CN111199072A - All-aluminum vehicle body riveting system based on online process library self-learning and implementation method thereof - Google Patents
All-aluminum vehicle body riveting system based on online process library self-learning and implementation method thereof Download PDFInfo
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Abstract
The invention relates to an all-aluminum vehicle body riveting system based on-line process library self-learning and an implementation method thereof, wherein the system comprises the following steps: the on-line riveting process library cloud server comprises a process database based on orthogonal test design, a data storage unit and a riveting process CAE simulation analysis platform; the all-aluminum vehicle body collaborative riveting operation network comprises a plurality of riveting robots as network nodes, and the riveting robots exchange remote information with an on-line riveting process library cloud server; the all-aluminum vehicle body riveting process detection feedback system is connected with an online riveting process library cloud server and comprises a plurality of visual cameras, a plurality of laser radars and a multi-sensor information fusion unit. Compared with the prior art, the invention guides the riveting robot to carry out process operation on line and carries out real-time monitoring and analysis on the riveting process, thereby improving the intelligent level of the all-aluminum vehicle body riveting production line and accelerating the application of a new material new process on the premise of ensuring the efficiency and the quality.
Description
Technical Field
The invention relates to the field of automobile manufacturing, in particular to an all-aluminum automobile body riveting system based on-line process library self-learning and an implementation method thereof.
Background
With the development requirements of light weight, intellectualization and high performance of electric automobiles and automobiles, in order to improve the battery endurance mileage, reduce the fuel consumption rate of automobiles, improve the environment and meet the requirements on safety, reliability, comfort, attractiveness and the like of automobiles, the aluminum alloy composite material is applied more and more in the automobile industry abroad and domestically. Currently, most cost effective lightweight solutions can be achieved through the design of multiple materials. This limits conventional thermal joining techniques and shows the need for cost effective mechanical and adhesive joining techniques. The self-piercing riveting technology plays a role in the automobile industry with the advantages of unique cold extrusion deformation process, low cost, high automation rate, high mechanical performance and the like.
The self-piercing riveting technology is a cold connection technology, and can fix two or more materials including steel, aluminum, nylon woven belts, plastics and rubber. It also allows for the attachment of galvanised or pre-coated materials without damaging the coating. Self-piercing riveting has many advantages over other connection methods: pre-drilling or surface treatment is not needed; the device is quiet, easy to automate and free from smoke or sparks; the resulting joint has high static and fatigue strength and can produce a watertight joint. For materials difficult to spot weld, the self-punching riveting technology has better adaptability, and the process is more suitable for robot application.
At present, domestic research on self-piercing riveting focuses on research on the performance of a joint by process parameters, and no domestic autonomous intelligent all-aluminum vehicle body riveting method and system exist, because a process database of the system is lacked to guide the riveting process, the unpredictable defect can occur. The self-piercing riveting defects are numerous in form, and the defects cannot be intelligently detected, evaluated and improved after the defects appear. Meanwhile, the intellectualization level of the self-piercing riveting robot is not enough, the technological parameters need to be programmed and set, and the self-learning online setting of the technological parameters cannot be carried out to achieve the optimal riveting quality.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provide an all-aluminum vehicle body riveting system based on-line process library self-learning and an implementation method thereof.
The purpose of the invention can be realized by the following technical scheme:
an all-aluminum vehicle body riveting system based on-line process library self-learning comprises:
the on-line riveting process library cloud server comprises a process database based on orthogonal test design, a data storage unit and a riveting process CAE simulation analysis platform;
the all-aluminum vehicle body collaborative riveting operation network comprises a plurality of riveting robots as network nodes, wherein the riveting robots exchange remote information with the on-line riveting process library cloud server;
the all-aluminum vehicle body riveting process detection feedback system is connected with the on-line riveting process library cloud server and comprises a plurality of visual cameras, a plurality of laser radars and a multi-sensor information fusion unit.
Preferably, the process database based on orthogonal test design is characterized in that the height of a boss of a die, the diameter of the die, the material and length of a rivet, the speed of a punch and the load of the punch are used as orthogonal factors, orthogonal tests are carried out to obtain a force-displacement change curve rule, and further optimized process parameters are obtained.
Preferably, the data stored in the data storage unit includes: and data of the whole process of riveting the all-aluminum vehicle bodies of different vehicle types on the production line, and dynamic correction data of the process database and the CAE simulation analysis platform of the riveting process.
Preferably, the riveting process CAE simulation analysis platform and the online riveting process library cloud server are formed in the process of synchronously developing the three-dimensional simulation analysis of the riveting process, and the simulation analysis result and the orthogonal test data keep consistent.
Preferably, the all-aluminum vehicle body collaborative riveting operation network adopts a node dynamic access and exit mechanism.
Preferably, the laser radar acquires laser point cloud data of a riveting position of the all-aluminum vehicle body, and guides the laser point cloud data into the CAE simulation analysis platform of the riveting process to reversely establish a three-dimensional digital analog for digital virtual analysis and evaluation.
Preferably, the all-aluminum vehicle body riveting process detection feedback system feeds back unqualified riveting process and vehicle body digital-to-analog converted by laser point cloud to the on-line riveting process library cloud server, and dynamically corrects the quality level of the riveting process library.
An implementation method of an all-aluminum vehicle body riveting system based on online process library self-learning comprises the following steps:
s1: constructing an online riveting process library cloud server by combining orthogonal test design and riveting mechanism finite element analysis;
s2: constructing an all-aluminum vehicle body cooperative riveting operation network facing a plurality of riveting robots, and completing connection and calling of access interfaces of distributed riveting robots connected with an online riveting process library cloud server;
s3: aiming at the riveting task of the all-aluminum vehicle body, obtaining self-learning process experience through a CAE simulation analysis platform of the riveting process;
s4: dynamically setting the operation mode information of the robot according to the self-learning process experience to realize the autonomous operation of the robot;
s5: and constructing an all-aluminum vehicle body riveting process detection feedback system based on a laser radar and a visual camera, and realizing automatic detection of riveting quality and feedback of operation results.
Preferably, the step of obtaining the self-learning process experience through the CAE simulation analysis platform in the riveting process in S3 includes: when new technological parameter requirements exist, the CAE simulation analysis platform of the riveting technology adopts an online technology library self-learning algorithm to obtain industrial parameters with optimized strength; and performing virtual analysis and riveting quality pre-evaluation on the riveting process through a riveting process CAE simulation analysis platform.
Preferably, the process of obtaining the strength-optimized industrial parameter by the online process library self-learning algorithm comprises the following steps:
different process parameters are simulated through a CAE simulation analysis platform of the riveting process to obtain a joint strength simulation result obtained under the process parameters, a relation of the process parameters with respect to strength is updated by a neural network method, and the strength relation is optimally designed through a multi-objective genetic algorithm to obtain the process parameters with optimized strength.
Compared with the prior art, the invention has the following advantages:
1. the intelligent all-aluminum vehicle body riveting system and the implementation method can make up for the lack of a process database and virtual analysis on riveting quality, guide a riveting robot to carry out process operation on line and carry out real-time monitoring and analysis on the riveting process, improve the intelligent level of the all-aluminum vehicle body riveting production line, and the process database cloud server comprises a self-piercing riveting process database and a CAE simulation platform.
2. The all-aluminum vehicle body collaborative operation network for the plurality of riveting robots can realize information exchange with the process library cloud server, can guide the robot to produce on line, does not need to manually set process parameters, realizes the autonomous operation of the riveting robots, and improves the automation degree of the riveting robots.
3. The all-aluminum vehicle body riveting process detection feedback system can realize automatic detection of riveting quality and feedback of operation results, can feed unqualified riveting process and vehicle body digital-to-analog converted by laser point cloud back to the process library cloud server, dynamically corrects the quality level of the riveting process library, realizes transparent riveting process and closed loop of process guidance and quality feedback control for traceability of quality defects, optimizes a new all-aluminum vehicle body riveting process, and evaluates the application benefit of a light new material.
Drawings
FIG. 1 is a schematic diagram of the system of the present invention;
FIG. 2 is a schematic flow chart of the process database for obtaining optimal process parameters according to the present invention;
FIG. 3 is a schematic diagram of the CAE simulation analysis platform according to the orthogonal test to obtain the riveting quality evaluation;
FIG. 4 is a schematic diagram of the working process of the all-aluminum vehicle body riveting process detection feedback system in the invention;
FIG. 5 is a flow chart of an online process library self-learning algorithm in an embodiment;
FIG. 6 is a schematic diagram of a stretching experiment performed on a riveted joint on a CAE simulation platform in the embodiment;
FIG. 7 is a schematic diagram illustrating a shearing experiment performed on a riveted joint on a CAE simulation platform in the embodiment;
FIG. 8 is a schematic diagram of the detection of the geometric parameters of the joint profile in the CAE simulation platform in the embodiment.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
Examples
As shown in fig. 1, the present application proposes an all-aluminum vehicle body riveting system based on-line process library self-learning, comprising: the on-line riveting process library cloud server comprises a process database based on orthogonal test design, a data storage unit and a riveting process CAE simulation analysis platform; the all-aluminum vehicle body collaborative riveting operation network comprises a plurality of riveting robots as network nodes, and the riveting robots exchange remote information with an on-line riveting process library cloud server; the all-aluminum vehicle body riveting process detection feedback system is connected with an online riveting process library cloud server and comprises a plurality of visual cameras, a plurality of laser radars and a multi-sensor information fusion unit. The system guides the production process of self-piercing riveting through the established process database, CAE three-dimensional simulation is synchronously performed with the process database, and quality detection is performed on a simulation result to achieve the purpose of optimizing riveting process parameters; the information exchange between the riveting robot and the on-line riveting process library cloud server is realized through the interface connection and calling between the riveting robot and the on-line riveting process library cloud server, and the intelligent level of the riveting robot is improved; the riveting result is monitored and improved in real time through the all-aluminum vehicle body riveting process detection feedback system, and unpredictable results caused by defects are avoided.
As shown in fig. 2, according to the requirements of the plate thickness, the plate material and the strength provided by the cloud server of the on-line riveting process library, the process database firstly inquires whether the library has optimal parameters, if not, the design of an orthogonal test scheme is carried out by taking the height of a boss of a die, the diameter of the die, the material and length of a rivet, the speed of a punch and the load of the punch as orthogonal factors, a force displacement change curve rule is obtained, and optimized process parameters are obtained through a CAE simulation platform to form the process database.
The data storage unit stores data including: the data of the whole process of riveting the all-aluminum vehicle bodies of different vehicle types on the production line, the dynamic correction data of the process database and the CAE simulation analysis platform of the riveting process.
As shown in fig. 3, the riveting process CAE simulation analysis platform and the on-line riveting process library cloud server are used for synchronously developing the three-dimensional simulation analysis of the riveting process, so as to provide reference suggestions for riveting quality evaluation and process improvement, and the simulation analysis result is consistent with the orthogonal test data.
The all-aluminum vehicle body is cooperated with the riveting operation network to realize high-dynamic, full-real-time, anti-interference and high-throughput data communication between the cloud server and the riveting robot, a node dynamic access and exit mechanism is mainly adopted, network loads are balanced, and the communication utilization rate is improved. The number of riveting robots is dynamically changed along with the number of requirements of riveting cooperative work in different time periods.
As shown in fig. 4, in the all-aluminum vehicle body riveting process detection feedback system, the laser radar is mainly used for detecting the all-aluminum vehicle body riveting quality, acquiring laser point cloud data of the all-aluminum vehicle body riveting position, importing the laser point cloud data into a riveting process CAE simulation analysis platform to reversely establish a three-dimensional digital model, and performing digital virtual analysis and evaluation. The camera is mainly used for monitoring the operation process of the robot and detecting the accuracy of the riveting position. The all-aluminum vehicle body riveting process detection feedback system can feed back unqualified riveting process and a vehicle body digital-analog converted by laser point cloud to the on-line riveting process library cloud server, dynamically modify the quality level of the riveting process library, realize transparent riveting process and quality defect traceable process guidance and quality feedback control closed loop, optimize a new all-aluminum vehicle body riveting process, and evaluate the application benefits of new material and light weight.
In this embodiment, the implementation method of the system includes:
s1: constructing an online riveting process library cloud server by combining orthogonal test design and riveting mechanism finite element analysis;
s2: constructing an all-aluminum vehicle body cooperative riveting operation network facing a plurality of riveting robots, and completing connection and calling of access interfaces of distributed riveting robots connected with an online riveting process library cloud server;
s3: aiming at the riveting task of the all-aluminum vehicle body, combining an online process library self-learning algorithm, realizing virtual analysis and riveting quality pre-evaluation in the riveting process through a riveting process CAE simulation analysis platform, and obtaining self-learning process experience;
as shown in fig. 5, in the online process library self-learning algorithm, when new process parameters are required, the CAE simulation analysis platform of the riveting process simulates different process parameters to obtain joint strength simulation results obtained under the process parameters, and stores the different process parameters and the joint strength obtained under the process parameters into a database; updating the relational expression of the process parameters about the strength by using a neural network method; optimally designing the strength relational expression through a multi-objective genetic algorithm to complete the process parameter design of strength optimization;
the virtual analysis process comprises the steps of carrying out stretching and shearing experiments on the riveted joint on a CAE simulation platform to obtain the tensile strength and the shear strength of the joint, wherein the experimental schematic diagrams are respectively shown in fig. 6 and 7; the riveting quality pre-evaluation comprises the steps of detecting the geometric parameters of the joint section, wherein the geometric parameters are regarded as qualified within a specified range, as shown in fig. 8;
s4: dynamically setting the operation mode information of the robot according to the self-learning process experience to realize the autonomous operation of the robot;
s5: and constructing an all-aluminum vehicle body riveting process detection feedback system based on a laser radar and a visual camera, and realizing automatic detection of riveting quality and feedback of operation results.
Claims (10)
1. The utility model provides an all-aluminum automobile body riveting system based on online technology storehouse is self-learning which characterized in that includes:
the on-line riveting process library cloud server comprises a process database based on orthogonal test design, a data storage unit and a riveting process CAE simulation analysis platform;
the all-aluminum vehicle body collaborative riveting operation network comprises a plurality of riveting robots as network nodes, wherein the riveting robots exchange remote information with the on-line riveting process library cloud server;
the all-aluminum vehicle body riveting process detection feedback system is connected with the on-line riveting process library cloud server and comprises a plurality of visual cameras, a plurality of laser radars and a multi-sensor information fusion unit.
2. The all-aluminum vehicle body riveting system based on-line process library self-learning of claim 1, wherein the process database based on orthogonal test design is characterized in that the die boss height, the die diameter, the rivet material and length, the punch speed and the punch load are used as orthogonal factors, and an orthogonal test is carried out to obtain a force-displacement change curve rule so as to obtain optimized process parameters.
3. The on-line process library self-learning based all-aluminum vehicle body riveting system according to claim 1, wherein the data stored by the data storage unit comprises: and data of the whole process of riveting the all-aluminum vehicle bodies of different vehicle types on the production line, and dynamic correction data of the process database and the CAE simulation analysis platform of the riveting process.
4. The all-aluminum vehicle body riveting system based on-line process library self-learning of claim 1, wherein the riveting process CAE simulation analysis platform and the on-line riveting process library cloud server are formed to synchronously develop riveting process three-dimensional simulation analysis, and simulation analysis results and orthogonal test data keep consistent.
5. The all-aluminum vehicle body riveting system based on-line process library self-learning of claim 1, wherein the all-aluminum vehicle body collaborative riveting operation network adopts a node dynamic access and exit mechanism.
6. The all-aluminum vehicle body riveting system based on-line process library self-learning of claim 1, wherein the laser radar acquires laser point cloud data of the all-aluminum vehicle body riveting position and guides the laser point cloud data into the riveting process CAE simulation analysis platform to reversely establish a three-dimensional digital model for digital virtual analysis and evaluation.
7. The all-aluminum vehicle body riveting system based on the on-line process library self-learning of claim 1, wherein the all-aluminum vehicle body riveting process detection feedback system feeds back unqualified riveting process and vehicle body digital-analog converted by laser point cloud to the on-line riveting process library cloud server to dynamically correct the quality level of the riveting process library.
8. The method for realizing the all-aluminum vehicle body riveting system based on the on-line process library self-learning of any one of claims 1 to 7 is characterized by comprising the following steps:
s1: constructing an online riveting process library cloud server by combining orthogonal test design and riveting mechanism finite element analysis;
s2: constructing an all-aluminum vehicle body cooperative riveting operation network facing a plurality of riveting robots, and completing connection and calling of access interfaces of distributed riveting robots connected with an online riveting process library cloud server;
s3: aiming at the riveting task of the all-aluminum vehicle body, obtaining self-learning process experience through a CAE simulation analysis platform of the riveting process;
s4: dynamically setting the operation mode information of the robot according to the self-learning process experience to realize the autonomous operation of the robot;
s5: and constructing an all-aluminum vehicle body riveting process detection feedback system based on a laser radar and a visual camera, and realizing automatic detection of riveting quality and feedback of operation results.
9. The method for implementing the all-aluminum vehicle body riveting system based on the online process library self-learning of claim 8, wherein the step of obtaining the self-learning process experience through the riveting process CAE simulation analysis platform in the step S3 comprises the following steps: when new technological parameter requirements exist, the CAE simulation analysis platform of the riveting technology adopts an online technology library self-learning algorithm to obtain industrial parameters with optimized strength; and performing virtual analysis and riveting quality pre-evaluation on the riveting process through a riveting process CAE simulation analysis platform.
10. The method for implementing the all-aluminum vehicle body riveting system based on the on-line process library self-learning of claim 9, wherein the process of obtaining the strength-optimized industrial parameter by the on-line process library self-learning algorithm comprises the following steps:
different process parameters are simulated through a CAE simulation analysis platform of the riveting process to obtain a joint strength simulation result obtained under the process parameters, a relation of the process parameters with respect to strength is updated by a neural network method, and the strength relation is optimally designed through a multi-objective genetic algorithm to obtain the process parameters with optimized strength.
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