Digital MES-driven full-lamination process flow tracing system and method

Abstract

The invention discloses a digital MES (Manufacturing Execution System)-driven full-lamination process flow tracing system and method, which are used for solving the problems of material tracing fault, process static out-of-control and lack of real-time interception in display panel manufacturing. The method comprises the following steps: S1, work order initialization: calling a BOM table to bind a material batch number, loading process parameters such as a cutting knife wheel specification and a UV curing energy value, and generating a unique traceability code; s2, process parameter dynamic loading: scanning a code to verify material consistency, activating a cleaning process and a silicone adhesive dispensing path, and starting white glass plumpness verification and UV curing countdown; s3, real-time risk interception: monitoring cleanliness and bending strength through a sensor, and automatically intercepting standard exceeding events that the height of the PIN is greater than 0.45 mm and the like; s4, constructing an electronic resume: integrating full-link data to generate a block chain tracing report, and supporting reverse tracing; and S5, closed-loop optimization: dynamically adjusting the process based on the reject ratio. According to the invention, accurate control of the whole process is realized, and the yield and the tracing efficiency are improved.

Description

[0001] This invention relates to the field of intelligent manufacturing technology, and in particular to a method for tracing the full lamination process driven by a digital MES. Background Technology

[0002] In the field of full lamination manufacturing of display panels, existing processes generally use Manufacturing Execution Systems (MES) for basic work order management, but there are significant deficiencies in material traceability, dynamic process control, and real-time risk interception. Specifically: 1. Material traceability gaps: Reliance on manual recording of material batch numbers results in a lack of systematic binding between LCD panels, OCA adhesive, and FPC batches. When soldering pins exceed standards or FPC bending defects occur, it is impossible to quickly trace the problematic batch. 2. Static process control failure: Key process parameters are executed fixedly—debubbling time is not dynamically adjusted in conjunction with bubble defect rate, and the cleaning process cannot optimize the frequency of cleanroom cloth replacement based on scratch type (such as lint contamination), resulting in a lag in process response. 3. Lack of real-time interception: Quality risks rely on downstream sampling inspections, and there is a lack of monitoring mechanisms in the process: excessive CG cleanliness and excessive FPC bending force cannot be intercepted in time; UV curing aging is not monitored with a countdown timer, and overdue products flow into the next process. Summary of the Invention

[0003] To solve the above-mentioned technical problems, the technical solution adopted by the present invention, a digital MES-driven full lamination process traceability method, includes the following steps: S1. Work order initialization and data binding: Retrieve the BOM table corresponding to the product model from the MES database to obtain the bill of materials for LCD panel part number, OCA adhesive part number, and FPC part number; load the associated process card parameter set, including cutting wheel specifications, UV curing energy value, and degassing pressure-time curve; generate a globally unique traceability code and bind it to the work order number, batch quantity, and material batch number; S2. First article activation and dynamic loading of process parameters: At the incoming material inspection station, scan the barcode and enter the LCD panel part number to verify its consistency with the BOM: if it matches, activate the 100% cleaning process of the cleaning machine and the CNC chamfering parameters; if it fails, freeze the batch and trigger an alarm. At the bonding key station, scanning the code triggers the execution of process parameters: at the silicone sealant coating station, scanning the code loads the dispensing path and starts the fullness verification process of the white glass; at the UV curing station, scanning the code activates the 4-hour aging countdown and verifies that the CG side of the product is facing up through an infrared sensor. S3. Real-time risk interception and closed-loop control: Real-time monitoring is deployed at process nodes: the CG cleaning station uploads cleanliness inspection data in real time, and the batch is frozen if the particle count is >5 particles / cm². The FPC bending station monitors the bending force through a pressure sensor. If the force is greater than 3N, an alarm "Do not pull with force" is triggered. Automatic interception of process exceeding standards: When the soldering inspection station detects a pin height greater than 0.45mm, it automatically marks the product as a reserved item. If three consecutive pieces from the same batch report errors, the batch number will be locked and feeding will be suspended. S4. End-to-end electronic resume construction: Integrate MES barcode scanning records, process parameter execution logs, and quality inspection results from various stations; Based on blockchain, an immutable traceability report is generated, supporting reverse tracing back to the LCD panel part number by work order number; S5. Closed-loop optimization of process parameters: Statistical analysis of the distribution of defect types at the material handling inspection station: S510. Classify the causes of bubble defects and perform targeted optimization: S511. Bubble type identification: At the material inspection station, a high-resolution optical imaging system is used to capture the morphological characteristics of bubbles. Combined with the MES historical defect database, pattern matching is performed to automatically distinguish between nucleated bubbles and non-nucleated bubbles. The nucleated bubbles are those with ≥1μm foreign matter diffraction characteristics in the core area, while the non-nucleated bubbles are uniformly transparent and have no foreign matter core. S512. Closed-loop processing of nucleated bubbles: Triggering environmental traceability: The system associates the location coordinates of the bubbles with the production period, retrieves the cleanliness sensor data of the corresponding CG cleaning station and bonding room, and the operator's number, and triggers a deep cleaning alarm; Material batch freezing: If nucleated bubbles are detected in 3 consecutive LCD panels of the same batch, the material batch is automatically frozen and a supplier improvement report is generated. S513. Coreless bubble dynamic pressurization optimization: Pressure gradient control: In the degassing process, the initial pressure increment is dynamically set according to the size and density distribution of the nucleus-free bubbles; Iterative pressurization and verification: After pressurization, the bubble elimination rate is evaluated in real time. If the target is not met, the pressure is increased in increments of 0.2 MPa, with the total pressure not exceeding the equipment safety threshold; After the target is met, the optimization parameters are recorded and synchronized to the process card for this product model; If the overall bubble defect rate is >10%, the degassing time is automatically extended; S520. If the scratch rate is >5%, revise the cleaning process parameters.

[0004] As a further aspect of the present invention, the loading logic of the process card parameter set in S1 includes: Cutting process loading rules: Match the cutting process card according to the product model, dynamically set the cutter wheel life to ≤30,000 times, and associate it with the air pressure abnormality handling protocol; Bonding process parameter binding: During barcode scanning, retrieve the ACF bonding temperature profile and Plasma cleaning power; Full-bonding error prevention control: After scanning the OCA adhesive part number, the degassing status is forcibly verified.

[0005] As a further aspect of the present invention, the fullness verification process of the white glass in S2 specifically includes: During the first operation, place a clear glass sheet below the silicone sealant coating path and detect the continuity of the sealant line through light transmission imaging: if a break in the sealant, missed coating, or overflow of sealant is detected with a width > 0.2mm, the process is interrupted and an alarm is triggered; During the inspection phase, one piece is randomly selected every 2 hours, and the imaging data is uploaded to the MES in real time. When the difference rate between the data and the first piece of reference value is greater than 15%, parameter calibration is triggered.

[0006] As a further aspect of this invention, the quantification rules for real-time risk interception in S3 include: Welding height control: At station L3-07, a laser height gauge is used to scan the pins. Units with a height > 0.45mm are automatically sorted to the Reten area and associated with the station operator's employee number; FPC batch locking mechanism: When the same FPC batch reports consecutive errors, the traceability system automatically associates it with the supplier batch, freezes the unused inventory, and generates a report.

[0007] As a further aspect of the present invention, the S4 electronic resume data association method includes: Environmental parameter binding: At the fitting appearance inspection station, the ambient light intensity value is simultaneously recorded to the quality inspection results; Time-tracking chain: UV curing countdown status is written into the history, and products that exceed the time limit are automatically marked and downgraded.

[0008] As a further aspect of the present invention, the parameter revision logic for S5 closed-loop optimization includes: Dynamic iteration of degassing time: based on the bubble defect rate P, according to the formula Extend the defoaming time; among which The new debubbling time represents the execution time of the dynamically optimized debubbling procedure. P represents the original degassing time, and P represents the bubble defect rate, which is the percentage of bubble defect rate calculated by the material unloading inspection station.

[0009] Beneficial effects: This invention verifies process parameters in real time through MES barcode scanning, and accurately intercepts risks such as welding height >0.45mm through sensor networks, significantly improving yield; deploying error-proof mechanisms in key processes effectively eliminates human error and reduces rework rates; building electronic records based on blockchain enables second-level reverse traceability from work orders to material batch numbers, accelerating customer complaint response; dynamically optimizing degassing time using bubble rate data from material unloading inspection continuously improves capacity utilization, forming a closed-loop management system of "interception-traceability-optimization". Attached Figure Description

[0010] Figure 1 This is a flowchart illustrating the steps of a digital MES-driven full lamination process traceability method according to the present invention. Detailed Implementation

[0011] The present invention will be further described in detail below with reference to the embodiments.

[0012] S1. Work order initialization and data binding: Retrieve the BOM table corresponding to the product model from the MES database to obtain the bill of materials for LCD panel part number, OCA adhesive part number, and FPC part number; load the associated process card parameter set, including cutting wheel specifications, UV curing energy value, and degassing pressure-time curve; generate a globally unique traceability code and bind it to the work order number, batch quantity, and material batch number; S2. First article activation and dynamic loading of process parameters: At the incoming material inspection station, scan the barcode and enter the LCD panel part number to verify its consistency with the BOM: if it matches, activate the 100% cleaning process of the cleaning machine and the CNC chamfering parameters; if it fails, freeze the batch and trigger an alarm. At the bonding key station, scanning the code triggers the execution of process parameters: at the silicone sealant coating station, scanning the code loads the dispensing path and starts the fullness verification process of the white glass; at the UV curing station, scanning the code activates the 4-hour aging countdown and verifies that the CG side of the product is facing up through an infrared sensor. S3. Real-time risk interception and closed-loop control: Real-time monitoring is deployed at process nodes: the CG cleaning station uploads cleanliness inspection data in real time, and the batch is frozen if the particle count is >5 particles / cm². The FPC bending station monitors the bending force through a pressure sensor. If the force is greater than 3N, an alarm "Do not pull with force" is triggered. Automatic interception of process exceeding standards: When the soldering inspection station detects a pin height greater than 0.45mm, it automatically marks the product as a reserved item. If three consecutive pieces from the same batch report errors, the batch number will be locked and feeding will be suspended. S4. End-to-end electronic resume construction: Integrate MES barcode scanning records, process parameter execution logs, and quality inspection results from various stations; Based on blockchain, an immutable traceability report is generated, supporting reverse tracing back to the LCD panel part number by work order number; S5. Closed-loop optimization of process parameters: Statistical analysis of the distribution of defect types at the material handling inspection station: S510. Classify the causes of bubble defects and perform targeted optimization: S511. Bubble type identification: At the material inspection station, a high-resolution optical imaging system is used to capture the morphological characteristics of bubbles. Combined with the MES historical defect database, pattern matching is performed to automatically distinguish between nucleated bubbles and non-nucleated bubbles. The nucleated bubbles are those with ≥1μm foreign matter diffraction characteristics in the core area, while the non-nucleated bubbles are uniformly transparent and have no foreign matter core. S512. Closed-loop processing of nucleated bubbles: Triggering environmental traceability: The system associates the location coordinates of the bubbles with the production period, retrieves the cleanliness sensor data of the corresponding CG cleaning station and bonding room, and the operator's number, and triggers a deep cleaning alarm; Material batch freezing: If nucleated bubbles are detected in 3 consecutive LCD panels of the same batch, the material batch is automatically frozen and a supplier improvement report is generated. S513. Coreless bubble dynamic pressurization optimization: Pressure gradient control: In the degassing process, the initial pressure increment is dynamically set according to the size and density distribution of the nucleus-free bubbles; Iterative pressurization and verification: After pressurization, the bubble elimination rate is evaluated in real time. If the target is not met, the pressure is increased in increments of 0.2 MPa, with the total pressure not exceeding the equipment safety threshold; After the target is met, the optimization parameters are recorded and synchronized to the process card for this product model; If the overall bubble defect rate is >10%, the degassing time is automatically extended; S520. If the scratch rate is >5%, revise the cleaning process parameters.

[0013] Furthermore, the loading logic for the process card parameter set in S1 includes: Cutting process loading rules: Match the cutting process card according to the product model, dynamically set the cutter wheel life to ≤30,000 times, and associate it with the air pressure abnormality handling protocol; Bonding process parameter binding: During barcode scanning, retrieve the ACF bonding temperature profile and Plasma cleaning power; Full-bonding error prevention control: After scanning the OCA adhesive part number, the degassing status is forcibly verified.

[0014] Furthermore, the fullness verification process for clear glass in S2 specifically includes: During the first operation, place a clear glass sheet below the silicone sealant coating path and detect the continuity of the sealant line through light transmission imaging: if a break in the sealant, missed coating, or overflow of sealant is detected with a width > 0.2mm, the process is interrupted and an alarm is triggered; During the inspection phase, one piece is randomly selected every 2 hours, and the imaging data is uploaded to the MES in real time. When the difference rate between the data and the first piece of reference value is greater than 15%, parameter calibration is triggered.

[0015] Furthermore, the quantitative rules for real-time risk interception in S3 include: Welding height control: At station L3-07, a laser height gauge is used to scan the pins. Units with a height > 0.45mm are automatically sorted to the Reten area and associated with the station operator's employee number; FPC batch locking mechanism: When the same FPC batch reports consecutive errors, the traceability system automatically associates it with the supplier batch, freezes the unused inventory, and generates a report.

[0016] Furthermore, the data association methods for S4 electronic resumes include: Environmental parameter binding: At the fitting appearance inspection station, the ambient light intensity value is simultaneously recorded to the quality inspection results; Time-tracking chain: UV curing countdown status is written into the history, and products that exceed the time limit are automatically marked and downgraded.

[0017] Furthermore, the parameter revision logic for S5 closed-loop optimization includes: Dynamic iteration of degassing time: based on the bubble defect rate P, according to the formula Extend the defoaming time; among which The new debubbling time represents the execution time of the dynamically optimized debubbling procedure. P represents the original degassing time, and P represents the bubble defect rate, which is the percentage of bubble defect rate calculated by the material unloading inspection station.

[0018] The above description is merely an embodiment of this specification and is not intended to limit this specification. Various modifications and variations can be made to this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this specification should be included within the scope of the claims of this application.

Claims

1. A method for tracing the entire bonding process driven by a digital MES, characterized in that, Includes the following steps: S1. Work order initialization and data binding: Retrieve the BOM table corresponding to the product model from the MES database to obtain the bill of materials for LCD panel part number, OCA adhesive part number, and FPC part number; Load the associated process card parameter set, including cutting wheel specifications, UV curing energy value, and degassing pressure-time curve; Generate a globally unique traceability code and bind it to the work order number, the quantity of this batch, and the material batch number; S2. First article activation and dynamic loading of process parameters: Scan the LCD small piece part number at the incoming material inspection station to verify its consistency with the BOM: If it matches, activate the 100% cleaning process of the cleaning machine and the CNC chamfering parameters. If the process fails, freeze the batch and trigger an alarm. At the bonding key station, scanning the code triggers the execution of process parameters: at the silicone sealant coating station, scanning the code loads the dispensing path and starts the fullness verification process of the white glass; at the UV curing station, scanning the code activates the 4-hour aging countdown and verifies that the CG side of the product is facing up through an infrared sensor. S3. Real-time risk interception and closed-loop control: Real-time monitoring is deployed at process nodes: the CG cleaning station uploads cleanliness inspection data in real time, and the batch is frozen if the particle count is >5 particles / cm². The FPC bending station monitors the bending force through a pressure sensor. If the force is greater than 3N, an alarm "Do not pull with force" is triggered. Automatic interception of process exceeding standards: When the soldering inspection station detects a pin height greater than 0.45mm, it automatically marks the product as a reserved item. If three consecutive pieces from the same batch report errors, the batch number will be locked and feeding will be suspended. S4. End-to-end electronic resume construction: Integrate MES barcode scanning records, process parameter execution logs, and quality inspection results from various stations; Based on blockchain, an immutable traceability report is generated, supporting reverse tracing back to the LCD panel part number by work order number; S5. Closed-loop optimization of process parameters: Statistical analysis of the distribution of defect types at the material handling inspection station: S510. Classify the causes of bubble defects and perform targeted optimization: S511. Bubble type identification: At the material inspection station, a high-resolution optical imaging system is used to capture the morphological characteristics of bubbles. Combined with the MES historical defect database, pattern matching is performed to automatically distinguish between nucleated bubbles and non-nucleated bubbles. The nucleated bubbles are those with ≥1μm foreign matter diffraction characteristics in the core area, while the non-nucleated bubbles are uniformly transparent and have no foreign matter core. S512. Closed-loop processing of nucleated bubbles: Triggering environmental traceability: The system associates the location coordinates of the bubbles with the production period, retrieves the cleanliness sensor data of the corresponding CG cleaning station and bonding room, and the operator's number, and triggers a deep cleaning alarm; Material batch freezing: If nucleated bubbles are detected in 3 consecutive LCD panels of the same batch, the material batch is automatically frozen and a supplier improvement report is generated. S513. Coreless bubble dynamic pressurization optimization: Pressure gradient control: In the degassing process, the initial pressure increment is dynamically set according to the size and density distribution of the nucleus-free bubbles; Iterative pressurization and verification: After pressurization, the bubble elimination rate is evaluated in real time. If the target is not met, the pressure is increased in increments of 0.2 MPa, with the total pressure not exceeding the equipment safety threshold; After the target is met, the optimization parameters are recorded and synchronized to the process card for this product model; If the overall bubble defect rate is >10%, the degassing time is automatically extended; S520. If the scratch rate is >5%, revise the cleaning process parameters.

2. The method for tracing the full lamination process driven by a digital MES according to claim 1, characterized in that, The loading logic for the process card parameter set in S1 includes: Cutting process loading rules: Match the cutting process card according to the product model, dynamically set the cutter wheel life to ≤30,000 times, and associate it with the air pressure abnormality handling protocol; Bonding process parameter binding: During barcode scanning, retrieve the ACF bonding temperature profile and Plasma cleaning power; Full-bonding error prevention control: After scanning the OCA adhesive part number, the degassing status is forcibly verified.

3. The method for tracing the full lamination process driven by digital MES according to claim 1, characterized in that, The specific procedures for verifying the fullness of clear glass in S2 include: During the first operation, place a clear glass sheet below the silicone sealant coating path and detect the continuity of the sealant line through light transmission imaging: if a break in the sealant, missed coating, or overflow of sealant is detected with a width > 0.2mm, the process is interrupted and an alarm is triggered; During the inspection phase, one piece is randomly selected every 2 hours, and the imaging data is uploaded to the MES in real time. When the difference rate between the data and the first piece of reference value is greater than 15%, parameter calibration is triggered.

4. The method for tracing the entire bonding process driven by a digital MES according to claim 1, characterized in that, The quantitative rules for real-time risk interception in S3 include: Welding height control: At station L3-07, a laser height gauge is used to scan the pins. Units with a height > 0.45mm are automatically sorted to the Reten area and associated with the station operator's employee number; FPC batch locking mechanism: When the same FPC batch reports consecutive errors, the traceability system automatically associates it with the supplier batch, freezes the unused inventory, and generates a report.

5. The method for tracing the full lamination process driven by a digital MES according to claim 1, characterized in that, The data association methods for S4 electronic resumes include: Environmental parameter binding: At the fitting appearance inspection station, the ambient light intensity value is simultaneously recorded to the quality inspection results; Time-tracking chain: UV curing countdown status is written into the history, and products that exceed the time limit are automatically marked and downgraded.

6. The method for tracing the full lamination process driven by a digital MES according to claim 1, characterized in that, The parameter revision logic for S5 closed-loop optimization includes: Dynamic iteration of degassing time: based on the bubble defect rate P, according to the formula Extend the defoaming time; among which The new debubbling time represents the execution time of the dynamically optimized debubbling procedure. P represents the original degassing time, and P represents the bubble defect rate, which is the percentage of bubble defect rate calculated by the material unloading inspection station.

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