data. The workpiece deformation does not exceed the limit as a constraint, and the pressing force reaches the preset target value as the optimization objective, thereby adjusting the pressing speed and pressure in real time.

4. The system according to claim 2, characterized in that, The pressing station (408) integrates an in-situ electrical performance testing unit, including a test probe embedded in the pressing mold, which is used to acquire contact resistance and insulation resistance data immediately after the pressing action is completed. The workpiece performance data includes the contact resistance and insulation resistance data.

5. The system according to claim 1, characterized in that, The system is configured to control a composite material molding device, the device comprising: Rack (1); The following are set on the frame (1): feeding tray (3), mold forming station (5), auxiliary parts assembly station (407), pin pressing station (408) and sorting station (405). Conveyor lines (4) are installed on the frame (1) and connected to each workstation. The conveyor line (4) includes a conveyor rail (401) and a drive unit (402). The glue injection mechanism (404) is installed at the mold forming station (5). A three-dimensional vision sensor is installed at the auxiliary parts assembly station (407); A pressing force sensor, a displacement sensor, and a workpiece deformation monitoring sensor are installed at the pin pressing station (408); A visual inspection sensor is installed at the sorting station (405); Clamping mechanism (403) installed at one or more workstations. The drive unit (402), the glue injection mechanism (404), each clamping mechanism (403), each sensor, and the actuator of each station constitute the controlled terminal and sensing node of the control network, and are connected to the central controller through the sensing network.

6. The system according to claim 5, characterized in that, The mold forming station (5) is also equipped with a process monitoring unit for collecting data on injection pressure, resin flow rate and mold temperature. The central controller issues a feedforward compensation command to the glue injection mechanism (404) or the mold temperature control mechanism based on the mass deviation trend analyzed by the data.

7. The system according to claim 5, characterized in that, The equipment also includes an auxiliary station (406) that is mounted on the frame (1) and connected to the conveyor line (4). The central controller also communicates with the auxiliary workstation (406).

8. The system according to claim 1, characterized in that, The system also includes a material identification unit; The central controller automatically calls up the virtual assembly simulation model, pressing control parameter set, and sorting decision threshold associated with the identified material identity information.

9. The system according to claim 1, characterized in that, The central controller is also connected to a data management and optimization module, which is used to store and analyze the full-process traceability data chain, and to establish the correlation between process parameters, process data and final quality through machine learning models, and iteratively optimize the generation strategy of the collaborative control closed loop.

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