CN111539583A - Production process simulation optimization method based on digital twinning - Google Patents
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Abstract
The invention builds a manufacturing operation management system and a data acquisition and control system based on big data based on flexible production requirements of multiple varieties, small batch and variable beat, improves the production logistics intelligentization level and the assembly process equipment specialized level, forms an assembly intelligentization workshop taking a batch production product pulsation production line and an intelligent assembly unit for developing products as main bodies, and provides more optimization decisions for physical entities through information fusion and data interaction between a virtual model and the physical entities.
Description
Technical Field
The invention belongs to the technical field of simulation optimization, and particularly relates to a digital twinning technology.
Background
The general assembly of the existing large-scale complex products usually adopts a fixed station, manual vertical assembly is carried out based on a rigid frame, the assembly quality is unstable, the assembly efficiency is low, the labor intensity of workers is high, and the assembly operation management is difficult.
The Pulse Assembly line (Pulse Assembly Lines) was originally derived from the Ford mobile automotive line and is a transitional stage in a continuous mobile Assembly line. The difference is that the pulse assembly production line can set buffer time, the requirement on production rhythm is not high, when a certain production link has problems, the whole production line can not move or be left for the next station to solve, and when the assembly work is completely finished, the production line pulses once.
The data acquisition rate in the production process at the present stage is low, the data analysis and application capability is weak, the process control and tracing capability is not strong, and the quality control effect is not high. In the workshop management, resources and plan scheduling depend on manual experience, an informatization means is insufficient, the refinement degree of production plan scheduling is insufficient, the dynamic response capability is not strong, and the workshop operation management efficiency is low.
With the development of the information age, digital twin technology has become one of the methods for realizing intelligent manufacturing. The digital twin is to create a virtual model of a physical entity in an information space in a digital manner, and simulate the behavior of the physical entity in a real environment by using data. And more optimization decisions are provided for the physical entity through information fusion and data interaction between the virtual model and the physical entity.
Disclosure of Invention
The invention provides a production process simulation optimization method based on digital twins in order to solve the problems in the prior art, and adopts the following technical scheme in order to achieve the purpose.
The intelligent workshop management system has the advantages that functions such as the operation state, the key station operation state, the assembly process monitoring, the production process simulation and optimization are achieved, effective management of a workshop operation process is achieved, management and control capacity of an intelligent workshop is comprehensively improved, the progress deviation of each work step in actual assembly is calculated through real-time simulation of the workshop operation state, the assembly process flow is adjusted according to the work step deviation, and assembly efficiency is improved in the aspects of further optimizing the process flow, balancing station beats, improving the station assembly capacity and the like.
And adjusting the final assembly process flow according to the step deviation, and further optimizing the process flow, balancing the station beat, improving the station assembly capacity and the like to improve the final assembly efficiency.
Analyzing the product assembly process, and integrating and optimizing the operation content of each station.
According to the structure composition and the process characteristics of the product, different types of product production lines in different fields are respectively designed.
And (4) completing the construction of the intelligent plant digital twin model, and realizing the data acquisition and analysis of equipment in the plant.
Establishing utilization rates of tasks, process flows, people, devices, material complete sets, logistics distribution, process guidance models or files, measuring devices and the like and time series models of problem occurrence, analyzing the utilization rates of various resources changing along with time, and judging the occurrence mode of bottleneck resources by using a cluster analysis method.
The method has the advantages of realizing interconnection and intercommunication of key resource equipment, realizing controllable key parameters of risk components, realizing quick decision processing of simple quality problems in the assembling process and simultaneously providing basic hardware conditions for intelligent workshop integrated application construction.
The method realizes synchronous operation of a digital space and a logic space, researches a logic processing method of entity space data and signals, provides a real-time mapping operation process of the digital space, drives high simulation operation of a digital twin model by utilizing real-time data, and realizes mapping and interaction of products, equipment, personnel, systems and environments in the digital space and the entity space.
And the reworking and repairing, the quality visualization and the quality whole-process tracing management are realized. The accumulated historical data is used for carrying out statistical analysis and data-driven equipment state modeling, nonlinear prediction models, parameter optimization technologies, alarm strategies and the like for different types of equipment objects are developed, the degradation trend of the equipment state is accurately grasped, the equipment is prevented from being abnormal, and the smooth production is ensured.
The construction is developed from an operation layer and a control layer, the production management of a final assembly workshop is uniformly implemented and deployed, a material cache library and a line side library are arranged in the final assembly workshop according to the principle of hierarchical storage, the construction is developed according to the steps of system planning and hierarchical implementation, and the intelligent workshop which is efficient, transparent, highly flexible, timely in delivery and remarkable in man-machine cooperation characteristic is built.
The production line flexibility is increased, the production line efficiency is improved, and detailed technological process carding, analysis classification and optimization adjustment are carried out on various types of products.
Product clustering analysis, which is to design different types of product production lines in different fields respectively according to the structure composition and the process characteristics of products, establish utilization rates of tasks, process flows, people, devices, material complete sets, logistics distribution, process guidance models or files, measuring devices and the like and time series models of problem occurrence, analyze the utilization rates of various resources changing along with time, and judge the occurrence mode of bottleneck resources by using a clustering analysis method.
The technological process is reconstructed, the traditional design thought is broken through, the product production process is analyzed in the whole life cycle, and the technological process of the reconstructed product is adapted to the production of similar products.
And optimizing station layout, analyzing the product assembly process, and integrating and optimizing the operation content of each station.
Based on the analysis result of the process flow, the intelligent upgrading research of the assembly equipment is developed, the interconnection and intercommunication of key resource equipment is realized through the digitization, networking and intelligent upgrading of various equipment, the controllability of key parameters of risk pieces is realized, the quick decision processing of simple quality problems in the assembly process is realized, and meanwhile, the basic hardware condition is also provided for the integrated application construction of an intelligent workshop.
The method comprises the steps of establishing a twin model in a digital space in the face of physical entity types and diversified functions such as different types of products, parts, materials, equipment, personnel and environments and data generated by the entities, researching a logic processing method of entity space data and signals, providing a digital space real-time mapping operation process, driving high-fidelity operation of the digital twin model by utilizing real-time data, realizing synchronous operation of the digital space and the logic space, and realizing mapping and interaction of the products, the equipment, the personnel, the systems and the environments in the digital space and the entity space.
The production process digital twin model adopts a unified expression DTws＝DTequip∪DTprod∪DTpers∪DTenvWherein DTwsFor digital models of workshop processes, DTequipBeing a digital twin model of the plant, DTprodFor digital twinning models of products, DTpersBeing a person digital twin model, DTenvIs an environmental twin model.
Based on digital twin information and statistical learning and deep learning technologies, professional diagnosis of the operation state of the assembly workshop is completed, notification, judgment, processing, tracking, analysis compensation and closing of the abnormity are achieved, a closed loop of workshop abnormity management is formed, and the timeliness of abnormity processing is improved.
The method comprises the steps of collecting quality data in real time, correlating the collected data with information such as production orders, batches and the like, finding potential quality risks and problems in time, realizing rework repair and quality visualization, tracing and managing the whole quality process, carrying out statistical analysis and data-driven equipment state modeling by using accumulated historical data, accurately grasping the degradation trend of equipment states aiming at nonlinear prediction models, parameter optimization technologies and alarm strategies of equipment objects of different types, preventing the equipment from being abnormal, and ensuring the smooth production.
The method comprises the steps of inputting original data for fusion, extracting basic information from a lower layer of the original data, fusing the basic information into higher representation information and decisions of a middle layer of the original data, further fusing the decisions and the information into a higher layer of the original data to form a final classification result, automatically discovering a complex data structure, learning useful characteristics layer by layer from the original data, and adaptively optimizing combinations of different fusion levels so as to meet the requirements of intelligent decisions on data in production management.
The method comprises the steps of constructing a digital twin model of the full life cycle of a product, simulating the running state of a workshop in real time, calculating the progress deviation of each process step in actual assembly, adjusting the assembly process flow according to the process step deviation, further optimizing the process flow, balancing the beat of the process steps, enhancing the assembly capacity of the process steps and improving the assembly efficiency.
And performing process flow simulation in the digital twin model again, calculating the progress deviation of each process step in actual assembly, and completing a new process flow optimization.
In actual assembly operation, the process flow is often not in the best state, the stations are unreasonably arranged, the problem that the operation time difference of each station is large exists, the assembly tools and equipment in the stations are traditional, the assembly efficiency is low, process data cannot be automatically acquired, quality monitoring and process tracing are not facilitated, and the assembly efficiency is improved by further optimizing the process flow, balancing the station beat, improving the station assembly capacity and the like.
In the traditional process flow, the problems of low operation efficiency and long operation period are caused because the machine installation and the electric fitting content of an antenna array surface, a platform and a radar vehicle assembly are not operated in parallel, telecommunication test is carried out on a test frame, the assembling and separating time of the array surface and the test frame is increased, the panel assembling station, the high-frequency box assembling station and the high-frequency box assembling station are unreasonably divided, and the time of each station is unbalanced.
According to the optimized process flow, the content of the high-frequency box assembly station is split to the panel assembly station and the high-frequency box assembly station, the whole life cycle from the part delivery to the finished product storage is driven by actually generated data, the evolution of the product is completed, the process data, the quality data and the like of the product are dynamically stored and virtualized in the label of the product, and the whole life cycle of the product is accompanied.
The method comprises the steps of mapping the action, spatial position and running state of various equipment such as a robot, an AGV and processing equipment in a production line in real time, finishing the processing of each station, mapping the identity, position and other information of personnel in real time, finishing the visual management of the personnel, and mapping the information of production plan progress, operation plan progress, process progress and the like in real time, wherein the information of inventory state, logistics situation, the flow of processing stations, the data volume of work-in-process and the like can be visually analyzed and managed by a digital twin space.
The historical data is required to be subjected to associated modeling, the operation associated modeling of comprehensive factors such as man-machine material method environment measurement and the like under the task driving is comprehensively considered, and a comprehensive model is formed by taking a time axis as guidance.
Establishing utilization rates of tasks, process flows, people, devices, material complete sets, logistics distribution, process guidance models or files, measuring devices and the like and time series models of problem occurrence, analyzing the utilization rates of various resources changing along with time, judging the occurrence mode of bottleneck resources by using a cluster analysis method, and providing guidance for subsequent actual execution.
The method comprises the steps of establishing a core equipment database in a system, displaying the use condition of the core equipment, the use rate of the core equipment, the unqualified product count and the product qualification rate of the equipment in real time, displaying the current state monitoring, operation, shutdown, maintenance, failure and the like of the core equipment in real time in the system, displaying the operation condition, the operation parameters, the standby time, the moment press-mounting displacement and the like of the equipment when a mouse stops when moving to the equipment, associating the operation condition, the operation parameters, the standby time, the moment press-mounting displacement and the like with OEE, and viewing a statistical analysis report in software in real time.
The three-dimensional visual display platform is established for the final assembly intelligent workshop based on the digital twin model, has the functions of overall operation state of the workshop, assembly process monitoring, key station operation state, production process simulation and optimization and the like, realizes effective management of the workshop operation process, and comprehensively improves the management and control capability of the intelligent workshop; the operating state of a workshop is simulated in real time, the progress deviation of each process step in actual assembly is calculated, the assembly process flow is adjusted according to the process step deviation, the process flow is further optimized, the beat of the stations is balanced, and the assembly capacity of the stations is enhanced, so that the assembly efficiency is improved; based on flexible production requirements of multiple varieties, small batches and variable beats, a manufacturing operation management system and a data acquisition and control system based on big data are built, the intelligent level of production logistics and the professional level of final assembly process equipment are improved, and a final assembly intelligent workshop mainly comprising a batch production product pulsation production line and a development product intelligent final assembly unit is formed.
Drawings
Fig. 1 is a system design architecture, fig. 2 is a conventional process flow, fig. 3 is an optimized process flow, and fig. 4 is a cluster analysis process.
Detailed Description
The technical scheme of the invention is specifically explained in the following by combining the attached drawings.
The intelligent workshop management system has the advantages that functions such as the operation state, the key station operation state, the assembly process monitoring, the production process simulation and optimization are achieved, effective management of a workshop operation process is achieved, management and control capacity of an intelligent workshop is comprehensively improved, the progress deviation of each work step in actual assembly is calculated through real-time simulation of the workshop operation state, the assembly process flow is adjusted according to the work step deviation, and assembly efficiency is improved in the aspects of further optimizing the process flow, balancing station beats, improving the station assembly capacity and the like.
And adjusting the final assembly process flow according to the step deviation, and further optimizing the process flow, balancing the station beat, improving the station assembly capacity and the like to improve the final assembly efficiency.
Analyzing the product assembly process, and integrating and optimizing the operation content of each station.
According to the structure composition and the process characteristics of the product, different types of product production lines in different fields are respectively designed.
And (4) completing the construction of the intelligent plant digital twin model, and realizing the data acquisition and analysis of equipment in the plant.
Establishing utilization rates of tasks, process flows, people, devices, material complete sets, logistics distribution, process guidance models or files, measuring devices and the like and time series models of problem occurrence, analyzing the utilization rates of various resources changing along with time, and judging the occurrence mode of bottleneck resources by using a cluster analysis method.
The method has the advantages of realizing interconnection and intercommunication of key resource equipment, realizing controllable key parameters of risk components, realizing quick decision processing of simple quality problems in the assembling process and simultaneously providing basic hardware conditions for intelligent workshop integrated application construction.
The method realizes synchronous operation of a digital space and a logic space, researches a logic processing method of entity space data and signals, provides a real-time mapping operation process of the digital space, drives high simulation operation of a digital twin model by utilizing real-time data, and realizes mapping and interaction of products, equipment, personnel, systems and environments in the digital space and the entity space.
And the reworking and repairing, the quality visualization and the quality whole-process tracing management are realized. The accumulated historical data is used for carrying out statistical analysis and data-driven equipment state modeling, nonlinear prediction models, parameter optimization technologies, alarm strategies and the like for different types of equipment objects are developed, the degradation trend of the equipment state is accurately grasped, the equipment is prevented from being abnormal, and the smooth production is ensured.
The construction is carried out from an operation layer and a control layer, as shown in fig. 1, the production management of a final assembly workshop is uniformly implemented and deployed, a material cache library and a line side library are arranged in the final assembly workshop according to the principle of hierarchical storage, the construction is carried out according to the steps of system planning and hierarchical implementation, and the intelligent workshop which is efficient, transparent, highly flexible, on-time delivery and remarkable in man-machine cooperation characteristic is built.
The production line flexibility is increased, the production line efficiency is improved, and detailed technological process carding, analysis classification and optimization adjustment are carried out on various types of products.
Product clustering analysis, as shown in fig. 4, according to the structural composition and the process characteristics of the product, different types of product production lines in different fields are respectively designed, utilization rates of tasks, process flows, people, devices, material complete sets, logistics distribution, process guidance models or files, measuring devices and the like and time series models of problem occurrence are established, the utilization rates of various resources changing along with time are analyzed, and the occurrence mode of bottleneck resources is judged by using a clustering analysis method.
The technological process is reconstructed, the traditional design thought is broken through, the product production process is analyzed in the whole life cycle, and the technological process of the reconstructed product is adapted to the production of similar products.
And optimizing station layout, analyzing the product assembly process, and integrating and optimizing the operation content of each station.
Based on the analysis result of the process flow, the intelligent upgrading research of the assembly equipment is developed, the interconnection and intercommunication of key resource equipment is realized through the digitization, networking and intelligent upgrading of various equipment, the controllability of key parameters of risk pieces is realized, the quick decision processing of simple quality problems in the assembly process is realized, and meanwhile, the basic hardware condition is also provided for the integrated application construction of an intelligent workshop.
The method comprises the steps of establishing a twin model in a digital space in the face of physical entity types and diversified functions such as different types of products, parts, materials, equipment, personnel and environments and the like and data generated by the entities, researching a logic processing method of entity space data and signals, providing a digital space real-time mapping operation process, driving high-fidelity operation of the digital twin model by utilizing real-time data, realizing synchronous operation of the digital space and the logic space, and realizing mapping and interaction of the products, the equipment, the personnel, the systems and the environments in the digital space and the entity space.
The production process digital twin model adopts a unified expression DTws＝DTequip∪DTprod∪DTpers∪DTenvWherein DTwsFor digital models of workshop processes, DTequipBeing a digital twin model of the plant, DTprodFor digital twinning models of products, DTpersBeing a person digital twin model, DTenvIs an environmental twin model.
Based on digital twin information and statistical learning and deep learning technologies, professional diagnosis of the operation state of the assembly workshop is completed, notification, judgment, processing, tracking, analysis compensation and closing of the abnormity are achieved, a closed loop of workshop abnormity management is formed, and the timeliness of abnormity processing is improved.
The method comprises the steps of collecting quality data in real time, correlating the collected data with information such as production orders, batches and the like, finding potential quality risks and problems in time, realizing rework repair and quality visualization, tracing and managing the whole quality process, carrying out statistical analysis and data-driven equipment state modeling by using accumulated historical data, accurately grasping the degradation trend of equipment states aiming at nonlinear prediction models, parameter optimization technologies and alarm strategies of equipment objects of different types, preventing the equipment from being abnormal, and ensuring the smooth production.
The method comprises the steps of inputting original data for fusion, extracting basic information from a lower layer of the original data, fusing the basic information into higher representation information and decisions of a middle layer of the original data, further fusing the decisions and the information into a higher layer of the original data to form a final classification result, automatically discovering a complex data structure, learning useful characteristics layer by layer from the original data, and adaptively optimizing combinations of different fusion levels so as to meet the requirements of intelligent decisions on data in production management.
The method comprises the steps of constructing a digital twin model of the full life cycle of a product, simulating the running state of a workshop in real time, calculating the progress deviation of each process step in actual assembly, adjusting the assembly process flow according to the process step deviation, further optimizing the process flow, balancing the beat of the process steps, enhancing the assembly capacity of the process steps and improving the assembly efficiency.
And performing process flow simulation in the digital twin model again, calculating the progress deviation of each process step in actual assembly, and completing a new process flow optimization.
In actual assembly operation, the process flow is often not in the best state, the stations are unreasonably arranged, the problem that the operation time difference of each station is large exists, the assembly tools and equipment in the stations are traditional, the assembly efficiency is low, process data cannot be automatically acquired, quality monitoring and process tracing are not facilitated, and the assembly efficiency is improved by further optimizing the process flow, balancing the station beat, improving the station assembly capacity and the like.
The traditional process flow is shown in fig. 2, the problems of low operation efficiency and long operation period are caused by the fact that the machine installation and the electric installation content of an antenna array surface, a platform and a radar vehicle assembly do not perform parallel operation, telecommunication test is performed on a test frame, the assembling and separating time of the array surface and the test frame is increased, the panel assembling station, the high-frequency box assembling station and the high-frequency box assembling station are not reasonably divided, and the time of each station is not balanced.
The optimized process flow is shown in fig. 3, the content of the high-frequency box assembly station is split to the panel assembly station and the high-frequency box assembly station, the whole life cycle from the part delivery to the finished product storage is driven by the actually generated data, the evolution of the product is completed, the process data, the quality data and the like of the product are dynamically stored and virtualized in the label of the product, and the whole life cycle of the product is accompanied.
The method comprises the steps of mapping the action, spatial position and running state of various equipment such as a robot, an AGV and processing equipment in a production line in real time, finishing the processing of each station, mapping the identity, position and other information of personnel in real time, finishing the visual management of the personnel, and mapping the information of production plan progress, operation plan progress, process progress and the like in real time, wherein the information of inventory state, logistics situation, the flow of processing stations, the data volume of work-in-process and the like can be visually analyzed and managed by a digital twin space.
The historical data is required to be subjected to associated modeling, the operation associated modeling of comprehensive factors such as man-machine material method environment measurement and the like under the task driving is comprehensively considered, and a comprehensive model is formed by taking a time axis as guidance.
Establishing utilization rates of tasks, process flows, people, devices, material complete sets, logistics distribution, process guidance models or files, measuring devices and the like and time series models of problem occurrence, analyzing the utilization rates of various resources changing along with time, judging the occurrence mode of bottleneck resources by using a cluster analysis method, and providing guidance for subsequent actual execution.
The method comprises the steps of establishing a core equipment database in a system, displaying the use condition of the core equipment, the use rate of the core equipment, the unqualified product count and the product qualification rate of the equipment in real time, displaying the current state monitoring, operation, shutdown, maintenance, failure and the like of the core equipment in real time in the system, displaying the operation condition, the operation parameters, the standby time, the moment press-mounting displacement and the like of the equipment when a mouse stops when moving to the equipment, associating the operation condition, the operation parameters, the standby time, the moment press-mounting displacement and the like with OEE, and viewing a statistical analysis report in software in real time.
The assembly process flow is refined and combed, the assembly total debugging efficiency is improved by 28.7 percent through measures of multi-seed parallel operation, station integration optimization, test process optimization and the like, the period is shortened by 29 days, and the period is reduced from 101 days to 72 days.
The above-described embodiments are not intended to limit the present invention, and any modifications, equivalents, improvements, etc. made within the spirit and principle of the present invention are included in the scope of the present invention.
Claims (6)
1. A production process simulation optimization method based on digital twinning is characterized by comprising the following steps: monitoring an assembly process, an operation state and workshop management and control, collecting analysis equipment data, simulating the workshop operation state in real time, and building an intelligent workshop digital twin model; analyzing the structural composition, the assembly process and the process characteristics of the product, and designing different types of production lines in different fields; integrating the operation content of the stations, calculating the progress deviation of each process step, adjusting the process flow, balancing the station beats and improving the assembly capacity; establishing a database, displaying the use condition, the use rate and the qualification rate in real time, monitoring the current running, shutdown, maintenance and fault states, diagnosing the running state of the final assembly workshop based on the database depth technology, and forming a closed loop of workshop management.
2. The method for optimizing simulation of a digital twin-based production process according to claim 1, wherein the building of an intelligent workshop digital twin model comprises: models of products, equipment, personnel, systems and rings are established, the utilization rate of each resource is measured, a time sequence model of event occurrence is established, and synchronous operation of a digital space and a logic space is realized.
3. The method of claim 2, wherein the modeling of products, equipment, personnel, systems and loops comprises: using a unified expression DTws＝DTequip∪DTprod∪DTpers∪DTenvDescribe the plant process, in which DTwsFor digital twinning models in the production process of a workshop, DTequipBeing a digital twin model of the plant, DTprodFor digital twinning models of products, DTpersBeing a person digital twin model, DTenvIs an environmental twin model.
4. The method for optimizing simulation of a production process based on digital twinning as claimed in claim 2, wherein the implementing synchronous operation of digital space and logic space comprises: analyzing the utilization rate of each resource along with the change of time, acquiring the occurrence mode of bottleneck resources by adopting a clustering analysis method, acquiring entity space data and logic processing data of signals, and realizing the mapping and interaction of products, equipment, personnel, systems and environments in a digital space and an entity space by driving the simulation operation of a digital twin model through real-time data.
5. The method for optimizing simulation of a production process based on digital twin as set forth in claim 4, wherein the method for cluster analysis comprises: merging original data fusion, extracting basic information from a lower layer, fusing the basic information to the representation information and the decision of an intermediate layer, fusing the decision and the information at a higher layer to form a final classification result, and adaptively optimizing the combination of different fusion levels by combining the learning characteristics layer by layer from the original data to meet the requirement of intelligent decision on data in production management.
6. The method for optimizing simulation of a digital twin-based production process according to claim 1, wherein the operation contents of the integration station comprise: mapping the processing action, the spatial position and the running state of the equipment in real time; mapping the identity and position of the person in real time; mapping the production plan progress, the operation plan progress and the process progress in real time; and analyzing and managing inventory state, logistics situation, process of processing stations and work-in-process data volume.
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| CN117933101B (en) * | 2024-03-22 | 2024-06-11 | 山东星科智能科技股份有限公司 | Industrial production digital twin simulation system, method and storage medium |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107832497A (en) * | 2017-10-17 | 2018-03-23 | 广东工业大学 | A kind of intelligent workshop fast custom design method and system |
| CN110347131A (en) * | 2019-07-18 | 2019-10-18 | 中国电子科技集团公司第三十八研究所 | The digital twinned system of facing to manufacture |
| CN110399642A (en) * | 2019-06-21 | 2019-11-01 | 浙江大学 | It is a kind of for the twin body of number and its construction method of production line and application |
| CN110765589A (en) * | 2019-09-10 | 2020-02-07 | 上海大学 | Intelligent workshop virtual and real synchronous monitoring system and method based on digital twins |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11262722B2 (en) * | 2016-08-02 | 2022-03-01 | Siemens Aktiengesellschaft | Monitoring and controlling unit for use in an autonomous system with self-x properties |
| CN110704974B (en) * | 2019-09-30 | 2023-08-01 | 江苏科技大学 | Modeling and using method of process model based on digital twin driving |
| CN110989506A (en) * | 2019-11-02 | 2020-04-10 | 温州大学 | Management platform of automatic digital twin workshop of assembling of circuit breaker |
| CN111061232A (en) * | 2019-12-09 | 2020-04-24 | 中国科学院沈阳自动化研究所 | Production line design and optimization method based on digital twinning |
| CN111539583B (en) * | 2020-05-13 | 2023-10-24 | 中国电子科技集团公司第十四研究所 | Production process simulation optimization method based on digital twin |
-
2020
- 2020-05-13 CN CN202010402048.0A patent/CN111539583B/en active Active
- 2020-09-17 WO PCT/CN2020/115792 patent/WO2021227325A1/en active Application Filing
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107832497A (en) * | 2017-10-17 | 2018-03-23 | 广东工业大学 | A kind of intelligent workshop fast custom design method and system |
| CN110399642A (en) * | 2019-06-21 | 2019-11-01 | 浙江大学 | It is a kind of for the twin body of number and its construction method of production line and application |
| CN110347131A (en) * | 2019-07-18 | 2019-10-18 | 中国电子科技集团公司第三十八研究所 | The digital twinned system of facing to manufacture |
| CN110765589A (en) * | 2019-09-10 | 2020-02-07 | 上海大学 | Intelligent workshop virtual and real synchronous monitoring system and method based on digital twins |
Non-Patent Citations (4)
| Title |
|---|
| 于成龙;侯俊杰;陆菁;张伟;蒲洪波;郭旭凯;: "多品种变批量产品智能生产线应用框架" * |
| 张伟;: "数字孪生在智能装备制造中的应用研究" * |
| 郭安;于东;胡毅;毕筱雪;刘劲松;李浩;: "智能车间系统下面向加工深度分析的聚类应用" * |
| 陶飞;程颖;程江峰;张萌;徐文君;戚庆林;: "数字孪生车间信息物理融合理论与技术" * |
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