A mapping relationship model construction method suitable for aero-engine product production line simulation

Abstract

The application is a mapping relationship model construction method suitable for aero-engine production line simulation, comprising: analyzing the composition and structure of physical entities in the production line to complete geometric modeling of entity models, completing information modeling of entity models according to data information of the production line, and completing behavior modeling of entity models through kinematic analysis of the physical entities; classifying the equipment of the production line according to purposes, and building an aero-engine mechanical machining production line model library; building a production line simulation model according to a two-dimensional CAD layout diagram of an actual production line; decomposing the operation actions of the equipment and personnel at the workstation level according to production tasks, setting mechanical movement processes of moving objects in the production line simulation model; defining the production logic control of the production line, classifying the production logic, and compiling corresponding logic control simulation programs; simulating the production process of the production line, and evaluating the performance of the production line according to production line operation simulation data.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of digital factory, and relates to a mapping relationship model construction method suitable for aero-engine product production line simulation. BACKGROUND

[0002] Aero-engine parts have complex structures and high machining process requirements. The production of aero-engine parts belongs to multi-variety, small-batch and mixed-line production, and there are more special machining equipment and special tooling in the production line. The process and material flow logic are complex, the production operation data are complex, there are more special evaluation indexes of the production line, and the commercial production line modeling and simulation software does not have special equipment for aero-engine machining production line. The production line operation logic needs to be customized according to the production process, equipment operation action and man-machine cooperation of aero-engine parts machining, and the evaluation of aero-engine machining production line operation data also needs to be defined by itself. Therefore, there is a lack of simulation modeling method for aero-engine machining production line in the prior art. Therefore, a mapping relationship model construction method suitable for aero-engine product production line simulation is urgently needed. SUMMARY

[0003] To solve the above technical problems, the purpose of the present application is to provide a mapping relationship model construction method suitable for aero-engine product production line simulation. Based on the method, the construction of aero-engine production line entity model, logic model and evaluation model can be realized, which is used for evaluating production line production plan scheme and analyzing the utilization rate of equipment, personnel and the like.

[0004] The application provides a mapping relationship model construction method suitable for aero-engine product production line simulation, which comprises the following steps:

[0005] Step 1: analyze the composition and structure of physical entities in the production line, and sequentially perform device level, unit level and production line and geometric attribute setting to complete geometric modeling of each entity model;

[0006] Step 2: collect data information of the production line, define data structure, describe data relationship, arrange and classify data information, and input the data information into the geometric model to complete information modeling of each entity model;

[0007] Step 3: kinematic analysis is performed on the physical entities, and motion stroke setting, coordinate setting and motion mode setting are performed according to the analysis results to complete behavior modeling of each entity model;

[0008] Step 4: classify the equipment and facilities constituting the production line according to the purpose, and build an aero-engine machining production line model library based on the entity model according to the classification;

[0009] Step 5: According to the two-dimensional CAD layout of the actual production line, select the related entity model from the model library, place the entity model space position, and build the production line simulation model;

[0010] Step 6: According to the production task, the specific to the station level equipment, personnel, and operation action decomposition are carried out, and the mechanical movement process of the moving object is set in the production line simulation model;

[0011] Step 7: The production logic control of the aero-engine machining production line is defined, and the production logic is classified, and the corresponding logic control simulation program is compiled;

[0012] Step 8: The production line production process simulation is carried out, and the production line performance is evaluated according to the production line running simulation data.

[0013] The mapping relationship model construction method suitable for the simulation of the aero-engine product production line can construct the aero-engine part machining production line entity model and the production line model library, create the running logic of the production line and the operation logic of the production line equipment, and establish the production line performance evaluation method. Based on the constructed mapping relationship model of the aero-engine part machining production line simulation, the bottleneck point and the bottleneck resource in the production process of the aero-engine part machining production line can be identified, the key factors restricting the production capacity of the production line can be found, the overall production capacity can be improved by optimizing the bottleneck point; different production plan schemes, including order scheduling, production resource scheduling and material supply, can be analyzed to optimize the rationality and feasibility of the production plan; the utilization rate of equipment, manpower and materials and other resources can be evaluated, the resource utilization efficiency can be analyzed and improvement measures can be proposed; different process improvement schemes, equipment investment decisions and process optimization measures can be verified to improve production efficiency and reduce production cost. BRIEF DESCRIPTION OF DRAWINGS

[0014] Figure 1 is a flowchart of the mapping relationship model construction method suitable for the simulation of the aero-engine product production line of the application;

[0015] Figure 2 is a construction diagram of the entity model of the application;

[0016] Figure 3 is a classification diagram of the aero-engine machining production line model library of the application. DETAILED DESCRIPTION

[0017] The embodiment discloses a mapping relationship model construction method suitable for the simulation of the aero-engine product production line, and the simulation model covers three parts of the entity model, the logic model and the evaluation model, and the modeling process is as shown in Figure 1 The model construction method comprises the following steps:

[0018] Step 1: Analyze the composition and structure of physical entities in the production line, and sequentially perform device level, unit level, and production line and geometric property settings to complete the geometric modeling of each entity model.

[0019] In specific implementation, a geometric model is established by CAD three-dimensional modeling software, and the geometric properties include device shape and size, appearance color, texture, and material. When constructing the geometric model, the object topology structure is established according to the assembly relationship of the object, the topological relationship between parts is defined, and the corresponding motion properties are added to depict the motion properties of the object. The motion properties include coordinate origin, joint speed, and travel limit.

[0020] Step 2: Collect data information of the production line, define data structure, describe data relationship, organize and classify data information, and enter data information into the geometric model to complete information modeling of each entity model.

[0021] In specific implementation, the data information includes basic data information, task data information, state data information, and production process information.

[0022] The basic data information is the static attribute information of the production line, including device name, device model, device code, device area, device IP address, device manufacturer, and note information.

[0023] The task data information is the task attribute of the part in the production process, including part name, part code, production time, production shift, and note information.

[0024] The state data information is the production line state attribute of the part in the production process, including initialization state, program running state, reset state, and fault state.

[0025] The production process information is various information generated in the production process of the part.

[0026] Based on these data information, the production line entity model can simulate the actual production process.

[0027] Step 3: Perform kinematic analysis on the physical entity, set motion travel, coordinates, and motion mode according to the analysis results to complete behavior modeling of each entity model.

[0028] The behavior model can intuitively reflect the behavior actions of each element at the physical entity layer. The behavior actions of each element are subdivided into each joint action, and the trigger signal of the corresponding action is set according to different action behaviors. The working path and key points of each actuator are recorded to ensure the motion consistency of the entity model and the physical model. The entity model modeling process is shown in Figure 2 .

[0029] Step 4: Classify the equipment and facilities in the production line according to their functions, and build an aero-engine machining production line model library based on the classified entity models, as shown in Figure 3

[0030] The production line model library includes machining unit entity models, detection unit entity models, polishing unit entity models, auxiliary unit entity models, storage equipment entity models, logistics equipment entity models, tooling fixture entity models, and building facility entity models.

[0031] The machining unit entity models include five-axis machining center entity models, etc. The detection unit entity models include three-coordinate measuring machine entity models, etc. The polishing unit entity models include polishing robot entity models, etc. The auxiliary unit entity models include loading and unloading station entity models and marking machine entity models, etc. The storage equipment entity models include buffer rack entity models and tool magazine entity models, etc. The physical equipment entity models include AGV trolley entity models, etc. The tooling fixture entity models include pallet models, etc. The building facility entity models include fence entity models and table and chair entity models, etc.

[0032] Step 5: According to the actual production line's two-dimensional CAD layout, select related entity models from the model library, place the entity models' spatial positions, and build a production line simulation model.

[0033] Step 6: According to the production tasks, decompose the equipment and personnel's operation actions at the workstation level, and set the mechanical motion process of the moving objects in the production line simulation model.

[0034] In specific implementation, the mechanical motion process includes product flow process, logistics line motion process, logistics element motion process, robot collaborative motion process, machining equipment operation process, three-dimensional warehouse operation process, and state light start-stop process.

[0035] Step 7: Define the production logic control of the aero-engine machining production line, as shown in Table 1. Then, classify the production logic and compile the corresponding logic control simulation program.

[0036] Table 1 Logic structure

[0037]

[0038] The production logic classification includes resource logic class, personnel logic class, buffer logic class, conveyor belt logic class, and AGV logic class.

[0039] Step 8: Perform production line production process simulation, and evaluate the production line performance according to the production line operation simulation data.

[0040] ​In specific implementation, the production line operation simulation data includes product production data, equipment state data, personnel state data and station process data.

[0041] The product production data includes product online flow and positioning simulation data, product production data volume and production plan tracking data.

[0042] The equipment state data includes:

[0043] 1) Equipment state: running, shutdown, standby, alarm.

[0044] 2) Machine tool movement data: door opening, door closing, spindle movement, tool movement.

[0045] 3) Robot movement data: posture, movement trajectory.

[0046] 4) AGV trolley movement data: position, posture, movement trajectory.

[0047] 5) Conveying device movement data: tray position, conveyor movement.

[0048] 6) Vertical warehouse information: warehouse out, warehouse in, temporary storage information.

[0049] The personnel state data includes position positioning data and online time data.

[0050] The station process data includes processing process data, tooling, tool state data and work time calculation data.

[0051] The production line performance evaluation indexes include:

[0052] 1) Capacity: output per unit of time / unit of time.

[0053] 2) Production rhythm: production rhythm, also known as production distance time, refers to the necessary market time for a customer to demand one product within a certain length of time.

[0054] 3) Output rate: indicates the number of products that can be completed by the production line in unit time.

[0055] 4) Manufacturing cycle: indicates the time required for the entire production cycle from order to completion.

[0056] 5) Capacity utilization rate: indicates the ratio between actual production capacity and theoretical maximum production capacity.

[0057] 6) Bottleneck: the slowest link in a production process, the bottleneck will limit the output speed of the production process and also affect the production capacity of other links.

[0058] 7) Equipment utilization: Actual number of operating equipment divided by the number of available equipment or the number of equipment on record.

[0059] 8) Production balance rate: Measures the average operation time of each process of the production line and the production efficiency of the production line. The calculation formula is as follows:

[0060]

[0061] In the formula, P is the production line balance rate; ΣT i is the operation time of each process; T max is the operation time of the bottleneck process; and N is the total number of processes.

[0062] 9) Personnel utilization: Actual working time of workers divided by the total working time.

[0063] The above description is only the preferred embodiment of the present application and is not intended to limit the idea of the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for constructing a mapping relationship model suitable for simulation of an aero-engine product production line, characterized in that: include: Step 1: Analyze the composition and structure of the physical entities in the production line, and set the equipment level, unit level, production line and geometric attributes in sequence to complete the geometric modeling of each entity model; Step 2: Collect data information of the production line, define the data structure, describe the data relationship, organize and classify the data information, enter the data information into the geometric model, and complete the information modeling of each solid model; Step 3: Perform kinematic analysis on the physical entity, set the motion range, coordinates, and motion mode based on the analysis results, and complete the behavioral modeling of each entity model; Step 4: Classify the equipment and facilities that make up the production line according to their uses, and build an aircraft engine machining production line model library based on the classification and the physical model; Step 5: Based on the 2D CAD layout of the actual production line, select relevant physical models from the model library, arrange the physical models in space, and build a production line simulation model; Step 6: Based on the production tasks, decompose the work actions of the equipment and personnel at the workstation level, and set the mechanical motion process of the moving object in the production line simulation model; Step 7: Define the production logic control of the aircraft engine machining production line, classify the production logic, and compile the corresponding logic control simulation program; Step 8: Simulate the production process of the production line and evaluate the production line performance based on the production line operation simulation data.

2. The method for constructing a mapping relationship model suitable for simulation of an aero-engine product production line according to claim 1, characterized in that: In step 1, a geometric model is established using CAD three-dimensional modeling software, and the geometric attributes include the shape and size of the device, appearance color, texture and material; when constructing the geometric model, the object topology structure is established according to the assembly relationship of the object, the topological relationship between the components is defined, and corresponding motion attributes are added to characterize the object motion attributes, and the motion attributes include: coordinate origin, joint speed and travel limit.

3. The method for constructing a mapping relationship model suitable for simulating an aerospace product production line according to claim 1, wherein: Data information includes: basic data information, task data information, status data information, and production process information; Basic data information is the static attribute information of the production line, including: equipment name, equipment model, equipment code, equipment region, equipment IP address, equipment manufacturer and remarks; Task data information is the task attributes of the part during the production process, including: part name, part code, production time, production shift and remarks; Status data information is the production line status attributes of the parts during the production process, including: initialization status, program running status, reset status and fault status; Production process information refers to various types of information generated during the production process of parts; Based on this data information, a production line physical model can be built to simulate the actual production process.

4. The method for constructing a mapping relationship model suitable for simulating an aerospace product production line according to claim 1, wherein: The production line model library in step 4 includes: processing unit entity model, detection unit entity model, grinding and polishing unit entity model, auxiliary unit entity model, storage equipment entity model, logistics equipment entity model, tooling fixture entity model and building facility entity model.

5. The method for constructing a mapping relationship model suitable for simulation of an aero-engine product production line according to claim 1, characterized in that: The mechanical movement process in step 6 includes: product flow process, logistics line movement process, logistics element movement process, robot collaborative movement process, processing equipment operation process, stereoscopic warehouse operation process and status light start and stop process.

6. The method for constructing a mapping relationship model suitable for simulation of an aero-engine product production line according to claim 1, characterized in that: The production logic control in step 7 is specifically defined as follows: Initialization logic: controls production resources to complete the decision-making of the initialization process; Process logic: controls production resources to complete workpiece loading, cycle processing, and workpiece unloading process decisions; Artifact routing logic: controls decisions about how artifacts are routed from one object to another; Resource selection logic: controls resource selection behavior decisions when a process occurs; Queue logic: The order in which artifacts are routed from the cache to other production resources.

7. The method for constructing a mapping relationship model suitable for simulation of an aero-engine product production line according to claim 1, characterized in that: The production logic in step 7 is classified into resource logic class, personnel logic class, cache logic class, conveyor belt logic class and AGV logic class.

8. The method for constructing a mapping relationship model suitable for simulation of an aero-engine product production line according to claim 1, characterized in that: The production line operation simulation data in step 8 includes: Product production data: product online flow and positioning simulation data, product production data volume, and production plan tracking data; Device status data: 1) Equipment status: running, shutdown, standby, alarm; 2) Machine tool motion data: door opening, door closing, spindle motion, tool motion; 3) Robot motion data: posture, motion trajectory; 4) AGV motion data: position, posture, and motion trajectory; 5) Conveyor motion data: pallet position, conveyor belt motion; 6) Warehouse information: outbound, inbound, and temporary storage information; Personnel status data: location data and online time data; Workstation process data: processing data, tooling and tool status data, and working time calculation data.

9. The method for constructing a mapping relationship model suitable for simulation of an aero-engine product production line according to claim 1, characterized in that: The production line performance evaluation indicators in step 8 include: 1) Capacity: output per unit time / unit time; 2) Production takt: Production takt, also known as lead time, refers to the market necessary time for customers to demand a product within a certain period of time; 3) Output rate: refers to the number of products that the production line can complete in a unit of time; 4) Manufacturing cycle: refers to the time required for the entire production cycle from the start of the order to the completion; 5) Capacity utilization: represents the ratio between the actual capacity of the production line and the theoretical maximum capacity; 6) Bottleneck: The slowest production step in a production process. The bottleneck will limit the output speed of the production process and also affect the production capacity of other links; 7) Equipment utilization rate: the percentage of the number of production equipment actually in operation to the number of production equipment available for use or the number of registered production equipment that has been transferred to production; 8) Production balance rate: This measures the average operation time of each process in the production line and the production efficiency of the production line. The calculation formula is as follows: Where: P is the balance rate of the production line; ΣT i T is the operation time of each process; max is the operation time of the bottleneck process; N is the total number of processes; 9) Staff utilization rate: the percentage of workers' actual working time to total working time.

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