An intelligent production system for FPC flexible circuit boards based on image recognition

Abstract

The present invention relates to the technical field of FPC production control, and specifically to an intelligent production system for FPC flexible circuit boards based on image recognition. The system comprises a demand analysis module for performing information analysis on production entities on the FPC production line, building a production information model, and allocating production tasks based on the production information; a process monitoring module for performing image recognition on key processes on the FPC production line, and processing the key processes based on the production characteristics of the FPC flexible circuit boards to generate FPC labels, wherein the FPC labels include at least time, key process characteristics, production characteristics, and processing data; and an intelligent analysis module for performing FPC quality traceability and management, including obtaining electrical performance test results of the FPC flexible circuit boards, generating FPC labels, searching for and comparing similar FPC label sets, checking whether there are problems with the same batch of FPC flexible circuit boards, and providing feedback on key FPC process steps.

Description

Technical Field

[0001] The present invention relates to the technical field of FPC production control, and in particular to an FPC flexible circuit board intelligent production system based on image recognition. Background Art

[0002] In the field of flexible circuit board manufacturing, due to its thin, flexible substrate and sophisticated production processes, higher requirements are placed on automated handling, high-precision alignment, online quality inspection, and process parameter control. Existing production workshops generally suffer from low levels of informatization and networking, stand-alone equipment, and numerous manual operations, resulting in wear and inaccurate positioning. Consequently, experts have begun exploring the construction of intelligent manufacturing unit management and control systems. By establishing databases and knowledge bases, and applying data analysis and machine learning techniques, these systems enable self-sensing and self-configuration of process parameters and prediction of product quality. Furthermore, data-based supervisory and control systems are being developed to assess production challenges, monitor the environment and personnel flow, and perform fault prediction and simulation. Furthermore, modular production units and intelligent material management systems are also being gradually implemented to improve production flexibility and efficiency. However, existing technologies still lack the ability to achieve comprehensive, centralized, refined, real-time, and intelligent control and management of the entire FPC production process. Key challenges remain in effectively integrating multi-source information, accurately monitoring key process parameters and product status, and enabling rapid response and intelligent decision-making based on real-time conditions.

[0003] Therefore, an FPC flexible circuit board intelligent production system based on image recognition is proposed. Summary of the Invention

[0004] The present invention aims to provide an intelligent production system for flexible printed circuit boards (FPCs) based on image recognition, thereby improving the supervision and control of the entire FPC production process. First, a demand analysis module analyzes information on production entities on the FPC production line, constructs a production information model, and allocates production tasks based on this information. A process monitoring module performs image recognition on key processes on the FPC production line and processes the production characteristics of the FPCs during these key processes to generate FPC labels. These labels contain at least the time, key process characteristics, production characteristics, and processing data. Finally, an intelligent analysis module performs FPC quality traceability and management, including obtaining electrical performance test results for the FPCs, generating FPC labels, searching for and comparing similar FPC label sets, checking for issues with the same batch of FPCs, and providing feedback on key FPC process steps.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] An intelligent production system for FPC flexible circuit boards based on image recognition, comprising:

[0007] The demand analysis module is used to analyze the information of production entities on the FPC production line, build a production information model, and allocate production tasks based on the production information;

[0008] Furthermore, the production entity at least includes the FPC flexible circuit board materials to be produced, production equipment, fixtures, production personnel and production orders to be executed;

[0009] The production information model constructed based on the information of the production subject includes at least a material sub-model, an equipment sub-model, an order sub-model, a personnel sub-model and association relationships, where the association relationships record the matching compatibility rules between materials and orders, materials and equipment, and equipment and orders.

[0010] Furthermore, the process of production task allocation includes:

[0011] A scheduling algorithm is constructed based on order priority, delivery date, material availability and quality assessment, real-time equipment load and readiness status, and changeover cost. The scheduling algorithm continuously monitors data changes in the production information model. When it detects that the production information model receives information about a new production entity, the scheduling algorithm starts, generates an optimal production task sequence and equipment allocation plan, and executes the allocation plan.

[0012] The process monitoring module performs image recognition on the key processes on the FPC production line, and processes the production characteristics of the FPC flexible circuit board in the key processes to generate an FPC label. The FPC label includes at least time, key process characteristics, production characteristics and processing data;

[0013] Furthermore, the key processes include at least cutting, drilling, pattern transfer, etching, lamination, cutting and surface treatment;

[0014] The image recognition is to collect and analyze the production features of the FPC flexible circuit board in the key process, and the production features include at least registration targets, circuit patterns, hole positions, pads, character logos, surface defects, and alignment relationships between layers or materials;

[0015] The recognition algorithms used in the image recognition include but are not limited to edge detection, image segmentation, feature extraction, template matching, defect classification based on convolutional neural networks, and target detection algorithms.

[0016] Furthermore, the process of generating the FPC label includes:

[0017] Directly obtain the visual fingerprint of the production characteristics of the FPC flexible circuit board in each key process through image recognition, and the visual fingerprint includes at least the time, key process characteristics, and processing data characteristics of the FPC flexible circuit board in the corresponding key process;

[0018] After completing image recognition and data collection in each key process, the visual fingerprints of the FPC flexible circuit board in all key processes are linked in chronological order and marked with a unique identifier, which is generated and attached in the first key process.

[0019] The intelligent analysis module performs FPC quality traceability and management, including obtaining the electrical performance test results of FPC flexible circuit boards, generating FPC labels, finding and comparing similar FPC label sets, checking whether there are problems with FPC flexible circuit boards in the same batch, and providing feedback on key FPC process steps.

[0020] Furthermore, the process of obtaining the electrical performance test results of the FPC flexible circuit board and generating the FPC label includes:

[0021] After completing all manufacturing processes, the FPC flexible circuit board enters the electrical performance test phase; the electrical performance test includes at least a flying probe tester, an ICT tester, and a high-frequency tester;

[0022] The electrical performance of the FPC flexible circuit board is tested through an electrical performance test, wherein the electrical performance includes at least conduction, insulation, impedance, and capacitance; the test results are automatically uploaded to the intelligent analysis module through the device interface and associated with the unique identifier of the FPC flexible circuit board;

[0023] If the test result of the FPC flexible circuit board is not up to standard, the status of the FPC flexible circuit board will be marked immediately, and detailed failure test data will be added to the FPC label. The failure test data shall at least include a list of failed test items, failure type, failure location information, comparison between actual measurement values ​​and standard values, test conditions, test equipment and fixture information, and test timestamp.

[0024] Furthermore, the process of finding and comparing similar FPC label sets to check whether there are any problems with FPC products in the same batch includes:

[0025] Retrieving FPC labels from a database, and performing similarity search through several search methods, wherein the search methods at least include searching based on product attributes and batches, searching based on production characteristics, and searching based on test results; constructing a set of similar FPC labels based on the search results;

[0026] After finding a similar FPC label set, analyze the electrical performance test results of the FPC flexible circuit boards in the set. If the test results of the FPC flexible circuit boards in the set are unqualified in several electrical performance test items, and the unqualified ratio exceeds the set threshold ratio, then it is determined that the similar FPC label set has quality problems;

[0027] When it is determined that a similar FPC label set has quality problems, the intelligent analysis module retrospectively analyzes the FPC label data corresponding to each key process of the unqualified FPC flexible circuit board in the similar FPC label set, and compares it with the FPC label data of the qualified FPC flexible circuit board. If the difference exceeds the preset difference threshold, the key process is marked as abnormal;

[0028] The intelligent analysis module counts the number of abnormalities in key processes and generates a detailed analysis report, which at least includes the key processes with problems, the causes of the abnormalities, related production characteristics and FPC label data.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] 1. The production information model builds a digital map that reflects the current status of personnel, equipment, materials, process methods, environment, etc. at the production site in real time, providing a comprehensive, accurate, and dynamically updated digital map of the production site, providing high-quality input for subsequent scheduling algorithms. The scheduling algorithm continuously monitors the production task allocation process of the dynamic changes in the production information model and performs production scheduling, which can greatly improve the responsiveness and execution efficiency of production plans.

[0031] 2. By deploying intelligent monitoring based on image recognition in key processes and generating digital labels for each FPC flexible circuit board containing the visual fingerprint and processing data of the entire process, high-precision, real-time, non-contact monitoring of the FPC flexible circuit board production process is achieved. This makes the flexible circuit board production process, which was originally difficult to quantify and trace, completely transparent and traceable.

[0032] 3. By performing similarity search and set analysis on FPC labels, it is possible to automatically discover product groups with common characteristics and potential quality problems from massive production data, changing passive search to active early warning, and accurately locate the production batch or process window where the problem occurs. By retrospectively comparing the detailed label data of the problem product and the qualified product in the key process, the root cause of the problem can be diagnosed efficiently and scientifically. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 A schematic diagram of the structure of an FPC flexible circuit board intelligent production system based on image recognition provided by an embodiment of the present invention;

[0034] Figure 2 A flowchart of a demand analysis module provided by an embodiment of the present invention;

[0035] Figure 3 This is a flowchart of the intelligent analysis module provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0036] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0037] Example 1:

[0038] In order to improve the supervision and control of the entire production process and increase the production efficiency of FPC flexible circuit boards, a certain FPC flexible circuit board manufacturer introduced an FPC flexible circuit board intelligent production system based on image recognition provided by the present invention. The system structure is as follows: Figure 1 As shown, the specific implementation is as follows:

[0039] The demand analysis module is used to analyze the information of production entities on the FPC production line, build a production information model, and allocate production tasks based on the production information;

[0040] Furthermore, the production entity at least includes the FPC flexible circuit board materials to be produced, production equipment, fixtures, production personnel and production orders to be executed;

[0041] The production information model constructed based on the information of the production subject includes at least a material sub-model, an equipment sub-model, an order sub-model, a personnel sub-model and association relationships, where the association relationships record the matching compatibility rules between materials and orders, materials and equipment, and equipment and orders.

[0042] Furthermore, after receiving and integrating the original information collected from the above-mentioned production entities, the demand analysis module performs information analysis and constructs the production information model. Specifically, the process of constructing the production information model includes: data collection and preprocessing, information extraction and identification, data association and integration, etc.

[0043] Furthermore, the integrated structured information is used to dynamically update the production information model in memory. When new materials arrive, the equipment status changes, tooling and fixtures are in place, personnel log in, or new orders are imported, the corresponding material sub-model, equipment sub-model, tooling sub-model, personnel sub-model, or order sub-model in the model is created, updated, or the status change is marked. For example, a new batch of flexible copper clad laminates will create a record in the material sub-model, including its model, quantity, preliminary quality score based on image analysis, and marked as "pending allocation". A piece of equipment that has completed maintenance will update its status to "idle" in the equipment sub-model and record the type of tooling and fixture currently installed.

[0044] Furthermore, the core of the production information model lies in the relationships and rules it contains. These rules are pre-configured based on the product's process flow and equipment capabilities and stored in the model's rule base. These relationships and rules include at least certain requirements: specific product models require specific material types and batch ranges; specific process steps must be performed on specific types or equipment with specific configurations; specific equipment requires the use of specific types or versions of fixtures; and the relationship between materials and orders. These relationships and rules are referenced during the model construction process to verify the accuracy and compatibility of information and are used as constraints by the scheduling algorithm during scheduling.

[0045] The production information model is a digital map that reflects the current status of personnel, equipment, materials, process methods, environment, etc. at the production site in real time. It is a dynamically changing graph or network structure. Nodes represent production entities, and edges represent the relationships and current status between these entities. It provides a comprehensive, accurate, and dynamically updated digital map of the production site, providing high-quality input for subsequent scheduling algorithms, enabling them to plan production processes more effectively, reduce waiting and changeover time, improve equipment utilization and production throughput, and quickly respond to sudden production changes or new high-priority orders, thereby enhancing the flexibility and adaptability of the entire production system.

[0046] Furthermore, the process of production task allocation includes:

[0047] A scheduling algorithm is constructed based on order priority, delivery date, material availability and quality assessment, real-time equipment load and readiness status, and changeover cost. The scheduling algorithm continuously monitors data changes in the production information model. When it detects that the production information model receives information about a new production entity, the scheduling algorithm starts, generates an optimal production task sequence and equipment allocation plan, and executes the allocation plan.

[0048] Furthermore, first, the scheduling algorithm can be a rule-based expert system, linear programming, mixed integer programming, constraint programming or more complex heuristic algorithms, simulation-based optimization, or even use reinforcement learning to cope with dynamic changes. The goal of the algorithm is to generate a production task sequence and equipment allocation plan that can optimize one or more goals; the scheduling algorithm runs continuously as a background service and monitors data changes in the production information model in real time. Once the model receives new production subject information, such as the import of new production orders, these changes will trigger the restart or adjustment of the scheduling algorithm; the system can also set trigger thresholds or cycles, such as starting scheduling at fixed intervals or when the status of key resources changes significantly.

[0049] Furthermore, the scheduling algorithm accesses the latest, panoramic data from the production information model, including all pending tasks, the status of all available and active production resources, and their relationships. Based on the preset optimization objectives and constraints, the algorithm calculates the benefits of different task sequences and resource allocation schemes and selects the optimal one. This scheme specifies which products, batches, equipment, and time periods will be processed within the next timeframe, using which batches of materials, and the corresponding process parameter program numbers.

[0050] After generating the optimal production task sequence and equipment allocation plan, the demand analysis module converts the plan into specific execution instructions, which are then sent to the control system layer at the production site via the network.

[0051] The scheduling algorithm continuously monitors the production task allocation process of the dynamic changes in the production information model and performs production scheduling, which can greatly improve the responsiveness and execution efficiency of production plans. It breaks away from the limitations of static plans, can quickly respond to emergencies at the production site, make dynamic adjustments, and reduce production interruptions and resource waste caused by delayed or inaccurate information.

[0052] The process monitoring module performs image recognition on the key processes on the FPC production line, and processes the production characteristics of the FPC flexible circuit board in the key processes to generate an FPC label. The FPC label includes at least time, key process characteristics, production characteristics and processing data;

[0053] Furthermore, the key processes include at least cutting, drilling, pattern transfer, etching, lamination, cutting and surface treatment;

[0054] The image recognition is to collect and analyze the production features of the FPC flexible circuit board in the key process, and the production features include at least registration targets, circuit patterns, hole positions, pads, character logos, surface defects, and alignment relationships between layers or materials;

[0055] The recognition algorithms used in the image recognition include but are not limited to edge detection, image segmentation, feature extraction, template matching, and defect classification and target detection algorithms based on convolutional neural networks.

[0056] Furthermore, the process monitoring module deploys automated visual inspection stations at key process points in the FPC production process, capturing and analyzing real-time images of the product. These key process points were chosen because they are crucial to the final performance and reliability of the FPC and are susceptible to fluctuations in process parameters, which can lead to defects.

[0057] In these key processes, image recognition algorithms are used to capture and analyze production features, achieving significant quality control benefits. This enables the system to perform real-time, high-precision, and objective monitoring and measurement of key production features such as registration targets, circuit patterns, hole positions, pads, character identification, surface defects, and inter-layer alignment during the manufacturing process of FPC flexible circuit boards.

[0058] Furthermore, the process of generating the FPC label includes:

[0059] Directly obtain the visual fingerprint of the production characteristics of the FPC flexible circuit board in each key process through image recognition, and the visual fingerprint includes at least the time, key process characteristics, and processing data characteristics of the FPC flexible circuit board in the corresponding key process;

[0060] After completing image recognition and data collection in each key process, the visual fingerprints of the FPC flexible circuit board in all key processes are linked in chronological order and marked with a unique identifier, which is generated and attached in the first key process.

[0061] Furthermore, the visual fingerprint includes at least: the precise timestamp of the key process of this image acquisition; key process features obtained through image analysis, such as the average line width, minimum line spacing, hole position deviation, cover film alignment offset and other key geometric measurement values; a list of detected production characteristic defects, such as defect type, defect coordinates on the board, defect severity assessment, etc.; and any processing data feature identifiers read from the image.

[0062] Furthermore, in addition to the visual fingerprint obtained by image recognition, the equipment in this key process will also provide processing data through sensors or communication interfaces, such as actual temperature, pressure, speed, current, drug concentration, etc. These processing data are associated with the visual fingerprint of this visual inspection in time and space.

[0063] Furthermore, when an FPC enters the first critical step of the production process, the system generates a unique identifier for it, such as a laser-etched QR code or a specific coded pattern formed by drilling. This identifier is then attached to or physically associated with the FPC. After image recognition and data collection are completed at each subsequent critical step, the generated visual fingerprint and associated processing data for that step are packaged and uploaded to a central database via the network, where they are stored in association with the FPC's unique identifier. Based on the recorded timestamps, the system automatically links and organizes all visual fingerprints and processing data belonging to the same unique identifier in chronological order, forming a complete FPC label archive. This archive serves as a digital record of the entire FPC process, from raw materials to the current step. Table 1 shows detailed visual fingerprint data acquired through image recognition during the etching process for some FPCs.

[0064] Table 1. Partial visual fingerprint data

[0065]

[0066] By deploying intelligent monitoring based on image recognition in key processes and generating digital labels containing full-process visual fingerprints and processing data for each FPC flexible circuit board, high-precision, real-time, non-contact monitoring of the FPC flexible circuit board production process is achieved. This makes the flexible circuit board production process, which was originally difficult to quantify and trace, completely transparent and traceable.

[0067] The intelligent analysis module performs FPC quality traceability and management, including obtaining the electrical performance test results of FPC flexible circuit boards, generating FPC labels, finding and comparing similar FPC label sets, checking whether there are problems with FPC flexible circuit boards in the same batch, and providing feedback on key FPC process steps.

[0068] Furthermore, the process of obtaining the electrical performance test results of the FPC flexible circuit board and generating the FPC label includes:

[0069] After completing all manufacturing processes, the FPC flexible circuit board enters the electrical performance test phase; the electrical performance test includes at least a flying probe tester, an ICT tester, and a high-frequency tester;

[0070] The electrical performance of the FPC flexible circuit board is tested through an electrical performance test, wherein the electrical performance includes at least conduction, insulation, impedance, and capacitance; the test results are automatically uploaded to the intelligent analysis module through the device interface and associated with the unique identifier of the FPC flexible circuit board;

[0071] If the test result of the FPC flexible circuit board is not up to standard, the status of the FPC flexible circuit board will be marked immediately, and detailed failure test data will be added to the FPC label. The failure test data shall at least include a list of failed test items, failure type, failure location information, comparison between actual measurement values ​​and standard values, test conditions, test equipment and fixture information, and test timestamp.

[0072] The electrical performance test results of FPC flexible circuit boards are automatically obtained and associated with FPC labels. In particular, for products that do not meet the standards, detailed failure test data is added to their labels, providing indispensable data for the intelligent analysis module to perform accurate root cause analysis. This enables quality engineers and intelligent systems to quickly and accurately understand "where" and "how" electrical failures occurred in the product, and based on this, they can trace back its manufacturing process and efficiently identify potential process problems or visual anomalies that lead to failure, greatly improving the efficiency of problem diagnosis.

[0073] Furthermore, the process of finding and comparing similar FPC label sets to check whether there are any problems with FPC products in the same batch includes:

[0074] Retrieving FPC labels from a database, and performing similarity search through several search methods, wherein the search methods at least include searching based on product attributes and batches, searching based on production characteristics, and searching based on test results; constructing a set of similar FPC labels based on the search results;

[0075] After finding a similar FPC label set, analyze the electrical performance test results of the FPC flexible circuit boards in the set. If the test results of the FPC flexible circuit boards in the set are unqualified in several electrical performance test items, and the unqualified ratio exceeds the set threshold ratio, then it is determined that the similar FPC label set has quality problems;

[0076] When it is determined that a similar FPC label set has quality problems, the intelligent analysis module retrospectively analyzes the FPC label data corresponding to each key process of the unqualified FPC flexible circuit board in the similar FPC label set, and compares it with the FPC label data of the qualified FPC flexible circuit board. If the difference exceeds the preset difference threshold, the key process is marked as abnormal;

[0077] The intelligent analysis module counts the number of abnormalities in key processes and generates a detailed analysis report, which at least includes the key processes with problems, the causes of the abnormalities, related production characteristics and FPC label data.

[0078] Furthermore, the process aims to intelligently mine and analyze the accumulated label data of a large number of FPC flexible circuit board products, identify product groups with similar characteristics, find common problems in these groups, and ultimately trace back to the possible root process.

[0079] Furthermore, searching based on product attributes and batches is the most basic search method. The system directly aggregates FPC product labels belonging to the same category or batch based on information such as product model, production order number, production batch number or raw material batch number in the FPC label;

[0080] Furthermore, the intelligent analysis module reads the "production features" and "key process features" of each key process recorded on the FPC labels and uses feature vector matching, clustering algorithms, or dimensionality reduction techniques to find groups of FPC labels with dense data distribution or close distances in the multi-dimensional production feature space. For example, all FPC labels with line width measurements fluctuating within a specific range during etching process image inspection can be grouped together; or all FPC labels with similar bubble defect patterns during the lamination process can be grouped together.

[0081] Furthermore, the test result-based search involves the intelligent analysis module reading the electrical performance test results recorded on the FPC label and searching for FPC labels that exhibit similar results on specific test items. For example, it can search for all product labels that failed a specific short-circuit test point, or search for all product labels that failed a specific impedance test item with similar measured values.

[0082] Furthermore, after finding a set of similar FPC labels, the intelligent analysis module first performs a statistical analysis on the electrical performance test results of all FPC flexible circuit boards in the set, and checks the test results of the FPC flexible circuit boards in the set on several electrical performance test items. If the unqualified proportion of the FPC flexible circuit boards in the set on the test item exceeds the set threshold proportion, for example, more than 5% of the products fail a specific short-circuit test, the system determines that there is a quality problem with the similar FPC label set.

[0083] Furthermore, when it is determined that a certain similar FPC label set has quality problems, the intelligent analysis module retrospectively analyzes the FPC label data corresponding to each key process of the unqualified FPC flexible circuit boards in the set. At the same time, the system will retrieve and compare the label data of qualified FPC flexible circuit boards produced in the same period from the database. The focus of the comparison is to find the differences in the production characteristics of the key processes between the unqualified product set and the qualified product set.

[0084] By performing similarity searches and set analysis on FPC labels, it is possible to automatically discover product groups with common characteristics and potential quality issues from massive production data, turning passive searches into active early warnings. It is also possible to accurately locate the production batches or process windows where the problems occur, and by retrospectively comparing the detailed label data of problem products and qualified products in key processes, it is possible to efficiently and scientifically diagnose the root cause of the problem.

[0085] This system provides a comprehensive intelligent solution for FPC flexible circuit board production by deeply integrating demand analysis, process monitoring and intelligent analysis functions based on image recognition. It significantly improves the automation and intelligence level of the production process and overcomes the limitations brought by traditional manual dependence and information islands. Precision image monitoring throughout key processes realizes real-time, objective and fine control of product quality and process status, and can detect and prevent defects earlier and more accurately, thereby significantly improving production yield and reducing rework and waste. Integrated data collection and intelligent analysis, especially quality traceability and root cause diagnosis combined with full-process visual information, make problem solving more efficient and accurate, and provide strong data support for continuous process optimization and management decision-making.

[0086] Example 2:

[0087] A company introduced an FPC flexible circuit board intelligent production system based on image recognition provided by the present invention during the production process of FPC flexible circuit boards to improve the supervision and control efficiency of the entire production process. The specific implementation method is as follows:

[0088] The demand analysis module is used to analyze the information of production entities on the FPC production line, build a production information model, and allocate production tasks based on the production information;

[0089] Furthermore, the production entity at least includes the FPC flexible circuit board materials to be produced, production equipment, fixtures, production personnel and production orders to be executed;

[0090] The production information model constructed based on the information of the production subject includes at least a material sub-model, an equipment sub-model, an order sub-model, a personnel sub-model and association relationships, where the association relationships record the matching compatibility rules between materials and orders, materials and equipment, and equipment and orders.

[0091] Furthermore, the process of production task allocation includes:

[0092] A scheduling algorithm is constructed based on order priority, delivery date, material availability and quality assessment, real-time equipment load and readiness status, and changeover cost. The scheduling algorithm continuously monitors data changes in the production information model. When it detects that the production information model receives information about a new production entity, the scheduling algorithm starts, generates an optimal production task sequence and equipment allocation plan, and executes the allocation plan.

[0093] The process monitoring module performs image recognition on the key processes on the FPC production line, and processes the production characteristics of the FPC flexible circuit board in the key processes to generate an FPC label. The FPC label includes at least time, key process characteristics, production characteristics and processing data;

[0094] Furthermore, the key processes include at least cutting, drilling, pattern transfer, etching, lamination, cutting and surface treatment;

[0095] The image recognition is to collect and analyze the production features of the FPC flexible circuit board in the key process, and the production features include at least registration targets, circuit patterns, hole positions, pads, character logos, surface defects, and alignment relationships between layers or materials;

[0096] The recognition algorithms used in the image recognition include but are not limited to edge detection, image segmentation, feature extraction, template matching, defect classification based on convolutional neural networks, and target detection algorithms.

[0097] Furthermore, the process of generating the FPC label includes:

[0098] Directly obtain the visual fingerprint of the production characteristics of the FPC flexible circuit board in each key process through image recognition, and the visual fingerprint includes at least the time, key process characteristics, and processing data characteristics of the FPC flexible circuit board in the corresponding key process;

[0099] After completing image recognition and data collection in each key process, the visual fingerprints of the FPC flexible circuit board in all key processes are linked in chronological order and marked with a unique identifier, which is generated and attached in the first key process.

[0100] The intelligent analysis module performs FPC quality traceability and management, including obtaining the electrical performance test results of FPC flexible circuit boards, generating FPC labels, finding and comparing similar FPC label sets, checking whether there are problems with FPC flexible circuit boards in the same batch, and providing feedback on key FPC process steps.

[0101] Furthermore, the process of obtaining the electrical performance test results of the FPC flexible circuit board and generating the FPC label includes:

[0102] After completing all manufacturing processes, the FPC flexible circuit board enters the electrical performance test phase; the electrical performance test includes at least a flying probe tester, an ICT tester, and a high-frequency tester;

[0103] The electrical performance of the FPC flexible circuit board is tested through an electrical performance test, wherein the electrical performance includes at least conduction, insulation, impedance, and capacitance; the test results are automatically uploaded to the intelligent analysis module through the device interface and associated with the unique identifier of the FPC flexible circuit board;

[0104] If an FPC test result fails, the FPC status is immediately marked and detailed failure test data is added to the FPC label. This failure test data includes at least a list of failed test items, failure type, failure location, comparison of actual measured values ​​with standard values, test conditions, test equipment and fixture information, and a test timestamp. Table 2 shows some FPC test data.

[0105] Table 2. Test data of some FPC flexible circuit boards

[0106]

[0107] Furthermore, the process of finding and comparing similar FPC label sets to check whether there are any problems with FPC products in the same batch includes:

[0108] Retrieving FPC labels from a database, and performing similarity search through several search methods, wherein the search methods at least include searching based on product attributes and batches, searching based on production characteristics, and searching based on test results; constructing a set of similar FPC labels based on the search results;

[0109] After finding a similar FPC label set, analyze the electrical performance test results of the FPC flexible circuit boards in the set. If the test results of the FPC flexible circuit boards in the set are unqualified in several electrical performance test items, and the unqualified ratio exceeds the set threshold ratio, then it is determined that the similar FPC label set has quality problems;

[0110] When it is determined that a similar FPC label set has quality problems, the intelligent analysis module retrospectively analyzes the FPC label data corresponding to each key process of the unqualified FPC flexible circuit board in the similar FPC label set, and compares it with the FPC label data of the qualified FPC flexible circuit board. If the difference exceeds the preset difference threshold, the key process is marked as abnormal;

[0111] The intelligent analysis module counts the number of abnormalities in key processes and generates a detailed analysis report. The report includes at least the key process with the problem, the cause of the abnormality, relevant production characteristics, and FPC label data. Table 3 shows a portion of the analysis report.

[0112] Table 3. Analysis report

[0113]

[0114] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An FPC flexible circuit board intelligent production system based on image recognition, characterized in that: include: The demand analysis module is used to analyze the information of production entities on the FPC production line and build a production information model. The scheduling algorithm continuously monitors the production information model. When it detects that the production information model receives information about new production entities, the scheduling algorithm is activated to allocate production tasks based on the production information. The process monitoring module performs image recognition on the key processes on the FPC production line to obtain visual fingerprints. The visual fingerprints of all key processes are linked in chronological order and marked as unique identifiers. The visual fingerprints are processed according to the production characteristics of the FPC flexible circuit board in the key process to generate an FPC label. The FPC label includes at least time, key process characteristics, production characteristics and processing data. The intelligent analysis module performs FPC quality traceability and management, including obtaining the electrical performance test results of the FPC flexible circuit board. The electrical performance test results are automatically uploaded to the intelligent analysis module through the device interface and associated with the unique identifier to generate an FPC label. If the test result of the FPC flexible circuit board is not up to standard, the status of the FPC flexible circuit board is immediately marked and the failure test data is ensured to be added to the FPC label. FPC labels are retrieved from the database, and similarity searches are performed using several search methods to check whether there are any problems with the same batch of FPC flexible circuit boards. When it is determined that a set of similar FPC labels has quality problems, the intelligent analysis module retrospectively analyzes the FPC label data corresponding to each key process of the unqualified FPC flexible circuit boards in the similar FPC label set, and compares the FPC label data of qualified FPC flexible circuit boards. If the difference exceeds the preset difference threshold, the key process is marked as abnormal; a detailed analysis report is generated and feedback is provided on the key FPC process steps.

2. The FPC flexible circuit board intelligent production system based on image recognition according to claim 1 is characterized in that: The production entity at least includes the FPC flexible circuit board materials to be produced, production equipment, fixtures, production personnel and production orders to be executed; The production information model constructed based on the information of the production subject includes at least a material sub-model, an equipment sub-model, an order sub-model, a personnel sub-model and association relationships, where the association relationships record the matching compatibility rules between materials and orders, materials and equipment, and equipment and orders.

3. The FPC flexible circuit board intelligent production system based on image recognition according to claim 1 is characterized in that: The process of production task allocation includes: A scheduling algorithm is constructed based on order priority, delivery date, material availability and quality assessment, real-time equipment load, readiness status, and changeover cost. The scheduling algorithm continuously monitors the production information model. When it detects that the production information model receives information from a new production entity, the scheduling algorithm is activated to generate the optimal production task sequence and equipment allocation plan, and then executes the allocation plan.

4. The FPC flexible circuit board intelligent production system based on image recognition according to claim 1 is characterized in that: The key processes include at least cutting, drilling, pattern transfer, etching, lamination, cutting and surface treatment; The image recognition is to collect and analyze the production features of the FPC flexible circuit board in the key process, and the production features include at least registration targets, circuit patterns, hole positions, pads, character logos, surface defects and alignment relationships between materials; The recognition algorithms used in the image recognition include edge detection, image segmentation, feature extraction, template matching, defect classification based on convolutional neural network and target detection algorithm.

5. The FPC flexible circuit board intelligent production system based on image recognition according to claim 1 is characterized in that: The process of generating an FPC label by processing the production characteristics of the FPC flexible circuit board in key processes includes: Directly obtain the visual fingerprint of the production characteristics of the FPC flexible circuit board in each key process through image recognition, and the visual fingerprint includes at least the time, key process characteristics, and processing data characteristics of the FPC flexible circuit board in the corresponding key process; After completing image recognition and data collection in each key process, the visual fingerprints of the FPC flexible circuit board in all key processes are linked in chronological order and marked with a unique identifier, which is generated and attached in the first key process.

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