CN110705882A - Twin data driven ship assembly product quality control system and configuration method - Google Patents
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Abstract
The invention discloses a twin data driven ship assembly product quality control system and a configuration method. The intelligent building equipment forms a combined product manufacturing production line according to a building flow, the data acquisition module acquires process data of the combined product and current running state data of the equipment in real time, and the wireless transmission module is used for communicating the data acquisition module with the building quality control module. The construction quality control module firstly fuses process data, equipment state data and historical data to form twin data. Secondly, establishing a multi-digital twin model, simulating to obtain process parameters to be optimized, optimizing the process parameters according to a deep neural network algorithm, and finally converting the process parameters into an instruction which can be recognized by intelligent construction equipment, thereby completing quality control of ship assembly products. The invention also provides a configuration method of the system, and further illustrates the operation feasibility of the system.
Description
Technical Field
The invention belongs to the field of ship construction, particularly relates to quality control of assembled products in a ship segmental construction process, and particularly relates to a twin data drive-based quality control system of ship assembled products and a configuration method thereof.
Background
Since the 21 st century, the marine industry has developed dramatically with the development of computer information technology. The marine industry, as a modern integrated industry supporting water transportation, ocean development and defense construction, is an indispensable component in the national manufacturing industry. Among the manufacturing industries, the development level of the ship manufacturing industry is an important embodiment of the national comprehensive national force. The ship building technology is vigorously developed, the conversion of the ship building industry to integrated manufacturing and agile manufacturing is promoted, the intelligent degree of ship building is improved, the digital twin technology is comprehensively applied to the ship building process, high-level digital ship building is realized, and the method has important practical significance for the development of the ship industry in China.
The digital twin technology is characterized in that a virtual model of a physical entity is created in a digital mode, data is used as a bridge between the two, the physical entity and the virtual model are communicated, the virtual model simulates the behavior of the physical entity by means of the data, and the purpose of controlling reality in a virtual mode is achieved through means of virtual-real interactive feedback, data fusion analysis, iterative optimization and the like. The digital twin is a technology which utilizes data, models and entities and integrates multiple disciplines, and plays a great role in the segmented construction process of modern ships.
In the prior art, a shipbuilding model oriented with intermediate products as a guide occurs, and a ship is a final product, and the intermediate products are assembled into the final product by various methods. The intermediate product is called assembly, twin data are formed by collecting process data of small, medium and large assembly in the processes of machining, assembling and welding and fusing the process data with the created digital twin model, and the quality control of the ship assembly product driven by the twin data is realized.
At present, patent technologies for improving the ship segment construction quality appear, for example, patent CN 105785944B discloses a "ship hull construction precision control process method and system", and patent CN 103434611 a discloses a "large ship high precision control construction method", two patents manage and analyze data affecting the construction process quality, and perform quality control by comparing or setting compensation amount, but only perform quality control on the machined component, and fail to predict the component to be machined in advance, which is passive control; patent CN 105808891B discloses "a system precision control method for ship segment digital manufacturing", in this patent, there is no analysis and simulation of the building process, and the quality to be processed still cannot be predicted in advance.
Disclosure of Invention
In order to solve the defects in the prior art, the invention provides a twin data driven ship assembly product quality control system and a configuration method, wherein the twin data is formed by collecting process data and equipment running state data in the building process and fusing the process data and historical data, a multi-digital twin model is driven to carry out analog simulation, process parameters are optimized, the closed-loop process data control of 'perception-analysis-decision-feedback' of a ship assembly product can be realized, the quality control of the ship assembly product is realized, and the process determines the quality and guides the process.
The technical scheme is as follows: the twin data-driven ship assembly product quality control system comprises intelligent construction equipment, a data acquisition module, a wireless transmission module and a construction quality control module;
the intelligent construction equipment is used for finishing issued instructions including machining, assembling and welding construction tasks;
the data acquisition module is used for acquiring process data of small assemblage, medium assemblage and large assemblage and equipment running state data in the ship construction process in real time;
the wireless transmission module is used for realizing data interaction between the intelligent construction equipment and the construction quality control module;
the construction quality control module comprises a virtual model simulation submodule, a process parameter optimization submodule and an intelligent construction equipment control submodule;
the virtual model simulation submodule simulates the construction process of the assembled product according to the twin data by establishing a multi-digital twin model;
the process parameter optimization submodule is used for obtaining process parameters influencing quality defects according to a simulation result and process reasoning knowledge of the digital twin process model, and optimizing the process parameters by adopting a deep neural network algorithm;
the intelligent construction equipment control submodule is used for converting twin data into instructions which can be executed by equipment on a production line, sending the instructions to specific equipment and controlling the equipment to execute corresponding actions.
Furthermore, twin data is formed by fusing collected process data and equipment running state data with historical data; the twin data is the basis for carrying out simulation on a multi-digital twin model, and the multi-digital twin model comprises a digital twin equipment model, a digital twin process model and a digital twin product model.
Further, the digital twinning process model comprises a digital twinning machining process model, a digital twinning assembly process model and a digital twinning welding process model.
Further, the process reasoning refers to that the quality influencing factors are analyzed and judged through the process parameters and are compared with the quality indexes, so that the process parameters needing to be optimized can be obtained for the quality simulation result of the digital twin process model;
further, the quality indexes are classified into three types according to the process: the quality indexes of the machining process, the assembly process and the welding process are obtained; wherein, the quality indexes of the mechanical processing technology are the size, the shape and the surface roughness of the part; the quality indexes of the assembly process are the gap and the parallelism of butt joints; the welding process quality indexes are the size and shape of a welding seam, welding deformation and water tightness.
Further, the process data comprises machining process parameters, assembly process parameters and welding process parameters; the machining process parameters comprise size, shape, plate thickness and surface roughness parameters; the assembly process data comprises dimensional accuracy, position accuracy and verticality parameters; the welding process data includes a welding current. Arc voltage, welding speed, gas flow and power supply polarity parameters;
in order to achieve the above object, the present invention further provides a method for configuring a twin data-driven ship assembly product quality control system, including the following steps:
1) intelligent build equipment configuration SWE
Firstly, configuring a plurality of jig frame process equipment with sensor equipment, machining equipment, assembling and welding robots, a PLC (programmable logic controller) embedded system device and a camera device for an assembled product in a ship sectional construction process, and autonomously forming a flexible processing production line of the assembled product, wherein a modeling expression configured by intelligent construction equipment is as follows:
SWE＝{SCi，ME，WR，ES，CE}
SCi＝{SC1，SC2…SCn-1，SCn}，
{SC1，SC2…SCn-1，SCn}＝{SS，TS，…CS，VS}
in the formula, after the plate and the section are cut into corresponding shapes and sizes on mechanical processing equipment ME, the plate and the section are lifted to moulding bed process equipment SC with sensor equipmentiThe matched assembly and welding robot WR carries out assembly and welding processes on the plate and the section bar to form the shipThe intermediate products are assembled into small, medium and large products;
2) data acquisition module configuration DAM
Configuring a PLC (programmable logic controller) embedded system device ES and a plurality of intelligent sensing devices to acquire process data of assembled products in the ship segmental construction process, wherein a RFID (radio frequency identification) handheld data acquisition device is configured to acquire information of resources used in the construction process, sensor equipment including a laser sensor SS, a thickness sensor TS, a current sensor CS and a voltage sensor VS is configured to acquire data including a machining process, an assembly process and a welding process in the construction process, and the configured PLC embedded system device ES uploads the data acquired in real time to a construction quality control module through a wireless transmission module; configuring a human-computer interaction terminal for displaying the real-time construction task condition collected by the camera device CE in real time, providing a corresponding data input request interface, and controlling a processing production line in the construction process, wherein the collected process data modeling expression is as follows:
PD∈{MP，AP，WP}
in the formula, PD represents the process data type of the current component, MP represents the machining process parameter of the current component, AP represents the assembly process parameter of the current component, and WP represents the welding process parameter of the current component.
3) WTM configured wireless transmission module
According to the characteristics of diversity and complexity of data acquisition equipment in the ship segment building process, a ZigBee wireless transmission network is arranged in a ship building workshop, a star network is adopted for arrangement, and a multi-source heterogeneous data communication interface is configured; real-time process data are accessed to a building quality control module of an upper computer through a ZigBee wireless network and a standardized data communication interface, so that a local Internet of things of a ship building workshop is formed, and process data circulation of the building workshop is realized;
4) construction quality management and control module configuration QC
The construction quality control module is divided into three submodules, and the modeling expression among the three submodules is as follows:
in the formula, QC represents a construction quality control module, VM represents a virtual model simulation submodule, PO represents a process parameter optimization submodule, EC represents an intelligent construction equipment control submodule,
representing the flow direction processed in the logical sequence of the building quality control module;
firstly, fusing collected process data, equipment running state data and historical data to form twin data, wherein the twin data is the basis of simulation of a multi-digital twin model, a data communication interface and the multi-digital twin model establish a bidirectional data channel, the multi-digital twin model comprises a digital twin equipment model, a digital twin process model and a digital twin product model, simulation is carried out according to a certain building flow logical relationship, actual building workshop operation and a ship intermediate product manufacturing process are simulated, and the modeling expression of the multi-digital twin model is as follows:
wherein MDT ' represents a multi-digital twin model, EDT ' represents a digital twin equipment model, PDT ' represents a digital twin process model, pDT represents a digital twin product model,
representing a physical space and digital twin model true mapping;
secondly, according to the simulation result of the digital twin process model on the process parameters, the process reasoning is combined to obtain the process parameters to be optimized, then the deep neural network algorithm is combined to optimize the process parameters, finally the optimized process parameter result is converted into an instruction which can be identified by equipment, the instruction is sent to intelligent construction equipment of a physical construction workshop through an interface, and the process flow of the ship segmental construction assembly product is adjusted, so that the quality control of the ship segmental construction process is realized.
The invention has the beneficial effects that:
compared with the prior art, the twin data drive-based ship assembly product quality control system and the twin data drive-based ship assembly product quality control configuration method are provided, the system is composed of intelligent construction equipment, a data acquisition module, a wireless transmission module and a construction quality control module, technological parameters of machining, assembling and welding processes can be effectively optimized, physical workshop manufacturing is guided, and therefore quality control of ship assembly products is achieved.
The system configuration method further embodies the feasibility of the system establishment, and provides a foundation for the realization of the quality control of ship assembly products.
Drawings
FIG. 1 is a diagram of a twin data driven ship assembly product quality control system;
FIG. 2 is a diagram of a twin data driven ship assembly product quality control system configuration method;
FIG. 3 is a process reasoning diagram of a digital twin process model.
Detailed Description
The following examples further illustrate the present invention but are not to be construed as limiting the invention. Modifications and substitutions to methods, procedures, or conditions of the invention may be made without departing from the spirit of the invention.
Example one
The twin data-driven ship assembly product quality control system is structurally shown in fig. 1 and comprises intelligent construction equipment, a data acquisition module, a wireless transmission module and a construction quality control module.
The intelligent building equipment is an execution object of a physical space and is used for completing built tasks of assigned machining, assembling, welding and the like, and specifically comprises machining equipment, an assembling robot, a welding robot, a jig frame and other auxiliary equipment. And forming a flexible production line according to the construction flow of processing, assembling and welding.
The data acquisition module is used for acquiring process data of small assemblage, medium assemblage and large assemblage and equipment running state data in the ship building process in real time, and configuring a PLC (programmable logic controller) embedded system device, an RFID (radio frequency identification) handheld data acquisition device, a camera device, a laser sensor, a voltage sensor and other devices to acquire process data of assembled products in the ship building process.
The wireless transmission module is used for data interaction between the intelligent construction equipment and the construction quality control module, a ZigBee wireless transmission network is adopted, a standardized data interface is configured, and the collected process data are uploaded to the construction quality control module in real time.
The construction quality control module comprises a virtual model simulation submodule, a process parameter optimization submodule and an intelligent construction equipment control submodule. The construction quality control module is used for real-time data processing, digital twin process model simulation, process parameter optimization, control decision information generation and virtual simulation modeling of a physical space and a virtual space.
Example two
A twin data drive-based ship assembly product quality control system configuration method is shown in fig. 2, and physical space resource configuration includes intelligent construction equipment configuration SWE, data acquisition module configuration DAM and wireless transmission module configuration WTM.
Firstly, the intelligent building equipment configures a plurality of jig frame process equipment with sensor equipment, machining equipment, assembling and welding robots, a PLC embedded system device and a camera device for the assembled product in the ship sectional building process, and a flexible processing production line of the assembled product is formed automatically. The modeling expression for the configuration of the intelligent construction equipment is as follows:
SWE＝{SCi，ME，WR，ES，CE}
SCi＝{SC1，SC2…SCn-1，SCn}，
{SC1，SC2…SCn-1，SCn}＝{SS，TS，…CS，VS}
and secondly, the data acquisition module is provided with a PLC embedded system device ES and a plurality of intelligent sensing devices to acquire process data of the assembled product in the ship section construction process. The system comprises a building quality control module, a wireless transmission module, a Radio Frequency Identification (RFID) handheld data acquisition unit, a laser sensor (SS), a Thickness Sensor (TS), a Current Sensor (CS), a Voltage Sensor (VS) and other sensors, wherein the RFID handheld data acquisition unit is configured to acquire resource information used in the building process, the sensors such as the laser sensor, the thickness sensor, the Current Sensor (CS), the Voltage Sensor (VS) and the like are configured to acquire data such as a machining process, an assembly process, a welding process and the like in the building process, and; and configuring a human-computer interaction terminal for displaying the real-time construction task condition acquired by the camera device CE in real time, providing a corresponding data input request interface and controlling the construction process processing production line. The collected process data modeling expression is as follows:
PD∈{MP，AP，WP}
and finally, the wireless transmission module is provided with a ZigBee wireless transmission network, and real-time process data are accessed to a construction quality control module of an upper computer through a standardized data communication interface, so that bidirectional data circulation is realized.
The virtual space VS is provided with a construction quality control module, the construction quality control module is divided into three sub-modules, and a modeling expression among the three sub-modules is as follows:
in the formula, QC represents a construction quality control module, VM represents a virtual model simulation submodule, PO represents a process parameter optimization submodule, EC represents an intelligent construction equipment control submodule,
the flow direction is shown to be processed in the logical order of the build quality management module.
The collected process data, the equipment running state data and the historical data are fused to form twin data, and the twin data is the basis of the multi-digital twin model simulation. The data communication interface and the multi-digital twin model establish a bidirectional data channel. The multi-digital twinning model includes a digital twinning equipment model, a digital twinning process model, and a digital twinning product model. And simulating the operation of an actual building workshop and the manufacturing process of the intermediate product of the ship according to a certain building process logical relation. The modeling expression of the multi-digit twin model is as follows:
wherein MDT ' represents a multi-digital twin model, EDT ' represents a digital twin equipment model, PDT ' represents a digital twin process model, pDT represents a digital twin product model,
representing a true mapping of the physical space to the digital twin model.
Secondly, according to the simulation result of the digital twin process model on the process parameters, the process reasoning is combined to obtain the process parameters to be optimized, then the deep neural network algorithm is combined to optimize the process parameters, finally the optimized process parameters and the historical data are fused and converted into instructions which can be recognized by intelligent equipment, the instructions are sent to the intelligent equipment of the physical construction workshop through an interface, the process flow of the ship segmental construction assembly product is adjusted, and therefore quality control of the ship segmental construction process is achieved.
EXAMPLE III
The technological reasoning process of the digital twin process model is shown in figure 3, the ship sectional construction intermediate product is composed of parts, small assemblies, middle assemblies and large assemblies, and the parts and the machining process, the small assemblies, the middle assemblies and the large assemblies are respectively matched with the assembling process and the welding process. The machining process parameters comprise parameters such as size, shape, plate thickness and surface roughness; the assembly process parameters comprise the parameters of size precision, position precision, verticality and the like; the welding process parameters comprise parameters such as welding current, arc voltage, welding speed, gas flow, power supply polarity and the like, the quality defects caused by the process parameters can be deduced through process reasoning, and the process parameters influencing the construction quality can be accurately mastered through further confirmation of a digital twin process model, so that the subsequent process parameter optimization is facilitated.
The foregoing illustrates and describes the principles, general features, and advantages of the present invention. However, the above description is only an example of the present invention, the technical features of the present invention are not limited thereto, and any other embodiments that can be obtained by those skilled in the art without departing from the technical solution of the present invention should be covered by the claims of the present invention.
Claims (8)
1. The twin data-driven ship assembly product quality control system is characterized by comprising intelligent construction equipment, a data acquisition module, a wireless transmission module and a construction quality control module;
the intelligent construction equipment is used for finishing issued instructions including machining, assembling and welding construction tasks;
the data acquisition module is used for acquiring process data of small assemblage, medium assemblage and large assemblage and equipment running state data in the ship construction process in real time;
the wireless transmission module is used for realizing data interaction between the intelligent construction equipment and the construction quality control module;
the construction quality control module comprises a virtual model simulation submodule, a process parameter optimization submodule and an intelligent construction equipment control submodule;
the virtual model simulation submodule simulates the construction process of the assembled product according to twin data by establishing a multi-digital twin model;
the process parameter optimization submodule is used for obtaining process parameters influencing quality defects according to a simulation result and process reasoning knowledge of the digital twin process model, and optimizing the process parameters by adopting a deep neural network algorithm;
the intelligent construction equipment control submodule is used for converting twin data into instructions which can be executed by equipment on a production line, sending the instructions to specific equipment and controlling the equipment to execute corresponding actions.
2. The twin data driven ship assembly product quality control system according to claim 1, wherein the twin data is formed by fusing collected process data and equipment operating state data with historical data; the twin data is the basis for carrying out simulation on a multi-digital twin model, and the multi-digital twin model comprises a digital twin equipment model, a digital twin process model and a digital twin product model.
3. The twin data driven vessel assemblage product quality management and control system of claim 1, wherein digital twin process model comprises digital twin machining process model, digital twin assembly process model and digital twin welding process model.
4. The twin data-driven ship assembly product quality control system according to claim 1, wherein the process reasoning means that factors affecting quality are judged through process parameter analysis and compared with quality indexes, so that process parameters to be optimized can be obtained for a quality simulation result of the digital twin process model.
5. The twin data driven ship assembly product quality control system according to claim 1, wherein the process data includes machining process parameters, assembly process parameters, and welding process parameters; the machining process parameters comprise size, shape, plate thickness and surface roughness parameters; the assembly process data comprises dimensional accuracy, position accuracy and verticality parameters; the welding process data includes a welding current. Arc voltage, welding speed, gas flow, and power supply polarity parameters.
6. The twin data-driven ship assembly product quality control system according to claim 4, wherein the quality indexes are classified into three types according to the process: the quality index of the machining process, the quality index of the assembly process and the quality index of the welding process.
7. The twin data-driven ship assembly product quality control system according to claim 6, wherein machining process quality indicators are part size, shape, and surface roughness; the quality indexes of the assembly process are the gap and the parallelism of butt joints; the welding process quality indexes are the size and shape of a welding seam, welding deformation and water tightness.
8. The configuration method of the twin data driven ship assembly product quality control system according to any one of claims 1 to 7, comprising the steps of:
1) intelligent build equipment configuration SWE
Firstly, configuring a plurality of jig frame process equipment with sensor equipment, machining equipment, assembling and welding robots, a PLC (programmable logic controller) embedded system device and a camera device for an assembled product in a ship sectional construction process, and autonomously forming a flexible processing production line of the assembled product, wherein a modeling expression configured by intelligent construction equipment is as follows:
SWE＝{SCi，ME，WR，ES，CE}
{SC1，SC2…SCn-1，SCn}＝{SS，TS，…CS，VS}
in the formula, after the plate and the section are cut into corresponding shapes and sizes on mechanical processing equipment ME, the plate and the section are lifted to moulding bed process equipment SC with sensor equipmentiThe matched assembly and welding robot WR carries out assembly and welding processes on the plate and the section bar to form small, medium and large assemblies of the intermediate product of the ship;
2) data acquisition module configuration DAM
Configuring a PLC (programmable logic controller) embedded system device ES and a plurality of intelligent sensing devices to acquire process data of assembled products in the ship segmental construction process, wherein a RFID (radio frequency identification) handheld data acquisition device is configured to acquire information of resources used in the construction process, sensor equipment including a laser sensor SS, a thickness sensor TS, a current sensor CS and a voltage sensor VS is configured to acquire data including a machining process, an assembly process and a welding process in the construction process, and the configured PLC embedded system device ES uploads the data acquired in real time to a construction quality control module through a wireless transmission module; configuring a human-computer interaction terminal for displaying the real-time construction task condition collected by the camera device CE in real time, providing a corresponding data input request interface, and controlling a processing production line in the construction process, wherein the collected process data modeling expression is as follows:
PD∈{MP，AP，WP}
in the formula, PD represents the process data type of the current part, MP represents the machining process parameter of the current part, AP represents the assembly process parameter of the current part, and WP represents the welding process parameter of the current part;
3) WTM configured wireless transmission module
According to the characteristics of diversity and complexity of data acquisition equipment in the ship segment building process, a ZigBee wireless transmission network is arranged in a ship building workshop, a star network is adopted for arrangement, and a multi-source heterogeneous data communication interface is configured; real-time process data are accessed to a building quality control module of an upper computer through a ZigBee wireless network and a standardized data communication interface, so that a local Internet of things of a ship building workshop is formed, and process data circulation of the building workshop is realized;
4) construction quality management and control module configuration QC
The construction quality control module is divided into three submodules, and the modeling expression among the three submodules is as follows:
in the formula, QC represents a construction quality control module, VM represents a virtual model simulation submodule, PO represents a process parameter optimization submodule, EC represents an intelligent construction equipment control submodule,logic to represent to build quality management modulesSequentially processing the flow direction;
firstly, fusing collected process data, equipment running state data and historical data to form twin data, wherein the twin data is the basis of simulation of a multi-digital twin model, a data communication interface and the multi-digital twin model establish a bidirectional data channel, the multi-digital twin model comprises a digital twin equipment model, a digital twin process model and a digital twin product model, simulation is carried out according to a certain building flow logical relationship, actual building workshop operation and a ship intermediate product manufacturing process are simulated, and the modeling expression of the multi-digital twin model is as follows:
wherein MDT ' represents a multi-digital twin model, EDT ' represents a digital twin equipment model, PDT ' represents a digital twin process model, pDT represents a digital twin product model,
representing a physical space and digital twin model true mapping;
secondly, according to the simulation result of the digital twin process model on the process parameters, the process reasoning is combined to obtain the process parameters to be optimized, then the deep neural network algorithm is combined to optimize the process parameters, finally the optimized process parameter result is converted into an instruction which can be identified by equipment, the instruction is sent to intelligent construction equipment of a physical construction workshop through an interface, and the process flow of the ship segmental construction assembly product is adjusted, so that the quality control of the ship segmental construction process is realized.
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Cited By (17)
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| CN111461431A (en) * | 2020-03-31 | 2020-07-28 | 广东工业大学 | Optimization method and system based on screw locking process in mobile phone manufacturing |
| CN111580478A (en) * | 2020-05-13 | 2020-08-25 | 中国电子科技集团公司第十四研究所 | Complex electronic equipment final assembly digital twin workshop |
| CN111859566A (en) * | 2020-07-17 | 2020-10-30 | 重庆大学 | Surface roughness stabilizing method based on digital twinning |
| CN111968004A (en) * | 2020-08-07 | 2020-11-20 | 东华大学 | High-precision product assembling and adjusting integrated system based on digital twins |
| CN112101725A (en) * | 2020-08-12 | 2020-12-18 | 中车青岛四方机车车辆股份有限公司 | Digital manufacturing system and method for product, electronic device and storage medium |
| CN112276388A (en) * | 2020-10-19 | 2021-01-29 | 上海振华重工(集团)股份有限公司 | Deformation digital twinning optimization method for welding and manufacturing large crane box girder |
| CN113126569A (en) * | 2021-04-19 | 2021-07-16 | 北京航空航天大学 | Digital twin equipment construction method and system |
| CN113219912A (en) * | 2021-03-31 | 2021-08-06 | 成都飞机工业（集团）有限责任公司 | Multi-Agent-based numerical control machining flexible manufacturing system machining process early warning method |
| CN113467319A (en) * | 2021-07-21 | 2021-10-01 | 江苏科技大学 | Intelligent jig frame three-dimensional visual regulation and control system based on digital twinning and regulation and control method thereof |
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| JP2022051560A (en) * | 2020-09-18 | 2022-03-31 | 国立研究開発法人 海上・港湾・航空技術研究所 | Construction simulation method and construction simulation program for marine vessel |
| JP2022068836A (en) * | 2020-10-22 | 2022-05-10 | 国立研究開発法人 海上・港湾・航空技術研究所 | Shipbuilding simulation system |
| CN114781054A (en) * | 2022-04-06 | 2022-07-22 | 国科治慧(苏州)智能科技有限公司 | Aviation product digital twin geometric model quality control method based on gate closing |
| CN114905179A (en) * | 2022-06-30 | 2022-08-16 | 中船黄埔文冲船舶有限公司 | Three-dimensional model-based assembling method and device |
| CN115092346A (en) * | 2022-07-08 | 2022-09-23 | 上海外高桥造船有限公司 | Simulation system and method for rib plate pulling-in assembly |
| CN115230902A (en) * | 2022-07-13 | 2022-10-25 | 上海江南长兴造船有限责任公司 | Intelligent building method for ship section intermediate product assembly |
| CN115365779A (en) * | 2021-05-18 | 2022-11-22 | 中移(上海)信息通信科技有限公司 | Assembling method, assembling device and assembling equipment for fuel cell |
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| CN111461431A (en) * | 2020-03-31 | 2020-07-28 | 广东工业大学 | Optimization method and system based on screw locking process in mobile phone manufacturing |
| CN111580478A (en) * | 2020-05-13 | 2020-08-25 | 中国电子科技集团公司第十四研究所 | Complex electronic equipment final assembly digital twin workshop |
| CN111859566A (en) * | 2020-07-17 | 2020-10-30 | 重庆大学 | Surface roughness stabilizing method based on digital twinning |
| CN111859566B (en) * | 2020-07-17 | 2023-11-17 | 重庆大学 | Digital twinning-based surface roughness stabilization method |
| CN111968004A (en) * | 2020-08-07 | 2020-11-20 | 东华大学 | High-precision product assembling and adjusting integrated system based on digital twins |
| CN112101725A (en) * | 2020-08-12 | 2020-12-18 | 中车青岛四方机车车辆股份有限公司 | Digital manufacturing system and method for product, electronic device and storage medium |
| WO2022059786A1 (en) * | 2020-09-18 | 2022-03-24 | 国立研究開発法人 海上・港湾・航空技術研究所 | Shipbuilding simulation method, simulation program, and simulation system |
| JP2022051560A (en) * | 2020-09-18 | 2022-03-31 | 国立研究開発法人 海上・港湾・航空技術研究所 | Construction simulation method and construction simulation program for marine vessel |
| JP7397501B2 (en) | 2020-09-18 | 2023-12-13 | 国立研究開発法人 海上・港湾・航空技術研究所 | Ship construction simulation method and construction simulation program |
| CN112276388A (en) * | 2020-10-19 | 2021-01-29 | 上海振华重工(集团)股份有限公司 | Deformation digital twinning optimization method for welding and manufacturing large crane box girder |
| CN112276388B (en) * | 2020-10-19 | 2022-03-18 | 上海振华重工(集团)股份有限公司 | Deformation digital twinning optimization method for welding and manufacturing large crane box girder |
| JP7397502B2 (en) | 2020-10-22 | 2023-12-13 | 国立研究開発法人 海上・港湾・航空技術研究所 | Ship construction simulation system |
| JP2022068836A (en) * | 2020-10-22 | 2022-05-10 | 国立研究開発法人 海上・港湾・航空技術研究所 | Shipbuilding simulation system |
| CN113219912A (en) * | 2021-03-31 | 2021-08-06 | 成都飞机工业（集团）有限责任公司 | Multi-Agent-based numerical control machining flexible manufacturing system machining process early warning method |
| CN113219912B (en) * | 2021-03-31 | 2022-03-15 | 成都飞机工业（集团）有限责任公司 | Multi-Agent-based numerical control machining flexible manufacturing system machining process early warning method |
| CN113126569A (en) * | 2021-04-19 | 2021-07-16 | 北京航空航天大学 | Digital twin equipment construction method and system |
| CN115365779A (en) * | 2021-05-18 | 2022-11-22 | 中移(上海)信息通信科技有限公司 | Assembling method, assembling device and assembling equipment for fuel cell |
| CN115365779B (en) * | 2021-05-18 | 2024-05-03 | 中移(上海)信息通信科技有限公司 | Method, device and equipment for assembling fuel cell |
| CN113467319A (en) * | 2021-07-21 | 2021-10-01 | 江苏科技大学 | Intelligent jig frame three-dimensional visual regulation and control system based on digital twinning and regulation and control method thereof |
| CN114781054B (en) * | 2022-04-06 | 2023-10-20 | 国科治慧(苏州)智能科技有限公司 | Door-closing-based quality control method for digital twin geometric model of aviation product |
| CN114781054A (en) * | 2022-04-06 | 2022-07-22 | 国科治慧(苏州)智能科技有限公司 | Aviation product digital twin geometric model quality control method based on gate closing |
| CN114905179A (en) * | 2022-06-30 | 2022-08-16 | 中船黄埔文冲船舶有限公司 | Three-dimensional model-based assembling method and device |
| CN114905179B (en) * | 2022-06-30 | 2024-02-23 | 中船黄埔文冲船舶有限公司 | Middle assembling and assembling method and device based on three-dimensional model |
| CN115092346A (en) * | 2022-07-08 | 2022-09-23 | 上海外高桥造船有限公司 | Simulation system and method for rib plate pulling-in assembly |
| CN115092346B (en) * | 2022-07-08 | 2023-12-01 | 上海外高桥造船有限公司 | Simulation system and method for rib plate pull-in assembly |
| CN115230902A (en) * | 2022-07-13 | 2022-10-25 | 上海江南长兴造船有限责任公司 | Intelligent building method for ship section intermediate product assembly |
| CN115230902B (en) * | 2022-07-13 | 2023-12-08 | 沪东中华造船(集团)有限公司 | Intelligent building method for ship segmented intermediate product assembly |
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