Production line rapid reconstruction method
By constructing a digital twin model and modular design, combined with AGV carts, the aerospace product production line can be rapidly restructured, solving the problem that traditional production lines are difficult to adapt to the iteration of multiple varieties and high complexity of products, and improving production efficiency and quality stability.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CAPITAL AEROSPACE MACHINERY
- Filing Date
- 2025-12-08
- Publication Date
- 2026-04-21
AI Technical Summary
Traditional rigid production lines are ill-suited to the diverse, complex, and iterative nature of aerospace products, resulting in low production efficiency, unstable quality, and long delivery cycles.
By constructing a digital twin model and combining it with modular design and management, the production line can be rapidly reconfigured, including data acquisition, reconfiguration requirement analysis, multi-dimensional solution generation, simulation verification and physical execution. AGVs are used to achieve high-precision positioning adjustments and dynamically adapt to production line resources.
It enables efficient switching between multiple product specifications on the production line, shortens the reconfiguration cycle, improves the flexibility and reliability of the production line, and reduces production costs.
Abstract
Description
Technical Field
[0001] This invention relates to the field of product final assembly and testing production line technology, and specifically to a method for rapid reconfiguration of a production line. Background Technology
[0002] In the aerospace manufacturing field, aerospace products are characterized by "multiple varieties, high complexity, high reliability, and high iteration." Traditional rigid production lines, centered on fixed single processes, specialized equipment, and specialized systems, struggle to adapt to the accelerating pace of product iteration and the dynamic changes in demand from research and development to mass production. They also fail to accommodate changes in new structures and processes, severely restricting production efficiency and quality stability, leading to extended delivery cycles. Rapid production line reconfiguration, as a key technology for improving production flexibility, breaks down the rigid constraints of hardware, software, and management, enabling agile and dynamic reorganization and functional adaptation of production resources. This has become a key technology for addressing the pain points of "insufficient flexibility and slow response" in aerospace manufacturing and a core path for the efficient development of aerospace equipment. Summary of the Invention
[0003] The technical problem to be solved by this invention is to overcome the shortcomings of the prior art and provide a method for rapid reconfiguration of production lines. Through flexible and modular design and management, the production line can be efficiently switched between multiple product specifications to adapt to the diversification and iteration speed of aerospace missions, especially the production tasks and product iteration needs from development to mass production and emergency products.
[0004] The objective of this invention is achieved through the following technical solutions:
[0005] This invention provides a method for rapid reconfiguration of a production line, comprising the following steps:
[0006] S1, Constructing a digital model of the production line: Based on physical production line data, construct a digital twin model simulating the physical production line;
[0007] S2, Restructuring Requirements Analysis and Prioritization: Receive the production line restructuring trigger signal, extract restructuring requirement parameters, prioritize the restructuring requirements based on the analytic hierarchy process, and determine the core constraints.
[0008] S3, Intelligent generation of multi-dimensional reconstruction solutions: Build a basic library, and based on the reconstruction requirement parameters, call the resources of the basic library to generate candidate reconstruction solutions for the production line;
[0009] S4, Scheme simulation verification and optimal scheme selection: Based on the digital model of the production line, the production process is simulated in its entirety, the candidate schemes for production line reconstruction are evaluated, and the optimal reconstruction scheme is selected.
[0010] S5, Physical Production Line Execution and Dynamic Adaptation: Drives the modular station movement, equipment debugging, tooling change and process update of the physical production line, and dynamically corrects the reconfiguration parameters.
[0011] According to the rapid production line reconfiguration method of the present invention, step S1 includes the following steps:
[0012] S11, Data Acquisition: Acquire the physical production line data and product process data; the physical production line data includes the station layout, equipment parameters, tooling specifications, and conveying system parameters of the pulsed production line; the product process data includes the process requirements and constraints for the final assembly and testing of the target product.
[0013] S12, Model Construction: Construct a four-layer digital twin model including a physical entity layer, a virtual simulation layer, a data interaction layer, and a constraint mapping layer; the physical entity layer includes the physical form and operating status of production line stations, equipment, tooling, and conveying systems; the virtual simulation layer is a simulation module integrating final assembly and final testing processes; the data interaction layer enables real-time data synchronization between the physical entity layer and the virtual simulation layer; the constraint mapping layer is used to embed special constraints for final assembly and final testing of the product.
[0014] According to the rapid production line reconfiguration method of the present invention, in step S11, data acquisition adopts OPC UA protocol for transmission, with a transmission delay ≤10ms and data accuracy ≥0.01m.
[0015] According to the rapid production line reconfiguration method of the present invention, in step S12, the error of real-time data synchronization is ≤ ±0.05mm.
[0016] According to the rapid production line reconfiguration method of the present invention, in step S2, the reconfiguration trigger signal includes a product switching signal, a capacity adjustment signal, an equipment failure signal, a process iteration signal, and an emergency order insertion signal; the reconfiguration requirement parameters include the target product's model and specifications, capacity indicators, delivery cycle, equipment availability, final assembly and testing process requirements, and constraints.
[0017] According to the rapid production line reconfiguration method of the present invention, step S3 includes the following steps:
[0018] S31, Construct a basic library, which includes a station modular library, an equipment adaptation rule library, and a process optimization algorithm library;
[0019] S32, Generate a reconstruction scheme; Based on the digital twin model and the reconstruction requirement parameters, call the basic library resources to generate at least 3 reconstruction candidate schemes, including station adjustment, equipment reorganization, process optimization and pulse cycle recalculation.
[0020] According to the rapid production line reconfiguration method of the present invention, the station adjustment includes increasing or decreasing the number of modular stations and moving their positions; the equipment reconfiguration includes selecting and switching testing equipment and assembly equipment and interface docking; the process optimization includes splitting and merging the final assembly process and adjusting the order of the final testing process; and the cycle time recalculation is to recalculate the pulse cycle time based on the reconfigured process and equipment status.
[0021] According to the rapid production line reconfiguration method of the present invention, the station position adjustment is automatically positioned by an AGV trolley with a positioning accuracy of ±0.03mm.
[0022] According to the rapid production line reconfiguration method of the present invention, step S4 includes the following steps:
[0023] S41, In the digital twin model, the candidate scheme is simulated and tested in the whole process to simulate the real scenario of product final assembly and testing. The core test indicators include reconstruction time, pulse cycle balance rate, equipment utilization rate, positioning accuracy error, process connection failure rate and constraint satisfaction.
[0024] S42, Optimal Reconfiguration Scheme Screening: The candidate reconfiguration schemes are comprehensively evaluated to select the optimal reconfiguration scheme. The optimal reconfiguration scheme requires reconfiguration time ≤ 4 hours, pulse cycle balance rate ≥ 90%, equipment utilization rate ≥ 85%, positioning accuracy error ≤ ±0.1mm, process connection failure rate ≤ 0.5%, and constraint satisfaction of 100%.
[0025] According to the rapid production line reconfiguration method of the present invention, step S5 includes the following steps:
[0026] S51, Instruction Issuance and Execution: The parameter instructions of the optimal reconfiguration scheme are issued to the production line PLC control system to drive modular station movement, automatic equipment debugging, rapid tooling changeover, and process flow system update.
[0027] S52, Real-time monitoring and dynamic correction: The operation data of the physical production line is collected in real time through the digital twin model. When deviations or emergencies occur, the backup plan is automatically triggered, and the reconfiguration parameters are dynamically corrected to ensure the continuous operation of the production line.
[0028] Compared with the prior art, the present invention has the following advantages:
[0029] (1) A digital reconfiguration model for the entire process of assembly and testing was constructed, and special constraints such as electromagnetic compatibility and mechanical performance testing in the aerospace testing process were embedded into the calculation and scheduling of the entire production line, which solved the problem of ignoring the testing constraints in the traditional reconfiguration scheme;
[0030] (2) A standardized docking scheme for modular station positions and aerospace-specific equipment is proposed. Combined with AGV automatic positioning technology, the station position adjustment is made with high precision and speed, and adaptable to the size and process differences of multiple aerospace products.
[0031] (3) Integrate large model algorithms to establish a decision-making system for reconstruction schemes in aerospace scenarios, taking into account core requirements such as delivery cycle, accuracy requirements, and equipment utilization, and improve the scientific nature of decision-making;
[0032] (4) Adopting a closed-loop reconstruction process of “simulation verification-physical execution-dynamic correction”, and through real-time monitoring and deviation correction by digital twin, the failure rate of the reconstructed production line is controlled within 0.5%, ensuring the reliability of aerospace product production.
[0033] (5) By optimizing equipment utilization and shortening the reconfiguration cycle, a single production line can save ≥2 million yuan in production costs per year, thereby improving the economic efficiency of aerospace product production. Detailed Implementation
[0034] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below.
[0035] This invention provides a method for rapid reconfiguration of a production line, comprising the following steps:
[0036] S1, Constructing a digital model of the production line: Based on physical production line data, construct a digital twin model simulating the physical production line;
[0037] S2, Restructuring Requirements Analysis and Prioritization: Receive the production line restructuring trigger signal, extract restructuring requirement parameters, prioritize the restructuring requirements based on the analytic hierarchy process, and determine the core constraints.
[0038] S3, Intelligent generation of multi-dimensional reconstruction solutions: Build a basic library, and based on the reconstruction requirement parameters, call the resources of the basic library to generate candidate reconstruction solutions for the production line;
[0039] S4, Scheme simulation verification and optimal scheme selection: Based on the digital model of the production line, the production process is simulated in its entirety, the candidate schemes for production line reconstruction are evaluated, and the optimal reconstruction scheme is selected.
[0040] S5, Physical Production Line Execution and Dynamic Adaptation: Drives the modular station movement, equipment debugging, tooling change and process update of the physical production line, and dynamically corrects the reconfiguration parameters.
[0041] According to a specific embodiment of the present invention, step S1 includes the following steps:
[0042] S11, Data Acquisition: Acquire the physical production line data and product process data; the physical production line data includes the station layout, equipment parameters, tooling specifications, and conveying system parameters of the pulsed production line; the product process data includes the final assembly and testing process requirements and constraints of the target product; the process requirements include assembly sequence, bolt tightening torque, and weld requirements; the constraints include electromagnetic compatibility standards, mechanical performance test parameters, and data acquisition interface protocols;
[0043] S12, Model Construction: Construct a four-layer digital twin model including a physical entity layer, a virtual simulation layer, a data interaction layer, and a constraint mapping layer; the physical entity layer includes the physical form and operating status of production line stations, equipment, tooling, and conveying systems; the virtual simulation layer is a simulation module integrating final assembly and final testing processes; the data interaction layer enables real-time data synchronization between the physical entity layer and the virtual simulation layer; the constraint mapping layer is used to embed special constraints for final assembly and final testing of the product.
[0044] According to a specific embodiment of the present invention, in step S11, data acquisition is performed in real time by laser rangefinder, force sensor, and industrial camera equipment, and transmitted using OPC UA protocol with a transmission delay ≤10ms and data accuracy ≥0.01m; in step S12, the error of real-time data synchronization is ≤±0.05mm, and the special constraints include high temperature environment adaptation, high-precision positioning requirements, and test data integrity standards.
[0045] According to a specific embodiment of the present invention, in step S2, the reconfiguration trigger signal includes a product switching signal, a capacity adjustment signal, an equipment failure signal, a process iteration signal, and an emergency order insertion signal; the reconfiguration requirement parameters include the target product's model specifications, capacity indicators, delivery cycle, equipment availability, final assembly and testing process requirements, and constraints; the system is written in Python, combined with a large model and Java, and prioritizes the reconfiguration requirements, such as in the emergency order scenario, where the delivery cycle has the highest priority.
[0046] According to a specific embodiment of the present invention, step S3 includes the following steps:
[0047] S31, Construct a basic library, which includes a station modularization library, an equipment adaptation rule library, and a process optimization algorithm library; the station modularization library includes a movable assembly station, a flexible testing station, and an intelligent conveyor line, and the module interfaces conform to the general standards of aerospace tooling; the equipment adaptation rule library stores the adaptation relationships between different aerospace products and equipment; the process optimization algorithm library integrates particle swarm optimization algorithm and genetic algorithm to optimize the serial / parallel process constraints of final assembly and testing.
[0048] S32, Generate a reconstruction scheme; Based on the digital twin model and the reconstruction requirement parameters, call the basic library resources to generate at least 3 reconstruction candidate schemes, including station adjustment, equipment reorganization, process optimization and pulse cycle recalculation.
[0049] According to a specific embodiment of the present invention, in step S32, the station adjustment includes increasing or decreasing the number of modular stations and moving their positions; the equipment reorganization includes selecting and switching test equipment and assembly equipment and interface docking; the process optimization includes splitting and merging the final assembly process and adjusting the order of the final testing process; the cycle time recalculation is to recalculate the pulse cycle time based on the reorganized process and equipment status.
[0050] According to one specific embodiment of the present invention, the station position adjustment is achieved by automatic positioning through an AGV trolley with a positioning accuracy of ±0.03mm.
[0051] According to a specific embodiment of the present invention, step S4 includes the following steps:
[0052] S41, conduct full-process simulation testing of candidate solutions in the digital twin model, simulate the real scenario of product final assembly and testing, and test the core indicators including reconstruction time, pulse cycle balance rate, equipment utilization rate, positioning accuracy error, process connection failure rate and constraint satisfaction.
[0053] S42, Optimal Reconfiguration Scheme Screening: The candidate reconfiguration schemes are comprehensively evaluated to select the optimal reconfiguration scheme. The optimal reconfiguration scheme requires reconfiguration time ≤ 4 hours, pulse cycle balance rate ≥ 90%, equipment utilization rate ≥ 85%, positioning accuracy error ≤ ±0.1mm, process connection failure rate ≤ 0.5%, and constraint satisfaction of 100%.
[0054] According to a specific embodiment of the present invention, step S5 includes the following steps:
[0055] S51, Instruction Issuance and Execution: The parameter instructions of the optimal reconfiguration scheme are issued to the production line PLC control system to drive modular station movement, automatic equipment debugging, rapid tooling changeover, and process flow system update.
[0056] S52, Real-time monitoring and dynamic correction: The operation data of the physical production line is collected in real time through the digital twin model. When deviations or emergencies occur, the backup plan is automatically triggered, and the reconfiguration parameters are dynamically corrected to ensure the continuous operation of the production line.
[0057] According to one specific embodiment of the present invention, the backup plan includes activating redundant equipment and adjusting the process sequence.
[0058] The contents not described in detail in this specification are common knowledge to those skilled in the art.
[0059] Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make possible changes and modifications to the technical solutions of the present invention by utilizing the methods and techniques disclosed above without departing from the spirit and scope of the present invention. Therefore, any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solutions of the present invention shall fall within the protection scope of the technical solutions of the present invention.
Claims
1. A method for rapid production line reconfiguration, comprising the following steps: S1, Constructing a digital model of the production line: Based on physical production line data, construct a digital twin model simulating the physical production line; S2, Restructuring Requirements Analysis and Prioritization: Receive the production line restructuring trigger signal, extract restructuring requirement parameters, prioritize the restructuring requirements based on the analytic hierarchy process, and determine the core constraints. S3, Intelligent generation of multi-dimensional reconstruction solutions: Build a basic library, and based on the reconstruction requirement parameters, call the resources of the basic library to generate candidate reconstruction solutions for the production line; S4, Scheme simulation verification and optimal scheme selection: Based on the digital twin model, the production process is simulated in its entirety, the candidate schemes for production line reconstruction are evaluated, and the optimal reconstruction scheme is selected; S5, Physical Production Line Execution and Dynamic Adaptation: Drives the modular station movement, equipment debugging, tooling change and process update of the physical production line, and dynamically corrects the reconfiguration parameters.
2. The rapid production line reconfiguration method according to claim 1, characterized in that: Step S1 includes the following steps: S11, Data Acquisition: Acquire the physical production line data and product process data; the physical production line data includes the station layout, equipment parameters, tooling specifications, and conveying system parameters of the pulsed production line; the product process data includes the final assembly and testing process requirements and constraints of the target product. S12, Model Construction: Construct a four-layer digital twin model including a physical entity layer, a virtual simulation layer, a data interaction layer, and a constraint mapping layer; the physical entity layer includes the physical form and operating status of production line stations, equipment, tooling, and conveying systems; the virtual simulation layer is a simulation module integrating final assembly and testing processes; the data interaction layer realizes real-time data synchronization between the physical entity layer and the virtual simulation layer; the constraint mapping layer is used to embed special constraints for final assembly and testing of the product.
3. The rapid production line reconfiguration method according to claim 2, characterized in that: In step S11, data acquisition uses the OPC UA protocol for transmission, with a transmission delay of ≤10ms and a data accuracy of ≥0.01m.
4. The rapid production line reconfiguration method according to claim 2, characterized in that: In step S12, the error of real-time data synchronization is ≤ ±0.05mm.
5. The rapid production line reconfiguration method according to claim 1, characterized in that: In step S2, the reconfiguration trigger signals include product switching signals, capacity adjustment signals, equipment failure signals, process iteration signals, and emergency order insertion signals; the reconfiguration requirement parameters include the target product's model and specifications, capacity indicators, delivery cycle, equipment availability, final assembly and testing process requirements, and constraints.
6. The rapid production line reconfiguration method according to claim 1, characterized in that: Step S3 includes the following steps: S31, Construct a basic library, which includes a station modular library, an equipment adaptation rule library, and a process optimization algorithm library; S32, Generate a reconstruction scheme; Based on the digital twin model and the reconstruction requirement parameters, call the basic library resources to generate at least 3 reconstruction candidate schemes, including station adjustment, equipment reorganization, process optimization and cycle time recalculation.
7. The rapid production line reconfiguration method according to claim 6, characterized in that: The station adjustment includes increasing or decreasing the number of modular stations and moving their positions; the equipment reorganization includes selecting and switching testing and assembly equipment and interface docking; the process optimization includes splitting and merging the final assembly process and adjusting the order of the final testing process; the cycle time recalculation is to recalculate the pulse cycle time based on the reorganized process and equipment status.
8. The rapid production line reconfiguration method according to claim 7, characterized in that: The station position adjustment is achieved through automatic positioning by an AGV trolley with a positioning accuracy of ±0.03mm.
9. The rapid production line reconfiguration method according to claim 1, characterized in that: Step S4 includes the following steps: S41, In the digital twin model, the candidate scheme is simulated and tested in the whole process to simulate the real scenario of product final assembly and testing. The core test indicators include reconstruction time, pulse cycle balance rate, equipment utilization rate, positioning accuracy error, process connection failure rate and constraint satisfaction. S42, Optimal Reconfiguration Scheme Screening: The candidate reconfiguration schemes are comprehensively evaluated to select the optimal reconfiguration scheme. The optimal reconfiguration scheme requires reconfiguration time ≤ 4 hours, pulse cycle balance rate ≥ 90%, equipment utilization rate ≥ 85%, positioning accuracy error ≤ ±0.1mm, process connection failure rate ≤ 0.5%, and constraint satisfaction of 100%.
10. The method for rapid reconfiguration of a production line according to claim 1, characterized in that: Step S5 includes the following steps: S51, Instruction Issuance and Execution: The parameter instructions of the optimal reconfiguration scheme are issued to the production line PLC control system to drive modular station movement, automatic equipment debugging, rapid tooling changeover, and process flow system update. S52, Real-time monitoring and dynamic correction: The operation data of the physical production line is collected in real time through the digital twin model. When deviations or emergencies occur, the backup plan is automatically triggered, and the reconfiguration parameters are dynamically corrected to ensure the continuous operation of the production line.