Intelligent line speed automatic switching system based on work order identification in brown machine production

Abstract

The invention discloses an intelligent line speed automatic switching system based on work order identification in brown machine production, and relates to the technical field of brown machine production, comprising a work order identification module used for acquiring work order information in real time through a data interface of an enterprise production management system, and a data processing module connected with the work order identification module, the optimal line speed of a current work order product in the browning process is calculated by using an algorithm model according to a preset product model and line speed corresponding relation database in combination with parameters such as product size and circuit board layer number, the data processing module is connected, and the transmission line speed of the browning machine is automatically adjusted according to the optimal line speed calculated by the data processing module. The feedback and monitoring module is used for collecting browned product quality data in real time in the production process of the browning machine; by accurately matching the optimal line speeds of different work order products, the quality problems of poor browning effect and the like caused by improper line speeds are effectively avoided, and the stability and the consistency of the product quality are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of brownization machine production, and specifically relates to an intelligent line speed automatic switching system based on work order recognition in brownization machine production. Background Art

[0002] During the production process of the brownization machine, there are differences in the product specifications and process requirements corresponding to different work orders. For example, the sizes, thicknesses of different circuit boards, and the required brownization degrees are all different. The traditional line speed control method of the brownization machine usually relies on manual adjustment based on experience, and this method has many drawbacks. On the one hand, it is difficult for manual operation to accurately match the optimal line speed of different work orders, which easily leads to unstable product quality. For example, too fast a line speed may result in insufficient brownization effect, while too slow a line speed will reduce production efficiency and increase production costs. On the other hand, frequent manual line speed adjustment not only consumes manpower, but also easily causes operation errors, further affecting the continuity of production and the consistency of product quality. Summary of the Invention

[0003] To solve the above technical problems, an intelligent line speed automatic switching system based on work order recognition in brownization machine production is provided, which solves the problems of unstable product quality, low production efficiency, and easy occurrence of operation errors in manually adjusting the line speed of the brownization machine in the prior art.

[0004] To achieve the above object, the technical solution adopted by the present invention is: an intelligent line speed automatic switching system based on work order recognition in brownization machine production, including a work order recognition module for obtaining work order information in real time through a data interface with the enterprise production management system. The work order information includes product model, product size, number of circuit board layers, and brownization process requirements, and can use optical character recognition technology to scan and recognize paper work orders, and transmit the recognized work order information to the data processing module;

[0005] A data processing module, connected to the work order recognition module, receives the work order information and parses and processes it. Based on the preset database of the corresponding relationship between product model and line speed, combined with parameters such as product size and number of circuit board layers, an algorithm model is used to calculate the optimal line speed of the current work order product during the brownization process. And this algorithm model will comprehensively consider the influence of process parameters such as brownization liquid concentration, temperature, and spraying pressure on the optimal line speed, and continuously optimize and update according to actual production data;

[0006] A line speed control module, connected to the data processing module, according to the optimal line speed calculated by the data processing module, communicates with the drive control system of the brownization machine to automatically adjust the conveyor line speed of the brownization machine, and has a function of real-time monitoring of the line speed. When there is a deviation between the actual line speed and the target line speed, automatic correction is immediately performed;

[0007] The feedback and monitoring module is used to collect real-time quality data of the products after browning during the production process of the browning machine, such as the thickness of the browning film, adhesion, etc., and transmit the data to the data processing module. The data processing module compares and analyzes the actual quality data with the preset quality standards. If the product quality is abnormal, it re-evaluates the line speed and other process parameters, and sends an adjustment instruction to the line speed control module. At the same time, the feedback and monitoring module can monitor the running state of the system in real time, including the working states of each module and the communication connection situation, and issues an alarm and records relevant information in time when a fault or abnormality is found.

[0008] Compared with the prior art, the advantages of the present invention are as follows:

[0009] (1) By accurately matching the optimal line speed for different work order products, it effectively avoids quality problems such as poor browning effect caused by inappropriate line speed, and significantly improves the stability and consistency of product quality;

[0010] (2) The automatic line speed switching function reduces the time required for manual line speed adjustment, realizes the rapid connection of production for different work order products, greatly improves the production efficiency of the browning machine, and reduces the production cost;

[0011] (3) The system automatically completes work order identification and line speed adjustment, avoids mistakes that may occur in manual operations, reduces product scrapping and production interruption caused by improper operations, and improves the reliability of the production process;

[0012] (4) The feedback and monitoring module realizes real-time monitoring and data feedback of the production process, provides strong support for the production management of the enterprise, and helps to achieve intelligent production decision-making and continuous optimization of the production process. Description of the Drawings

[0013] Figure 1 It is a schematic diagram of the system structure of the present invention. Detailed Embodiments

[0014] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments in the following description are only examples, and those skilled in the art can think of other obvious variations.

[0015] Referring to Figure 1 As shown, when the overall system is put into use, it includes system initialization, work order identification and data processing, automatic line speed switching, production process monitoring and feedback adjustment, and system optimization and update;

[0016] Among them, system initialization requires data entry and calibration, preliminary training of the algorithm model, and debugging of hardware devices;

[0017] Data entry and calibration: Before the system is put into use, technicians need to enter the detailed information of various products into the database of the data processing module. These information include but are not limited to product models, length, width and height dimensions, number of circuit board layers, required level of brownification, and corresponding key process parameters such as the optimal line speed, applicable concentration range of brownification solution, temperature setting value, and spray pressure standard. After the entry is completed, multiple rounds of data calibration are required to ensure the accuracy and integrity of the data. For example, randomly select some product data and verify whether the entered line speed and process parameters can achieve the expected brownification effect through actual brownification experiments. If there are deviations, correct them in a timely manner;

[0018] Initial training of the algorithm model: Use historical production data to conduct initial training on the algorithm model. Input the production data of different products under various process conditions in the past, including actual line speed, brownification solution-related parameters, product quality inspection results, etc., into the algorithm model. Through data analysis and machine learning algorithms, let the model initially learn the association and influence rules between different parameters, laying a foundation for subsequent real-time calculation of the optimal line speed. During the training process, set reasonable evaluation indicators, such as product quality compliance rate, line speed adjustment error rate, etc., to monitor and evaluate the training effect of the model. When the indicators reach the preset standards, stop the initial training;

[0019] Debugging of hardware devices: Complete the data interface configuration between the work order recognition module and the enterprise production management system (such as the ERP system), conduct multiple rounds of data transmission tests, simulate the transmission scenarios of different work order information, check the accuracy, integrity, and timeliness of the data. At the same time, conduct a comprehensive debugging of the OCR device used for scanning and recognizing paper work orders, adjust the scanning resolution, image recognition parameters, etc. of the device to ensure that it can clearly and accurately recognize the content of paper work orders in various formats and fonts. During the debugging process, use a large number of different types of paper work order samples for testing, and count the recognition accuracy rate. When the accuracy rate reaches more than 99% (which can be set according to actual needs), it is considered that the OCR device debugging is qualified.

[0020] Among them, work order recognition and data processing require obtaining and verifying electronic work orders, recognizing and complementing information of paper work orders, and parsing data and calculating the line speed;

[0021] Obtaining and verifying electronic work orders: When a new work order is issued, the work order recognition module will immediately attempt to obtain the electronic work order information from the enterprise production management system through the data interface. After obtaining the information, immediately conduct data verification, check whether all key information in the work order is complete, such as whether there are missing values in product models, dimensions, etc.; conduct format verification on key information such as product models to ensure compliance with the system's preset specifications. For example, the product model should follow specific character combination rules. If it does not conform, prompt data anomalies. If the electronic work order information is complete and passes the verification, directly transmit it to the data processing module;

[0022] Paper work order recognition and information completion: If the information in the electronic work order is missing or incorrect, the system automatically activates the OCR device to scan and recognize the paper work order. After the OCR device converts the scanned image into text information, it extracts and organizes the information through specific algorithms. For some fuzzy or uncertain recognition results, the system will adopt the method of manual-assisted verification, pop up relevant information for the operator to confirm and correct. After recognition, the information in the paper work order is integrated and completed with the obtained electronic work order information to ensure that the work order information received by the data processing module is accurate and error-free;

[0023] Data parsing and line speed calculation: After receiving the work order information, the data processing module first performs in-depth parsing on it. Classify and extract parameters such as product model, size, and number of circuit board layers, and match them with the preset data in the database. According to the preset algorithm model, combined with current actual process parameters such as brownification liquid concentration, temperature, and spraying pressure, calculate the optimal line speed of the product in the current work order during the brownification process. For example, for products with larger sizes and more circuit board layers, the algorithm model will consider increasing the line speed to ensure brownification uniformity. At the same time, considering the slightly lower current brownification liquid concentration, appropriately reduce the line speed adjustment range to ensure the brownification effect. During the calculation process, the calculation progress and intermediate results are displayed in real time to facilitate technicians to monitor and analyze.

[0024] Among them, the automatic line speed switching requires instruction reception and conversion, motor speed regulation operation, and real-time line speed monitoring and correction;

[0025] Instruction reception and conversion: The line speed control module receives the optimal line speed instruction sent by the data processing module. The instruction is transmitted in a specific data format, including information such as the target line speed value and the allowable line speed adjustment error range. The line speed control module parses and converts the received instruction, and converts the target line speed value into a control signal that the motor speed regulation device can recognize. For example, convert the line speed value in meters per minute into the motor control pulse frequency value to ensure that the motor can accurately respond to the line speed adjustment requirement;

[0026] Motor speed regulation operation: Use a high-precision motor speed regulation device to adjust the conveyor line speed of the brownification machine. The speed regulation device changes the motor's power supply frequency or voltage according to the converted control signal to achieve precise control of the motor speed, thereby driving the change of the conveyor line speed. During the adjustment process, an incremental speed regulation strategy is adopted to avoid the impact on products and equipment caused by a large instantaneous change in line speed. For example, gradually increase or decrease the motor speed within a short period of time to make the line speed smoothly transition to the target value, and the entire speed regulation process is completed within a few seconds (the specific time can be set according to the equipment performance and production requirements);

[0027] Line speed real-time monitoring and correction: During the production process, the line speed control module uses speed sensors installed on the conveyor line to monitor the line speed in real-time. The sensors feed the collected line speed data back to the line speed control module, which compares the actual line speed with the target line speed. Once it is found that the deviation between the actual line speed and the target line speed exceeds the allowable error range, the automatic correction mechanism is immediately activated. By fine-tuning the control parameters of the motor speed regulation device, the motor speed is adjusted to restore the line speed to the target value. At the same time, the line speed deviation situation and the correction operation process are recorded for subsequent data analysis and equipment maintenance.

[0028] Among them, for production process monitoring and feedback adjustment, it is necessary to collect product quality data, analyze and evaluate quality data, make decisions on process parameter adjustment, and issue and execute adjustment instructions;

[0029] Product quality data collection: The feedback and monitoring module collects the product quality data after brownization in real-time through various sensors installed at the outlet of the brownization machine. For example, a film thickness sensor is used to measure the thickness of the brownized film, and an adhesion test device is used to detect the adhesion between the brownized film and the circuit board. The sensors digitally process the collected data and transmit it to the data processing module at a certain time interval (such as once per second). During the data collection process, the working status of the sensors is monitored in real-time. Once a sensor failure or data anomaly is found, an alarm is immediately issued and the data collection is switched to a backup sensor;

[0030] Quality data analysis and evaluation: After receiving the product quality data, the data processing module conducts a detailed comparative analysis with the preset quality standards. Statistical methods are used to analyze the data, and statistical quantities such as the average value and standard deviation of the data are calculated to determine whether the product quality is stable within the qualified range. For example, for the thickness of the brownized film, if the measured values of multiple consecutive products deviate from the preset mean value and the standard deviation is large, it indicates that the thickness of the brownized film is uneven and there are quality problems with the product. At the same time, combined with information such as product model and production time, a product quality traceability system is established to facilitate finding the root cause of quality problems in the future;

[0031] Process parameter adjustment decision-making: If it is found that the product quality is abnormal, the data processing module will comprehensively consider the influence of the line speed and other process parameters (such as brownizing solution concentration, temperature, spray pressure, etc.) on the product quality, and use an optimization algorithm to re-evaluate and formulate an adjustment plan. For example, if the thickness of the brownized film is too thin and the current line speed is relatively fast, the algorithm may recommend appropriately reducing the line speed and simultaneously increasing the brownizing solution concentration; if the adhesion is insufficient, it may be necessary to adjust the spray pressure and brownizing time. During the formulation of the adjustment plan, historical production data and the experience knowledge base are referred to to ensure the rationality and effectiveness of the adjustment plan;

[0032] Adjustment instruction issuance and execution: The data processing module sends the formulated adjustment instructions to the line speed control module and related process parameter control devices (such as the brownization liquid concentration adjustment device, temperature control system, etc.). The line speed control module readjusts the brownization machine line speed according to the adjustment instructions, and the process parameter control devices correspondingly adjust parameters such as the brownization liquid concentration, temperature, and spraying pressure. During the adjustment process, the changes of each parameter are monitored in real time to ensure the accurate execution of the adjustment operation. At the same time, the product quality data and process parameters before and after the adjustment are recorded to evaluate the adjustment effect.

[0033] Among them, system optimization and update require production data accumulation and sorting, algorithm model optimization, and database update;

[0034] Production data accumulation and sorting: As production continues, the system continuously accumulates a large amount of production data, including work order information, product quality data, process parameters, and equipment operation status data, etc. These data are regularly sorted and classified for storage, and data indexes are established according to dimensions such as product model, production batch, and time for subsequent query and analysis. For example, the production data of the current month is summarized and sorted every month, stored in a dedicated data warehouse, and corresponding data reports are established;

[0035] Algorithm model optimization: The algorithm model is continuously optimized using the accumulated production data. The online learning algorithm in machine learning is adopted to enable the model to continuously adjust and optimize its own parameters according to new data, improving the accuracy of calculating the line speed of different work order product lines. For example, by analyzing the deviation data between the actual line speed and the optimal line speed, and the relationship between product quality and line speed and process parameters, the weight coefficients and decision rules in the algorithm model are adjusted. The optimized algorithm model is regularly verified and evaluated. By comparing the actual production results with the model prediction results, the optimization effect of the model is judged. When the model performance improvement reaches a certain standard (such as the line speed calculation error is reduced by more than 10%), the optimized model is applied to actual production;

[0036] Database update: According to the needs of the enterprise to launch new products and improve processes, the product information and process parameters in the database are updated in a timely manner. When a new product is put into production, technical personnel need to enter information such as the detailed specifications, process requirements, and corresponding optimal line speed of the new product into the database, and conduct targeted training and adjustment on the algorithm model. At the same time, if the enterprise improves the process of existing products, such as changing the brownization liquid formula, adjusting the spraying method, etc., the process parameters in the database are updated accordingly, and the corresponding relationship between line speed and process parameters is re-evaluated and optimized. During the database update process, the data backup and recovery mechanism is strictly followed to ensure the security and integrity of the data.

[0037] The foregoing has shown and described the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and what is described in the above embodiments and the specification is only the principle of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent line speed automatic switching system based on work order recognition in the production of a brownization machine, characterized in that, It includes a work order recognition module, which is used to obtain work order information in real time through the data interface with the enterprise production management system; A data processing module, connected to the work order recognition module, receives the work order information and parses and processes it. Based on the preset database of the corresponding relationship between product models and line speeds, combined with parameters such as product size and number of circuit board layers, an algorithm model is used to calculate the optimal line speed of the current work order product during the brownification process; A line speed control module, connected to the data processing module, communicates with the drive control system of the brownification machine according to the optimal line speed calculated by the data processing module, and automatically adjusts the conveyor line speed of the brownification machine; A feedback and monitoring module, which is used to collect real-time product quality data after brownification during the production process of the brownification machine, such as brownification film thickness, adhesion, etc., and transmits the data to the data processing module. The data processing module compares and analyzes the actual quality data with the preset quality standards.

2. The intelligent line speed automatic switching system based on work order recognition in the production of a brownification machine according to claim 1, characterized in that, The data interface of the work order recognition module and the enterprise production management system adopts a standard data transmission protocol to ensure real-time and accurate data transmission. The optical character recognition technology has been trained and optimized to accurately identify paper work order information in various formats and fonts.

3. An intelligent line speed automatic switching system based on work order recognition in the production of a brownification machine according to claim 1, characterized in that, The preset database of the corresponding relationship between product models and line speeds in the data processing module stores a large amount of corresponding data between different product models and optimal line speeds, and is updated regularly. The algorithm model uses machine learning algorithms and adjusts and optimizes parameters through continuous input of actual production data.

4. An intelligent line speed automatic switching system based on work order recognition in the production of a brownization machine according to claim 1, characterized in that, The motor speed control device adopted by the line speed control module is a high-precision variable frequency speed regulator, which can achieve precise control of the line speed. The line speed adjustment time is within a specified short time range to ensure that the line speed quickly and smoothly reaches the target value when switching between different work orders.

5. An intelligent line speed automatic switching system based on work order recognition in the production of a brownization machine according to claim 1, characterized in that, The sensors of the feedback and monitoring module for collecting product quality data are calibrated to ensure data accuracy. The system operation status monitoring has a fault warning function and can issue an alarm prompt in advance before a fault occurs.

6. The intelligent line speed automatic switching system based on work order recognition in the production of a brownification machine according to claim 1, wherein, The system has an initialization function. Before being put into use, data such as various product models, sizes, number of circuit board layers, corresponding optimal line speeds, and brownification process requirements are entered into the database of the data processing module, and the algorithm model is initially trained and optimized. At the same time, the data interface configuration between the work order recognition module and the enterprise production management system and the debugging of the OCR device are completed.

7. An intelligent line speed automatic switching system based on work order recognition in the production of a brownification machine according to claim 1, characterized in that, With the accumulation of production data, the data processing module can continuously optimize and update the algorithm model. When the enterprise launches new products or conducts process improvements, it can timely update the product information and process parameters in the database.

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