Substrate screen printing method and device based on intelligent screen printing unit

Abstract

The invention relates to the technical field of industrial Internet of Things, edge calculation, silk-screen and the like, and provides a substrate silk-screen method and equipment based on an intelligent silk-screen unit, and the method comprises the steps: configuring a feeding mechanism, a dust removal mechanism, a pre-printing alignment mechanism and a printing mechanism which are connected in sequence, and establishing an industrial Internet of Things information sensing layer and an edge gateway which are in two-way communication with the feeding mechanism, the dust removal mechanism, the pre-printing alignment mechanism and the printing mechanism. Order and formula data are received through a gateway, multi-source sensing point location real-time data are fused, a production model with a unified time reference and batch identification is constructed, and process parameters are dynamically issued. And during feeding, the model and the warping degree of the substrate are automatically recognized, and dust removal parameters are synchronously adjusted. In the printing process, screen tension, scraper stress, motor current and printing ink rheological property are monitored in real time and fused to obtain a printing ink comprehensive rheological characteristic value, scraper parameters and an ink return time sequence are cooperatively regulated according to the printing ink comprehensive rheological characteristic value, and a printing quality estimated value is output. According to the method, collaborative optimization and quality estimation of the whole printing process are achieved, and the production adaptability and consistency of product printing are improved.

Description

Technical Field

[0001] This invention relates to the fields of industrial Internet of Things, edge computing, and screen printing, and particularly to a substrate screen printing method and equipment based on an intelligent screen printing unit. Background Technology

[0002] Screen printing, as a widely used traditional printing process, plays an important role in industries such as electronics, packaging, textiles, and decorative materials. However, with the deepening of industrial intelligent transformation, traditional screen printing production models have gradually exposed several problems: First, information is isolated between production units, and there is a lack of coordination in processes such as material feeding, dust removal, alignment, and printing, resulting in low production efficiency and a high error rate. Second, the identification of substrate characteristics (such as model and warpage) and the adjustment of process parameters rely on manual experience, making it difficult to achieve precise and adaptive control. Third, the monitoring of key parameters such as screen tension, squeegee dynamics, and ink rheology during the printing process is insufficient, mostly relying on offline sampling inspection, which cannot provide real-time feedback and closed-loop control, resulting in large fluctuations and poor consistency in printing quality. Furthermore, although some existing automated screen printing equipment has basic sensing capabilities, the degree of collection and fusion of multi-source heterogeneous data is low, lacking a unified time-series benchmark and batch correlation, making it difficult to support dynamic optimization and traceability of process parameters. Order and formula data from the upper-level production management system are difficult to channel down to the equipment level and drive execution in real time, while equipment status information cannot be effectively transmitted back, creating an information gap that restricts production flexibility and refined management. Therefore, there is an urgent need for a screen printing method that can deeply integrate information perception, edge computing, and process control to achieve collaborative optimization and quality prediction throughout the entire process from substrate loading to printing output, effectively addressing modern production scenarios with requirements for multiple varieties, small batches, and high quality. Summary of the Invention

[0003] To address the shortcomings of the existing technologies, this invention provides a substrate screen printing method and equipment based on an intelligent screen printing unit, which enables collaborative optimization and quality prediction of the entire process from substrate feeding to printing output, effectively addressing modern production scenarios with requirements for multiple varieties, small batches, and high quality.

[0004] In a first aspect, the present invention provides a substrate screen printing method based on an intelligent screen printing unit, comprising: The system is equipped with an intelligent screen printing unit, which includes a feeding mechanism, a dust removal mechanism, a pre-printing alignment mechanism, and a printing mechanism in sequence according to the production process. An industrial Internet of Things (IoT) information sensing layer is established, which establishes bidirectional data communication connections with the feeding mechanism, the dust removal mechanism, the pre-printing alignment mechanism, and the printing mechanism, and communicates with the upper-level production management system through an edge gateway. The edge gateway receives order data and process formula data from the production management system, and collects real-time monitoring data from multiple source sensor points in the intelligent screen printing unit through the industrial IoT information sensing layer. The order data, process formula data and real-time monitoring data are fused and processed by the edge gateway to establish a production operation model with a unified time base and batch identification. The generated process parameters are then distributed to the corresponding institutions through the industrial IoT information sensing layer. Based on the batch identifier, the loading mechanism performs a transfer loading operation on the substrate to be printed, and identifies the substrate model using a barcode scanner set at the loading station, and detects the substrate warpage using a visual imaging device; the identified substrate model data and warpage detection data are used as the identification result, and the identification result is associated with the unified time reference and stored through the industrial Internet of Things information sensing layer, and control commands are generated according to the identification result, and the dust removal mechanism is controlled by the industrial Internet of Things information sensing layer to synchronously adjust its dust removal action parameters, so as to complete the dust removal treatment of the substrate printing surface during the loading process of the substrate to be printed; By controlling the alignment of the substrate to be printed through multi-source sensing points set at the pre-printing alignment mechanism, and by monitoring the screen tension, squeegee force, and drive motor current in real time at the printing mechanism and estimating the ink rheological characteristics, the comprehensive rheological characteristic value of the ink is obtained by integrating the monitoring data of screen tension, squeegee force, and drive motor current with the estimated value of ink rheological characteristics. Based on this comprehensive rheological characteristic value, the squeegee angle, squeegee pressure, squeegee movement speed, and the timing of ink return action are adjusted in a coordinated manner to complete the screen printing operation and output the estimated value of printing quality.

[0005] Secondly, the present invention provides a substrate screen printing device based on an intelligent screen printing unit, wherein the substrate screen printing device based on the intelligent screen printing unit uses the above-mentioned substrate screen printing method based on the intelligent screen printing unit.

[0006] Compared with the prior art, the beneficial effects of this invention are as follows: This invention provides a method and equipment for screen printing substrates based on an intelligent screen printing unit. The method includes: configuring an intelligent screen printing unit, which sequentially includes a feeding mechanism, a dust removal mechanism, a pre-printing alignment mechanism, and a printing mechanism according to the production process; establishing an industrial Internet of Things (IoT) information sensing layer, wherein the IoT information sensing layer establishes bidirectional data communication connections with the feeding mechanism, dust removal mechanism, pre-printing alignment mechanism, and printing mechanism, and communicates with an upper-level production management system through an edge gateway; receiving order data and process formula data from the production management system through the edge gateway, and collecting real-time monitoring data from multiple source sensor points in the intelligent screen printing unit through the IoT information sensing layer; fusing the order data, process formula data, and real-time monitoring data at the edge gateway to establish a production operation model with a unified time base and batch identifier, and distributing the generated process parameters to the corresponding mechanisms through the IoT information sensing layer; and, based on the batch identifier, having the feeding mechanism perform a transfer and feeding operation on the substrate to be printed. The substrate model is identified by a barcode scanner installed at the loading station, and the warpage of the substrate is detected by a visual imaging device. The identified substrate model data and warpage detection data are used as the identification result. This identification result is associated with the unified time reference and stored through the industrial IoT information sensing layer. At the same time, control commands are generated based on the identification result, and the dust removal mechanism is controlled by the industrial IoT information sensing layer to synchronously adjust its dust removal action parameters, completing the dust removal treatment of the substrate printing surface during the loading process. The alignment of the substrate to be printed is controlled by multi-source sensor points set at the pre-printing alignment mechanism, and the screen tension, squeegee force, and drive motor current are monitored in real time at the printing mechanism to estimate the ink rheological characteristics. By integrating the monitoring data of screen tension, squeegee force, and drive motor current with the estimated value of ink rheological characteristics, a comprehensive rheological characteristic value of the ink is obtained. Based on this comprehensive rheological characteristic value, the squeegee angle, squeegee pressure, squeegee movement speed, and ink return action sequence are adjusted in a coordinated manner to complete the screen printing operation and output the estimated value of printing quality. This method can achieve collaborative optimization and quality prediction of the entire process from substrate feeding to printing output, effectively addressing modern production scenarios with requirements for multiple varieties, small batches, and high quality. Attached Figure Description

[0007] The accompanying drawings, which are provided to further illustrate the invention and constitute a part of this invention, are illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention.

[0008] Figure 1 This is a schematic flowchart of a substrate screen printing method based on an intelligent screen printing machine according to an embodiment of the present invention.

[0009] Figure 2 This is a schematic diagram of a feeding mechanism according to an embodiment of the present invention; Figure 3 This is a schematic diagram of a dust removal mechanism according to an embodiment of the present invention; Figure 4 This is a schematic diagram of a pre-printing alignment mechanism according to an embodiment of the present invention; Figure 5 This is a schematic diagram of a printing mechanism according to an embodiment of the present invention; Figure 6 This is a schematic diagram of the drying mechanism according to an embodiment of the present invention.

[0010] Explanation of reference numerals in the attached figures: 1. Feeding mechanism; 10. Belt drive mechanism; 11. Feeding belt; 2. Dust removal mechanism; 20. Dust removal drive module; 21. Electrostatic dust removal roller brush assembly; 3. Pre-printing alignment mechanism; 30. Machine base; 31. Alignment reference sensor position; 32. Loading detection sensor position; 4. Printing mechanism; 40. Screen printing assembly; 41. Squeegee assembly; 42. Motor drive assembly; 5. Drying mechanism. Detailed Implementation

[0011] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are merely some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0012] See Figures 1-6 This invention provides a substrate screen printing method and equipment based on an intelligent screen printing machine. The substrate screen printing equipment based on the intelligent screen printing machine uses the substrate screen printing method based on the intelligent screen printing machine, which includes the following steps: S101. Configure an intelligent screen printing unit, which includes a feeding mechanism 1, a dust removal mechanism 2, a pre-printing alignment mechanism 3, and a printing mechanism 4 in sequence according to the production process; establish an industrial Internet of Things (IoT) information sensing layer, which establishes bidirectional data communication connections with the feeding mechanism 1, the dust removal mechanism 2, the pre-printing alignment mechanism 3, and the printing mechanism 4, and communicates with the upper-level production management system through an edge gateway. S102. Receive order data and process formula data from the production management system through the edge gateway, and collect real-time monitoring data from multiple source sensor points in the intelligent screen printing unit through the industrial IoT information sensing layer; fuse the order data, process formula data and real-time monitoring data through the edge gateway to establish a production operation model with a unified time base and batch identifier, and distribute the generated process parameters to the corresponding institutions through the industrial IoT information sensing layer. S103. Based on the batch identifier, the loading mechanism 1 performs a transfer loading operation on the substrate to be printed, and identifies the substrate model using a barcode scanner set at the loading station, and detects the substrate warpage using a visual imaging device; the identified substrate model data and warpage detection data are used as identification results, and the identification results are associated with the unified time reference and stored through the industrial Internet of Things information sensing layer. At the same time, control commands are generated according to the identification results, and the dust removal mechanism 2 is controlled by the industrial Internet of Things information sensing layer to synchronously adjust its dust removal action parameters, and the dust removal treatment of the substrate printing surface is completed during the loading process of the substrate to be printed. S104. The alignment status of the substrate to be printed is determined by multiple sensors set at the pre-printing alignment mechanism 3, and the screen tension, squeegee force, and drive motor current are monitored in real time at the printing mechanism 4 to estimate the ink rheological characteristics. By integrating the monitoring data of screen tension, squeegee force, and drive motor current with the estimated value of ink rheological characteristics, the comprehensive rheological characteristic value of the ink is obtained. Based on the comprehensive rheological characteristic value, the squeegee angle, squeegee pressure, squeegee movement speed, and ink return action timing are adjusted in a coordinated manner to complete the screen printing operation and output the estimated value of printing quality.

[0013] In this embodiment, an intelligent screen printing unit is configured and includes, in sequence according to the production process, a feeding mechanism 1, a dust removal mechanism 2, a pre-printing alignment mechanism 3, and a printing mechanism 4. An industrial IoT information sensing layer is established, which has bidirectional data communication connections with all these mechanisms. Furthermore, an edge gateway connects the unit to the upper-level production management system. This addresses the problems of information isolation and lack of collaboration between production units, as well as information gaps with the management level. It achieves vertical information integration from upper-level management to lower-level execution and horizontal collaboration between various production stages, providing the hardware and network foundation for intelligent control of the entire process. The edge gateway receives order and formula data and collects real-time monitoring data from multiple sensor points. This data is then integrated and processed on the gateway to establish a production operation model with a unified time reference and batch identifier. Process parameters are then distributed to each mechanism. This solves the problems of lacking a unified time reference and batch correlation for multi-source heterogeneous data, making dynamic optimization and traceability difficult. It achieves precise alignment and fusion of production data in the time and batch dimensions, providing core model support for data-driven real-time decision-making and dynamic distribution of process parameters, thus improving production flexibility and traceability. The material is fed by the feeding mechanism 1 based on batch identification, and the substrate model is identified by a barcode scanner and the warpage of the substrate is detected by a visual imaging device. The identification results are associated with and stored with a unified time reference. At the same time, control instructions are generated based on the results to control the dust removal mechanism 2 to adjust its dust removal action parameters synchronously. This can solve the problems of substrate characteristic identification relying on manual experience and dust removal process parameters being fixed and unable to be adjusted adaptively. It realizes automatic identification and status detection of the fed substrate, and accurately and adaptively adjusts the parameters of subsequent dust removal processes based on real-time detection results, thereby improving the accuracy of processing and production efficiency. The alignment status is determined by multiple sensors at the pre-printing alignment mechanism 3. At the printing mechanism 4, the screen tension, squeegee force, and drive motor current are monitored in real time, and the ink rheological characteristics are estimated. These data are fused to obtain a comprehensive rheological characteristic value. Based on this characteristic value, the squeegee angle, pressure, speed, and ink return timing are adjusted to complete the screen printing operation and output the estimated printing quality value. This can solve the problems of insufficient monitoring of key process parameters, reliance on offline sampling inspection, and inability to control in real time, which leads to quality fluctuations. It realizes real-time perception and fusion analysis of multiple physical quantities in the printing process, and based on this, adaptive and collaborative control of core printing actions is performed, thereby improving the stability and consistency of printing quality. It realizes online real-time prediction and feedback of printing quality, and provides key data support for continuous optimization of the production process and closed-loop quality control.

[0014] In summary, the solution provided in this embodiment constructs a highly interconnected, data-driven intelligent system, utilizing the Industrial Internet of Things and edge computing technologies as the nerve center. It organically combines multi-source sensing, data fusion, adaptive control, and quality prediction, ultimately achieving the following: once an order is placed, the system can automatically identify the workpiece, adaptively adjust the parameters of each process, ensure quality in real time during the printing process, and output the predicted results. This can effectively solve the challenges of changeover efficiency, quality consistency, and process control under high-quality requirements in multi-variety, small-batch production, realizing intelligent and flexible manufacturing.

[0015] It should be noted that in the method provided in this embodiment, a unified time reference can be established between the feeding mechanism 1, dust removal mechanism 2, pre-printing alignment mechanism 3, and printing mechanism 4 through the industrial IoT information sensing layer using the PTP protocol; a lightweight machine learning model is deployed in the edge gateway for early warning of anomalies and feature data compression; all batch data is synchronized to the Manufacturing Execution System (MES) and the digital twin is updated; the defect risk of the next substrate is predicted through the digital twin, and alignment adjustment parameters and scraper adjustment parameters are sent to the corresponding mechanisms in advance. The use of the PTP protocol to establish a unified time reference between the various mechanisms can solve the problem of data timing disorder and inaccurate correlation analysis caused by clock asynchrony between distributed sensors and actuators, achieving time synchronization of the entire system. Deploying a lightweight machine learning model in the edge gateway for early warning of anomalies and feature data compression can solve the problems of high network bandwidth and cloud computing pressure and high warning latency caused by uploading a large amount of real-time data, enabling intelligent analysis and information purification at the data source, uploading only warning results or feature values, significantly reducing system load and improving real-time response capabilities. By synchronizing all batch data to the MES to update the digital twin, and using it to predict the defect risk of the next substrate and issue adjustment parameters in advance, the problem of production process control lag can be solved. This enables the production line digital twin driven by historical and real-time data to perform advanced simulation and prediction, and to perform forward-looking parameter optimization accordingly.

[0016] It should also be noted that in the method provided in this embodiment, a first online visual inspection node can be set in the post-printing process to detect defects such as broken lines, ink bleeding, and jagged edges; a second online visual inspection node can be set in the post-drying process to detect defects such as pinholes, bubbles, and color differences; the detected defect topology and location information are mapped to the screen coordinate system through the industrial IoT information sensing layer; when defects appear on multiple substrates in the same area and the number of occurrences exceeds a set threshold, it is determined that the screen and process coupling is abnormal; a backtracking control strategy is triggered: for subsequent substrates in the same batch, the dust removal mechanism 2 is prioritized to perform enhanced dust removal, and the pre-printing alignment mechanism 3 is prioritized to perform alignment compensation; then the printing mechanism 4 is controlled to use conservative printing parameters such as reducing the squeegee pressure and increasing the squeegee angle to print, so as to block the spatial diffusion of defects. By setting up first and second online visual inspection nodes and mapping defect information to the screen coordinate system, the problem of quality inspection only judging the quality of individual products and being unable to locate the root cause of defects (especially periodic defects related to the screen) can be solved. This enables accurate classification and location of defects and their correlation analysis with the screen position, providing a visual basis for accurately tracing the causes of process or equipment problems. When defects exceed the threshold consecutively in the same area, it is determined that the screen and process coupling is abnormal, and a backtracking control strategy is triggered. This can solve the problem of lacking an automatic degradation operation mechanism to avoid continuous scrap when there is a systemic defect source (such as slight screen blockage or wear). It enables automatic identification of systemic risks based on statistical laws and immediately starts a conservative but safe printing strategy (enhanced dust removal, alignment compensation, and squeegee adjustment) to quickly suppress defect spread without stopping the machine, buy time for maintenance, and reduce the scrap rate.

[0017] Furthermore, the intelligent screen printing unit also includes a drying mechanism 5 located in the next process after the printing mechanism 4; at the drying mechanism 5, the following steps are performed: according to the solvent evaporation kinetic model and the bonding and curing model, the drying process is divided into multiple independent temperature zones and air zones, and gradient heating, heat preservation and cooling strategies are formulated for each temperature zone and air zone respectively; data is collected in real time by online VOC sensors, temperature and humidity sensors and infrared temperature measuring devices set at the drying mechanism 5, and the thermal management curves of each temperature zone and air zone are dynamically corrected according to the collected data, and the drying sufficiency index used to characterize the degree of drying of the substrate is output.

[0018] In this embodiment, a drying mechanism 5 is set up in the next process after the printing mechanism 4. Based on the solvent evaporation kinetics model and the adhesion and curing model, the drying process is divided into multiple independent temperature zones and air zones. Gradient heating, heat preservation and cooling strategies are formulated for each temperature zone and air zone. This can solve the problem that the drying process uses a fixed temperature curve, which cannot be adapted to the characteristics of different inks and substrates, and is prone to insufficient drying (affecting adhesion) or over-drying (causing the substrate to become brittle and deformed). It realizes refined and adaptive drying control based on physicochemical models, provides a better curing path for different products, and improves energy efficiency while ensuring quality. By collecting data in real time through online VOC sensors, temperature and humidity sensors, and infrared temperature measuring devices installed at 5 points in the drying mechanism, and dynamically correcting the thermal management curves of each temperature and air zone based on the collected data, the system outputs a drying sufficiency index to characterize the degree of substrate drying. This solves the problems of unstable effects of fixed drying strategies due to environmental fluctuations, differences in ink batches, etc., as well as the inability to perceive the drying process in real time, the lack of quantitative evaluation standards for drying quality, and the reliance on manual experience or offline sampling inspection, which often results in delayed feedback. The system enables real-time online perception and feedback of the drying status, and dynamically optimizes drying parameters based on actual monitoring data to ensure that each substrate can be cured under optimal conditions. This allows for digital and quantitative evaluation of the drying results, improving drying consistency and reliability.

[0019] Furthermore, the multiple independent temperature and air zones specifically include a pre-evaporation zone, a main curing zone, and a setting zone. The method also includes the following steps at the drying mechanism 5: determining the inflection point of the VOC release curve based on data from the online VOC sensor; calculating the surface temperature rise rate of the substrate based on data from the infrared thermometer; and adaptively adjusting the set temperature, airflow, and residence time of the substrate in each zone in conjunction with the estimated heat capacity of the substrate. When the infrared thermometer detects that the temperature on the back of the substrate is close to its thermal deformation threshold, the control mode of the main curing zone is switched to a low-temperature long-time mode, and the directional airflow intensity of the main curing zone is increased. Simultaneously, a control command is generated and sent to the printing mechanism 4 via the industrial IoT information sensing layer, causing the printing mechanism 4 to reduce the amount of ink applied per unit area. In the setting zone, a point-check delay strategy is implemented for solvent residue based on the drying sufficiency index, automatically extending the residence time of single substrate pieces that have not reached the drying sufficiency threshold in the setting zone.

[0020] In this embodiment, the drying zone is divided into a pre-evaporation zone, a main curing zone, and a setting zone. Based on VOC sensor and infrared temperature measurement data, the set temperature, wind speed, and substrate dwell time in each zone are adaptively adjusted. This solves the problem that drying ovens using fixed temperature curves cannot respond to differences in ink evaporation characteristics and substrate heat capacity, easily leading to surface drying but internal incomplete drying or thermal damage. It enables refined, dynamic, zoned control of the drying process based on real-time process data, ensuring sufficient solvent evaporation and curing while preventing substrate overheating, thus improving drying quality and energy efficiency. When the temperature on the back of the substrate is detected to be close to the heat deformation threshold, the main curing zone is switched to a low-temperature, long-duration mode with enhanced directional airflow. Simultaneously, the printing mechanism 4 is controlled to reduce the ink application. This solves the problem of substrate deformation and scrap due to excessive local ink application or thermal shock when printing thick-film circuits or heat-sensitive materials. It achieves cross-process collaborative control based on temperature risk (printing and drying linkage), fundamentally preventing thermal defects and protecting expensive substrates by proactively reducing ink application and adjusting drying strategies. In the setting area, a point inspection delay strategy is implemented based on the drying adequacy index. For individual substrates that do not meet the standard, the dwell time is automatically extended. This can solve the problem that individual products are not fully dried due to slight differences during batch drying, but uniformly extending the time would reduce efficiency. This achieves personalized drying quality closed-loop control for individual products, maximizing the overall production cycle while ensuring that each substrate is fully dried.

[0021] Preferably, the industrial IoT information sensing layer further performs the following steps: writing the operating status, process parameters, and quality index data from the feeding mechanism 1, dust removal mechanism 2, pre-printing alignment mechanism 3, printing mechanism 4, and drying mechanism 5 into a batch file according to the unified time benchmark; based on the written batch file data, if any of the following situations is detected: alignment deviation, screen blockage, or drying sufficiency index is lower than a set threshold, the relevant process parameters are dynamically adjusted, and the dust removal mechanism 2, the pre-printing alignment mechanism 3, the printing mechanism 4, or the drying mechanism 5 are linked to execute corresponding correction strategies, including re-dust removal, re-alignment, reprinting, or extending the drying time.

[0022] In this embodiment, the operating status, process parameters, and quality index data from the feeding mechanism 1, dust removal mechanism 2, pre-printing alignment mechanism 3, printing mechanism 4, and drying mechanism 5 are written into the batch archive according to the unified time benchmark. This can solve the problem of scattered and unrelated data throughout the production process, making it difficult to quickly and accurately trace the root cause once a quality problem occurs. It enables the construction of a complete digital twin archive covering the entire process from feeding, dust removal, alignment, printing to drying, providing a solid data foundation for production traceability, big data analysis, and full life cycle quality management. Based on the batch archive data, if any of the following conditions are detected: alignment deviation, screen blockage, or drying adequacy index falling below a set threshold, the relevant process parameters are dynamically adjusted, and the dust removal mechanism 2, the pre-printing alignment mechanism 3, the printing mechanism 4, or the drying mechanism 5 are linked to execute corresponding correction strategies. This can solve the problems that abnormal situations in the production process usually require manual intervention, resulting in slow response, low efficiency, and the inability of each device to coordinate error correction when operating independently. It achieves proactive quality early warning and self-healing capabilities based on real-time data throughout the entire process. Through intelligent linkage across devices (such as re-alignment, reprinting, and extending drying time), it automatically eliminates or compensates for the impact of abnormalities, significantly improving the overall yield and adaptability of the production line.

[0023] Preferably, the feeding mechanism 1 includes: a belt drive mechanism 10 and a feeding belt 11 driven by the belt drive mechanism 10; when performing the feeding operation, the feeding scheduler performs predictive scheduling according to the order cycle provided by the edge gateway, and controls the belt drive mechanism 10 to drive the feeding belt 11 to complete the transfer of the substrate to be printed; when the warpage of the substrate is detected by the visual imaging device to exceed the set threshold, the substrate with warpage exceeding the threshold is transferred to the rework buffer area, and at the same time, the rework reason and image feature data containing the warpage of the substrate are recorded in the batch file through the industrial Internet of Things information perception layer.

[0024] In this embodiment, the material feeding scheduler performs predictive scheduling based on the order cycle provided by the edge gateway, and controls the belt drive mechanism 10 to drive the material feeding belt 11 to complete the transfer of the substrate to be printed. This can solve the problem of mismatch between the material feeding rhythm and the production order cycle, and the problem of low production efficiency or waiting waste caused by reliance on manual operation. It realizes efficient collaboration between the material feeding link and the production management system, and achieves accurate and efficient automated material feeding, providing a basic guarantee for the high cycle and flexible production of the entire intelligent screen printing unit. When the visual imaging device detects that the substrate warpage exceeds a set threshold, the substrate with warpage exceeding the threshold is transferred to the rework buffer area. The reason for rework and the image feature data including the substrate warpage are recorded in the batch file through the industrial IoT information sensing layer. This can solve the problem that defective substrates flowing into subsequent printing stages can lead to poor printing, wasted ink, or even damage to the screen. It also solves the problem that after rejecting defective products, the information record is incomplete, making it difficult to conduct supplier quality management and statistical analysis of incoming material problems. This enables strict control of incoming material quality at the forefront of production, automatic isolation of defective products, prevention of defect expansion and resource waste, and plays a role in cost control and quality prediction. It also enables refined, digital recording and traceability of quality problems.

[0025] Preferably, the dust removal mechanism 2 includes a dust removal drive module 20 and an electrostatic dust removal roller brush assembly 21, wherein the dust removal drive module 20 is connected to the electrostatic dust removal roller brush assembly 21; when the dust removal mechanism 2 performs a dust removal operation, the dust removal drive module 20 drives the electrostatic dust removal roller brush assembly 21 to move, so as to complete the dust removal treatment of the printing surface during the feeding process of the substrate to be printed; the dust removal action parameters synchronously adjusted by the dust removal mechanism 2 include at least one of ion wind intensity, airflow field parameters and roller brush speed.

[0026] In this embodiment, the dust removal mechanism 2 includes a dust removal drive module 20 and an electrostatic dust removal roller brush assembly 21. The dust removal drive module 20 drives the electrostatic dust removal roller brush assembly 21 to move, thereby completing the dust removal treatment of the printing surface of the substrate during the feeding process. This solves the problems of low efficiency, easy secondary pollution, or ineffective dust removal in high-speed movement of the production line of traditional dust removal methods (such as air showers and brushes). It achieves non-contact, high-efficiency dynamic dust removal that is seamlessly integrated with the feeding process, improving cleaning efficiency and avoiding damage to the substrate. The synchronously adjusted dust removal action parameters include at least one of ion wind intensity, airflow field parameters, and roller brush speed. This solves the problem of fixed dust removal process parameters that cannot be adaptively adjusted according to different substrate characteristics (such as material and warpage) and environmental conditions, resulting in unstable dust removal effect. It enables precise and flexible real-time control of key dust removal parameters based on upstream identification results (such as substrate type and warpage), thereby achieving better and more consistent dust removal effect under various working conditions.

[0027] Preferably, when controlling alignment at the pre-printing alignment mechanism 3, the following steps are performed: reference detection is performed using alignment reference sensor points 31 distributed around the substrate to be printed on the machine 30; substrate arrival detection is performed using loading detection sensor points 32 set in the substrate loading area on the machine 30; when the loading detection sensor point 32 detects that the substrate is in place and all alignment reference sensor points 31 do not return any obstruction signal, it is determined that the substrate to be printed has completed pre-printing alignment.

[0028] In this embodiment, the alignment reference sensor points 31 distributed around the substrate to be printed on the machine 30 are used for reference detection. This solves the problems of low accuracy and poor reliability of alignment methods such as mechanical limiting, and enables accurate and digital judgment of whether the substrate is in the correct position, providing a basic guarantee for high-precision printing. The loading detection sensor points 32 set in the substrate loading area on the machine 30 are used for substrate arrival detection. This solves the problem of not being able to automatically sense whether the substrate has been accurately delivered to the alignment station, requiring manual confirmation or posing a risk of collision. This enables automatic monitoring of the substrate delivery status, ensuring the safety and coordination of subsequent alignment and printing operations. When the loading detection sensor 32 detects that the substrate is in place and all alignment reference sensor points 31 do not return any obstruction signals, it is determined that the substrate to be printed has completed the alignment before printing. This can solve the problems of complex sensor signal logic being difficult to unify, unclear alignment status judgment criteria, and easy misjudgment. It realizes a clear, reliable, and automatically executable alignment success judgment logic, ensuring that only substrates with completely correct positions will enter the printing process, thus eliminating printing defects caused by alignment deviations from the source.

[0029] Preferably, the printing mechanism 4 includes: a screen assembly 40, a doctor blade assembly 41, and a motor drive assembly 42 mechanically connected to the doctor blade assembly 41 for driving the doctor blade assembly 41 to move on the screen assembly 40; the screen assembly 40 is provided with a tension sensor for monitoring the screen tension, the motor drive assembly 42 is provided with a current sensor for detecting the operating current of the motor drive assembly 42, and an ultrasonic viscosity estimation device for estimating the rheological properties of the ink is provided on the ink path of the printing mechanism 4; the tension sensor, the current sensor, the ultrasonic viscosity estimation device, and the motor drive assembly 42 are signal connected to the control unit.

[0030] In this embodiment, a tension sensor for monitoring screen tension is installed on the screen assembly 40, a current sensor for detecting operating current is installed on the motor drive assembly 42, and an ultrasonic viscosity estimation device for estimating ink rheological properties is installed on the ink path. This solves the problem of difficulty in online real-time monitoring of core process parameters (screen tension, actual force on the squeegee, and ink state) during printing, resulting in a black box effect on production status. It enables comprehensive and real-time perception and quantification of the most critical physical quantities (mechanical, electrical, and rheological) affecting printing quality. The tension sensor, current sensor, ultrasonic viscosity estimation device, and motor drive assembly 42 are signal-connected to the control unit, which solves the problem of independent sensors, actuators, and controllers, resulting in data incompatibility and difficulty in coordinated control. This achieves interconnection between all key components and the central control unit, forming a complete infrastructure for perception, decision-making, and execution.

[0031] Furthermore, the control unit is configured to perform the following actions: receiving monitoring and estimation data from a tension sensor, a current sensor, and an ultrasonic viscosity estimation device; fusing monitoring data of screen tension, doctor blade force, and drive motor current with estimated values ​​of ink rheological properties to obtain a comprehensive rheological characteristic value of the ink; based on the comprehensive rheological characteristic value, calculating a feasible working range where the doctor blade is not stretched, the ink does not leak, and the screen openings are not clogged using a constraint solving algorithm; and controlling the motor drive assembly 42 to operate within the feasible working range with an optimized combination of doctor blade angle, pressure, speed, and ink return timing to achieve the coordinated adjustment.

[0032] In this embodiment, monitoring data of screen tension, doctor blade force, and drive motor current are integrated with estimated values ​​of ink rheological properties to obtain a comprehensive rheological characteristic value of the ink. This solves the problem that a single parameter cannot comprehensively and accurately characterize the actual transfer state of ink in complex printing processes. It achieves multi-source information fusion to construct a characteristic index that more comprehensively and profoundly reflects the overall state of the current printing system, providing a more reliable basis for precise control. Based on the comprehensive rheological characteristic value, a constraint-solving algorithm is used to calculate the feasible working range where the doctor blade is not stretched, the ink does not leak, and the screen is not clogged. This solves the problem that printing parameter adjustments rely on operator experience and it is difficult to find the optimal solution under multiple mutually restrictive process constraints (such as quality and defects). It enables the control algorithm to automatically and scientifically find a safe parameter working range that ensures both printing quality (no defects) and meets process limits, transforming manual experience into replicable digital decisions. The control motor drive component 42 operates within the feasible working range with an optimized combination of doctor blade angle, pressure, speed, and ink return timing. This solves the problem that even if a feasible range is found, specific parameter combinations still need to be manually selected, making it impossible to achieve optimal control and adaptive adjustment. Within the safety boundary calculated by the algorithm, it automatically finds and executes a set of optimal and mutually matching process parameter combinations, achieving refined and adaptive optimization control of the printing process, stabilizing and improving printing quality.

[0033] It should be noted that the above embodiments are merely preferred embodiments of the present invention, and the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention, and the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A substrate screen printing method based on an intelligent screen printing unit, characterized in that, include: The system is equipped with an intelligent screen printing unit, which includes a feeding mechanism, a dust removal mechanism, a pre-printing alignment mechanism, and a printing mechanism in sequence according to the production process. An industrial Internet of Things (IoT) information sensing layer is established, which establishes bidirectional data communication connections with the feeding mechanism, the dust removal mechanism, the pre-printing alignment mechanism, and the printing mechanism, and communicates with the upper-level production management system through an edge gateway. The edge gateway receives order data and process formula data from the production management system, and the industrial Internet of Things information sensing layer collects real-time monitoring data from multiple source sensor points in the intelligent screen printing unit. The order data, process formula data and real-time monitoring data are fused and processed at the edge gateway to establish a production operation model with a unified time base and batch identification, and the generated process parameters are distributed to the corresponding institutions through the industrial Internet of Things information perception layer. Based on the batch identifier, the loading mechanism performs a transfer loading operation on the substrate to be printed, and identifies the substrate model using a barcode scanner set at the loading station, and detects the substrate warpage using a visual imaging device; the identified substrate model data and warpage detection data are used as the identification result, and the identification result is associated with the unified time reference and stored through the industrial Internet of Things information sensing layer, and control commands are generated according to the identification result, and the dust removal mechanism is controlled by the industrial Internet of Things information sensing layer to synchronously adjust its dust removal action parameters, so as to complete the dust removal treatment of the substrate printing surface during the loading process of the substrate to be printed; By controlling the alignment of the substrate to be printed through multi-source sensing points set at the pre-printing alignment mechanism, and by monitoring the screen tension, squeegee force, and drive motor current in real time at the printing mechanism and estimating the ink rheological characteristics, the comprehensive rheological characteristic value of the ink is obtained by integrating the monitoring data of screen tension, squeegee force, and drive motor current with the estimated value of ink rheological characteristics. Based on this comprehensive rheological characteristic value, the squeegee angle, squeegee pressure, squeegee movement speed, and the timing of ink return action are adjusted in a coordinated manner to complete the screen printing operation and output the estimated value of printing quality.

2. The method according to claim 1, characterized in that, The intelligent screen printing unit also includes a drying mechanism located in the next process after the printing mechanism. In the drying mechanism, the following steps are performed: based on the solvent evaporation kinetic model and the bonding and curing model, the drying process is divided into multiple independent temperature zones and air zones, and gradient heating, heat preservation and cooling strategies are formulated for each temperature zone and air zone respectively; data is collected in real time by online VOC sensors, temperature and humidity sensors and infrared temperature measuring devices set in the drying mechanism, and the thermal management curves of each temperature zone and air zone are dynamically corrected based on the collected data, and the drying sufficiency index used to characterize the degree of drying of the substrate is output.

3. The method according to claim 2, characterized in that, The industrial IoT information sensing layer also performs the following steps: writing the operating status, process parameters, and quality index data from the feeding mechanism, dust removal mechanism, pre-printing alignment mechanism, printing mechanism, and drying mechanism into the batch file according to the unified time benchmark; based on the written batch file data, if any of the following situations are detected: alignment deviation, screen blockage, or drying sufficiency index is lower than the set threshold, the relevant process parameters are dynamically adjusted, and the dust removal mechanism, the pre-printing alignment mechanism, the printing mechanism, or the drying mechanism are linked to execute the corresponding correction strategy, which includes re-dust removal, re-alignment, reprinting, or extending the drying time.

4. The method according to claim 3, characterized in that, The feeding mechanism includes a belt drive mechanism and a feeding belt driven by the belt drive mechanism. When performing the feeding operation, the feeding scheduler performs predictive scheduling based on the order cycle provided by the edge gateway and controls the belt drive mechanism to drive the feeding belt to complete the transfer of the substrate to be printed. When the visual imaging device detects that the substrate warpage exceeds a set threshold, the substrate with warpage exceeding the threshold is transferred to the rework buffer area. At the same time, the rework reason and image feature data containing the substrate warpage are recorded in the batch file through the industrial IoT information perception layer.

5. The method according to claim 1, characterized in that, The dust removal mechanism includes a dust removal drive module and an electrostatic dust removal roller brush assembly. The dust removal drive module is connected to the electrostatic dust removal roller brush assembly. When the dust removal mechanism performs a dust removal operation, the dust removal drive module drives the electrostatic dust removal roller brush assembly to move, so as to complete the dust removal treatment of the printing surface during the feeding process of the substrate to be printed. The dust removal action parameters that the dust removal mechanism adjusts synchronously include at least one of ion wind intensity, airflow field parameters, and roller brush rotation speed.

6. The method according to claim 1, characterized in that, When controlling alignment at the pre-printing alignment mechanism, the following steps are performed: reference detection is performed using alignment reference sensor points distributed around the substrate to be printed on the machine platform; substrate arrival detection is performed using loading detection sensor points set in the substrate loading area on the machine platform; when the loading detection sensor points detect that the substrate is in place and all alignment reference sensor points do not return any obstruction signals, it is determined that the substrate to be printed has completed pre-printing alignment.

7. The method according to claim 1, characterized in that, The printing mechanism includes: a screen assembly, a doctor blade assembly, and a motor drive assembly mechanically connected to the doctor blade assembly for driving the doctor blade assembly to move on the screen assembly; a tension sensor for monitoring screen tension is provided on the screen assembly, a current sensor for detecting the operating current of the motor drive assembly is provided on the motor drive assembly, and an ultrasonic viscosity estimation device for estimating the rheological properties of the ink is provided on the ink path of the printing mechanism; the tension sensor, the current sensor, the ultrasonic viscosity estimation device, and the motor drive assembly are signal connected to the control unit.

8. The method according to claim 7, characterized in that, The control unit is configured to perform the following: receive monitoring and estimation data from a tension sensor, a current sensor, and an ultrasonic viscosity estimation device; and fuse the monitoring data of screen tension, doctor blade force, and drive motor current with the estimated values ​​of ink rheological properties to obtain the comprehensive rheological characteristic value of the ink. Based on the comprehensive rheological characteristic values, a feasible working range in which the doctor blade is not stretched, the ink does not leak, and the mesh is not clogged is calculated through a constraint solving algorithm; the motor drive component is controlled to operate within the feasible working range with an optimized combination of doctor blade angle, pressure, speed, and ink return timing to achieve the coordinated adjustment.

9. The method according to claim 2, characterized in that, The multiple independent temperature and air zones specifically include a pre-evaporation zone, a main curing zone, and a setting zone; the method further includes the following steps at the drying mechanism: determining the inflection point of the VOC release curve based on the data fed back by the online VOC sensor, calculating the surface temperature rise rate of the substrate based on the data fed back by the infrared temperature measuring device, and adaptively adjusting the set temperature, air velocity, and residence time of the substrate in each zone in conjunction with the estimated heat capacity of the substrate; When the infrared temperature measuring device detects that the temperature on the back of the substrate is close to the thermal deformation threshold of the material, the control mode of the main curing zone is switched to low-temperature long-time mode and the directional air supply intensity of the main curing zone is increased. At the same time, a control command is generated and sent to the printing mechanism via the industrial Internet of Things information sensing layer, so that the printing mechanism performs the operation of reducing the amount of ink applied per unit area. In the setting zone, a point inspection delay strategy is implemented for solvent residue based on the drying sufficiency index. For single substrates that have not reached the drying sufficiency threshold, the residence time in the setting zone is automatically extended.

10. A substrate screen printing device based on an intelligent screen printing unit, characterized in that, The substrate screen printing equipment based on the intelligent screen printing unit uses the substrate screen printing method based on the intelligent screen printing unit as described in any one of claims 1-9.

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