A big data self-piercing riveting joint key geometrical parameter prediction system and method

A technology of geometric parameters and self-piercing riveting, applied in geometric CAD, neural learning methods, special data processing applications, etc., can solve the problems of large detection error, poor effect of thin joints, and inability to judge the internal deformation characteristics of self-piercing riveting joints, etc. To achieve the effect of solving low cost

Pending Publication Date: 2020-12-18
CHINALCO MATERIALS APPL RES INST CO LTD
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Problems solved by technology

[0005] In view of the fact that the existing technology cannot judge the internal deformation characteristics of the self-piercing riveting joint, only whether the self-piercing riveting joint is defective, and the detection effect of the thinner joint is extremely poor, the detection process is extremely dependent on operation skills, and the detection error is relatively large. Provides a big data prediction system for key geometric parameters of self-piercing riveting joints, including: data call module, data input module and data calculation module;

Method used

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  • A big data self-piercing riveting joint key geometrical parameter prediction system and method
  • A big data self-piercing riveting joint key geometrical parameter prediction system and method
  • A big data self-piercing riveting joint key geometrical parameter prediction system and method

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Embodiment 1

[0063] combine figure 1 and figure 2 , the present invention provides a large data prediction system for key geometric parameters of self-piercing riveting joints, including: a data call module, a data input module and a data calculation module;

[0064] Data calling module: according to the type of joint mold to be tested, determine the pre-trained network model corresponding to the mold type;

[0065] Data input module: input the process parameters of the joint to be tested to the pre-trained network model to obtain the opening value of the nail foot and the self-locking value of the nail foot of the joint to be tested;

[0066] Data calculation module: calculate the key geometric parameters of the joint to be tested according to the opening value of the nail foot of the joint to be tested;

[0067] The key geometric parameters include the height of the nail head and the bottom thickness of the joint;

[0068] The pre-trained network model is obtained by training the his...

Embodiment 2

[0099] combine figure 2 , a system and method for predicting key geometric parameters of self-piercing riveting joints based on big data. The system includes a material database, a training sample database, a data call module, a data input module, a data calculation module, and a result display module.

[0100] Material database includes material alloy grade, thickness, hardness, yield strength and tensile strength;

[0101] The training sample database is obtained through actual process tests and simulation model calculations. For different bottom mold types, different samples are formed. Determine the training sample database under four bottom mold parameters, namely φ3.0~3.4mm rivet flat mold, φ3.0~3.4mm rivet punch, φ5.0~5.4mm rivet flat mold, φ5.0~5.4mm Rivet punch; the training sample database includes alloy grades, plate thickness, rivet specifications, bottom die depth, riveting speed, and nail foot opening and self-locking values ​​under corresponding process condi...

Embodiment 3

[0119] 5182-O state aluminum alloy plate, the thickness of the upper and lower plates is 1.5mm, the size of the rivet is φ5.3×5.5mm, the size of the bottom mold, combined Figure 5 , the depth is h=2mm. 5182-O state aluminum alloy plate riveting process parameters are as follows: t 1 = 1.5mm, h 1 =80HV,σ 1 =280MPa; t 2 = 1.5mm, h 2 =80HV,σ 2 =280MPa; D 0 =5.3mm, V=60cm / min.

[0120] (1) Use the data calling module to call the φ5.3 punch.

[0121] (2) put t 1 = 1.5mm, h 1 =80HV,σ 1 =280MPa; t 2 = 1.5mm, h 2 =80HV,σ 2 =280MPa; D 0 =5.3mm, V=60cm / min parameters are input to the data input module;

[0122] (3) According to the training sample called by the φ5.3 punch, calculate the opening value of the joint nail foot D = 7.32mm, and the self-locking value u = 0.43mm;

[0123] (4) Using h=(D 模 -D 0 )·tanα / 2-L 帽 , s=t 1 +t 2 +H 模 -L 帽 -L 脚 cos α-h; α=arcsin(D-D 0 ) / 2L 脚 The formula calculates the nail head height h=0.05mm, and the bottom thickness value s=...

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Abstract

A big data self-piercing riveting joint key geometrical parameter prediction system comprises a data calling module, a data input module and a data calculation module, and the data calling module is used for calling a pre-trained network model corresponding to a mold type according to the mold type of a to-be-tested joint; the data input module is used for inputting process parameters of the to-be-tested joint into the called pre-trained network model to obtain an opening value and a nail foot self-locking value of a nail foot of the to-be-tested joint; the data calculation module is used forcalculating to obtain key geometrical parameters of the joint to be measured according to the opening value of the nail foot of the joint to be measured; according to the invention, the pre-trained network model is selected according to the type of the mold to calculate the input technological parameters, the nail head height and bottom thickness values of the joint are obtained, and then qualification judgment is conducted on the predicted value result according to the set judgment standard.

Description

technical field [0001] The invention relates to the prediction of key geometric parameters of a self-piercing riveting joint, in particular to a system and method for predicting key geometric parameters of a self-piercing riveting joint with big data. Background technique [0002] Self-piercing riveting technology is a new type of connection technology for thin plate materials. During the plate connection process, a semi-hollow rivet is pressed into the plate through a punch, and after piercing the upper plate, the rivet legs are embedded in the lower plate under the action of the punch and the bottom die, so that It forms a self-locking effect and realizes the connection of two or more layers of plates. It has the characteristics of simple process, no pollution and high connection strength. It has been applied in more and more automobile companies. [0003] Due to the characteristics of the self-piercing riveting process, the quality of the joint is mainly affected by the p...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F30/17G06F30/27G06K9/62G06N3/08
CPCG06F30/17G06F30/27G06N3/08G06F18/24
Inventor 毛晓东朱光磊刘庆永羊浩苗海宾王立娟赵丕植
Owner CHINALCO MATERIALS APPL RES INST CO LTD
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