PCBA electronic assembly process parameter adjusting system

Abstract

The invention discloses a PCBA (Printed Circuit Board Assembly) electronic assembly process parameter adjusting system, and relates to the technical field of PCBA electronic assembly. The regulation and control center is in communication connection with an equipment parameter acquisition module, an equipment control interface module, a process parameter optimization module, an equipment coordination module, a parameter adjustment execution module, a monitoring feedback module and a data analysis report module, and the equipment parameter acquisition module is used for acquiring real-time process parameter data of each equipment in the PCBA production process. By monitoring and analyzing the operation data of each device on the production line in real time, the abnormal mode in the production process can be accurately identified, the process parameters are adjusted in time to optimize the production flow, the dynamic adjustment mechanism not only reduces the breakdown time of the production line, but also remarkably improves the production efficiency, and the production cost is reduced. And each production link can be operated according to the preset standard value, so that the quality problem caused by improper process parameters is effectively reduced.

Description

Technical Field

[0001] The invention relates to the technical field of PCBA electronic assembly, and in particular to a PCBA electronic assembly process parameter adjustment system. Background Art

[0002] With the advancement of science and technology, electronic products have penetrated into all aspects of people's lives, from communication and network equipment, consumer electronics to industrial automation and control systems, medical equipment, automotive electronics, energy and power equipment, and even the military and defense fields. All of these are inseparable from the support of electronic products. This trend has promoted the continuous growth of the demand for PCBA electronic assembly technology, which makes the functions of electronic products more and more complex and the integration higher and higher, which means that the design of PCB has also become more complex, which requires the assembly process to adapt to different needs and ensure the accuracy and quality of each link.

[0003] In the prior art, a variety of equipment is used in PCBA production, and different equipment has different control systems and parameter requirements, which leads to certain coordination difficulties and the difficulty in adjusting process parameters. Therefore, how to coordinate and optimize process parameters between different equipment to achieve cross-equipment parameter adjustment and ensure that each equipment operates under the best working conditions is the problem we need to solve. To this end, a PCBA electronic assembly process parameter adjustment system is proposed. Summary of the invention

[0004] The present invention aims to provide a PCBA electronic assembly process parameter adjustment system to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0006] A PCBA electronic assembly process parameter adjustment system includes a control center, wherein the control center is communicatively connected with an equipment parameter acquisition module, an equipment control interface module, a process parameter optimization module, an equipment coordination module, a parameter adjustment execution module, a monitoring feedback module, and a data analysis report module;

[0007] The equipment parameter acquisition module is used to obtain the real-time process parameter data of each device in the PCBA production process, and pre-process the collected raw data to ensure that the system can accurately obtain the real-time status of the production line and provide a data basis for subsequent parameter adjustments;

[0008] The device control interface module is used to provide an interface with each device control system to achieve communication between the system and the device and the sending of control instructions;

[0009] The process parameter optimization module is used to optimize the process parameters by combining the collected data with the preset optimization target, find the best process parameter combination for each device, and improve the overall performance of the production line;

[0010] The equipment coordination module is used to coordinate the process parameter adjustment between different equipment according to the analysis results of the parameter optimization algorithm module to ensure that the working status and parameter settings of each equipment are coordinated with each other;

[0011] The parameter adjustment execution module is used to send adjustment instructions to the control system of each device in combination with the equipment coordination result, adjust the process parameters of the equipment, ensure that each device operates under the best working conditions, and improve production efficiency and product quality;

[0012] The monitoring feedback module is used to monitor the operating status and process parameters of each device in real time, and feed back real-time data to the system so that the system can be further adjusted and optimized;

[0013] The data analysis and reporting module is used to analyze the collected process data and quality inspection data, generate visual reports, and help managers quickly understand the production status.

[0014] A further improvement of the technical solution of the present invention is that: the device parameter acquisition module includes a data acquisition unit and a data preprocessing unit;

[0015] The data acquisition unit is used to collect process parameters and operating status of different equipment through sensors to provide real-time data support for the system;

[0016] The data preprocessing unit is used to standardize the parameter data of different devices, unify the data format, solve the problem of data heterogeneity between devices, and improve the compatibility and availability of data.

[0017] A further improvement of the technical solution of the present invention is that the device parameter acquisition module specifically includes:

[0018] Based on the sensors equipped on each production equipment in the PCBA production process, the data acquisition unit is combined with the set sampling frequency to obtain the process parameters and real-time operating status of different equipment from the sensors and equipment control system. The collected data includes process parameters such as temperature, pressure, speed, position, current, voltage, and the real-time operating status of the equipment. Among them, the sensors include temperature sensors, pressure sensors, speed sensors, position sensors, current sensors, voltage sensors, etc.;

[0019] The collected data is transmitted to the data preprocessing unit by wireless means, and preprocessing operations including data cleaning and data format unification are performed on the data;

[0020] Add a device identifier to each piece of pre-processed data to ensure the traceability of the data source, and add an accurate timestamp to each piece of data, and then classify the data according to the data type (process parameters, operating status) to facilitate targeted processing in subsequent modules;

[0021] For data collected at a high frequency after identification and classification, a data compression algorithm is used to reduce storage space while retaining key information, and the preprocessed data is stored in the database for subsequent module calls and analysis.

[0022] A further improvement of the technical solution of the present invention is that: the device control interface module specifically includes:

[0023] The equipment control interface module is connected to each equipment control system through a serial port interface to ensure that the physical connection of the interface is stable and reliable to meet the needs of data transmission. A unified communication protocol is used between the equipment control interface module and each equipment control system to ensure accurate data transmission and effective execution of control instructions.

[0024] The system generates corresponding control instructions according to production needs and process parameter adjustment requirements. The equipment control interface module converts the control instructions into a format that can be recognized by the equipment control system and sends them to the corresponding equipment through the interface;

[0025] The device control interface module receives status information from the device and feeds the status information back to the system so that the system can timely understand the operating status of the device and perform corresponding processing;

[0026] The equipment control interface module obtains the operating status information of the equipment in real time and displays it on the monitoring interface. According to the production needs and process parameter adjustment requirements, the system sends parameter adjustment instructions to the equipment through the equipment control interface module.

[0027] A further improvement of the technical solution of the present invention is that the process parameter optimization module specifically includes:

[0028] The process parameter optimization module calls the pre-processed process parameter data and operation status data from the database, and sets the optimization target according to the overall needs of the production line, wherein the optimization target includes maximizing product quality, minimizing production cost or shortening production cycle;

[0029] Combined with the set optimization goal, the particle swarm optimization algorithm is selected for optimization calculation, and the particle swarm optimization algorithm is used to analyze the collected data;

[0030] Through iterative calculation, try different combinations of process parameters, evaluate the impact of each combination on the optimization target, find the optimal solution among multiple targets, and then gradually approach the optimal combination of process parameters. When the stop condition is met, stop the optimization.

[0031] After finding the best process parameter combination, verify whether the optimized process parameter combination meets the expected effect;

[0032] The optimization scheme is adjusted according to the verification results, the optimized process parameter combination is stored in the database, and output to the equipment coordination module for application.

[0033] A further improvement of the technical solution of the present invention is that the process of optimizing calculation using the particle swarm optimization algorithm is:

[0034] According to the range of process parameters, the position vector and velocity vector of each particle are defined, the initial particle position (i.e., process parameter value) and velocity (i.e., the direction and magnitude of parameter adjustment) are randomly generated, and the particle swarm size, maximum number of iterations, inertia weight, and acceleration factor are determined;

[0035] Initialize the personal best position of each particle as its initial position, record the corresponding optimization target value, and initialize the global best position as the position of the particle with the best optimization target value among all particles;

[0036] According to the optimization goal (maximizing product quality, minimizing production costs or shortening production cycle), construct the objective function, and for each particle, calculate the objective function value corresponding to its current position vector, which is the fitness value of the particle;

[0037] Compare the fitness value of the current particle with the previously recorded individual optimal solution. If the current value is better, update the individual optimal solution and select the optimal value from the individual optimal solutions of all particles as the global optimal solution.

[0038] According to the particle's current speed, personal best position, global best position, inertia weight, and acceleration factor parameters, the particle's speed is updated, and according to the updated speed, the particle's position (i.e., process parameter value) is updated;

[0039] Repeat the steps of evaluating particles and updating particle speed and position until the stopping condition is met, where the stopping condition includes reaching the maximum number of iterations or the change of the global optimal solution is less than the set threshold. If the stopping condition is met, the algorithm is terminated, otherwise, the fitness value calculation step is returned and the iteration continues;

[0040] Apply the optimal process parameter combination in the simulation environment, collect data to verify whether the optimized process parameter combination meets the expected effect, and compare whether the optimized product quality, production cost or production cycle indicators are consistent with the expected goals. If the optimization effect meets expectations, accept the optimal process parameter combination, otherwise, further optimization is required.

[0041] A further improvement of the technical solution of the present invention is that the device coordination module specifically includes:

[0042] The equipment coordination module receives the optimized process parameter combination from the process parameter optimization module, including the key process parameters of each equipment, recommended values, analysis of the mutual impact between equipment, and improvement suggestions;

[0043] The equipment coordination module analyzes the received analysis results, clarifies the optimization goals of each device, ensures that the optimization goals are consistent with the optimization goals of the overall production system, and analyzes the current working status and parameter settings of each device. By comparing with the optimal parameter settings, it identifies the equipment and its parameters that need to be adjusted;

[0044] Based on the analysis results, the equipment coordination module formulates coordination strategies between devices, including adjusting the parameters of a certain device to adapt to changes in other devices, or adjusting the parameters of multiple devices to achieve overall optimization. The equipment coordination module combines the formulated coordination strategy with the received optimal process parameter combination to form a complete adjustment plan, and then sends the adjustment plan to the parameter adjustment execution module.

[0045] A further improvement of the technical solution of the present invention is that: the parameter adjustment execution module specifically includes:

[0046] The parameter adjustment execution module receives a detailed adjustment plan from the equipment coordination module. The adjustment plan includes detailed information such as the specific process parameter adjustment values ​​for each device, as well as the order and timing of the adjustment. The module also analyzes the received adjustment plan to clarify the parameters, target values, adjustment time points and conditions that need to be adjusted for each device.

[0047] According to the analyzed adjustment plan, the parameter adjustment execution module generates specific adjustment instructions, which include parameter name, target value, adjustment speed and execution time;

[0048] The parameter adjustment execution module sends the generated adjustment instructions to the control system of each device through the communication interface. After receiving the adjustment instructions, the control system of the device adjusts the process parameters of the device in real time according to the content of the instructions;

[0049] After completing the parameter adjustment, the equipment control system will feedback the adjustment results to the parameter adjustment execution module, including the adjusted parameter values, the working status of the equipment, and any abnormal or error information. The parameter adjustment execution module will analyze the feedback results to ensure that the adjustment achieves the expected effect and record the key data in the adjustment process for subsequent analysis.

[0050] A further improvement of the technical solution of the present invention is that: the monitoring feedback module includes a quality detection unit, a fault diagnosis and early warning unit and a fault-tolerant adaptive unit;

[0051] The quality inspection unit is used to perform quality inspection on the PCBA products produced after optimization, timely discover quality problems, provide a basis for further optimization of process parameters, and ensure product quality;

[0052] The fault diagnosis and early warning unit is used to diagnose the operating status of the production line, predict potential faults, and issue early warnings to reduce the downtime of the production line;

[0053] The fault-tolerant adaptive unit is used to perform fault-tolerant adjustments for predicted potential faults, and uses adaptive control technology to automatically adjust process parameters according to changes in equipment.

[0054] A further improvement of the technical solution of the present invention is that the monitoring feedback module specifically includes:

[0055] The PCBA products on the production line are received through the quality inspection unit, and multiple quality inspections are carried out on the PCBA according to the preset inspection standards and processes, including but not limited to solder joint quality, component installation position accuracy and electrical performance test, etc. The quality inspection scores are calculated, and the quality inspection scores are compared with the corresponding benchmark values. The product quality score is calculated comprehensively to determine whether the product is qualified. For unqualified products, the defect type and location are recorded, and the quality inspection data is fed back to the system in real time for further analysis and processing by other modules;

[0056] The fault diagnosis and early warning unit collects the operating data of each device on the production line in real time, including sensor data (temperature, pressure, vibration, etc.) and control signals (motor speed, valve opening, etc.). The collected operating data parameters of each device are compared with the preset standard values, and the abnormality score is calculated to identify the abnormal pattern. Based on the analysis results, the fault type of the equipment is predicted, and a fault early warning signal is issued in advance to notify maintenance personnel to conduct targeted inspections and repairs, thereby reducing the downtime of the production line.

[0057] The fault-tolerant adaptive unit receives the fault warning signal issued by the fault diagnosis and warning unit, as well as the real-time equipment operation status data, and formulates a fault-tolerant adjustment strategy based on the fault type and prediction results, including adjusting the process parameters of the equipment operation speed, temperature, and pressure. The adaptive control technology is used to automatically adjust the process parameters according to the real-time changes of the equipment to ensure that the production process can remain stable in the event of a fault. The adjusted production process and product quality are then monitored, the effectiveness of the fault-tolerant adjustment strategy is verified, and key data in the fault-tolerant adjustment process is recorded to provide a basis for subsequent fault prediction and fault-tolerant design.

[0058] Due to the adoption of the above technical solution, the present invention has the following technical advances compared with the prior art:

[0059] 1. The present invention provides a PCBA electronic assembly process parameter adjustment system, which can accurately identify abnormal modes in the production process by real-time monitoring and analyzing the operating data of each device on the production line, and timely adjust the process parameters to optimize the production process. This dynamic adjustment mechanism not only reduces the downtime of the production line, but also significantly improves the production efficiency, and can ensure that each production link operates according to the preset standard value, effectively reducing the quality problems caused by improper process parameters.

[0060] 2. The present invention provides a PCBA electronic assembly process parameter adjustment system, which can optimize the consumption of raw materials, reduce the scrap rate and rework rate, thereby reducing production costs by accurately controlling the PCBA electronic assembly process parameters. It can also monitor the operating status of the equipment in real time, promptly discover and repair potential faults, and avoid production losses caused by equipment downtime due to faults. The preventive maintenance mechanism not only reduces the maintenance cost of the equipment, but also extends the service life of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0062] Figure 1 It is a schematic diagram of the functional modules of the system of the present invention;

[0063] Figure 2 Schematic diagram of the workflow of the process parameter optimization module of the present invention. DETAILED DESCRIPTION

[0064] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are 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 creative work are within the scope of protection of the present invention.

[0065] Embodiment 1, as Figure 1 , Figure 2 As shown, the present invention provides a PCBA electronic assembly process parameter adjustment system, including a control center, the control center is communicatively connected with an equipment parameter acquisition module, an equipment control interface module, a process parameter optimization module, an equipment coordination module, a parameter adjustment execution module, a monitoring feedback module and a data analysis report module;

[0066] The equipment parameter acquisition module is used to obtain the real-time process parameter data of each device in the PCBA production process, and pre-process the collected raw data to ensure that the system can accurately obtain the real-time status of the production line and provide a data basis for subsequent parameter adjustments. The equipment parameter acquisition module includes a data acquisition unit and a data pre-processing unit. The data acquisition unit is used to collect the process parameters and operating status of different devices through sensors to provide real-time data support for the system and ensure that subsequent modules can be analyzed and adjusted based on the latest data. The data pre-processing unit is used to standardize the parameter data of different devices, unify the data format, solve the problem of data heterogeneity between devices, improve data compatibility and availability, and provide a basis for cross-device parameter coordination. Based on the sensors equipped on each production equipment in the PCBA production process, the data acquisition unit is combined with the set sampling frequency to obtain the process parameters and real-time operating status of different devices from the sensors and equipment control system. The collected data includes process parameters such as temperature, pressure, speed, position, current, voltage, and the real-time operating status of the equipment. The sensors include temperature sensors, pressure sensors, speed sensors, position sensors, current sensors and voltage sensors, etc. The collected data are transmitted to the data preprocessing unit by wireless, and the data is preprocessed including data cleaning and data format unification. The noise in the sensor data is removed by filtering algorithm to ensure the accuracy of the data. The missing data that may appear in the collection process is filled by interpolation method, abnormal data points are identified and processed, the data collected by different devices are converted into a unified format and unit, and the collected unstructured data is converted into structured data for subsequent processing. A device identifier is added to each preprocessed data to ensure the traceability of the data source, and an accurate timestamp is added to each data, and then the data is classified according to the data type (process parameters, operating status) to facilitate the targeted processing of subsequent modules. For the data collected at a high frequency after identification and classification, a data compression algorithm is used to reduce the storage space, while retaining key information, and the preprocessed data is stored in the database for subsequent module call and analysis;

[0067] The equipment control interface module is used to provide an interface with each equipment control system, realize communication between the system and the equipment and send control instructions, ensure that the system can accurately and timely control the operating status of each equipment, and realize real-time adjustment of parameters. The equipment control interface module is connected to each equipment control system through a serial port interface to ensure that the physical connection of the interface is stable and reliable to meet the needs of data transmission, and a unified communication protocol is adopted between the equipment control interface module and each equipment control system to ensure accurate data transmission and effective execution of control instructions. The system generates corresponding control instructions according to production needs and process parameter adjustment requirements. The equipment control interface module converts the control instructions into a format that can be recognized by the equipment control system and sends them to the corresponding equipment through the interface. The equipment control interface module receives status information from the equipment and feeds back the status information to the system so that the system can timely understand the operating status of the equipment and perform corresponding processing. The equipment control interface module obtains the operating status information of the equipment in real time and displays it on the monitoring interface. According to production needs and process parameter adjustment requirements, the system sends parameter adjustment instructions to the equipment through the equipment control interface module;

[0068] The process parameter optimization module is used to optimize the process parameters by combining the collected data with the preset optimization goals, find the best process parameter combination for each device, and improve the overall performance of the production line. The process parameter optimization module calls the pre-processed process parameter data and operation status data from the database, and sets the optimization goals according to the overall needs of the production line. The optimization goals include maximizing product quality, minimizing production costs, or shortening production cycles. In combination with the set optimization goals, the particle swarm optimization algorithm is selected for optimization calculation. The collected data is analyzed using the particle swarm optimization algorithm. Through iterative calculations, different process parameter combinations are tried, and the impact of each combination on the optimization goal is evaluated. The optimal solution is found between multiple goals, and then the optimal process parameter combination is gradually approached. When the stop condition is met, the optimization is stopped. After finding the best process parameter combination, it is verified whether the optimized process parameter combination meets the expected effect. The optimization plan is adjusted according to the verification results, and the optimized process parameter combination is stored in the database and output to the equipment coordination module for application.

[0069] In addition, the process of optimizing calculation using particle swarm optimization algorithm is as follows:

[0070] Define the position vector of each particle according to the range of process parameters and the velocity vector , where i represents the i-th particle, randomly generates the initial particle position (i.e., process parameter value) and speed (i.e., the direction and size of parameter adjustment), ensures that they are within the preset feasible range, and determines the particle swarm size, maximum number of iterations T, inertia weight w, acceleration factors c1 and c2 parameters, initializes the personal best position of each particle as its initial position, records the corresponding optimization target value, and initializes the global best position as the position of the particle with the best optimization target value among all particles. According to the optimization goal (maximizing product quality, minimizing production cost or shortening production cycle), constructs the objective function, and for each particle, calculates its current position vector The corresponding objective function value is the fitness value of the particle. Compare the fitness value of the current particle with the previously recorded individual optimal solution. , if the current value is better, update the individual optimal solution , and from the individual optimal solutions of all particles Select the best value as the global optimal solution , according to the current speed, personal best position, global best position, inertia weight and acceleration factor parameters of the particle, update the speed of the particle, and according to the updated speed, update the position of the particle (i.e., the process parameter value), repeat the steps of evaluating the particle and updating the particle speed and position until the stop condition is met, where the stop condition includes reaching the maximum number of iterations T or the change of the global optimal solution is less than the set threshold ϵ. If the stop condition is met, the algorithm is terminated, otherwise the fitness value calculation step is returned, and the iteration is continued. The optimal process parameter combination is applied to the simulation environment, and data is collected to verify whether the optimized process parameter combination meets the expected effect. Compare whether the optimized product quality, production cost or production cycle indicators are consistent with the expected target. If the optimization effect meets expectations, the optimal process parameter combination is accepted, otherwise, further optimization is required;

[0071] The expression for updating the particle's velocity is:

[0072] ;

[0073] In the formula, is the updated particle velocity, For the The particle in The speed of the dimension, is the inertia weight, and is the acceleration factor, and is a random number between [0,1], For particles In the The optimal position of an individual in dimension The global optimal solution is The location of the dimension, is the time variable;

[0074] The expression for updating the particle's position is:

[0075] ;

[0076] In the formula, is the updated particle position, The particle in Dimensional position in time The value of

[0077] The equipment coordination module is used to coordinate the process parameter adjustments between different equipment according to the analysis results of the parameter optimization algorithm module, to ensure that the working status and parameter settings of each equipment are coordinated with each other, and to avoid process inconsistency problems caused by improper settings of different equipment;

[0078] The parameter adjustment execution module is used to send adjustment instructions to the control system of each device based on the equipment coordination results, adjust the process parameters of the equipment, ensure that each device operates under the best working conditions, and improve production efficiency and product quality;

[0079] The monitoring feedback module is used to monitor the operating status and process parameters of each device in real time, and feed back real-time data to the system so that the system can make further adjustments and optimizations, ensuring that the system can promptly detect problems in the production process and take corresponding measures to make adjustments;

[0080] The data analysis report module is used to analyze the collected process data and quality inspection data and generate visual reports to help managers quickly understand the production status.

[0081] Embodiment 2, as Figure 1 , Figure 2 As shown, based on Example 1, the present invention provides a technical solution: preferably, the device coordination module specifically includes:

[0082] The equipment coordination module receives the optimized process parameter combination from the process parameter optimization module, including the key process parameters of each device, recommended values, analysis of mutual influence between devices, and improvement suggestions. The equipment coordination module analyzes the received analysis results, clarifies the optimization goals of each device, ensures that the optimization goals are consistent with the optimization goals of the overall production system, and analyzes the current working status and parameter settings of each device. By comparing with the optimal parameter settings, the equipment and its parameters that need to be adjusted are identified. Based on the analysis results, the equipment coordination module formulates a coordination strategy between devices, including adjusting the parameters of a certain device to adapt to changes in other devices, or adjusting the parameters of multiple devices to achieve the overall optimal. The equipment coordination module combines the formulated coordination strategy with the received optimal process parameter combination to form a complete adjustment plan, and then sends the adjustment plan to the parameter adjustment execution module;

[0083] The parameter adjustment execution module specifically includes:

[0084] The parameter adjustment execution module receives a detailed adjustment plan from the equipment coordination module. The adjustment plan includes the specific process parameter adjustment values ​​for each device, as well as detailed information such as the order and timing of the adjustment, and parses the received adjustment plan to clarify the parameters, target values, adjustment time points and conditions that need to be adjusted for each device. According to the parsed adjustment plan, the parameter adjustment execution module generates specific adjustment instructions. The adjustment instructions include parameter names, target values, adjustment speeds and execution times. The generation of instructions must be consistent with the control system interface and communication protocol of the device to ensure that the instructions can be correctly received and executed by the device. The parameter adjustment execution module sends the generated adjustment instructions to the control system of each device through the communication interface. After receiving the adjustment instructions, the control system of the device adjusts the process parameters of the device in real time according to the content of the instructions. After completing the parameter adjustment, the device control system feeds back the adjustment results to the parameter adjustment execution module, including the adjusted parameter values, the working status of the device, and any abnormal or error information. The parameter adjustment execution module analyzes the feedback results to ensure that the adjustment achieves the expected effect, and records key data during the adjustment process for subsequent analysis.

[0085] The monitoring feedback module includes a quality inspection unit, a fault diagnosis and early warning unit, and a fault-tolerant adaptive unit. The quality inspection unit is used to perform quality inspection on the PCBA products produced after optimization, timely discover quality problems, provide a basis for further optimization of process parameters, and ensure product quality. The fault diagnosis and early warning unit is used to diagnose the operating status of the production line, predict potential faults, and issue early warnings in advance to reduce the downtime of the production line and improve the stability and reliability of the production line. The fault-tolerant adaptive unit is used to make fault-tolerant adjustments for predicted potential faults, and use adaptive control technology to automatically adjust process parameters according to changes in equipment to ensure that product quality is not affected, improve the robustness of the system, reduce downtime in production, improve the stability and reliability of the production process, and ensure the continuity of the production process.

[0086] The monitoring feedback module specifically includes:

[0087] The PCBA products on the production line are received through the quality inspection unit, and multiple quality inspections are carried out on the PCBA according to the preset inspection standards and processes, including but not limited to solder joint quality, component installation position accuracy and electrical performance test, etc. The quality inspection scores are calculated, and the quality inspection scores are compared with the corresponding benchmark values. The product quality score is calculated comprehensively to determine whether the product is qualified. For unqualified products, the defect type and location are recorded, and the quality inspection data is fed back to the system in real time for further analysis and processing by other modules. The operating data of each device on the production line is collected in real time through the fault diagnosis and early warning unit, including sensor data (temperature, pressure, vibration, etc.) and control signals (motor speed, valve opening, etc.). The collected operating data parameters of each device are compared with the preset standard values, and the abnormality is calculated. Regular scoring to identify abnormal patterns, and based on the analysis results, predict the type of equipment failure, issue fault warning signals in advance, notify maintenance personnel to conduct targeted inspections and repairs, and reduce the downtime of production lines. The fault-tolerant adaptive unit receives the fault warning signals issued by the fault diagnosis and warning unit, as well as real-time equipment operation status data, and formulates fault-tolerant adjustment strategies based on the fault type and prediction results, including adjusting the process parameters of equipment operation speed, temperature, and pressure. Adaptive control technology is used to automatically adjust process parameters according to real-time changes in equipment to ensure that the production process can remain stable in the event of a failure. The adjusted production process and product quality are then monitored to verify the effectiveness of the fault-tolerant adjustment strategy, and key data in the fault-tolerant adjustment process are recorded to provide a basis for subsequent fault prediction and fault-tolerant design.

[0088] The expression of product quality score is:

[0089] ;

[0090] In the formula, It is the final quality score, which is used to measure the overall quality level of the product and comprehensively reflect the degree of deviation of the product from the standard. For the The actual quality inspection scores of the quality inspection items, For the The benchmark value corresponding to the quality test item, For the The target quality inspection score set for each quality inspection item, The number of quality inspection projects involved in the assessment;

[0091] The expression of anomaly score is:

[0092] ;

[0093] In the formula, It is an abnormality score, which is used to measure the degree to which the equipment operating status deviates from the normal range and comprehensively reflects whether the current operating status of the equipment is close to the critical point of failure. For the The actual measured values ​​of the operating data parameters, For the The standard value of the operating data parameter, is the sensitivity coefficient, which is used to adjust the sensitivity to deviation. is the number of operating data parameters involved in the evaluation. equal hour, , indicating that the equipment is operating normally. If the actual measured value Far from the standard value ,but will increase significantly, indicating that the device may have an abnormality or potential failure. Approaching , It gradually decreases until it approaches zero, indicating that the equipment is in good operating condition.

[0094] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A PCBA electronic assembly process parameter adjustment system, including a control center, characterized in that: The control center is communicatively connected to an equipment parameter acquisition module, an equipment control interface module, a process parameter optimization module, an equipment coordination module, a parameter adjustment execution module, a monitoring feedback module, and a data analysis report module; The equipment parameter acquisition module is used to obtain the real-time process parameter data of each equipment in the PCBA production process and perform preprocessing operations on the collected raw data; The device control interface module is used to provide an interface with each device control system; The process parameter optimization module is used to optimize the process parameters by combining the collected data with the preset optimization target, and find the best process parameter combination for each device; The equipment coordination module is used to coordinate the process parameter adjustment between different equipment according to the analysis results of the parameter optimization algorithm module; The parameter adjustment execution module is used to send adjustment instructions to the control system of each device in combination with the device coordination result to adjust the process parameters of the device; The monitoring feedback module is used to monitor the operating status and process parameters of each device in real time and feed back real-time data to the system; The data analysis and reporting module is used to analyze the collected process data and quality inspection data and generate visual reports.

2. A PCBA electronic assembly process parameter adjustment system according to claim 1, characterized in that: The device parameter acquisition module includes a data acquisition unit and a data preprocessing unit; The data acquisition unit is used to collect process parameters and operating status of different equipment through sensors; The data preprocessing unit is used to perform standardization processing on parameter data of different devices and unify the data format.

3. A PCBA electronic assembly process parameter adjustment system according to claim 2, characterized in that: The device parameter acquisition module specifically includes: Based on the sensors equipped on each production equipment in the PCBA production process, the data acquisition unit is combined with the sensor and equipment control system to obtain the process parameters and real-time operation status of different equipment at a set sampling frequency; The collected data is transmitted to the data preprocessing unit by wireless means, and preprocessing operations including data cleaning and data format unification are performed on the data; Add a device identifier to each piece of pre-processed data and add an accurate timestamp to each piece of data, thereby classifying the data according to the data type; For the data collected at a high frequency after identification and classification, a data compression algorithm is used to reduce the storage space, and the preprocessed data is stored in the database.

4. A PCBA electronic assembly process parameter adjustment system according to claim 3, characterized in that: The device control interface module specifically includes: The device control interface module is connected to each device control system through a serial port interface, and a unified communication protocol is used between the device control interface module and each device control system; The system generates corresponding control instructions according to production needs and process parameter adjustment requirements. The equipment control interface module converts the control instructions into a format that can be recognized by the equipment control system and sends them to the corresponding equipment through the interface; The device control interface module receives status information from the device, feeds the status information back to the system, and performs corresponding processing; The equipment control interface module obtains the operating status information of the equipment in real time and displays it on the monitoring interface. According to the production needs and process parameter adjustment requirements, the system sends parameter adjustment instructions to the equipment through the equipment control interface module.

5. A PCBA electronic assembly process parameter adjustment system according to claim 4, characterized in that: The process parameter optimization module specifically includes: The process parameter optimization module calls the pre-processed process parameter data and operation status data from the database, and sets the optimization target according to the overall needs of the production line, wherein the optimization target includes maximizing product quality, minimizing production cost or shortening production cycle; Combined with the set optimization goal, the particle swarm optimization algorithm is selected for optimization calculation, and the particle swarm optimization algorithm is used to analyze the collected data; Through iterative calculation, try different combinations of process parameters, evaluate the impact of each combination on the optimization target, find the optimal solution among multiple targets, and then gradually approach the optimal combination of process parameters. When the stop condition is met, stop the optimization. After finding the best process parameter combination, verify whether the optimized process parameter combination meets the expected effect; The optimization scheme is adjusted according to the verification results, the optimized process parameter combination is stored in the database, and output to the equipment coordination module for application.

6. A PCBA electronic assembly process parameter adjustment system according to claim 5, characterized in that: The process of using the particle swarm optimization algorithm to perform optimization calculation is as follows: According to the range of process parameters, the position vector and velocity vector of each particle are defined, the initial particle position and velocity are randomly generated, and the particle group size, maximum number of iterations, inertia weight and acceleration factor are determined; Initialize the personal best position of each particle as its initial position, record the corresponding optimization target value, and initialize the global best position as the position of the particle with the best optimization target value among all particles; According to the optimization goal, the objective function is constructed. For each particle, the objective function value corresponding to its current position vector is calculated. This value is the fitness value of the particle. Compare the fitness value of the current particle with the previously recorded individual optimal solution. If the current value is better, update the individual optimal solution and select the optimal value from the individual optimal solutions of all particles as the global optimal solution. Update the particle's velocity according to the particle's current velocity, personal best position, global best position, inertia weight, and acceleration factor parameters, and update the particle's position according to the updated velocity; Repeat the steps of evaluating particles and updating particle speed and position until the stopping condition is met, where the stopping condition includes reaching the maximum number of iterations or the change of the global optimal solution is less than the set threshold. If the stopping condition is met, the algorithm is terminated, otherwise, the fitness value calculation step is returned and the iteration continues; Apply the optimal process parameter combination in the simulation environment, collect data to verify whether the optimized process parameter combination meets the expected effect, and compare whether the optimized product quality, production cost or production cycle indicators are consistent with the expected goals. If the optimization effect meets expectations, accept the optimal process parameter combination, otherwise, further optimization is required.

7. A PCBA electronic assembly process parameter adjustment system according to claim 6, characterized in that: The device coordination module specifically includes: The equipment coordination module receives the optimized process parameter combination from the process parameter optimization module, including the key process parameters of each equipment, recommended values, analysis of the mutual impact between equipment, and improvement suggestions; The equipment coordination module analyzes the received analysis results, clarifies the optimization goals of each device, ensures that the optimization goals are consistent with the optimization goals of the overall production system, and analyzes the current working status and parameter settings of each device. By comparing with the optimal parameter settings, it identifies the equipment and its parameters that need to be adjusted; Based on the analysis results, the equipment coordination module formulates coordination strategies between devices, including adjusting the parameters of a certain device to adapt to changes in other devices, or adjusting the parameters of multiple devices to achieve overall optimization. The equipment coordination module combines the formulated coordination strategy with the received optimal process parameter combination to form a complete adjustment plan, and then sends the adjustment plan to the parameter adjustment execution module.

8. A PCBA electronic assembly process parameter adjustment system according to claim 7, characterized in that: The parameter adjustment execution module specifically includes: The parameter adjustment execution module receives a detailed adjustment plan from the equipment coordination module. The adjustment plan includes the specific process parameter adjustment values ​​for each device, as well as detailed information on the order and timing of the adjustment. It also analyzes the received adjustment plan to clarify the parameters, target values, adjustment time points and conditions that need to be adjusted for each device. According to the analyzed adjustment plan, the parameter adjustment execution module generates specific adjustment instructions, which include parameter name, target value, adjustment speed and execution time; The parameter adjustment execution module sends the generated adjustment instructions to the control system of each device through the communication interface. After receiving the adjustment instructions, the control system of the device adjusts the process parameters of the device in real time according to the content of the instructions; After completing the parameter adjustment, the equipment control system feeds back the adjustment results to the parameter adjustment execution module, including the adjusted parameter values, the working status of the equipment, and any abnormal or error information. The parameter adjustment execution module analyzes the feedback results to ensure that the adjustment achieves the expected effect.

9. A PCBA electronic assembly process parameter adjustment system according to claim 8, characterized in that: The monitoring feedback module includes a quality detection unit, a fault diagnosis and early warning unit and a fault-tolerant adaptive unit; The quality inspection unit is used to perform quality inspection on the PCBA products produced after optimization; The fault diagnosis and early warning unit is used to diagnose the operating status of the production line, predict potential faults, and issue early warnings; The fault-tolerant adaptive unit is used to perform fault-tolerant adjustments for predicted potential faults, and uses adaptive control technology to automatically adjust process parameters according to changes in equipment.

10. A PCBA electronic assembly process parameter adjustment system according to claim 9, characterized in that: The monitoring feedback module specifically includes: The quality inspection unit receives PCBA products from the production line and conducts multiple quality inspections on the PCBA according to the preset inspection standards and processes, including solder joint quality, component installation position accuracy and electrical performance test, calculates the scores of each quality inspection, compares the quality inspection scores with the corresponding benchmark values, calculates the product quality score comprehensively, determines whether the product is qualified, records the defect type and location of unqualified products, and feeds back the quality inspection data to the system in real time; The fault diagnosis and early warning unit collects the operating data of each device on the production line in real time, including sensor data and control signals, compares the collected operating data parameters of each device with the preset standard values, calculates the abnormality score to identify the abnormal pattern, and predicts the type of equipment failure based on the analysis results, issues a fault early warning signal in advance, and notifies maintenance personnel to conduct targeted inspections and repairs; The fault-tolerant adaptive unit receives the fault warning signal issued by the fault diagnosis and warning unit, as well as the real-time equipment operation status data, and formulates a fault-tolerant adjustment strategy based on the fault type and prediction results, including adjusting the process parameters of the equipment operation speed, temperature, and pressure. The adaptive control technology is used to automatically adjust the process parameters according to the real-time changes of the equipment, and then the adjusted production process and product quality are monitored to verify the effectiveness of the fault-tolerant adjustment strategy.

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