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Neural network modeling approach of electron-beam welding consolidation zone shape factor

A technology of neural network modeling and electron beam welding, which is applied in the field of neural network modeling, can solve the problems of few input layer parameters and insufficient comprehensiveness, and achieve the effect of concise model, saving materials and improving the progress of engineering research

Inactive Publication Date: 2008-12-24
BEIJING AVIATION MFG ENG INST CHINA AVIATION NO 1 GRP
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Problems solved by technology

First of all, the input layer parameters (influencing factors) considered are few and not comprehensive enough. In addition to the influence of welding process factors, for high-energy beam welding, it is also necessary to consider various factors such as beam quality, material characteristic factors, and environmental factors. influence of shape

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  • Neural network modeling approach of electron-beam welding consolidation zone shape factor
  • Neural network modeling approach of electron-beam welding consolidation zone shape factor
  • Neural network modeling approach of electron-beam welding consolidation zone shape factor

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Effect test

Embodiment 1

[0069] 1. Electron beam welded joint sample preparation

[0070] Electron beam welding δ = 20mm TC4 titanium alloy test plate process, adopts flat plate surfacing welding method to simulate the best butt joint form, and the size of welding material TC4 titanium alloy is 300mm×90mm×20mm. Welding is performed in an electron beam welder after removing the oxide film with a wire brush.

[0071] The welding parameters are as follows:

[0072] u a - Accelerating voltage, I b - Beam I f -focus current V-welding speed, H-distance from the gun to the test piece

[0073] Parameter 1: U a =150kV, I b = 42mA, I f =342mA, V=600mm / min, H=251mm, vacuum 6×10 -3 Pa

[0074] Parameter 2: U a =150kV, I b =69mA, I f =366mA, V=600mm / min, H=251mm, vacuum 4×10 -3 Pa

[0075] Parameter 3: U a =150kV, I b = 42mA, I f =324mA, V=400mm / min, H=251mm, vacuum 4×10 -3 Pa

[0076] Parameter 4: U a =90kV, I b =57mA, I f =1654mA, V=750mm / min, H=370mm, vacuum 3.2×10 -4 Pa

[0077] Paramet...

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Abstract

The invention belongs to a neural network modeling method which is applicable to the electron-beam welding techniques of various metal materials, and relates to the neural network modeling method of shape factors in a fusing region of the electron-beam welding. The neural network modeling method adopts neural network methods and systems to set up a mathematical model of the shape factors in the fusing region of the electron-beam welding, takes multiple non-related factors into consideration in all aspects to be used as an input layer for model solving, and belongs to the modeling methods with non-related multi-input and multi-output processing.

Description

technical field [0001] The invention belongs to a neural network modeling method, which is applicable to the electron beam welding technology of various metal materials, and relates to a neural network modeling method of the shape factor of the fusion zone of electron beam welding. Background technique [0002] In the field of high energy beam welding, neural network methods are mainly used in laser welding research. Xiong Jiangang, Zhang Wei and others established a BP network-based optimization model for laser welding process parameters of titanium alloy (TI-6AL-4V); the model takes laser power, welding speed and defocus as input, and takes penetration depth and weld width as Output, realizing the prediction of weld shape by process parameters (Xiong Jiangang, Zhang Wei, Hu Qianwu. Optimization of titanium alloy YAG laser welding process parameters based on artificial neural network model [J]. Applied Laser, 2001, 4(21): 243-246 ). Geng Changsong et al. used the method o...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01B11/24G06N3/02
Inventor 王亚军关永军付鹏飞卢志军
Owner BEIJING AVIATION MFG ENG INST CHINA AVIATION NO 1 GRP
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