CN114115147A - SMT manufacturing process intelligent management and control system based on digital twins - Google Patents
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Abstract
The invention discloses an SMT manufacturing process intelligent control system based on digital twins, which belongs to the technical field of electronic surface mounting processes and comprises a system management module, a data processing and analyzing module, a twins simulation monitoring module and a prediction optimization control module. The method can analyze the coupling relation of all elements in the SMT manufacturing process, excavate the internal operation rule influencing the production performance, and dynamically regulate and control key performance parameters, comprehensively improve the intelligent, refined and precise control level, form precise control on the process, quality and efficiency related to the SMT manufacturing process, and lay an important technical foundation for the construction of electronic equipment and intelligent component workshops.
Description
Technical Field
The invention relates to the technical field of electronic surface mounting processes, in particular to an SMT manufacturing process intelligent control system based on digital twins.
Background
SMT (surface mount technology) plays an important role in the manufacturing process of military products (radars, universal power supplies, cryogenic equipment) and civilian products (home appliances, displays, notebook computers, etc.). Military electronic equipment, typically represented by radar, is now increasingly showing two-polarization trends: maximization of system scale and complexity, minimization of units and core modules due to high integration, and the like, put higher demands on the manufacturing efficiency and manufacturing quality of radar core components. However, in the radar core component manufacturing mode of 'multi-variety, variable-batch and mixed-line production', the parameter debugging of the manufacturing process is complex, the quality is controlled afterwards, the dynamic decision is delayed, the data utilization rate is low, and the potential production abnormity, the production performance prediction and the like in the manufacturing process are difficult to quantify. The above factors cause the problems of low equipment efficiency utilization, poor product quality stability and the like in the SMT manufacturing process. Therefore, how to fully improve the efficiency, quality and fineness of the SMT manufacturing process control has become a troublesome problem for electronic enterprises.
At present, the application of the digital twin technology in products and manufacturing systems is mainly concentrated in developed countries, and the application stage of theoretical exploration and preliminary verification is still in China. The research of the existing digital twin technology in the production and manufacturing field is mostly focused on the aspects of product data management, product information tracking, product operation monitoring and the like, the application environment difference is large aiming at the research of the digital twin production line, the research mostly stays in the development trend, the operation mechanism and the initial verification (data visualization monitoring and twin model simulation analysis) stage, and the research depends on manual quantification and identification of potential production abnormity, insufficient prediction capability and lack of a real-time accurate regulation and control mechanism. How to realize intelligent management and control of the SMT manufacturing process through a digital twin technology is an urgent problem to be solved. Therefore, an SMT manufacturing process intelligent management and control system based on digital twinning is provided.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: how to realize real-time monitoring, key performance prediction and optimal control of the SMT manufacturing process provides an SMT manufacturing process intelligent management and control system based on digital twinning. The method comprises the steps of firstly, collecting equipment, process, personnel data and the like in the SMT manufacturing process, secondly, constructing a twin model, carrying out virtual-real data interaction, simulating and monitoring the production running state; and finally, constructing a performance prediction model, mining the data operation association rule of the manufacturing process, optimizing the optimal parameters, and carrying out production regulation and control strategy after simulation verification is reasonable.
The invention solves the technical problems through the following technical scheme, and the system comprises a system management module, a data processing and analyzing module, a twin simulation monitoring module, a performance prediction module and a decision control module;
the system management module is used for managing a system user, a database and an algorithm library;
the data processing and analyzing module is used for collecting typical process data, quality data and equipment running state data of the SMT production line from an enterprise information system, and cleaning and normalizing the data;
the twin simulation monitoring module is used for constructing a digital twin model of the SMT production line, and simulating and monitoring the current production running state in real time through the interaction of the twin model and data;
the prediction optimization control module is used for fusing twin data, constructing a performance prediction model, predicting potential production abnormity, optimizing optimal characteristic parameters and producing a regulation and control strategy;
furthermore, the system management module comprises a user management sub-module, a database management sub-module and an algorithm library management sub-module; the user management submodule is used for distributing different login authorities of a user and performing daily management and maintenance on user basic information and an operation log in the system; the database management submodule is used for storing twin data, collected equipment, process and quality data in a database in a classified manner, regularly updating the database and cleaning redundant data in the database; and the algorithm library management submodule is used for classifying and storing the intelligent algorithm, the optimization algorithm, the corresponding parameter setting, the optimization result and the regulation and control strategy.
Furthermore, the data processing and analyzing module comprises a data reading sub-module, a data cleaning sub-module and a data association analysis sub-module; the data reading submodule is used for reading printing, surface mounting, reflow soldering process data, equipment running state data and process quality data in the SMT manufacturing process through an enterprise information system; the data cleaning submodule is used for processing the missing values and the noise data of the various data, deleting the redundant values and normalizing the various data; the data correlation analysis submodule is used for selecting the characteristics of the various data, introducing correlation analysis and quantifying the coupling degree of the data.
Furthermore, the twin simulation monitoring module comprises a twin model building submodule, a data model building submodule and a simulation monitoring submodule; the twin model construction submodule is used for carrying out twin modeling (a plate feeding machine, a printing machine, a chip mounter, reflow soldering and the like), personnel, materials (a PCB (printed circuit board), components and parts), facilities and other entities on the 'man-machine-material-method-ring' entity with different attributes from the dimensions of geometry, physics, behavior, rules and the like; a data model construction submodule: adopting an object-oriented storage mode, corresponding to entities such as resources, processes, PCB boards and the like of a physical SMT production line, constructing a data model containing attribute information such as topology, motion, logic and the like, and forming virtual and real data mapping; the twin simulation monitoring submodule is used for providing multi-view monitoring and backtracking of the manufacturing process on the basis of real-time data acquisition, converting twin data in the SMT manufacturing process into key information of the manufacturing process through statistical analysis, and pushing the key information to workers to form visual monitoring of the manufacturing process.
Furthermore, the prediction optimization control module comprises a prediction submodule, an optimization submodule and a control submodule; the prediction submodule is used for fusing twin data and real-time data to construct a performance prediction model, mining association rules of production operation, predicting product quality, processing period and the like, and finding potential abnormality in advance; the optimization submodule is used for inputting random production populations into the prediction model through a meta-heuristic algorithm and obtaining optimal characteristic parameters through optimizing iteration on the output result of the prediction model; and the control submodule is used for further simulating and verifying the effectiveness of the optimal characteristic parameter in the twin model, and generating a physical entity parameter regulation and control strategy after determining that the simulation result is in the tolerance range.
Compared with the prior art, the invention has the following advantages: the intelligent management and control system based on the digital twin SMT manufacturing process integrates twin data and real-time data, analyzes the coupling relation of all elements in the SMT manufacturing process, excavates the internal operation rule influencing the production performance, predicts the potential production abnormity, dynamically regulates and controls key performance parameters, comprehensively improves the intelligent, refined and accurate management and control levels, and forms accurate management and control on the process, quality, efficiency and the like involved in the SMT manufacturing process.
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FIG. 1 is a schematic flow chart illustrating an implementation of an SMT manufacturing process intelligent management and control system according to an embodiment of the present invention;
fig. 2 is a schematic diagram of an intelligent management and control system for an SMT manufacturing process according to a second embodiment of the present invention.
Detailed Description
The following examples are given for the detailed implementation and specific operation of the present invention, but the scope of the present invention is not limited to the following examples.
Example one
The embodiment provides a technical scheme: an SMT manufacturing process intelligent management and control system based on digital twinning comprises the following functional modules:
s1: system management module
The system management module specifically comprises the following sub-modules:
s11: the user management submodule comprises: managing user authority, and performing daily management and maintenance on user basic information and operation logs in the system;
s12: a database management sub-module: the twin data, the acquisition equipment, the process, the quality and other data are classified and stored in an SQL database, the database is periodically updated, redundant data in the database are cleaned, and the normal operation of the database is guaranteed and maintained;
s13: an algorithm library management submodule: classifying and storing intelligent learning algorithms (algorithms such as clustering, deep learning and reinforcement learning), meta-heuristic algorithms (particle swarm, ant colony, difference algorithm and the like), algorithm parameters, optimization results and the like, and updating the algorithms periodically;
s2: data processing and analyzing module
The data processing and analyzing module specifically comprises the following sub-modules:
s21: a data reading submodule: reading the running state (plate loading machine, printing machine and chip mounter) of SMT manufacturing process equipment in the server, technological parameters (scraper pressure, printing speed, demolding distance, suction nozzle pressure, furnace temperature of each temperature zone, belt speed and the like), quality data (SPI data, AOI detection data, material throwing rate data) and the like through an MES (manufacturing execution system) system;
s22: a data cleaning submodule: in order to ensure the quality of data, smoothing processing is carried out by adopting an SMURF algorithm of an adaptive sliding window to fill in a vacancy value, a spatio-temporal data cube model is combined, a spatio-temporal bloom filter is used for deleting a repeated value, filtering and cleaning of the data are completed by adopting smooth noise data of a hidden Markov model, and normalization processing is carried out on the data by adopting a linear function normalization method;
s23: the data association analysis submodule comprises: a feature selection method (such as a mutual information method, a distance coefficient, RFE recursive feature elimination, a Pearson coefficient and the like) is adopted to quantify the correlation degree between data in the step S22, and features with strong correlation and low redundancy are obtained through screening;
s3: twin simulation monitoring module
The twin simulation monitoring module specifically comprises the following sub-modules:
s31: a twin model construction submodule: the method comprises the steps of constructing models such as facilities (workshop dimension, space position and the like), a board loading machine, a printing machine, a chip mounter, reflow soldering and the like by adopting three-dimensional software (such as Solidworks, Creo, CATIA and the like), and adding logic, constraint and rules to carry out twin modeling on entities with different attributes after finishing basic geometric models, such as Unity3D and plantations.
S32: a data model construction submodule: adopting an object-oriented storage mode (such as AutomationML), corresponding to entities such as resources, processes, PCB (printed Circuit Board) and the like of a physical SMT (surface mount technology) production line, constructing a data model containing attribute information such as topology, motion, logic and the like, and forming virtual-real data mapping;
s33: simulation monitoring submodule: providing multiple visual angles (such as a whole line level, a station level, an operator visual angle and the like), backtracking the manufacturing process, monitoring by multiple terminals, and pushing SMT production state monitoring, manufacturing process detailed data, statistical information, simulation results and the like to specific personnel according to different authorities of users.
S4: predictive optimization control module
The predictive optimization control module specifically comprises the following sub-modules:
s41: a prediction submodule: based on historical data in the twin model, after feature selection of corresponding performance is completed, effective features with strong correlation and low redundancy are obtained, and algorithms such as a gradient lifting tree algorithm (GBM), a distributed gradient lifting tree (lightGBM) algorithm and deep learning in an algorithm library are called to construct a performance prediction model (such as printing, patch, SPI detection, AOI detection quality prediction, equipment OEE and rejection rate analysis). And setting prediction calculation related parameters (such as learning rate, hidden layer number, node number and the like in deep learning), and evaluating the prediction algorithm precision according to indexes such as Root Mean Square Error (RMSE), mean percentage error (MAE), mean absolute error (MAPE) and the like.
S43: optimizing a submodule: and (3) constructing a performance prediction target function (printing, patching, SPI detection and AOI detection quality minimization variance), after the characteristics are selected in the step S23, adopting a meta-heuristic algorithm (such as particle swarm, difference, genetic algorithm and the like), referring to the step S42, randomly generating a plurality of groups of characteristic parameters as the input of a prediction model, substituting the output of the prediction model into the performance prediction target function, updating and iteratively optimizing to obtain a group of optimal characteristic parameters.
S44: a control sub-module: based on the optimal characteristic parameters (such as printing process parameters, chip mounter process parameters and reflow soldering process parameters) of the step S43, a twin model (such as a printer and reflow soldering) is input for simulation verification, then the relative error is calculated to be within the tolerance range, and a regulation and control strategy is generated.
Example two
An SMT manufacturing process intelligent management and control system based on digital twin, the overall system architecture of which is shown in fig. 2, the system is implemented as follows:
the S1 system management module is implemented as follows: managing user authorities through different management user authorities, and performing daily management and maintenance on user basic information and operation logs in the system; collecting equipment parameters of a printing machine through an SECS/GEM communication protocol universal in the semiconductor industry, reading personnel information and product information based on an MES system universal interface, constructing a shared file to communicate with an MES system data acquisition end, reading data files generated in the operation process of equipment such as a chip mounter, an SPI (serial peripheral interface), an AOI (automatic optic inspection) furnace, a reflow oven and the like, analyzing the files, and storing the data into a database; all algorithms in the algorithm library are written by python, are connected with the database through the pymssql module, and are packaged into dll files, so that the calling is convenient.
The S2 data processing module is implemented as follows: reading equipment data (equipment state parameters and process parameters) such as SMT (surface mount technology) near-one-month printing, surface mounting, SPI (serial peripheral interface), AOI (automatic optical inspection) and reflow soldering furnaces, personnel information and product information, performing smoothing processing by adopting an SMURF (surface mounted device) algorithm of a self-adaptive sliding window to fill vacancy values, deleting repeated values by using a space-time bloom filter in combination with a space-time data cube model, and filtering and cleaning the data by adopting smooth noise data of a hidden Markov model; because the multidimensional data dimensions are not uniform, a linear regression method is adopted to carry out data normalization processing.
The S3 twin simulation monitoring module is implemented as follows: the method comprises the steps of constructing models of an SMT production line facility, a board loading machine, a printing machine, a chip mounter, a reflow soldering furnace and the like by adopting three-dimensional software Creo, importing Unity3D software after transferring the models into an intermediate format through 3dMax software, adding custom attributes of the models, packaging the models into a model base, and conveniently and quickly establishing a virtual SMT production line manufacturing scene. Corresponding to equipment, personnel, processes and the like of a physical SMT production line, a unified data model is constructed from a role class library, a system unit library and an instance hierarchy based on AutomationML, and virtual and real data synchronous mapping is formed by means of an OPC UA transmission technology. Equipment state information, product information, personnel information, process information, manufacturing process statistical analysis information and the like in the twin model are displayed in a data visualization mode, various data visualization interfaces are pushed to specific users according to different user permissions, multi-view monitoring and prediction optimization (whole line aerial view, key station tour, operator view and the like) is provided for the users, and the process of key procedures in the SMT manufacturing process is reproduced.
The prediction optimization control module of S4 is implemented as follows: based on the data processed in step S2, the lightGBM algorithm packaged in the algorithm library is called to construct a prediction model for predicting reflow soldering process parameters, algorithm parameters (learning rate, maximum depth, leaf number, and the like) are adjusted, and the accuracy of the prediction algorithm is evaluated according to indexes such as Root Mean Square Error (RMSE), mean percentage error (MAE), mean absolute error (MAPE), and the like. And (3) constructing a reflow soldering quality minimum variance objective function, constructing a particle population by using reflow soldering quality multidimensional characteristic parameters (solder paste thickness, pad volume, belt speed, nitrogen pressure, oxygen content, furnace temperature, heating rate, environment temperature and the like), inputting the characteristic parameters into the constructed prediction model, outputting a quality prediction result, substituting the quality prediction result into the objective function, and performing iterative optimization through a particle swarm algorithm to obtain a group of optimal characteristic parameters. And finally, inputting the optimal characteristic parameters into the twin model, after simulation verification, determining that the result is within a tolerance range, and generating a parameter regulation and control strategy to feed back to a manager for decision.
To sum up, the SMT manufacturing process intelligent management and control system based on the digital twin of the embodiment can analyze the coupling relation of all elements in the SMT manufacturing process, excavate the internal operation rule influencing the production performance, predict the potential production abnormity, dynamically regulate and control key performance parameters, comprehensively improve the intelligent, refined and precise management and control levels, form precise management and control on the process, quality, efficiency and the like related to the SMT manufacturing process, establish an important technical basis for the construction of electronic equipment and component intelligent workshops, and is worthy of being popularized and used.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (5)
1. The utility model provides a SMT manufacture process intelligence management and control system based on digit twin which characterized in that: the system comprises a system management module, a data processing and analyzing module, a twin simulation monitoring module and a prediction optimization control module;
the system management module is used for managing a system user, a database and an algorithm library;
the data processing and analyzing module is used for collecting typical process data, quality data and equipment running state data of the SMT production line from an enterprise information system, and cleaning and normalizing the data;
the twin simulation monitoring module is used for constructing a digital twin geometric model and a data model of the SMT production line, and simulating and monitoring the running states of all manufacturing elements in the current manufacturing process through mapping interaction among the twin geometric model, the data model and a physical entity;
the prediction optimization control module is used for fusing twin data and real-time data, constructing a performance prediction model, mining the data operation association rule of the manufacturing process, correcting the prediction model and completing the prediction of key performance indexes.
2. A digital twin based SMT manufacturing process intelligent management and control system as defined in claim 1, wherein: the system management module comprises a user management sub-module, a database management sub-module and an algorithm library management sub-module; the user management submodule is used for distributing different login authorities of a user and performing daily management and maintenance on user basic information and an operation log in the system; the database management submodule is used for storing twin data, collected equipment, process and quality data in a database in a classified manner, regularly updating the database and cleaning redundant data in the database; and the algorithm library management submodule is used for performing classified storage on parameter setting, a prediction result, an optimization result and a verification result corresponding to the learning algorithm.
3. A digital twin based SMT manufacturing process intelligent management and control system as defined in claim 2, wherein: the data processing and analyzing module comprises a data reading sub-module, a data cleaning sub-module and a data association analysis sub-module; the data reading submodule is used for reading printing, surface mounting, reflow soldering process data, equipment running state data and quality data in the SMT manufacturing process through an enterprise information system; the data cleaning submodule is used for processing the missing values and the noise data of the various data, deleting the redundant values, extracting the numerical values of the required category data and carrying out normalization processing on the various data; the data association analysis submodule is used for quantifying association degree among various data characteristics by adopting a data mining method.
4. A digital twin based SMT manufacturing process intelligent management and control system as defined in claim 3, wherein: the twin simulation monitoring module comprises a twin model building submodule, a data model building submodule and a simulation monitoring submodule; the twin model construction submodule is used for carrying out twin modeling on entities with different attributes from geometric, physical, behavioral and regular dimensions; the data model construction submodule is used for constructing a data model containing topology, motion and logic attribute information corresponding to resources, processes and PCB entities of a physical SMT production line by adopting an object-oriented storage mode to form virtual and real data mapping; the simulation monitoring submodule is used for synchronously mapping the virtual model and the data model on the basis of real-time data acquisition, converting twin data in the SMT manufacturing process into key information and a simulation result in the manufacturing process through statistical analysis, and pushing the key information and the simulation result to workers to form visual monitoring of the manufacturing process.
5. A digital twin-based SMT manufacturing process intelligent management and control system according to claim 4, wherein: the prediction optimization control module comprises a prediction submodule, an optimization submodule and a control submodule; the prediction submodule is used for taking the real-time data after the feature selection as an input parameter, constructing a prediction model and predicting potential abnormality; the optimization submodule is used for inputting random production populations into the prediction model through a meta-heuristic algorithm and obtaining optimal characteristic parameters through optimizing iteration on the output result of the prediction model; and the control submodule is used for further simulating and verifying the effectiveness of the optimal characteristic parameter in the twin model, and generating a physical entity parameter regulation and control strategy after determining that the simulation result is in the tolerance range.
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