A simulation method and system for flexible production lines of aero-engines

Abstract

This invention provides a flexible production line simulation method and system for aero-engines, relating to the field of production line simulation technology. The method includes: collecting and storing various types of multi-source heterogeneous data from multiple systems; fusing and processing the multi-source heterogeneous data; constructing and storing an auxiliary decision-making model and a production line unit model for the aero-engine; constructing a digital twin simulation project for the production line; configuring simulation parameters and monitoring model data flow on the digital twin simulation project based on the multi-source heterogeneous data and the auxiliary decision-making model to achieve simulation calculations; and analyzing the simulation results based on the auxiliary decision-making model to obtain optimization solutions. This invention has the advantage of flexibly simulating and analyzing production scenarios based on different instantiated aero-engine manufacturing schemes.

Description

Technical Field

[0001] This invention relates to the field of production line simulation technology, and more specifically, to a method and system for simulating a flexible production line for aero-engines. Background Technology

[0002] In the era of intelligent manufacturing, the diversification and personalization of market demand are becoming increasingly prominent, and the pace of product upgrades is accelerating significantly.

[0003] Enterprises are rapidly shifting their production models from traditional large-scale, single-product production to flexible production involving multiple varieties, multiple batches, and small quantities. From the perspective of actual aero-engine production needs, flexible production requires production lines to possess strong rapid adjustment and adaptability capabilities. However, a lack of effective planning during production line adjustments can easily lead to production interruptions and increased costs. By simulating and analyzing the production process of different models and batches of aero-engines on the same production line, key information such as equipment load distribution and material flow efficiency can be clearly understood, providing precise decision-making basis for actual production adjustments.

[0004] Therefore, there is an urgent need for a practical and effective production line flexible simulation method and environment for the aero-engine manufacturing field, which can simulate and analyze production scenarios based on different instantiated aero-engine production and manufacturing schemes, such as changes in the production process of different product combinations on the production line, and predict the production line operation status in advance. Summary of the Invention

[0005] The purpose of this invention is to provide a simulation method and system for flexible production lines of aero-engines, which can flexibly simulate and analyze production scenarios based on different instantiated aero-engine manufacturing schemes.

[0006] This invention is achieved through the following technical solution:

[0007] A simulation method for a flexible production line of aero-engines includes the following steps:

[0008] Collect and store diverse types of heterogeneous data from multiple sources across multiple systems;

[0009] The multi-source heterogeneous data is fused.

[0010] Build and store the decision support model and the production line unit model for aero-engines;

[0011] Construct a digital twin simulation project for the production line, retrieve the production unit model and the auxiliary decision model of the production line, and configure the data flow parameters between the production unit models, the data flow parameters between the production unit models and the auxiliary decision model, and the multi-source heterogeneous data call interface parameters of the production unit models according to the nodes of the aero-engine production process.

[0012] Based on the multi-source heterogeneous data and the auxiliary decision-making model, simulation parameters are configured and model data flow is monitored in the production line digital twin simulation project to realize simulation calculation;

[0013] The simulation results are analyzed based on the aforementioned auxiliary decision-making model to obtain an optimization scheme.

[0014] Preferably, in the method for collecting and storing multi-source heterogeneous data of various types, data is collected at fixed times and frequencies through a data interaction interface between systems. The collection method includes:

[0015] Define a categorized storage resource directory for the multi-source heterogeneous data, including types such as production planning, process design, trial runs, and production line status;

[0016] When collecting the multi-source heterogeneous data, the data interaction interface type is defined and configured to interact with the system, and the data collection frequency and corresponding data resource directory are configured.

[0017] Preferably, the data interaction interface includes an FTP interface, an API interface, an SQL / ODBC / JDBC database interface, and a Web Service interface.

[0018] Preferably, the multi-source heterogeneous data includes aero-engine production plan data, process design data, test run data, and aero-engine production line status data. The method for fusing the multi-source heterogeneous data is as follows:

[0019] Assign feature identifiers to parameters in the multi-source heterogeneous data according to their parameter types;

[0020] Obtain the feature identifiers of parameters in aero-engine production plan data;

[0021] Construct a data BOM structure tree based on the characteristic identifiers of parameters in the aero-engine production plan data;

[0022] Based on the characteristic identifiers of parameters in the aero-engine production plan data, the corresponding parameters are identified from the process design data and test data, and loaded into the data BOM structure tree to form an instantiated production and manufacturing plan.

[0023] The instantiated production and manufacturing scheme and the aero-engine production line status data are encapsulated.

[0024] Preferably, the method for constructing the auxiliary decision-making model and the production line unit model of the aero-engine is as follows:

[0025] Create the primitives of the auxiliary decision-making model for the simulation of the flexible production line of aero-engines, edit the mathematical calculation expressions of the auxiliary decision-making model and bind them with the corresponding primitives, and define the variable input interface and result output interface of the auxiliary decision-making model;

[0026] Create the production unit model of the aero-engine production line, define the internal production team or production equipment model, define the variable input interface and result output interface of the production team or production equipment model, and define the variable input interface and result output interface of the production unit model.

[0027] Preferably, the method for constructing a digital twin simulation engineering of a production line is as follows:

[0028] Based on the simulation requirements of flexible production lines for aero-engines, a digital twin simulation project for aero-engine production lines was created.

[0029] Based on the simulation requirements of the flexible production line for aero-engines, input the model name or number, retrieve the required production line unit model and auxiliary decision-making model, and put them into the aero-engine production line digital twin simulation project.

[0030] Based on the simulation requirements of the flexible production line for aero-engines, the data flow parameters between production unit models and between different production stages are configured according to the production process nodes of aero-engines. The data flow parameters between the auxiliary decision-making model and the production unit model of the production line are also configured.

[0031] Based on the simulation requirements of the flexible production line for aero-engines and the defined API data interface of the production unit model, configure and define the API data interface to meet the need to call the multi-source heterogeneous data during the simulation process.

[0032] Preferably, the method for implementing the simulation calculation is as follows:

[0033] Configure the simulation parameters for the digital twin simulation project of the aero-engine production line, including the number of simulation executions and the simulation cycle parameters;

[0034] From the simulation data visualization components, select the required components and define the model objects to be observed, including the model interface and data flow;

[0035] Start the simulation.

[0036] Preferably, when analyzing simulation results and obtaining optimization solutions based on the auxiliary decision-making model, the optimization content includes logistics flow time and adjusting production unit capacity. The optimization method of the auxiliary decision-making model is as follows:

[0037] Establish the objective optimization function:

[0038] ;

[0039] ;

[0040] in, For the optimization target, T represents the total production time of the aero-engine. , and These represent the actual production time during the parts manufacturing stage, the actual production time during the assembly stage, and the time spent during the trial run and inspection stage. The additional shift cost for production unit u when producing equipment b. This represents the truth value of whether the production team or equipment b in production unit u has increased the number of work shifts; it is 1 if so, and 0 otherwise. This represents the set of production units within production stage S. For a production unit u, it refers to a set of production teams or equipment.

[0041] Establish constraints for assigning parts production tasks:

[0042] ;

[0043] in, Representative production parts Whether to use the true value of production shift or equipment b in production unit u; if yes, set to 1; otherwise, set to 0. The stage represented is the parts production stage;

[0044] Establish component assembly task allocation constraints:

[0045] ;

[0046] in, Representative production components Whether to use the true value of production shift or equipment b in production unit u; if yes, set to 1; otherwise, set to 0. The stage represented is the assembly stage;

[0047] Establish constraints for the allocation of tasks related to the final assembly and testing of the complete machine:

[0048] ;

[0049] in, Represents the production of complete machines Whether to use the true value of production shift or equipment b in production unit u; if yes, set to 1; otherwise, set to 0. This refers to the production team or equipment group within the overall machine trial and inspection production unit.

[0050] Establish capacity constraints:

[0051] ;

[0052] ;

[0053] ;

[0054] Where P represents the set of all parts, B represents the set of all components, and O represents the set of all finished production machines. The capacity increase coefficient after adding shifts to production team or equipment b in production unit u, and its value is not less than 1. This refers to the unit-time production capacity of production shift or equipment b within production unit u. This represents the total duration of the production planning period. The estimated remaining completion time for the production task currently being performed by production team or equipment b in production unit u. The downtime for maintenance of production shifts or equipment b within production unit u;

[0055] Establish overall time constraints:

[0056] T≤D;

[0057] Where D is the set threshold.

[0058] Preferably, the method for obtaining the actual production time in the part manufacturing stage, the actual production time in the assembly stage, and the time spent in the trial run and inspection stage is as follows:

[0059] ;

[0060] ;

[0061] ;

[0062] ;

[0063] ;

[0064] ;

[0065] in, For parts The time spent by the production team or equipment b in production unit u. For components The time spent assembling equipment in production unit u by production shifts or equipment b. For the whole machine The time spent in production unit u by production team or equipment b assembling or testing. For parts The number of For components The number of For the whole machine The number of Producing parts for production unit u The time required Producing components for production unit u The time required Producing complete machines for production unit u The time required For production units within the same production stage To the production unit The logistics transit time between them Indicates production unit within the same production stage To the production unit The waiting time for logistics transfer between them Indicates the production stage To the production stage The logistics transit time between them Indicates the production stage To the production stage The waiting time for logistics transfer between i=1,2,3,4,j=1,2.

[0066] This invention also provides a flexible production line simulation system for aero-engines, applied to the aforementioned flexible production line simulation method for aero-engines, comprising:

[0067] Multi-source heterogeneous data belongs to the acquisition module and is used to collect and store various types of multi-source heterogeneous data from multiple systems.

[0068] A multi-source heterogeneous data fusion processing module is used to fuse the multi-source heterogeneous data.

[0069] A library of decision support models and a library of production unit models, used to build and store decision support models and production unit models for aero-engine production lines;

[0070] The aero-engine production line digital twin module is used to construct a production line digital twin simulation project. It retrieves the production line unit model and the auxiliary decision-making model, and configures data flow parameters between the production line unit models, data flow parameters between the production line unit models and the auxiliary decision-making model, and multi-source heterogeneous data call interface parameters for the production line unit models according to the aero-engine production process nodes. Furthermore, based on the multi-source heterogeneous data and the auxiliary decision-making model, it configures simulation parameters on the production line digital twin simulation project and monitors the model data flow to achieve simulation calculations.

[0071] The intelligent auxiliary decision-making module is used to analyze the simulation results and obtain optimization solutions based on the auxiliary decision-making model.

[0072] The technical solution of the present invention has at least the following advantages and beneficial effects:

[0073] This invention solves the problem that existing technologies are unable to systematically and accurately simulate and analyze production line flexibility planning and resource demand bottlenecks based on the production needs of different models / batch / units of aero engines, and provide auxiliary decision-making suggestions.

[0074] This invention provides a set of aero-engine flexible production line simulation system based on digital twin technologies such as virtual-real fusion, data fusion and decision support. It can carry out flexible production line simulation analysis based on the construction of a digital twin of the aero-engine production line before production to meet the needs of multi-batch small-volume production in aero-engine manufacturing.

[0075] The auxiliary decision-making model of this invention can promptly identify bottlenecks in the production line and assist in optimizing and adjusting the aero-engine production line, such as optimizing the production line layout to shorten the logistics flow time / logistics flow waiting time and adjusting the production unit capacity, thereby improving the capacity efficiency of the aero-engine production line.

[0076] This invention is reasonably designed, cost-effective, and easy to apply to various production lines through simple module changes, making it highly scalable. Attached Figure Description

[0077] Figure 1 This is a flowchart illustrating the aero-engine flexible production line simulation method provided in Embodiment 1 of the present invention.

[0078] Figure 2 This is a schematic diagram of the data BOM structure tree provided in Embodiment 1 of the present invention;

[0079] Figure 3 This is a schematic diagram of the data BOM structure tree after mounting, as provided in Embodiment 1 of the present invention;

[0080] Figure 4 This is a schematic diagram of the production line unit model framework provided in Embodiment 2 of the present invention;

[0081] Figure 5 This is a schematic diagram of the structure of the production line digital twin simulation engineering project provided in Embodiment 2 of the present invention;

[0082] Figure 6 This is a schematic diagram of the principle of the aero-engine flexible production line simulation system provided in Embodiment 3 of the present invention. Detailed Implementation

[0083] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0084] Example 1

[0085] This embodiment provides a simulation method for a flexible production line of aero-engines, including the following steps:

[0086] Collect and store diverse types of heterogeneous data from multiple sources across multiple systems;

[0087] The multi-source heterogeneous data is fused.

[0088] Build and store the decision support model and the production line unit model for aero-engines;

[0089] Construct a digital twin simulation project for the production line, retrieve the production unit model and the auxiliary decision model of the production line, and configure the data flow parameters between the production unit models, the data flow parameters between the production unit models and the auxiliary decision model, and the multi-source heterogeneous data call interface parameters of the production unit models according to the nodes of the aero-engine production process.

[0090] Based on the multi-source heterogeneous data and the auxiliary decision-making model, simulation parameters are configured and model data flow is monitored in the production line digital twin simulation project to realize simulation calculation;

[0091] The simulation results are analyzed based on the aforementioned auxiliary decision-making model to obtain an optimization scheme.

[0092] In this embodiment, see Figure 1First, through the system data interaction interface, multi-source heterogeneous data is collected from relevant business systems such as the production planning management system, aero-engine process design system, aero-engine test management system, and aero-engine production line status monitoring system. Specifically, this includes production planning, parts processing technology, assembly technology, test inspection, and production line status. The collected multi-source heterogeneous data is then managed. Next, the multi-source heterogeneous data is fused. This step mainly focuses on aero-engine production planning data, performing data feature identification, extracting and calling relevant data from process design data and test data to form an instantiated aero-engine production and manufacturing scheme. Standardized data fusion and encapsulation are then performed, and production line status data is also encapsulated. Finally, an auxiliary decision-making model and an aero-engine production line production unit model are built and stored in the modeling environment. The constructed auxiliary decision-making model and production line production unit model are then stored in the auxiliary decision-making system and production line status monitoring system, respectively. The model library and production unit model library are used for modeling digital twins of aero-engine production lines. Then, a digital twin simulation project of the aero-engine production line is constructed and simulation calculations are performed. The simulation results can be obtained with a specific model / batch / unit aero-engine production and manufacturing scheme as the simulation calculation object. Specifically, the production line digital twin project can be created first, then the model can be retrieved and called and the twin can be constructed. Next, multi-source heterogeneous data can be called, and then the production line simulation calculation can be performed. Finally, based on the called auxiliary decision-making model, the simulation results of the specific model / batch / unit aero-engine production are analyzed, and production line optimization and adjustment suggestions are given based on the delivery time in the specific model / batch / unit aero-engine production and manufacturing scheme. Specifically, the auxiliary decision-making model and production unit model constructed above have been stored in the corresponding model library. Based on this, intelligent auxiliary decision-making can be realized, that is, simulation result analysis can be realized to obtain auxiliary decision recommendations.

[0093] In this embodiment, the method for collecting and storing various types of multi-source heterogeneous data involves collecting data at fixed times and frequencies through a data interaction interface between systems. The collection method includes:

[0094] Define a categorized storage resource directory for the multi-source heterogeneous data, including types such as production planning, process design, trial runs, and production line status;

[0095] When collecting the multi-source heterogeneous data, the data interaction interface type is defined and configured to interact with the system, and the data collection frequency and corresponding data resource directory are configured.

[0096] Furthermore, the data interaction interface includes an FTP interface, an API interface, an SQL / ODBC / JDBC database interface, and a Web Service interface.

[0097] Next, the multi-source heterogeneous data includes aero-engine production plan data, process design data, test run data, and aero-engine production line status data. The method for fusing the multi-source heterogeneous data is as follows:

[0098] Assign feature identifiers to parameters in the multi-source heterogeneous data according to their parameter types;

[0099] Obtain the feature identifiers of parameters in aero-engine production plan data;

[0100] Construct a data BOM structure tree based on the characteristic identifiers of parameters in the aero-engine production plan data;

[0101] Based on the characteristic identifiers of parameters in the aero-engine production plan data, the corresponding parameters are identified from the process design data and test data, and loaded into the data BOM structure tree to form an instantiated production and manufacturing plan.

[0102] The instantiated production and manufacturing scheme and the aero-engine production line status data are encapsulated, specifically, data API interface encapsulation can be prioritized. The encapsulation method may include defining the interface URL, determining the data request method (using the GET request method), request parameters, and response data format to form an instantiated data model. This ensures that the instantiated production and manufacturing scheme data and production line status data required by the aero-engine production line digital twin module can be recognized and invoked during simulation operations.

[0103] In this embodiment, the feature identifiers can be set as the name, model, batch, and unit number of the aero-engine, such as: XX aero-engine, XX-XX model, batch 202501, unit number 01-10, etc., which are production plan data feature identifiers. Then, a data BOM structure tree of the aero-engine production and manufacturing plan can be constructed using the aero-engine name, model, batch, and unit number as identifiers. The data BOM structure tree of this example can be found in [reference needed]. Figure 2 Based on the identified aero-engine name, model, batch, and unit number, corresponding data is identified and extracted from the collected process design data (including process data for parts manufacturing, component assembly, and complete engine assembly) and test data. This data is then incorporated into the constructed aero-engine production and manufacturing scheme's data BOM structure tree, forming an instantiated production and manufacturing scheme for the specific aero-engine model, batch, and unit. For details on the incorporated data BOM structure tree, please refer to [link / reference needed]. Figure 3 The mounted data BOM structure tree can be visualized.

[0104] As a preferred embodiment, the method for constructing the auxiliary decision-making model and the production line unit model of the aero-engine is as follows:

[0105] Create the primitives of the auxiliary decision-making model for the simulation of the flexible production line of aero-engines, edit the mathematical calculation expressions of the auxiliary decision-making model and bind them with the corresponding primitives, and define the variable input interface and result output interface of the auxiliary decision-making model;

[0106] Create the production unit model of the aero-engine production line, define the internal production team or production equipment model, define the variable input interface and result output interface of the production team or production equipment model, and define the variable input interface and result output interface of the production unit model.

[0107] Next, the method for constructing a digital twin simulation engineering for the production line is as follows:

[0108] Based on the simulation requirements of flexible production lines for aero-engines, a digital twin simulation project for aero-engine production lines was created.

[0109] Based on the simulation requirements of the flexible production line for aero-engines, input the model name or number, retrieve the required production line unit model and auxiliary decision-making model, and put them into the aero-engine production line digital twin simulation project.

[0110] Based on the simulation requirements of the flexible production line for aero-engines, the data flow parameters between production unit models and between different production stages are configured according to the production process nodes of aero-engines. The data flow parameters between the auxiliary decision-making model and the production unit model of the production line are also configured.

[0111] Based on the simulation requirements of the flexible production line for aero-engines and the defined API data interface of the production unit model, configure and define the API data interface to meet the need to call the multi-source heterogeneous data during the simulation process.

[0112] Then, the method for implementing the simulation calculation is as follows:

[0113] Configure the simulation parameters for the digital twin simulation project of the aero-engine production line, including simulation parameters such as the number of simulation executions and the simulation cycle;

[0114] From the simulation data visualization components, select the required components and define the model objects to be observed, including the model interface and data flow;

[0115] Start the simulation.

[0116] After the production line unit model in this embodiment is completed, it can be stored in the production unit model library. Specifically, a practical example of establishing and running a production line digital twin simulation project is as follows:

[0117] Production line digital twin simulation engineering project creation:

[0118] Based on the current requirements for simulating a flexible production line for aero-engines, create a digital twin simulation project for the aero-engine production line and name the simulation project.

[0119] Selection of production unit model and decision support model:

[0120] Based on the simulation requirements of flexible production lines for aero-engines, enter the model name or number in the search boxes of the production unit model library and the auxiliary decision-making model library to retrieve the required production unit model and auxiliary decision-making model from the production unit model library and the auxiliary decision-making model library, and drag and drop them into the modeling work area of ​​the production line digital twin simulation project.

[0121] Data interaction configuration for production unit model and decision support model:

[0122] Based on the simulation requirements of aero-engine flexible production lines, after dragging and dropping production unit models into the production line, data flow parameters (such as material flow direction, material transfer time, and material transfer waiting time) are configured between each production unit model and between different production stages, according to the aero-engine production process nodes. Simultaneously, data flow parameters are configured between the auxiliary decision-making model and each production unit model or production stage. The design of the production line digital twin simulation project is attached. Figure 5 As shown.

[0123] During the simulation process, based on the acquired multi-source heterogeneous data and auxiliary decision-making models, simulation parameters (number of simulation executions, simulation cycle, etc.) are configured and model data flow (model interface, data flow) is monitored on the constructed digital twin of the aero-engine production line. Simulation calculations are then performed to obtain results using a specific model / batch / unit aero-engine production and manufacturing scheme as the simulation object. The production line digital twin simulation calculation includes the following steps:

[0124] Simulation parameter configuration:

[0125] Configure the simulation parameters of the constructed digital twin simulation project for the aero-engine production line, including the number of simulation executions (e.g., 20 simulations) and the simulation cycle (e.g., 100 seconds per simulation).

[0126] Simulation visualization configuration:

[0127] From the simulation data visualization components, select the required components to the simulation control panel, and define the model objects to be monitored (specifically, production unit models or production shift / equipment models), including model interfaces and data flows. This allows you to view the input or output results of specific model objects in real time during the simulation.

[0128] Simulation calculation:

[0129] Start the simulation and monitor the data of each model in real time during the simulation process. Finally, obtain the simulation results, including the simulation results of the total production time T of the aero-engine and the simulation results of the cost optimization objective function C.

[0130] Finally, based on the digital twin simulation of the aero-engine production line, and according to the auxiliary decision-making model used, the simulation results for a specific model / batch / unit of aero-engine production are analyzed. The simulation and analysis results are then presented, including the simulation results of the total production time T after multiple simulation runs and the simulation results of the cost optimization objective function C. Simultaneously, based on the delivery time of each production stage in the specific model / batch / unit of aero-engine production and manufacturing plan, production line optimization and adjustment suggestions are provided, including: shortening the logistics flow time / logistics waiting time between production units / different production stages, and adjusting production unit capacity (such as increasing work shifts and improving capacity coefficients).

[0131] Example 2

[0132] This embodiment is based on the technical solution of Embodiment 1, and further explains the auxiliary decision-making model and the production line production unit model.

[0133] The following is a specific implementation example of constructing the aforementioned decision support model:

[0134] Create an auxiliary decision model primitive in the modeling environment and name it "Aero-engine Flexible Production Line Simulation Auxiliary Decision Model";

[0135] To support simulation-based decision-making for flexible production lines of aero-engines, a production line optimization auxiliary decision-making algorithm is designed. This algorithm aims to design a mathematical model-based calculation method and formula for production line optimization by considering the production sequence, logistics flow time and frequency, and other relevant factors among production units. Specific design methods can be found in the objective function and constraints designed for this model, described later in this paper. The goal is to calculate a comprehensive index that minimizes both logistics costs and production time, thereby assisting in production decision analysis.

[0136] The following is a specific example of how to build a production line unit model:

[0137] Create a production line model for an aero-engine within the modeling environment, defining its internal production teams or production equipment models. Simultaneously, define the variable input and output interfaces for the production team or equipment models, as well as the variable input and output interfaces for the aero-engine production line model. The production line model construction includes the following steps:

[0138] (1) Creation of production line production unit model primitives:

[0139] Based on the aero-engine production process, production line characteristics and internal organizational structure, aero-engine production line production unit model primitives are created in the modeling environment and named respectively as machining production unit model, special processing production unit model, welding production unit model, surface treatment production unit model, component assembly production unit model, complete engine assembly production unit model, and complete engine test and inspection production unit model.

[0140] (2) Production line production unit modeling:

[0141] Each production unit in an aero-engine production line consists of multiple production teams or production equipment. In the modeling environment, the internal production team or production equipment models of the created production line production unit model are designed and defined, and the capacity parameters of these production team or production equipment models are designed.

[0142] Specific modeling includes:

[0143] 1) Data Model Building

[0144] ① Model data design and interface design

[0145] A data model is defined within the production unit model of the production line. This data model is used to acquire instantiated production and manufacturing scheme data and production line status data, which are encapsulated as data API interfaces. It also encapsulates the production characteristic data (i.e., the types of products that the production team or equipment can produce and the unit-time capacity of a single product type) of the production teams or equipment within the current production unit into the data model. The data model's input and output interfaces are defined. The input interface is used to acquire instantiated production and manufacturing scheme data, production line status data, and simulation results from external production unit models. The output interface is used to distribute data stimuli to the production team or equipment models.

[0146] ② Data distribution mechanism logic design

[0147] Define the data distribution mechanism logic of the design data model. In the process of production line data twin simulation calculation, the data model distributes the instantiated production manufacturing plan data and production line status data to the designated production team or production equipment model based on the acquired instantiated production manufacturing plan data and production line status data, combined with the production characteristic data of all production teams or production equipment in the production unit, as the incentive for simulation calculation.

[0148] For the data model of the production unit in the part manufacturing stage, the data distribution mechanism uses the part production task allocation constraint as the design logic.

[0149] For the data model of the component assembly production unit in the assembly stage, its data distribution mechanism uses the component assembly task allocation constraint as the design logic.

[0150] For the data models of the final assembly production unit in the assembly stage and the final testing and inspection production unit in the testing and inspection stage, the data distribution mechanism is designed with the task allocation constraints of final assembly and testing and inspection as the design logic.

[0151] 2) Modeling of production teams or production equipment

[0152] Within the production unit model, define multiple production teams or production equipment models, and define their... (Production capacity per unit time) (Estimated remaining completion time for currently ongoing production tasks) (Downtime for maintenance) (Whether or not the production team or production equipment is used) (Should we increase the number of work shifts?) Parameters such as (capacity increase coefficient after increasing work shifts) and production characteristic data of the production shifts or production equipment contained in the current production unit (i.e., the types of products that the production shifts or production equipment can produce, and the unit time capacity of a single product) are used to represent the capabilities of the production shifts or production equipment.

[0153] For production teams or production equipment models of all production units in the part manufacturing stage, the production time for a single type of part is calculated. mathematical formulas for calculation Construct a model of a single production team or production equipment; when producing multiple parts of different types, the total production time of that production team or equipment is [not specified]. for And the production capacity of the production team or production equipment is constrained by the parts manufacturing capacity.

[0154] For the production team or production equipment model of the component assembly production unit in the assembly stage, the production time of a single type of component is considered. Computational mathematical formulas Build a model of a single production team or production equipment. Representative production components The value indicates whether the production team or equipment b in production unit u is used; if so, it is 1, otherwise 0. When producing multiple parts of different types, the total production time of this production team or equipment is... And the production capacity of the production team or production equipment is constrained by the parts manufacturing capacity.

[0155] For production teams or production equipment models in the assembly stage and the trial and inspection stage of the final machine assembly production unit, the time consumed in the final machine assembly / trial and inspection is calculated. mathematical formulas for calculation Build a model of a single production team or production equipment. For the production of complete machines Whether to use the truth value of production team or equipment b in production unit u; when producing multiple units of different types, the total production time of the production team or equipment is... And the production capacity of the production team or production equipment is constrained by the parts manufacturing capacity.

[0156] At the same time, define the input interface and output interface of the production team or production equipment model. The input interface is used to obtain the distributed simulation calculation stimulus data from the data model, and the output interface is used to output the results of the simulation calculation process.

[0157] 3) Production cell modeling

[0158] The production unit model consists of multiple production team or production equipment models and a data model. Based on the data model and the production team or production equipment models, the data interaction relationships between the data model and the multiple production team or production equipment models are defined. Simultaneously, the input and output interfaces of the production unit model are defined. The input interface is used to obtain the output results of the external production unit model simulation calculations, and the output interface is used to output the simulation calculation results of the production unit model. The production line production unit model framework is as follows: Figure 4 As shown.

[0159] For production unit models at different production stages, mathematical formulas are used for calculation. Build models for all production units. This indicates the total production time for all production shifts or equipment in the production unit. This indicates the material flow time between this production unit and external production units. This indicates the waiting time for material flow between this production unit and external production units.

[0160] Based on the modeling results, when analyzing the simulation results and obtaining optimization solutions using the auxiliary decision-making model, the optimization content includes logistics flow time and adjusting production unit capacity. The optimization method of the auxiliary decision-making model is as follows:

[0161] To minimize the total production time of the instantiated aero-engine manufacturing scheme, the values ​​of each variable in the decision variables are continuously adjusted to achieve a minimum total production time T, i.e., T = min T. When considering the cost of adjusting the production unit u's capacity, the cost of increasing shifts is defined as... Based on this, the objective optimization function is established:

[0162] ;

[0163] ;

[0164] in, For the optimization target, T represents the total production time of the aero-engine. , and These represent the actual production time during the parts manufacturing stage, the actual production time during the assembly stage, and the time spent during the trial run and inspection stage. The additional shift cost for production unit u when producing equipment b. This represents the truth value of whether the production team or equipment b in production unit u has increased the number of work shifts; it is 1 if so, and 0 otherwise. This represents the set of production units within production stage S. For a production unit u, it refers to a set of production teams or equipment.

[0165] For each part During the production phase, parts can only be assigned to a specific shift or piece of equipment within a specific production unit of that phase for production, establishing constraints on parts production task allocation:

[0166] ;

[0167] in, Representative production parts Whether to use the true value of production shift or equipment b in production unit u; if yes, set to 1; otherwise, set to 0. The stage represented is the parts production stage;

[0168] For each component During the assembly phase, components can only be assigned to a specific work group or piece of equipment within a specific production unit for assembly, establishing constraints on component assembly task allocation:

[0169] ;

[0170] in, Representative production components Whether to use the true value of production shift or equipment b in production unit u; if yes, set to 1; otherwise, set to 0. The stage represented is the assembly stage;

[0171] For each complete machine During the final assembly or commissioning inspection phase, tasks can only be assigned to a specific work group or piece of equipment within a specific production unit for assembly or commissioning inspection. This establishes constraints on the allocation of final assembly and commissioning inspection tasks.

[0172] ;

[0173] in, Represents the production of complete machines Whether to use the true value of production shift or equipment b in production unit u; if yes, set to 1; otherwise, set to 0. This refers to the production team or equipment group within the overall machine trial and inspection production unit.

[0174] Establish capacity constraints:

[0175] Parts manufacturing capacity constraints:

[0176] ;

[0177] Component assembly capacity constraints:

[0178] ;

[0179] Overall assembly / commissioning and testing capacity constraints:

[0180] ;

[0181] Where P represents the set of all parts, B represents the set of all components, and O represents the set of all finished production machines. The capacity increase coefficient after adding shifts to production team or equipment b in production unit u, and its value is not less than 1. This refers to the unit-time production capacity of production shift or equipment b within production unit u. This represents the total duration of the production planning period. The estimated remaining completion time for the production task currently being performed by production team or equipment b in production unit u. The downtime for maintenance of production shifts or equipment b within production unit u;

[0182] Establish overall time constraints:

[0183] T≤D;

[0184] Where D is the set threshold.

[0185] Preferably, the method for obtaining the actual production time in the part manufacturing stage, the actual production time in the assembly stage, and the time spent in the trial run and inspection stage is as follows:

[0186] ;

[0187] ;

[0188] ;

[0189] ;

[0190] ;

[0191] ;

[0192] in, For parts The time spent by the production team or equipment b in production unit u. For components The time spent assembling equipment in production unit u by production shifts or equipment b. For the whole machine The time spent in production unit u by production team or equipment b assembling or testing. For parts The number of For components The number of For the whole machine The number of Producing parts for production unit u The time required Producing components for production unit u The time required Producing complete machines for production unit u The time required For production units within the same production stage To the production unit The logistics transit time between them Indicates production unit within the same production stage To the production unit The waiting time for logistics transfer between them Indicates the production stage To the production stage The logistics transit time between them Indicates the production stage To the production stage The waiting time for logistics transfer between i=1,2,3,4,j=1,2.

[0193] Optimization can be achieved by solving the established objective function and constraints. Heuristic algorithms (such as genetic algorithms and particle swarm optimization algorithms) can be used for approximate solutions. During the simulation process, the simulation can be continuously adjusted in conjunction with the constructed digital twin simulation project of the production line (including models of each production unit). , , , , , and The value of is used to calculate the optimal solution of the objective function while satisfying the constraints.

[0194] The objective function and constraints calculated above can be edited into a script using the program editor in the modeling environment, and then associated and bound to the primitives of the decision support model. The interface defined in the decision support model is divided into a variable input interface and a result output interface. The defined variable input interface includes... , , , , , and The system includes a variable input interface and a data interface for interacting with each production unit model; the result output interface includes the output interface for the calculation results of the total production time T of the aero-engine and the cost optimization objective function C. After the above-mentioned auxiliary decision-making model is modeled, it is stored in the auxiliary decision-making model library.

[0195] Aero-engine manufacturing production lines are characterized by multi-stage, multi-process, and multi-equipment collaboration, making production line optimization challenging. This embodiment proposes an auxiliary decision-making optimization method that integrates production process characteristics and resource constraints, addressing the characteristics of diverse parts, complex production lines, and highly collaborative operations in aero-engine production processes. In this scheme, the optimization process incorporates stages such as parts manufacturing, component assembly, final assembly, and testing. Comprehensive modeling is performed for each stage of the complex system production line, including process flow, logistics time, logistics waiting time, remaining equipment hours, and downtime maintenance. The dedicated resource allocation method for each type of production task is optimized, thereby achieving more adaptable and precise optimization for aero-engine manufacturing production lines.

[0196] Example 3

[0197] This embodiment provides a simulation system for a flexible production line of aero-engines, applied to a simulation method for a flexible production line of aero-engines as described in the above embodiment. (See reference...) Figure 6 ,include:

[0198] Multi-source heterogeneous data belongs to the acquisition module and is used to collect and store various types of multi-source heterogeneous data from multiple systems.

[0199] A multi-source heterogeneous data fusion processing module is used to fuse the multi-source heterogeneous data.

[0200] A library of decision support models and a library of production unit models, used to build and store decision support models and production unit models for aero-engine production lines;

[0201] The aero-engine production line digital twin module is used to construct a production line digital twin simulation project. It retrieves the production line unit model and the auxiliary decision-making model, and configures data flow parameters between the production line unit models, data flow parameters between the production line unit models and the auxiliary decision-making model, and multi-source heterogeneous data call interface parameters for the production line unit models according to the aero-engine production process nodes. Furthermore, based on the multi-source heterogeneous data and the auxiliary decision-making model, it configures simulation parameters on the production line digital twin simulation project and monitors the model data flow to achieve simulation calculations.

[0202] The intelligent auxiliary decision-making module is used to analyze the simulation results and obtain optimization solutions based on the auxiliary decision-making model.

[0203] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A simulation method for a flexible production line of aero-engines, characterized in that, Includes the following steps: Collect and store diverse types of heterogeneous data from multiple sources across multiple systems; The multi-source heterogeneous data is fused. Build and store the decision support model and the production line unit model for aero-engines; Construct a digital twin simulation project for the production line, retrieve the production unit model and the auxiliary decision model of the production line, and configure the data flow parameters between the production unit models, the data flow parameters between the production unit models and the auxiliary decision model, and the multi-source heterogeneous data call interface parameters of the production unit models according to the nodes of the aero-engine production process. Based on the multi-source heterogeneous data and the auxiliary decision-making model, simulation parameters are configured and model data flow is monitored in the production line digital twin simulation project to realize simulation calculation; The simulation results are analyzed based on the aforementioned auxiliary decision-making model to obtain an optimization scheme; the optimization includes logistics flow time and adjusting production unit capacity. The optimization method of the auxiliary decision-making model is as follows: Establish the objective optimization function: in, For the optimization target, T represents the total production time of the aero-engine. , and These represent the actual production time during the parts manufacturing stage, the actual production time during the assembly stage, and the time spent during the trial run and inspection stage. The additional shift cost for production unit u when producing equipment b. This represents the truth value of whether the production team or equipment b in production unit u has increased the number of work shifts; it is 1 if so, and 0 otherwise. This represents the set of production units within production stage S. For a production unit u, it refers to a set of production teams or equipment. Establish constraints for assigning parts production tasks: in, Representative production parts Whether to use the true value of production shift or equipment b in production unit u; if yes, set to 1; otherwise, set to 0. The stage represented is the parts production stage; Establish component assembly task allocation constraints: in, Representative production components Whether to use the true value of production shift or equipment b in production unit u; if yes, set to 1; otherwise, set to 0. The stage represented is the assembly stage; Establish constraints for the allocation of tasks related to the final assembly and testing of the complete machine: in, Represents the production of complete machines Whether to use the true value of production shift or equipment b in production unit u; if yes, set to 1; otherwise, set to 0. This refers to the production team or equipment group within the overall machine trial and inspection production unit. Establish capacity constraints: Where P represents the set of all parts, B represents the set of all components, and O represents the set of all finished production machines. The capacity increase coefficient after adding shifts to production team or equipment b in production unit u, and its value is not less than 1. This refers to the unit-time production capacity of production shift or equipment b within production unit u. This represents the total duration of the production planning period. The estimated remaining completion time for the production task currently being performed by production team or equipment b in production unit u. The downtime for maintenance of production shifts or equipment b within production unit u; Establish overall time constraints: T≤D; Where D is the set threshold.

2. The simulation method for a flexible production line of an aero-engine according to claim 1, characterized in that, When collecting and storing various types of multi-source heterogeneous data, the data is collected at fixed times and frequencies through data interaction interfaces between systems. The collection method includes: Define a categorized storage resource directory for the multi-source heterogeneous data, including types such as production planning, process design, trial runs, and production line status; When collecting the multi-source heterogeneous data, the data interaction interface type is defined and configured to interact with the system, and the data collection frequency and corresponding data resource directory are configured.

3. The simulation method for a flexible production line of an aero-engine according to claim 2, characterized in that, The data interaction interfaces include FTP interface, API interface, SQL / ODBC / JDBC database interface and Web Service interface.

4. The simulation method for a flexible production line of an aero-engine according to claim 1, characterized in that, The multi-source heterogeneous data includes aero-engine production plan data, process design data, test run data, and aero-engine production line status data. The method for fusing the multi-source heterogeneous data is as follows: Assign feature identifiers to parameters in the multi-source heterogeneous data according to their parameter types; Obtain the feature identifiers of parameters in aero-engine production plan data; Construct a data BOM structure tree based on the characteristic identifiers of parameters in the aero-engine production plan data; Based on the characteristic identifiers of parameters in the aero-engine production plan data, the corresponding parameters are identified from the process design data and test data, and loaded into the data BOM structure tree to form an instantiated production and manufacturing plan. The instantiated production and manufacturing scheme and the aero-engine production line status data are encapsulated.

5. The simulation method for a flexible production line of an aero-engine according to claim 1, characterized in that, The method for constructing the auxiliary decision-making model and the production line unit model of the aero-engine is as follows: Create the primitives of the auxiliary decision-making model for the simulation of the flexible production line of aero-engines, edit the mathematical calculation expressions of the auxiliary decision-making model and bind them with the corresponding primitives, and define the variable input interface and result output interface of the auxiliary decision-making model; Create the production unit model of the aero-engine production line, define the internal production team or production equipment model, define the variable input interface and result output interface of the production team or production equipment model, and define the variable input interface and result output interface of the production unit model.

6. The simulation method for a flexible production line of an aero-engine according to claim 1, characterized in that, The method for constructing a digital twin simulation engineering of a production line is as follows: Based on the simulation requirements of flexible production lines for aero-engines, a digital twin simulation project for aero-engine production lines was created. Based on the simulation requirements of the flexible production line for aero-engines, input the model name or number, retrieve the required production line unit model and auxiliary decision-making model, and put them into the aero-engine production line digital twin simulation project. Based on the simulation requirements of the flexible production line for aero-engines, the data flow parameters between production unit models and between different production stages are configured according to the production process nodes of aero-engines. The data flow parameters between the auxiliary decision-making model and the production unit model of the production line are also configured. Based on the simulation requirements of the flexible production line for aero-engines and the defined API data interface of the production unit model, configure and define the API data interface to meet the need to call the multi-source heterogeneous data during the simulation process.

7. The simulation method for a flexible production line of an aero-engine according to claim 6, characterized in that, The method for implementing the simulation calculation is as follows: Configure the simulation parameters for the digital twin simulation project of the aero-engine production line, including the number of simulation executions and the simulation cycle parameters; From the simulation data visualization components, select the required components and define the model objects to be observed, including the model interface and data flow; Start the simulation.

8. The simulation method for a flexible production line of an aero-engine according to claim 1, characterized in that, The methods for obtaining the actual production time in the part manufacturing stage, the actual production time in the assembly stage, and the time spent in the trial run and inspection stage are as follows: ； ； ； ； ； in, For parts The time spent by the production team or equipment b in production unit u. For components The time spent assembling equipment in production unit u by production shifts or equipment b. For the whole machine The time spent in production unit u by production team or equipment b assembling or testing. For parts The number of For components The number of For the whole machine The number of Producing parts for production unit u The time required Producing components for production unit u The time required Producing complete machines for production unit u The time required For production units within the same production stage To the production unit The logistics transit time between them Indicates production unit within the same production stage To the production unit The waiting time for logistics transfer between them Indicates the production stage To the production stage The logistics transit time between them Indicates the production stage To the production stage The waiting time for logistics transfer between i=1,2,3,4,j=1,2.

9. A flexible production line simulation system for aero-engines, applied to the flexible production line simulation method for aero-engines as described in any one of claims 1-8, characterized in that, include: Multi-source heterogeneous data belongs to the acquisition module and is used to collect and store various types of multi-source heterogeneous data from multiple systems. A multi-source heterogeneous data fusion processing module is used to fuse the multi-source heterogeneous data. A library of decision support models and a library of production unit models, used to build and store decision support models and production unit models for aero-engine production lines; The aero-engine production line digital twin module is used to construct a production line digital twin simulation project. It retrieves the production line unit model and the auxiliary decision-making model, and configures data flow parameters between the production line unit models, data flow parameters between the production line unit models and the auxiliary decision-making model, and multi-source heterogeneous data call interface parameters for the production line unit models according to the aero-engine production process nodes. Furthermore, based on the multi-source heterogeneous data and the auxiliary decision-making model, it configures simulation parameters on the production line digital twin simulation project and monitors the model data flow to achieve simulation calculations. The intelligent auxiliary decision-making module is used to analyze the simulation results and obtain optimization solutions based on the auxiliary decision-making model.

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