CN111199072B - 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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- 238000000034 method Methods 0.000 title claims abstract description 191
- 230000008569 process Effects 0.000 title claims abstract description 173
- 229910052782 aluminium Inorganic materials 0.000 title claims abstract description 55
- 238000004088 simulation Methods 0.000 claims abstract description 44
- 238000004458 analytical method Methods 0.000 claims abstract description 35
- 238000001514 detection method Methods 0.000 claims abstract description 20
- 238000012360 testing method Methods 0.000 claims abstract description 16
- 238000013461 design Methods 0.000 claims abstract description 12
- 239000000463 material Substances 0.000 claims abstract description 12
- XAGFODPZIPBFFR-UHFFFAOYSA-N aluminium Chemical compound [Al] XAGFODPZIPBFFR-UHFFFAOYSA-N 0.000 claims abstract description 10
- 238000004519 manufacturing process Methods 0.000 claims abstract description 9
- 238000013500 data storage Methods 0.000 claims abstract description 7
- 230000000007 visual effect Effects 0.000 claims abstract description 7
- 230000004927 fusion Effects 0.000 claims abstract description 4
- 238000011156 evaluation Methods 0.000 claims description 7
- 230000007246 mechanism Effects 0.000 claims description 6
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- 238000013528 artificial neural network Methods 0.000 claims description 3
- 230000008859 change Effects 0.000 claims description 3
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- 238000006073 displacement reaction Methods 0.000 claims description 3
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- 238000011112 process operation Methods 0.000 abstract description 2
- 230000007547 defect Effects 0.000 description 9
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- 238000005516 engineering process Methods 0.000 description 5
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- 238000004891 communication Methods 0.000 description 2
- 238000002474 experimental method Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 229910000838 Al alloy Inorganic materials 0.000 description 1
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- 229910000831 Steel Inorganic materials 0.000 description 1
- 238000004026 adhesive bonding Methods 0.000 description 1
- 230000003796 beauty Effects 0.000 description 1
- 239000011248 coating agent Substances 0.000 description 1
- 238000000576 coating method Methods 0.000 description 1
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- 239000002131 composite material Substances 0.000 description 1
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Abstract
The invention relates to an all-aluminum vehicle body riveting system based on online process library self-learning and an implementation method thereof, wherein the system comprises the following steps: the online 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 cooperative riveting operation network comprises a plurality of riveting robots serving as network nodes, wherein the riveting robots exchange remote information with an online riveting process library cloud server; the detection feedback system for the riveting process of the aluminum car body is connected with the cloud server of the online riveting process library 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 on line to perform process operation and monitors and analyzes the riveting process in real time, improves the intelligent level of the whole aluminum vehicle body riveting production line, and accelerates the application of new materials and new processes 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 online process library self-learning and an implementation method thereof.
Background
With the development demands of electric vehicles and vehicles for light weight, intelligence and high performance, in order to improve battery endurance mileage, reduce vehicle fuel consumption rate, improve environment and demands on safety, reliability, comfort, beauty and the like of vehicles, aluminum alloy composite materials have been increasingly used in foreign and domestic vehicle industries. Currently, most cost-effective lightweight solutions can be realized by the design of a variety of materials. This limits conventional thermal bonding techniques and represents a need for cost-effective mechanical and adhesive bonding techniques. The self-piercing riveting technology has the advantages of unique cold extrusion deformation technology, low cost, high automation rate, high mechanical property and the like, and can play a role in the automobile industry.
The self-piercing riveting technology is a cold connecting technology, and can fix two or more than two materials, including steel, aluminum, nylon webbing, plastics and rubber. It also allows the attachment of galvanized or precoating materials without damaging the coating. Compared to other joining methods, self-piercing riveting has a number of advantages: no pre-drilling or surface treatment is required; the device is quiet, is easy to automate, and does not generate 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-piercing riveting technology has better adaptability, and the process is more suitable for robot application.
At present, domestic research on self-piercing riveting is focused on research on joint performance by technological parameters, and an autonomous all-aluminum vehicle body intelligent riveting method and system in China do not exist, because a technological database of the system is lack to guide the riveting process, and unpredictable defects are caused. The self-piercing riveting defects have a plurality of forms, and the defects cannot be intelligently detected, evaluated and improved after the defects appear. Meanwhile, the self-piercing riveting robot has insufficient intelligent level, technological parameters are required to be programmed and set, and self-learning on-line setting of the technological parameters cannot be performed to achieve optimal riveting quality.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides an all-aluminum vehicle body riveting system based on online process library self-learning and an implementation method thereof.
The aim of the invention can be achieved by the following technical scheme:
an all-aluminum vehicle body riveting system based on online process library self-learning, comprising:
the online 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 cooperative riveting operation network comprises a plurality of riveting robots serving as network nodes, wherein the riveting robots exchange remote information with the online riveting process library cloud server;
the all-aluminum vehicle body riveting process detection feedback system is connected with the 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.
Preferably, the process database based on orthogonal test design uses the height of the die boss, the diameter of the die, the rivet material and length, the punch speed and the punch load as orthogonal factors to perform orthogonal test to obtain a force displacement change curve rule, so as to obtain optimized process parameters.
Preferably, the data stored in the data storage unit includes: and the whole riveting process data of all-aluminum vehicle bodies of different vehicle types on the production line, and the process database and the dynamic correction data of the CAE simulation analysis platform of the riveting process.
Preferably, the CAE simulation analysis platform of the riveting process and the online riveting process library cloud server form a process to synchronously carry out three-dimensional simulation analysis of the riveting process, and the simulation analysis result is consistent with the orthogonal test data.
Preferably, the all-aluminum vehicle body cooperative riveting operation network adopts a node dynamic access and exit mechanism.
Preferably, the laser radar acquires laser point cloud data of the riveting position of the all-aluminum vehicle body, and the laser point cloud data is imported into the CAE simulation analysis platform of the riveting process to reversely establish a three-dimensional model for digital virtual analysis and evaluation.
Preferably, the detection feedback system for the riveting process of the all-aluminum vehicle body feeds back unqualified riveting process and vehicle body digital-analog converted by laser point cloud to the cloud server of the online riveting process library, 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 for a plurality of riveting robots to finish the connection and the call of an access interface of the distributed riveting robots to the online riveting process library cloud server;
s3: the self-learning process experience is obtained through a riveting process CAE simulation analysis platform facing the riveting task of the all-aluminum vehicle body;
s4: according to self-learning process experience, the operation mode information of the robot is dynamically set, and the autonomous operation of the robot is realized;
s5: and a detection feedback system of the riveting process of the all-aluminum vehicle body based on the laser radar and the visual camera is constructed, so that automatic detection of riveting quality and feedback of operation results are realized.
Preferably, the step of obtaining the self-learning process experience through the riveting process CAE simulation analysis platform in step S3 includes: when a new technological parameter requirement exists, the riveting process CAE simulation analysis platform adopts an online process library self-learning algorithm to obtain industrial parameters with optimized strength; and carrying out virtual analysis and pre-evaluation of riveting quality in the riveting process by using a CAE simulation analysis platform of the riveting process.
Preferably, the process of obtaining the industrial parameters with optimized strength by the on-line process library self-learning algorithm comprises the following steps:
and simulating different process parameters through a CAE simulation analysis platform of the riveting process to obtain joint strength simulation results obtained under the process parameters, updating a relation of the process parameters with respect to strength by using a neural network method, and optimally designing the strength relation through a multi-objective genetic algorithm to obtain the strength-optimized process parameters.
Compared with the prior art, the invention has the following advantages:
1. the intelligent riveting system and the implementation method for the all-aluminum vehicle body based on the online process library self-learning can make up for the lack of a process database and virtual analysis of riveting quality, guide a riveting robot on line to perform process operation and monitor and analyze the riveting process in real time, improve the intelligent level of the all-aluminum vehicle body riveting production line, enable a process library cloud server to comprise a self-punching riveting process database and a CAE simulation platform, enable the process database to provide basis for guiding the riveting process production process and selecting riveting process parameters, enable the CAE simulation platform to perform virtual analysis and prediction on the riveting quality on line, and reduce the risk of generating riveting defects.
2. The all-aluminum vehicle body collaborative operation network for a plurality of riveting robots can realize information exchange with a process library cloud server, can guide the production of the robots 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 detection feedback system for the riveting process of the all-aluminum vehicle body can realize automatic detection of riveting quality and feedback of operation results, can feed back unqualified riveting process and digital-analog converted vehicle body of laser point cloud to a process library cloud server, dynamically corrects the quality level of a riveting process library, realizes process guidance and quality feedback control closed loop with transparency and traceable quality defects of the riveting process, optimizes a new riveting process of the all-aluminum vehicle body, and evaluates the application benefit of light new materials.
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 obtaining optimal process parameters in the present invention;
FIG. 3 is a schematic diagram of a CAE simulation analysis platform according to the invention for evaluating riveting quality according to an orthogonal test;
FIG. 4 is a schematic diagram of the operation process of the detection feedback system for the riveting process of the aluminum car body;
FIG. 5 is a flowchart of an on-line process library self-learning algorithm in an embodiment;
FIG. 6 is a schematic diagram of a tensile test performed on a riveted joint at a CAE simulation platform in an embodiment;
FIG. 7 is a schematic diagram of a shearing experiment performed on a riveted joint at a CAE simulation platform in an embodiment;
FIG. 8 is a schematic diagram illustrating the detection of geometric parameters of the interface section of the CAE simulation platform according to an embodiment.
Detailed Description
The invention will now be described in detail with reference to the drawings and specific examples. The present embodiment is implemented on the premise of the technical scheme of the present invention, and a detailed implementation manner and a specific operation process are given, but the protection scope of the present invention is not limited to the following examples.
Examples
As shown in fig. 1, the present application proposes an all-aluminum vehicle body riveting system based on online process library self-learning, comprising: the online 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 cooperative riveting operation network comprises a plurality of riveting robots serving as network nodes, wherein the riveting robots exchange remote information with an online riveting process library cloud server; the detection feedback system for the riveting process of the aluminum car body is connected with the cloud server of the online riveting process library 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, and synchronously carries out CAE three-dimensional simulation with the process database, and carries out quality detection on simulation results so as to achieve the aim of optimizing riveting process parameters; the information exchange between the riveting robot and the online riveting process library cloud server is realized through the interface connection and the call between the riveting robot and the online riveting process library cloud server, so that the intelligent level of the riveting robot is improved; the riveting result is monitored and improved in real time through the detection feedback system in the riveting process of the aluminum car body, and unpredictable results caused by defects are avoided.
As shown in fig. 2, according to the requirements of the thickness, the material and the strength of the plate material provided by the cloud server of the online riveting process library, the process database firstly inquires whether the optimal parameters exist in the library, if not, the orthogonal test scheme design is carried out by taking the height of the die boss, the diameter of the die, the material and the length of the rivet, the speed of the punch and the load of the punch as orthogonal factors, the force displacement change curve rule is obtained, and the optimal process parameters are obtained through the CAE simulation platform, so that the process database is formed.
The data stored in the data storage unit includes: all-aluminum car body riveting whole process data of different car types on the production line, and dynamic correction data of a process database and a riveting process CAE simulation analysis platform.
As shown in fig. 3, the riveting process CAE simulation analysis platform and the online riveting process library cloud server form a process to synchronously develop three-dimensional simulation analysis of the riveting process, provide reference suggestions for riveting quality evaluation and process improvement, and keep the consistency of simulation analysis results and orthogonal test data.
The full-aluminum vehicle body collaborative riveting operation network realizes high-dynamic, full-real-time, anti-interference and high-throughput data communication between the cloud server and the riveting robot, and mainly adopts a node dynamic access and exit mechanism to balance network load and improve communication utilization rate. The number of the riveting robots is dynamically changed along with the number of the riveting collaborative operation demands at different time periods.
As shown in fig. 4, in the detection feedback system of the riveting process of the aluminum vehicle body, the laser radar is mainly used for detecting the riveting quality of the aluminum vehicle body, acquiring laser point cloud data of the riveting position of the aluminum vehicle body, and importing the laser point cloud data into the CAE simulation analysis platform of the riveting process to reversely establish a three-dimensional model for 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 detection feedback system for the riveting process of the all-aluminum vehicle body can feed back unqualified riveting process and vehicle body digital-analog converted by laser point cloud to the cloud server of the online riveting process library, dynamically correct the quality level of the riveting process library, realize the process guidance and quality feedback control closed loop with transparency and traceable quality defects in the riveting process, optimize the novel riveting process of the all-aluminum vehicle body, and evaluate the application benefit of light weight of new materials.
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 for a plurality of riveting robots to finish the connection and the call of an access interface of the distributed riveting robots to the online riveting process library cloud server;
s3: aiming at the riveting task of the all-aluminum vehicle body, combining on-line process library self-learning Xi Suanfa, realizing virtual analysis and riveting quality pre-evaluation of the riveting process through a riveting process CAE simulation analysis platform, and obtaining self-learning process experience;
as shown in fig. 5, the online process library self-learning algorithm simulates different process parameters by the riveting process CAE simulation analysis platform when new process parameter requirements exist, so as to obtain joint strength simulation results obtained under the process parameters, and store the different process parameters and the joint strength obtained under the process parameters into a database; updating a relation of the process parameters with respect to the intensity by using a neural network method; optimally designing the strength relation by a multi-objective genetic algorithm to finish the process parameter design of strength optimization;
the virtual analysis process comprises the steps of carrying out tensile and shearing experiments on the riveted joint on a CAE simulation platform to obtain the tensile strength and the shearing strength of the joint, wherein the experimental schematic diagrams are shown in fig. 6 and 7 respectively; the pre-evaluation of riveting quality comprises the steps of detecting geometric parameters of the joint profile, wherein the geometric parameters are considered to be qualified in a specified range, as shown in fig. 8;
s4: according to self-learning process experience, the operation mode information of the robot is dynamically set, and the autonomous operation of the robot is realized;
s5: and a detection feedback system of the riveting process of the all-aluminum vehicle body based on the laser radar and the visual camera is constructed, so that automatic detection of riveting quality and feedback of operation results are realized.
Claims (5)
1. All-aluminum vehicle body riveting system based on online process library self-learning is characterized by comprising:
the online 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 cooperative riveting operation network comprises a plurality of riveting robots serving as network nodes, wherein the riveting robots exchange remote information with the online riveting process library cloud server;
the all-aluminum vehicle body riveting process detection feedback system is connected with the 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 process database based on orthogonal test design takes the height of a die boss, the diameter of a die, the rivet material and length, the speed of a punch and the load of the punch as orthogonal factors, and carries out orthogonal test to obtain a force displacement change curve rule so as to obtain optimized process parameters;
the data stored in the data storage unit includes: all-aluminum car body riveting whole process data of different car types on a production line, wherein the process database and dynamic correction data of a riveting process CAE simulation analysis platform;
the CAE simulation analysis platform of the riveting process and the online riveting process library cloud server form a process to synchronously carry out three-dimensional simulation analysis of the riveting process, and the simulation analysis result is consistent with orthogonal test data;
the laser radar acquires laser point cloud data of the riveting position of the aluminum vehicle body and guides the laser point cloud data to the CAE simulation analysis platform of the riveting process to reversely establish a three-dimensional model for digital virtual analysis and evaluation;
and 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 online riveting process library cloud server, and dynamically corrects the quality level of the riveting process library.
2. The online process library self-learning based all-aluminum vehicle body riveting system as claimed in claim 1, wherein the all-aluminum vehicle body cooperative riveting operation network adopts a node dynamic access and exit mechanism.
3. A method of implementing an all-aluminum body riveting system based on-line process library self-learning as claimed in claim 1 or 2, comprising:
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 for a plurality of riveting robots to finish the connection and the call of an access interface of the distributed riveting robots to the online riveting process library cloud server;
s3: the self-learning process experience is obtained through a riveting process CAE simulation analysis platform facing the riveting task of the all-aluminum vehicle body;
s4: according to self-learning process experience, the operation mode information of the robot is dynamically set, and the autonomous operation of the robot is realized;
s5: and a detection feedback system of the riveting process of the all-aluminum vehicle body based on the laser radar and the visual camera is constructed, so that automatic detection of riveting quality and feedback of operation results are realized.
4. The method for implementing the all-aluminum vehicle body riveting system based on the online process library self-learning according to claim 3, wherein the process of obtaining the self-learning process experience through the riveting process CAE simulation analysis platform in S3 comprises the following steps: when a new technological parameter requirement exists, the riveting process CAE simulation analysis platform adopts an online process library self-learning algorithm to obtain industrial parameters with optimized strength; and carrying out virtual analysis and pre-evaluation of riveting quality in the riveting process by using a CAE simulation analysis platform of the riveting process.
5. The method for realizing the all-aluminum vehicle body riveting system based on the online process library self-learning according to claim 4, wherein the process for obtaining the industrial parameters with optimized strength by the online process library self-learning algorithm comprises the following steps:
and simulating different process parameters through a CAE simulation analysis platform of the riveting process to obtain joint strength simulation results obtained under the process parameters, updating a relation of the process parameters with respect to strength by using a neural network method, and optimally designing the strength relation through a multi-objective genetic algorithm to obtain the strength-optimized process parameters.
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| CN112015149A (en) * | 2020-07-28 | 2020-12-01 | 安徽巨一科技股份有限公司 | Lightweight vehicle body connection quality auxiliary judgment method |
| CN112683774B (en) * | 2020-12-03 | 2021-12-14 | 中国科学技术大学 | Self-piercing riveting quality detection device and method for new energy automobile body |
| CN113514500B (en) * | 2021-05-26 | 2022-06-14 | 天津理工大学 | Automatic online detection system and detection method for riveting quality of automobile body |
| CN114491849A (en) * | 2022-01-24 | 2022-05-13 | 深圳职业技术学院 | Method and device for determining self-piercing riveting process parameters, electronic equipment and storage medium |
| CN115719011A (en) * | 2023-01-03 | 2023-02-28 | 苏州数算软云科技有限公司 | Simulation method, device, equipment and readable storage medium |
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