CN114218763A - Production line dynamic virtual recombination method based on digital twin - Google Patents
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Abstract
The invention discloses a production line dynamic virtual recombination method based on a digital twin, which is used for carrying out real-time dynamic data acquisition on production element information of a physical production line and carrying out pretreatment and standardized processing on the acquired data; constructing a production line digital twin model according to the collected physical production line data, and carrying out unified encapsulation on the physical production line data in the production line digital twin model; establishing a production line dynamic virtual recombination system, evaluating the simulation running state of the established production line digital twin model, and performing production line dynamic virtual recombination in the production line dynamic virtual recombination system according to the evaluation result; and performing performance evaluation on the production line subjected to the dynamic virtual reorganization through an intelligent learning algorithm of a support vector machine, and performing decision making according to a performance evaluation result to finally complete the virtual dynamic reorganization of the production line.
Description
Technical Field
The invention belongs to the field of intelligent manufacturing, and particularly relates to a production line dynamic virtual recombination method based on a digital twin production line.
Technical Field
With the coming of the industrial 4.0, industrial interconnection and the fourth industrial revolution which are mainly made of intelligent manufacturing, advanced technologies such as internet of things, cloud computing, big data and artificial intelligence provide strong and beneficial support for the realization of intelligent manufacturing. In the process of transforming the traditional manufacturing industry to intelligent manufacturing, a digital twin technology is rapidly developed and becomes one of important means for realizing intelligent manufacturing.
The digital twin technology is mainly used for creating behaviors of physical entities in a real environment in a digital form in a virtual world, and new capacity is added or expanded for the physical entities through means of virtual-real interaction, data fusion analysis, decision iteration and the like. In the manufacturing industry, the research on the digital twin technology at home and abroad at present mainly focuses on the aspects of model construction, information interaction fusion and the like. The construction of the digital twin model is the basis of the digital twin technology, and the twin model can carry out digital expression on a physical production line in a virtual world. The production elements of the physical production line change constantly in the production process, and information interaction between the physical production line and the virtual production line can be realized through means of data real-time acquisition and the like. Through the virtual manufacturing process, an enterprise producer can carry out production scheduling and production decision on a production line.
Although the digital twin technology is developed rapidly in recent years, and some cases and applications exist in related digital twin production lines, most of the word twin production lines only describe the actual production process of the production line, and lack guiding significance for production, scheduling and model changing of the production line. Therefore, it is necessary to deeply research the dynamic virtual recombination technology of the production line, so that the digital twin system can rapidly reconstruct the virtual production line, and provide a decision basis for enterprise producers.
Disclosure of Invention
In order to overcome the defects and shortcomings of the prior art, the invention aims to provide a production line dynamic virtual recombination method based on a digital twin, which can realize the rapid recombination of a virtual production line, carry out reliability analysis on a virtual recombination system by means of an intelligent algorithm and ensure the success rate of an actual manufacturing system.
In order to achieve the purpose, the invention adopts the following technical scheme:
a production line dynamic virtual recombination method based on digital twin comprises the following steps:
the method comprises the following steps of firstly, carrying out real-time dynamic data acquisition on production element information of a physical production line, and carrying out pretreatment and standardized processing on the acquired data;
secondly, constructing a production line digital twin model according to the physical production line data collected in the first step, and carrying out unified encapsulation on the physical production line data in the production line digital twin model;
step three, establishing a production line dynamic virtual recombination system, evaluating the simulation running state of the production line digital twin model established in the step two, and performing production line dynamic virtual recombination in the production line dynamic virtual recombination system according to the evaluation result;
and fourthly, performing performance evaluation on the production line after the dynamic virtual recombination of the production line in the third step through an intelligent learning algorithm of a support vector machine, and performing decision making according to a performance evaluation result to finally complete the virtual dynamic recombination of the production line.
Further, the first step comprises:
1.1) carrying out real-time dynamic data extraction on physical production line data according to the actual situation on site, wherein the data comprises machining equipment, production beats, tooling fixtures and logistics storage information of a physical production line;
1.2) dividing the collected physical production line data into static system data, dynamic system data and information system data according to the characteristics of the manufacturing system, and respectively carrying out characteristic value extraction and normalization processing on the classified data.
Furthermore, the static system data mainly refers to the environment, process and product data of the production line; the dynamic system data mainly refers to equipment, personnel and material data; the information system data mainly refers to production beat and service scheduling data.
Further, the second step comprises:
2.1) carrying out 3D digital modeling on the field environment, the manufacturing equipment and the manufacturing unit of the physical production line by applying NX software to obtain a 3D model of the production line;
2.2) importing the 3D model of the production line into Tecnomatix simulation software, and establishing a digital twin model of the production line;
2.3) adopting SysML modeling tool to carry out unified encapsulation on the physical production line data obtained in the first step, storing the data in a specific XML or TXT file format, and carrying out digital expression in a production line digital twin model;
and 2.4) dynamically simulating the established digital twin model of the production line through Tecnomatix simulation software, and returning simulation operation data of the digital twin model of the production line to the physical production line, so that the digital twin model of the production line and the physical production line are dynamically combined.
Further, the third step includes:
3.1) establishing a production line dynamic virtual recombination system by relying on Tecnomatix simulation software;
3.2) evaluating the simulation running state of the production line digital twin model established in the second step through the production line balance rate;
3.3) inputting each working position data in the dynamic virtual reorganization system of the production line, obtaining the standard time, the number of people and the balance time of each working procedure through an eM-plant module, further obtaining the working load of each working procedure, and generating a visual data chart in the dynamic virtual reorganization system of the production line;
and 3.4) adjusting and recombining the production line in the dynamic virtual recombination system of the production line through an ECRS principle.
Further, the simulation running state of the digital twin model of the production line in the step 3.2) is evaluated through the balance rate of the production line, and the formula is as follows:
W＝∑(ti×si)/(t0×a)
wherein W is the balance rate of the production line; t is tiWorking time of the ith station is; siThe number of the members of the ith station is determined; t is t0The beat of the assembly line; a is the number of the members of the assembly line.
Further, the fourth step includes:
4.1) adopting an SVM intelligent learning algorithm to establish a production line dynamic recombination comprehensive performance prediction model;
4.2) simulating the production line subjected to the dynamic virtual recombination of the three production lines in the step in a dynamic virtual recombination system;
4.3) inputting the characteristic data of the production line after the dynamic virtual recombination of the three production lines into the dynamic recombination comprehensive performance prediction model of the production line for prediction analysis, and outputting the comprehensive performance evaluation result of the production line;
and 4.4) carrying out reliability analysis and decision measurement on the dynamic virtual restructuring system of the production line according to the comprehensive performance evaluation result of the production line, and finally finishing the virtual dynamic restructuring of the production line.
Furthermore, the training process of the production line dynamic recombination comprehensive performance prediction model comprises the following steps:
taking the data characteristic value of the physical production line and the balance rate of the production line as the input end of the dynamic recombination comprehensive performance prediction model of the production line;
and dividing the characteristic values of the production line data into a sample group and a test group, and training in the SVM.
Furthermore, the dynamic recombination comprehensive performance prediction model output of the production line is the running state of the production line, and comprises the following steps: the production line runs stably; the production line operates efficiently; the production line runs in a congestion mode; the production line runs abnormally.
Compared with the prior art, the invention has the following advantages and effects:
the dynamic virtual restructuring method evaluates the overall layout, equipment operation, production rhythm and the like of a manufacturing system through real-time dynamic analysis of a virtual manufacturing process. And rapidly and dynamically reconstructing the virtual manufacturing equipment, the virtual manufacturing unit and the virtual manufacturing system, and further performing comprehensive evaluation including production efficiency, reliability and abnormal early warning capability on the virtual reconstruction system by means of an intelligent algorithm. The problems of single display, poor interactivity, incapability of prediction and the like of the traditional digital simulation are solved, the production line scheme is efficiently and dynamically evaluated and analyzed in the virtual system, and production optimization of the production line is guided.
Drawings
FIG. 1 is an overall framework diagram of a production line dynamic virtual reorganization method based on digital twin
FIG. 2 is a network structure diagram of an SVM-based intelligent learning algorithm
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention are described in further detail below with reference to the accompanying drawings.
Referring to fig. 1, a production line dynamic virtual reorganization method based on a digital twin includes: dynamically acquiring and processing production line data; establishing a production line digital twin model and encapsulating resource capacity; establishing a dynamic virtual recombination system of a production line; and carrying out reliability analysis on the production line after the dynamic virtual recombination system of the production line is recombined by means of an intelligent algorithm.
The dynamic acquisition and processing of the production line data are the basis for supporting the high fidelity of the digital twin model, and the construction of the digital twin model is the key of the production line dynamic virtual recombination system. And the production line data is timely and reliably matched with an intelligent algorithm to carry out comprehensive reliability evaluation on the recombination system, so that the production line dynamic recombination system provides decision basis for production line beat adjustment and production line model change.
The present invention will be described in further detail with reference to specific examples below:
example (b):
as shown in fig. 1, a dynamic virtual reorganization method for a production line based on a digital twin includes the following steps:
the method comprises the steps of firstly, carrying out real-time dynamic data acquisition on production element information of a physical production line, and carrying out preprocessing and standardized processing on the acquired data.
Step S101, according to actual conditions in the field, production element information of machining equipment, production beats, tool fixtures, logistics storage and the like of a physical production line is subjected to real-time dynamic data extraction by utilizing communication networks with different architectures and topologies, such as field buses, Ethernet, wifi and the like;
wherein, the data of the machining equipment can be read through a numerical control system interface; the control system can be read by a PLC; data such as production beat, workpiece, time and the like can be read through an MAS system or an RFID interface; the data of workshop environment, logistics storage and the like can be read according to the production line environment data and other digital data.
Step S102, dividing the collected physical production line data into static system data, dynamic system data and information system data according to the characteristics of a manufacturing system, and respectively carrying out characteristic value extraction and normalization processing on the classified data;
wherein, the static system data mainly refers to the environment, process and product data of the production line; the dynamic system data mainly refers to equipment, personnel and material data; the information system data mainly refers to production beat and service scheduling data.
And step two, constructing a production line digital twin model, and performing virtualization packaging of resources and capacity through a virtualization technology.
Step S201, applying NX software to the field environment, the manufacturing equipment and the manufacturing unit of the physical production line to carry out 3D digital modeling to obtain a 3D model of the production line;
step S202, importing the production line 3D model established in the step S201 into Tecnomatix simulation software, and establishing a production line digital twin model;
step S203, adopting a SysML modeling tool to carry out unified encapsulation on the physical production line data acquired in the step one, storing the data in a specific XML or TXT file format, and carrying out digital expression in the production line digital twin model established in the step S202;
and S204, dynamically simulating the established digital twin model of the production line through Tecnomatix simulation software, and transmitting the simulation operation data of the digital twin model of the production line back to the physical production line through data interfaces such as PLC (programmable logic controller) and the like, so that the digital twin model of the production line and the physical production line are dynamically combined.
And step three, establishing a production line dynamic virtual recombination system, evaluating the simulation running state of the production line digital twin model established in the step two, performing production line dynamic virtual recombination in the production line dynamic virtual recombination system according to the evaluation result, and rapidly reconstructing the manufacturing equipment, the virtual manufacturing unit and the virtual system.
Step S301, a dynamic virtual recombination system of a production line is established by relying on Tecnomatix simulation software, and the system can rapidly reconstruct manufacturing equipment, a virtual manufacturing unit and a virtual system;
step S302, evaluating the simulation running state of the digital twin model of the production line in the step S204, and providing a decision basis for physical production line recombination;
wherein, the simulation running state can be evaluated through the production line balance rate, and the formula is as follows:
W＝∑(ti×si)/(t0×a)
wherein W is the balance rate of the production line; t is tiWorking time of the ith station is; siThe number of the members of the ith station is determined; t is t0The beat of the assembly line; a is the number of the members of the assembly line.
Step S303, inputting each working position data into the dynamic virtual reorganization system of the production line, obtaining standard time, the number of people and balance time of each working procedure through an eM-plant module, further obtaining the working load of each working procedure, and generating a visual data chart in the dynamic virtual reorganization system of the production line;
step S304, analyzing and adjusting production procedures in the dynamic virtual recombination system of the production line through an ECRS principle, namely canceling, combining, adjusting sequence and simplifying;
and S305, adjusting the overall layout, equipment operation, manufacturing time and the like of the production line in the dynamic virtual reorganization system of the production line according to an ECRS principle.
And fourthly, carrying out manufacturability analysis on the dynamic virtual recombination result of the production line through an intelligent learning algorithm of a Support Vector Machine (SVM), and ensuring the success rate of the actual manufacturing system.
Step S401, as shown in FIG. 2, a Support Vector Machine (SVM) intelligent learning algorithm is adopted to establish a production line dynamic recombination comprehensive performance prediction model, and the SVM prediction model is trained;
the training of the SVM prediction model specifically comprises the following steps:
taking the static system characteristic value, the dynamic system characteristic value and the information system characteristic value of the production line extracted in the step S102 and the balance rate of the production line calculated in the step S301 as input ends of the SVM;
dividing the production line data characteristic value into a sample group and a test group, training in the SVM, and setting the maximum training frequency to 10000 times.
And step S402, simulating the production line adjusted in the step S303 and the step S304 in the dynamic virtual reorganization system.
Step S403, inputting the characteristic data of the production line after the dynamic virtual reorganization of the three-production line into a trained SVM model for prediction analysis, evaluating the comprehensive performance of the production line by an intelligent learning algorithm, and taking the output end of the SVM model as the running state of the production line as the comprehensive performance evaluation index of the production line, wherein the method comprises the following steps: the production line runs stably, and the code is [1 ]; the production line runs efficiently, and the code is [2 ]; the production line runs in a congestion mode, and the code is [3 ]; the production line runs abnormally, and the code is [4 ].
And S404, performing credibility analysis and decision measurement on the dynamic virtual restructuring system of the production line according to the comprehensive performance evaluation result of the production line output by the SVM model, and finally completing the virtual dynamic restructuring of the production line.
In summary, the invention provides a production line dynamic virtual restructuring method based on digital twin, which can realize dynamic mapping of a physical production line and a virtual production line. The dynamic virtual recombination of the production line is established, and the overall layout, equipment operation, production rhythm and the like of the manufacturing system are evaluated through the analysis of the virtual manufacturing process. And rapidly and dynamically reconstructing the virtual manufacturing equipment, the virtual manufacturing unit and the virtual manufacturing system, and further performing comprehensive evaluation including production efficiency, reliability and abnormal early warning capability on the virtual reconstruction system by means of an intelligent algorithm.
While particular embodiments of the present invention have been described above, the scope of the invention is not limited thereto. Any simple modification or equivalent changes and modifications of the above embodiments based on the principle and spirit of the present invention shall fall within the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the appended claims.
Claims (9)
1. A production line dynamic virtual recombination method based on digital twin is characterized by comprising the following steps:
the method comprises the following steps of firstly, carrying out real-time dynamic data acquisition on production element information of a physical production line, and carrying out pretreatment and standardized processing on the acquired data;
secondly, constructing a production line digital twin model according to the physical production line data collected in the first step, and carrying out unified encapsulation on the physical production line data in the production line digital twin model;
step three, establishing a production line dynamic virtual recombination system, evaluating the simulation running state of the production line digital twin model established in the step two, and performing production line dynamic virtual recombination in the production line dynamic virtual recombination system according to the evaluation result;
and fourthly, performing performance evaluation on the production line after the dynamic virtual recombination of the production line in the third step through an intelligent learning algorithm of a support vector machine, and performing decision making according to a performance evaluation result to finally complete the virtual dynamic recombination of the production line.
2. The production line dynamic virtual reorganization method based on the digital twin as set forth in claim 1, wherein the step one comprises:
1.1) carrying out real-time dynamic data extraction on physical production line data according to the actual situation on site, wherein the data comprises machining equipment, production beats, tooling fixtures and logistics storage information of a physical production line;
1.2) dividing the collected physical production line data into static system data, dynamic system data and information system data according to the characteristics of the manufacturing system, and respectively carrying out characteristic value extraction and normalization processing on the classified data.
3. The dynamic virtual restructuring method for production line based on digital twin as claimed in claim 2, wherein the static system data mainly refers to environment, process and product data of the production line; the dynamic system data mainly refers to equipment, personnel and material data; the information system data mainly refers to production beat and service scheduling data.
4. The production line dynamic virtual reorganization method based on the digital twin as set forth in claim 1, wherein the second step comprises:
2.1) carrying out 3D digital modeling on a physical production line by using NX software to obtain a 3D model of the production line;
2.2) importing the 3D model of the production line into Tecnomatix simulation software, and establishing a digital twin model of the production line;
2.3) adopting SysML modeling tool to carry out unified encapsulation on the physical production line data obtained in the first step, storing the data in a specific XML or TXT file format, and carrying out digital expression in a production line digital twin model;
and 2.4) dynamically simulating the established digital twin model of the production line through Tecnomatix simulation software, and returning simulation operation data of the digital twin model of the production line to the physical production line, so that the digital twin model of the production line and the physical production line are dynamically combined.
5. The production line dynamic virtual reorganization method based on the digital twin as set forth in claim 1, wherein the third step comprises:
3.1) establishing a production line dynamic virtual recombination system by relying on Tecnomatix simulation software;
3.2) evaluating the simulation running state of the production line digital twin model established in the second step through the production line balance rate;
3.3) inputting each working position data in the dynamic virtual reorganization system of the production line, obtaining the standard time, the number of people and the balance time of each working procedure through an eM-plant module, further obtaining the working load of each working procedure, and generating a visual data chart in the dynamic virtual reorganization system of the production line;
and 3.4) adjusting and recombining the production line in the dynamic virtual recombination system of the production line through an ECRS principle.
6. The dynamic virtual restructuring method for production line based on digital twin as claimed in claim 5, wherein in step 3.2), the simulation operating state of the digital twin model for production line is evaluated by the balance ratio of production line, and the formula is as follows:
W＝∑(ti×si)/(t0×a)
wherein W is the balance rate of the production line; t is tiWorking time of the ith station is; siThe number of the members of the ith station is determined; t is t0The beat of the assembly line; a is the number of the members of the assembly line.
7. The production line dynamic virtual reorganization method based on the digital twin as set forth in claim 1, wherein the fourth step comprises:
4.1) adopting an SVM intelligent learning algorithm to establish a production line dynamic recombination comprehensive performance prediction model;
4.2) simulating the production line subjected to the dynamic virtual recombination of the three production lines in the step in a dynamic virtual recombination system;
4.3) inputting the characteristic data of the production line after the dynamic virtual recombination of the three production lines into the dynamic recombination comprehensive performance prediction model of the production line for prediction analysis, and outputting the comprehensive performance evaluation result of the production line;
and 4.4) carrying out reliability analysis and decision measurement on the dynamic virtual restructuring system of the production line according to the comprehensive performance evaluation result of the production line, and finally finishing the virtual dynamic restructuring of the production line.
8. The production line dynamic virtual reorganization method based on the digital twin as claimed in claim 7, wherein the training process of the production line dynamic reorganization comprehensive performance prediction model is as follows:
taking physical production line data and production line balance rate as input ends of a production line dynamic recombination comprehensive performance prediction model;
and dividing the characteristic values of the production line data into a sample group and a test group, and training in the SVM.
9. The production line dynamic virtual reorganization method based on the digital twin, according to claim 8, wherein the production line dynamic reorganization comprehensive performance prediction model is output as a production line running state, and comprises: the production line runs stably; the production line operates efficiently; the production line runs in a congestion mode; the production line runs abnormally.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114493049A (en) * | 2022-04-07 | 2022-05-13 | 卡奥斯工业智能研究院(青岛)有限公司 | Production line optimization method and device based on digital twin, electronic equipment and medium |
| CN114815759A (en) * | 2022-06-27 | 2022-07-29 | 广州力控元海信息科技有限公司 | Virtual-real fusion flexible production line variable control method and system |
| CN117391625A (en) * | 2023-10-18 | 2024-01-12 | 上海形拓科技有限公司 | Intelligent manufacturing management system and method based on digital twinning |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114493049A (en) * | 2022-04-07 | 2022-05-13 | 卡奥斯工业智能研究院(青岛)有限公司 | Production line optimization method and device based on digital twin, electronic equipment and medium |
| CN114815759A (en) * | 2022-06-27 | 2022-07-29 | 广州力控元海信息科技有限公司 | Virtual-real fusion flexible production line variable control method and system |
| CN114815759B (en) * | 2022-06-27 | 2022-09-20 | 广州力控元海信息科技有限公司 | Virtual-real fusion flexible production line variable control method and system |
| CN117391625A (en) * | 2023-10-18 | 2024-01-12 | 上海形拓科技有限公司 | Intelligent manufacturing management system and method based on digital twinning |
| CN117391625B (en) * | 2023-10-18 | 2024-04-02 | 上海形拓科技有限公司 | Intelligent manufacturing management system and method based on digital twinning |
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