Mapping relation model construction method suitable for aviation product production line simulation

Abstract

The mapping relation model construction method suitable for aircraft engine product production line simulation comprises the steps of analyzing the composition and structure of a physical entity in a production line to complete geometric modeling of an entity model, completing information modeling of the entity model according to data information of the production line, performing kinematics analysis on the physical entity, and establishing a mapping relation model according to the kinematics analysis. Behavior modeling of the entity model is completed; equipment of the production line is classified according to purposes, and an aero-engine machining production line model library is built; building a production line simulation model according to the two-dimensional CAD layout of the actual production line; according to a production task, working actions of equipment and personnel which specifically reach a station level are decomposed, and the mechanical movement process of a moving object is set in a production line simulation model; the production logic control of the production line is defined, then production logic classification is carried out, and a corresponding logic control simulation program is compiled; and simulating the production process of the production line, and evaluating the performance of the production line according to the operation simulation data of the production line.

Description

Technical Field

[0001] The invention belongs to the technical field of digital factories and relates to a method for constructing a mapping relationship model suitable for simulating an aero-engine product production line. Background Art

[0002] The structure of aircraft engine parts is complex and requires high processing technology. The production of aircraft engine parts is multi-variety, small batch, mixed line production. There are many special processing equipment and special tooling for the production line, the process and material flow logic are complex, the production operation data is complex, and there are many special evaluation indicators for the production line. In addition, the commercial production line modeling and simulation software does not have its own special equipment for aircraft engine machining production lines. The production line operation logic needs to be defined according to the production process, equipment operation actions and human-machine collaboration of aircraft engine parts processing, and the evaluation of aircraft engine machining production line operation data also needs to be defined by itself. The existing technology lacks simulation modeling methods for aircraft engine machining production lines. Therefore, there is an urgent need for a mapping relationship model construction method suitable for the simulation of aircraft engine product production lines. Summary of the invention

[0003] In order to solve the above technical problems, the purpose of the present invention is to provide a mapping relationship model construction method suitable for the simulation of aero-engine product production lines. Based on this method, the construction of aero-engine production line physical model, logical model and evaluation model can be realized, which can be used to evaluate the production line production plan and analyze the utilization rate of equipment, personnel, etc.

[0004] The present invention provides a mapping relationship model construction method suitable for simulation of an aeroengine product production line, comprising:

[0005] Step 1: Analyze the composition and structure of the physical entities in the production line, set the equipment level, unit level, production line and geometric attributes in turn, and complete the geometric modeling of each entity model;

[0006] Step 2: Collect data information of the production line, define data structure, describe data relationship, organize and classify data information, enter data information into the geometric model, and complete information modeling of each entity model;

[0007] Step 3: Perform kinematic analysis on the physical entity, set the motion stroke, coordinates, and motion mode based on the analysis results, and complete the behavioral modeling of each entity model;

[0008] 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;

[0009] Step 5: According to the 2D CAD layout of the actual production line, select relevant physical models from the model library, place the physical models in space, and build a production line simulation model;

[0010] Step 6: According to the production tasks, the equipment and personnel at the workstation level are decomposed into operation actions, and the mechanical motion process of the moving object is set in the production line simulation model;

[0011] 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;

[0012] Step 8: Simulate the production process of the production line and evaluate the production line performance based on the production line operation simulation data.

[0013] The present invention provides a mapping relationship model construction method suitable for simulation of aero-engine product production lines, which can construct a physical model of an aero-engine parts machining production line and a production line model library, create the operation logic of the production line and the operation logic of the production line equipment, and establish a production line performance evaluation method. Based on the constructed mapping relationship model for simulation of the aero-engine parts machining production line, the bottleneck points and bottleneck resources in the production process of the aero-engine parts machining production line can be identified, and the key factors restricting the production capacity of the production line can be found. By optimizing the bottleneck points, the overall production capacity can be improved; different production plan schemes can be analyzed, including order scheduling, production resource scheduling and material supply, etc., to optimize the rationality and feasibility of the production plan; the utilization rate of resources such as equipment, manpower and materials 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 costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a flow chart of a method for constructing a mapping relationship model suitable for simulation of an aero-engine product production line according to the present invention;

[0015] Figure 2 It is a construction diagram of the entity model of the present invention;

[0016] Figure 3 It is a classification diagram of the aircraft engine machining production line model library of the present invention. DETAILED DESCRIPTION

[0017] This embodiment discloses a method for constructing a mapping relationship model suitable for simulation of an aeroengine product production line. The simulation model includes three parts: a physical model, a logical model, and an evaluation model. The modeling process is as follows: Figure 1 The model building method includes the following steps:

[0018] 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 properties in turn to complete the geometric modeling of each entity model.

[0019] During the specific implementation, a geometric model is established through CAD three-dimensional modeling software. The geometric properties include the shape and size of the equipment, appearance color, texture and material. When constructing the geometric model, the object topological structure is established according to the assembly relationship of the object, the topological relationship between the components is defined, and the corresponding motion attributes are added to characterize the object motion properties. The motion attributes include: coordinate origin, joint speed and travel limit.

[0020] 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 entity model.

[0021] During specific implementation, the data information includes: basic data information, task data information, status data information, and production process information.

[0022] Basic data information is the static attribute information of the production line, including: equipment name, equipment model, equipment code, equipment area, equipment IP address, equipment manufacturer and remarks information.

[0023] Task data information refers to the task attributes of parts during the production process, including: part name, part code, production time, production shift and remarks.

[0024] The status data information is the production line status attribute of the parts during the production process, including: initialization status, program running status, reset status and fault status.

[0025] Production process information refers to various types of information generated during the production process of parts.

[0026] Based on this data information, a production line physical model can be built to simulate the actual production process.

[0027] Step 3: Perform kinematic analysis on the physical entity, set the motion stroke, coordinates, and motion mode based on the analysis results, and complete the behavioral modeling of each entity model.

[0028] The behavior model can intuitively reflect the behavior of each element in the physical entity layer. The behavior of each element is 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 consistency of the action of the entity model and the physical model. Figure 2 shown.

[0029] 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, such as Figure 3 shown.

[0030] The production line model library includes: processing unit entity model, inspection 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.

[0031] The machining unit entity model includes the five-axis machining center entity model, etc. The inspection unit entity model includes the three-coordinate measuring machine entity model, etc. The grinding and polishing unit entity model includes the grinding and polishing robot entity model, etc. The auxiliary unit entity model includes the loading and unloading station entity model and the marking machine entity model, etc. The storage equipment entity model includes the cache rack entity model and the tool library entity model, etc. The physical equipment entity model includes the AGV trolley entity model, etc. The tooling fixture entity model includes the pallet model, etc. The building facility entity model includes the fence entity model and the table and chair entity model, etc.

[0032] Step 5: According to 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.

[0033] Step 6: According to the production tasks, the work actions of the equipment and personnel at the workstation level are decomposed, and the mechanical motion process of the moving object is set in the production line simulation model.

[0034] During specific implementation, the mechanical movement process 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.

[0035] Step 7: Define the production logic control of the aircraft 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 Logical structure

[0037]

[0038] Production logic is classified into resource logic, personnel logic, cache logic, conveyor logic and AGV logic.

[0039] Step 8: Simulate the production process of the production line and evaluate the production line performance based on the production line operation simulation data.

[0040] During specific implementation, the production line operation simulation data includes: product production data, equipment status data, personnel status data and workstation process data.

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

[0042] Device status data includes:

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

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

[0045] 3) Robot motion data: posture and motion trajectory.

[0046] 4) AGV vehicle motion data: position, posture, and motion trajectory.

[0047] 5) Conveyor motion data: pallet position, conveyor belt motion.

[0048] 6) Warehouse information: outbound, inbound and temporary storage information.

[0049] Personnel status data includes: location data and online time data.

[0050] The workstation process data includes: processing data, tooling and tool status data, and working time calculation data.

[0051] Production line performance evaluation indicators include:

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

[0053] 2) Production cycle: Production cycle, also known as production lead time, refers to the necessary market time for customers to demand a product within a certain period of time.

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

[0055] 4) Manufacturing cycle: refers to the time required for the entire production cycle from the start of an order to its completion.

[0056] 5) Capacity utilization rate: It refers to the ratio between the actual capacity of the production line and the theoretical maximum capacity.

[0057] 6) Bottleneck: The link with the slowest production rhythm 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 rate: the percentage of the actual number of production equipment in operation to the number of usable production equipment or the number of registered production equipment that has been transferred to production.

[0059] 8) Production balance rate: It 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:

[0060]

[0061] 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.

[0062] 9) Staff utilization rate: the percentage of workers’ actual working time to total working time.

[0063] The above description is only a preferred embodiment of the present invention and is not intended to limit the concept of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for constructing a mapping relationship model suitable for simulation of an aeroengine product production line, characterized in that: include: Step 1: Analyze the composition and structure of the physical entities in the production line, set the equipment level, unit level, production line and geometric attributes in turn, and complete the geometric modeling of each entity model; Step 2: Collect data information of the production line, define data structure, describe data relationship, organize and classify data information, enter data information into the geometric model, and complete information modeling of each entity model; Step 3: Perform kinematic analysis on the physical entity, set the motion stroke, 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: According to the 2D CAD layout of the actual production line, select relevant physical models from the model library, place the physical models in space, and build a production line simulation model; Step 6: According to the production tasks, the equipment and personnel at the workstation level are decomposed into operation actions, and the mechanical motion process of the moving object is set 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 aeroengine product production line according to claim 1, characterized in that: In the step 1, a geometric model is established by using CAD three-dimensional modeling software, and the geometric attributes include the shape and size of the equipment, appearance color, texture and material; when constructing the geometric model, the object topological structure is established according to the assembly relationship of the object, the topological relationship between the components is defined, and the 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 simulation of an aeroengine product production line according to claim 1, characterized in that: 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 area, equipment IP address, equipment manufacturer and remarks; Task data information refers to the task attributes of parts 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 simulation of an aeroengine product production line according to claim 1, characterized in that: The production line model library in step 4 includes: a processing unit entity model, a detection unit entity model, a grinding and polishing unit entity model, an auxiliary unit entity model, a storage equipment entity model, a logistics equipment entity model, a tooling fixture entity model and a building facility entity model.

5. The method for constructing a mapping relationship model suitable for simulation of an aeroengine product production line according to claim 1, characterized in that: The mechanical movement process in step 6 includes: product circulation 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 aeroengine 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 of initialization process; Processing logic: Control 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: control the resource selection behavior decision when the process occurs; Queue logic: The order in which artifacts are processed when they are routed from the cache to other production resources.

7. The method for constructing a mapping relationship model suitable for simulation of an aeroengine 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 aeroengine 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 circulation 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 vehicle motion data: position, posture, and motion trajectory; 5) Conveyor motion data: pallet position, conveyor belt motion; 6) Warehouse establishment information: outbound, inbound, and temporary storage information; Personnel status data: location data and online time data; Workstation process data: processing data, tooling, tool status data, and working time calculation data.

9. The method for constructing a mapping relationship model suitable for simulation of an aeroengine 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 cycle: Production cycle is also called production lead time, which 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 can be completed by the production line in a unit of time; 4) Manufacturing cycle: refers to the time required for the entire production cycle from the start of an order to its completion; 5) Capacity utilization rate: represents the ratio between the actual capacity of the production line and the theoretical maximum capacity; 6) Bottleneck: The slowest production process 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: It 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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