CN114781098A - Method and device for determining self-piercing riveting process parameters, electronic equipment and storage medium - Google Patents
Method and device for determining self-piercing riveting process parameters, electronic equipment and storage medium Download PDFInfo
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Abstract
The embodiment of the invention relates to the technical field of self-piercing riveting, in particular to a method and a device for determining self-piercing riveting technological parameters, electronic equipment and a storage medium. Specifically, the method comprises the following steps: acquiring plate parameters of a plate to be riveted; calling a pre-trained process parameter determination model; the technical parameter determining model is obtained by training sample plate parameters and sample technical parameters; and inputting the plate parameters into a process parameter determination model, and determining the process parameters for performing self-piercing riveting on the plate to be riveted. According to the technical scheme provided by the invention, the technical parameter determination model is arranged, so that the plate parameters of the plate to be riveted can be input into the technical parameter determination model in the self-piercing riveting process to obtain the technical parameters for self-piercing riveting of the plate to be riveted, a large number of trial and error experiments can be avoided before the self-piercing riveting, and the problem that a large amount of manpower and material resources are consumed in the related technology for determining the self-piercing riveting technical parameters is solved.
Description
Technical Field
The embodiment of the invention relates to the technical field of self-piercing riveting, in particular to a method and a device for determining self-piercing riveting technological parameters, electronic equipment and a storage medium.
Background
Self-piercing riveting (namely self-piercing riveting) is a novel connecting technology, is widely applied to the fields of aviation, aerospace, automobiles, ships and the like, and has the advantages of no need of punching, drilling, heating and the like compared with the traditional threaded connection, riveting and welding. The self-piercing riveting process is approximately as follows: under the action of a punch, the semi-hollow rivet is pressed into a plate fixed on a die, so that the rivet pierces through the upper plate and does not pierce through the lower plate; under the action of the punch and the die, the legs of the semi-hollow rivet are opened to the periphery to form a firm mechanical internal lock so as to realize the connection of the upper plate and the lower plate. In the self-piercing riveting process, the pre-tightening force, the piercing force and the pressure maintaining force with different sizes can cause the defects of piercing of the lower plate, cracking of the plate, clearance of a contact area and the like. In order to ensure the quality of self-piercing riveting, the parameters of the self-piercing riveting process need to be optimized.
In the related technology, a large number of self-piercing riveting trial and error experiments are often required in the early stage of self-piercing riveting, so that better self-piercing riveting process parameters are obtained by utilizing manual practical experience to ensure the quality of self-piercing riveting. However, this approach requires a lot of trial and error experiments to re-determine the self-piercing riveting process parameters, which consumes a lot of manpower and material resources.
Disclosure of Invention
In order to solve the problem that a large amount of manpower and material resources are consumed in the related technology for determining the self-piercing riveting process parameters, the embodiment of the invention provides a method and a device for determining the self-piercing riveting process parameters, electronic equipment and a storage medium.
In a first aspect, an embodiment of the present invention provides a method for determining parameters of a self-piercing riveting process, including:
acquiring plate parameters of a plate to be riveted;
calling a pre-trained process parameter determination model; the process parameter determination model is obtained by training sample plate parameters and sample process parameters;
and inputting the plate parameters into the process parameter determination model, and determining process parameters for performing self-piercing riveting on the plate to be riveted.
In one possible design, the sheet parameters include modulus of elasticity, yield strength, elongation, hardness, reduction of area, and thickness, and the sample sheet parameters include modulus of elasticity, yield strength, elongation, hardness, reduction of area, and thickness.
In one possible design, the process parameters include punch stroke, riveting speed, piercing force, holding force, pre-tightening force, rivet model and mold model, and the sample process parameters include punch stroke, riveting speed, piercing force, holding force, pre-tightening force, rivet model and mold model.
In one possible design, the sample sheet material parameters are determined by:
acquiring experimental parameters of a self-piercing riveting experiment; the experimental parameters comprise parameters of a plate to be screened and process parameters of a sample;
and screening the parameters of the sample plate from the parameters of the plate to be screened by utilizing a gray level correlation degree analysis method to obtain the parameters of the sample plate.
In one possible design, the process parameter determination model is determined by:
constructing a back propagation neural network; the back propagation neural network comprises an input layer, a hidden layer and an output layer, the number of neurons of the input layer is the same as that of sample plate parameters, and the number of neurons of the output layer is the same as that of sample process parameters;
optimizing the back propagation neural network;
and training the optimized back propagation neural network by using the known sample plate parameters and the known sample process parameters to obtain a process parameter determination model.
In one possible design, the optimizing the back propagation neural network includes:
and optimizing the back propagation neural network by utilizing a learning rate adjustment strategy with hot restart and a Dropout strategy.
In one possible design, the pre-trained process parameter determination model is deployed in the cloud.
In a second aspect, an embodiment of the present invention further provides a device for determining parameters of a self-piercing riveting process, including:
the acquisition module is used for acquiring plate parameters of a plate to be riveted;
the calling module is used for calling a pre-trained process parameter determination model; the process parameter determination model is obtained by training sample plate parameters and sample process parameters;
and the determining module is used for inputting the plate parameters into the process parameter determining model and determining the process parameters for performing self-piercing riveting on the plate to be riveted.
In a third aspect, an embodiment of the present invention further provides an electronic device, which includes a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the processor implements the method described in any embodiment of this specification.
In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed in a computer, the computer program causes the computer to execute the method described in any embodiment of the present specification.
The embodiment of the invention provides a method and a device for determining self-piercing riveting process parameters, electronic equipment and a storage medium, wherein a process parameter determination model is set, so that in the self-piercing riveting process, plate parameters of a plate to be riveted can be input into the process parameter determination model to obtain process parameters for self-piercing riveting of the plate to be riveted, a large number of trial and error experiments can be avoided before self-piercing riveting, and the problem that a large number of manpower and material resources are consumed in the related technology for determining the self-piercing riveting process parameters is solved.
Drawings
In order to more clearly illustrate the embodiments or technical solutions of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the description below are some embodiments of the present invention, and it is also possible for those skilled in the art to obtain other drawings based on the drawings without creative efforts.
Fig. 1 is a flowchart of a method for determining parameters of a self-piercing riveting process according to an embodiment of the present invention;
fig. 2 is a hardware architecture diagram of an electronic device according to an embodiment of the present invention;
fig. 3 is a structural diagram of an apparatus for determining parameters of a self-piercing riveting process according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer and more complete, the technical solutions in the embodiments of the present invention will be described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention, and based on the embodiments of the present invention, all other embodiments obtained by a person of ordinary skill in the art without creative efforts belong to the scope of the present invention.
Referring to fig. 1, an embodiment of the present invention provides a method for determining parameters of a self-piercing riveting process, where the method includes:
step 100: acquiring plate parameters of a plate to be riveted;
step 102: calling a pre-trained process parameter determination model; the technical parameter determining model is obtained by training sample plate parameters and sample technical parameters;
step 104: and inputting the plate parameters into a process parameter determination model, and determining process parameters for performing self-piercing riveting on the plate to be riveted.
In the embodiment of the invention, by setting the process parameter determination model, the plate parameters of the plate to be riveted can be input into the process parameter determination model in the self-piercing riveting process to obtain the process parameters for self-piercing riveting of the plate to be riveted, so that a large number of trial and error experiments can be avoided before the self-piercing riveting, and the problem that a large amount of manpower and material resources are consumed in the related technology for determining the self-piercing riveting process parameters is solved.
The manner in which the various steps shown in fig. 1 are performed is described below.
With respect to step 100:
the most essential difference of different materials is the plate parameters (or mechanical properties) of the materials, and the difference of the plate parameters is the main reason for the change of the materials after being stressed. During the self-piercing riveting process, the material may deform due to external forces such as piercing and shearing, and specifically, the sheet material parameters may include elastic modulus, yield strength, deformation strengthening, elongation, hardness, reduction of area, and thickness.
Through analysis of the plate parameters by a grey correlation analysis method, the inventor finds that: the deformation strengthening does not influence the quality of riveting forming, so the plate parameter of the deformation strengthening can be removed from the plate parameters. Thus, sheet parameters include modulus of elasticity, yield strength, elongation, hardness, reduction of area, and thickness.
With respect to step 102:
from the analysis for step 100, sample sheet parameters include modulus of elasticity, yield strength, elongation, hardness, reduction of area, and thickness.
Specifically, the sample sheet material parameters were determined as follows:
acquiring experimental parameters of a self-piercing riveting experiment; the experimental parameters comprise parameters of the plate to be screened and technological parameters of a sample;
and screening the parameters of the sample plate from the parameters of the plate to be screened by utilizing a gray level correlation degree analysis method.
For example, the parameters of the plate to be screened may include elastic modulus, yield strength, deformation strengthening, elongation, hardness, reduction of area and thickness, and the parameters of the sample plate are screened from the parameters of the plate to be screened by using a gray level correlation analysis method, that is, the parameters of the sample plate include elastic modulus, yield strength, elongation, hardness, reduction of area and thickness.
After the experimental parameters are obtained, the working personnel can classify the experimental parameters to respectively form parameters of the plate to be screened and process parameters of the sample, so that the subsequent model training according to the experimental parameters is facilitated.
In some embodiments, the sample process parameters include punch travel, rivet speed, piercing force, holding force, pretension, rivet type, mold type.
The sample process parameters all affect the quality of the self-piercing riveting, so the sample process parameters are used as the output parameters of the process parameter determination model.
In some embodiments, the process parameter determination model is determined by:
constructing a back propagation neural network; the back propagation neural network comprises an input layer, a hidden layer and an output layer, the number of neurons of the input layer is the same as that of parameters of a sample plate, and the number of neurons of the output layer is the same as that of parameters of a sample process;
optimizing a back propagation neural network;
and training the optimized back propagation neural network by using the known sample plate parameters and the known sample process parameters to obtain a process parameter determination model.
In the embodiment, the process parameter determination model is obtained by training through the back propagation neural network, so that the process parameter determination model can have a high model recognition rate.
In some embodiments, the back propagation neural network is optimized, comprising:
and optimizing the back propagation neural network by using a learning rate adjustment strategy with hot restart and a Dropout strategy.
It should be noted that it is difficult to select a reasonable learning rate, and if the learning rate is too small, the convergence rate of the model is slow. If the learning rate is too large, the weight of the model is initialized randomly, and if a larger learning rate is selected, instability (oscillation) of the model may be brought about, which may prevent convergence, i.e., oscillation around the extreme point. And the self-adaptive adjustment of the learning rate can be realized by using the attenuation strategy of the learning rate with the hot restart cosine, and the learning rate is initialized to a certain value and then gradually reduced according to cosine annealing when restarting, the optimization of the hot restart is not started from the beginning, but the optimization of the next stage is started from the parameter of model convergence in the last step, so that the local optimal solution can be effectively skipped, and the self-adaptive adjustment of the learning rate is realized. While using the Dropout strategy (randomly inactivating neurons), the effect of over-fitting the model can be prevented.
Furthermore, the inventors found that: because all materials cannot be tested to obtain all data, the test can be carried out only according to a certain amount of data, and the neural network prediction accuracy can be further improved by using a limited sample space through combining a learning rate adjustment strategy with hot restart and a Dropout strategy. That is to say, by combining the learning rate adjustment strategy with hot restart with the Dropout strategy, the problems of sparsity and abnormal data in data characteristics can be well solved, the model is ensured to be updated in the correct direction each time, the model iteration process is more stable, and the convergence speed is faster. After the model is trained, more accurate predicted values can be obtained by inputting untrained data into the neural network for prediction (namely, the model has better robustness and stronger universality).
In some embodiments, training the back propagation neural network until the loss value reaches a set convergence threshold value, and testing the back propagation neural network, wherein the prediction error is not more than 5%; and if the error exceeds 5%, adjusting the hidden layer of the back propagation neural network. The adjusting method comprises the steps of adjusting the number of neurons in a single hidden layer and the number of layers of the hidden layer, and retraining and testing after each adjustment.
In some embodiments, the pre-trained process parameter determination model is deployed in the cloud.
In the embodiment, the trained process parameter determination model which is determined by the test requirements is deployed to the cloud, so that data sharing with all terminals is realized. Particularly, when other terminals are required to perform self-piercing riveting, the terminals can communicate with the cloud in a wireless mode, after relevant material attributes are input by the input device, the cloud can output more accurate process parameters for self-piercing riveting under the complex combination conditions of different materials, different thicknesses and the like, and the cloud can feed back the optimal process parameters such as pretightening force, holding force, piercing force and the like for self-piercing riveting operation.
As shown in fig. 2 and fig. 3, an embodiment of the present invention provides a device for determining parameters of a self-piercing riveting process. The device embodiments may be implemented by software, or by hardware, or by a combination of hardware and software. From a hardware aspect, as shown in fig. 2, for a hardware architecture diagram of an electronic device where a device for determining self-piercing-riveting process parameters provided in an embodiment of the present invention is located, in addition to the processor, the memory, the network interface, and the nonvolatile memory shown in fig. 2, the electronic device where the device is located in the embodiment may generally include other hardware, such as a forwarding chip responsible for processing a message, and the like. Taking a software implementation as an example, as shown in fig. 3, as a logically meaningful device, the device is formed by reading a corresponding computer program in a nonvolatile memory into a memory by a CPU of an electronic device where the device is located and running the computer program.
As shown in fig. 3, the device for determining parameters of a self-piercing riveting process provided in this embodiment includes:
the acquisition module 300 is used for acquiring the plate parameters of the plate to be riveted;
a calling module 302, configured to call a pre-trained process parameter determination model; the technical parameter determining model is obtained by training sample plate parameters and sample technical parameters;
the determining module 304 is configured to input the plate parameters into the process parameter determining model, and determine the process parameters for performing self-piercing riveting on the plate to be riveted.
In an embodiment of the present invention, the obtaining module 300 may be configured to perform step 100 in the foregoing method embodiment, the calling module 302 may be configured to perform step 102 in the foregoing method embodiment, and the determining module 304 may be configured to perform step 104 in the foregoing method embodiment.
In one embodiment of the invention, the sheet parameters include modulus of elasticity, yield strength, elongation, hardness, reduction of area, and thickness, and the sample sheet parameters include modulus of elasticity, yield strength, elongation, hardness, reduction of area, and thickness.
In one embodiment of the invention, the process parameters comprise punch stroke, riveting speed, piercing force, pressure protection force, pre-tightening force, rivet model and mould model, and the sample process parameters comprise punch stroke, riveting speed, piercing force, pressure protection force, pre-tightening force, rivet model and mould model.
In one embodiment of the invention, the sample sheet material parameters are determined by:
acquiring experimental parameters of a self-piercing riveting experiment; the experimental parameters comprise parameters of the plate to be screened and technological parameters of a sample;
and screening the parameters of the sample plate from the parameters of the plate to be screened by utilizing a gray level correlation degree analysis method.
In one embodiment of the invention, the process parameter determination model is determined by:
constructing a back propagation neural network; the back propagation neural network comprises an input layer, a hidden layer and an output layer, the number of neurons of the input layer is the same as that of parameters of a sample plate, and the number of neurons of the output layer is the same as that of parameters of a sample process;
optimizing a back propagation neural network;
and training the optimized back propagation neural network by using the known sample plate parameters and the known sample process parameters to obtain a process parameter determination model.
In one embodiment of the invention, the back propagation neural network is optimized using a learning rate adjustment strategy with warm restart and a Dropout strategy.
In one embodiment of the invention, the pre-trained process parameter determination model is deployed in the cloud.
It is to be understood that the exemplary structure of the embodiment of the present invention does not constitute a specific limitation to the apparatus for determining the parameters of the self-piercing riveting process. In other embodiments of the invention, a device for determining parameters of a self-piercing riveting process can comprise more or fewer components than those shown, or certain components can be combined, or certain components can be separated, or different component arrangements can be adopted. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.
Because the content of information interaction, execution process, and the like among the modules in the device is based on the same concept as the method embodiment of the present invention, specific content can be referred to the description in the method embodiment of the present invention, and is not described herein again.
The embodiment of the invention also provides electronic equipment which comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method for determining the self-piercing riveting process parameters in any embodiment of the invention is realized.
The embodiment of the invention also provides a computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the processor is enabled to execute a method for determining the self-piercing rivet process parameter in any embodiment of the invention.
Specifically, a system or an apparatus equipped with a storage medium on which software program codes that realize the functions of any of the above-described embodiments are stored may be provided, and a computer (or a CPU or MPU) of the system or the apparatus is caused to read out and execute the program codes stored in the storage medium.
In this case, the program code itself read from the storage medium can realize the functions of any of the above-described embodiments, and thus the program code and the storage medium storing the program code constitute a part of the present invention.
Examples of the storage medium for supplying the program code include a floppy disk, a hard disk, a magneto-optical disk, an optical disk (e.g., CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD + RW), a magnetic tape, a nonvolatile memory card, and a ROM. Alternatively, the program code may be downloaded from a server computer by a communications network.
Further, it should be clear that the functions of any one of the above-described embodiments may be implemented not only by executing the program code read out by the computer, but also by causing an operating system or the like operating on the computer to perform a part or all of the actual operations based on instructions of the program code.
Further, it is to be understood that the program code read out from the storage medium is written to a memory provided in an expansion board inserted into the computer or to a memory provided in an expansion module connected to the computer, and then a CPU or the like mounted on the expansion board or the expansion module is caused to perform part or all of the actual operations based on instructions of the program code, thereby realizing the functions of any of the embodiments described above.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an …" does not exclude the presence of other similar elements in a process, method, article, or apparatus that comprises the element.
Those of ordinary skill in the art will understand that: all or part of the steps for realizing the method embodiments can be completed by hardware related to program instructions, the program can be stored in a computer readable storage medium, and the program executes the steps comprising the method embodiments when executed; and the aforementioned storage medium includes: ROM, RAM, magnetic or optical disks, etc. that can store program codes.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A method for determining self-piercing riveting process parameters is characterized by comprising the following steps:
acquiring plate parameters of a plate to be riveted;
calling a pre-trained process parameter determination model; the process parameter determination model is obtained by training sample plate parameters and sample process parameters;
and inputting the plate parameters into the process parameter determination model, and determining the process parameters for performing self-piercing riveting on the plate to be riveted.
2. The method of claim 1, wherein the sheet parameters include modulus of elasticity, yield strength, elongation, hardness, reduction of area, and thickness, and wherein the sample sheet parameters include modulus of elasticity, yield strength, elongation, hardness, reduction of area, and thickness.
3. The method of claim 1, wherein the process parameters comprise punch stroke, riveting speed, piercing force, holding pressure, pre-tightening force, rivet model, mold model, and the sample process parameters comprise punch stroke, riveting speed, piercing force, holding pressure, pre-tightening force, rivet model, mold model.
4. The method of claim 2, wherein the sample sheet material parameter is determined by:
acquiring experimental parameters of a self-piercing riveting experiment; the experimental parameters comprise parameters of a plate to be screened and process parameters of a sample;
and screening the parameters of the sample plate from the parameters of the plate to be screened by utilizing a gray level correlation degree analysis method to obtain the parameters of the sample plate.
5. The method of claim 1, wherein the process parameter determination model is determined by:
constructing a back propagation neural network; the back propagation neural network comprises an input layer, a hidden layer and an output layer, the number of neurons of the input layer is the same as that of the sample plate parameters, and the number of neurons of the output layer is the same as that of the sample process parameters;
optimizing the back propagation neural network;
and training the optimized back propagation neural network by using the known sample plate parameters and the known sample process parameters to obtain a process parameter determination model.
6. The method of claim 5, wherein the optimizing the back propagation neural network comprises:
and optimizing the back propagation neural network by utilizing a learning rate adjustment strategy with hot restart and a Dropout strategy.
7. The method of any one of claims 1-6, wherein the pre-trained process parameter determination model is deployed in the cloud.
8. A device for determining parameters of a self-piercing riveting process is characterized by comprising the following steps:
the acquisition module is used for acquiring plate parameters of a plate to be riveted;
the calling module is used for calling a pre-trained process parameter determination model; the process parameter determination model is obtained by training sample plate parameters and sample process parameters;
and the determining module is used for inputting the plate parameters into the process parameter determining model and determining the process parameters for performing self-piercing riveting on the plate to be riveted.
9. An electronic device comprising a memory having stored therein a computer program and a processor that, when executing the computer program, implements the method of any of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored which, when executed in a computer, causes the computer to carry out the method of any one of claims 1-7.
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| JINLIANG WU等: "Recent development of the novel riveting processes", 《THE INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY》 * |
| 刘洋等: "基于灰色理论和神经网络的自冲铆接头力学性能预测", 《塑性工程学报》 * |
| 吴丹等: "汽车用钢铝异种材料的自冲铆接工艺智能优化", 《锻压技术》 * |
| 张先炼等: "钛合金薄板自冲铆接工艺及失效行为研究", 《机械工程学报》 * |
| 杜爱民等: "自冲铆接工艺参数对接头性能影响的研究", 《新技术新工艺》 * |
| 靳文豪等: "酸性腐蚀环境下铝合金自冲铆接头的静力学性能研究", 《塑性工程学报》 * |
| 鲁墨武等: "飞机蒙皮锤铆参数优化方法研究", 《机械工程与自动化》 * |
| 龙江启等: "基于神经网络无铆钉自冲铆接头力学性能预测", 《计算机集成制造系统》 * |
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| Publication number | Publication date |
|---|---|
| CN114491849A (en) | 2022-05-13 |
| CN114781098B (en) | 2022-10-18 |
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