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A double-optimized bp neural network laser welding seam shape prediction method

A BP neural network, welding seam forming technology, used in laser welding equipment, welding equipment, instruments, etc., to achieve high prediction accuracy, improve initial weights and thresholds, and reduce input dimensions.

Active Publication Date: 2022-03-08
KUSN BAOJIN LASER TAILOR WELDED
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  • Abstract
  • Description
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  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The invention aims to solve the problems existing in the weld seam quality monitoring in the laser welding process, and proposes a BP neural network laser welding seam shape prediction method based on principal component analysis and genetic algorithm double optimization

Method used

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  • A double-optimized bp neural network laser welding seam shape prediction method
  • A double-optimized bp neural network laser welding seam shape prediction method
  • A double-optimized bp neural network laser welding seam shape prediction method

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Embodiment Construction

[0043] The present invention will be further described below in conjunction with the examples.

[0044] The present invention adopts laser welding molten pool coaxial monitoring system, such as figure 2 As shown, the laser used can be fiber laser, CO 2 Lasers, semiconductor lasers, etc. The motion system used for welding can be mechanical arms, CNC machine tools, CNC guide rails, etc. The cameras used can be CCD cameras, CMOS cameras, etc. The auxiliary light source can be fiber lasers, xenon lamps, etc. The protective gas can be argon gas, helium, helium-argon mixture, etc.

[0045] TA15 titanium alloy is used as the welding base material, and the T-joint form is taken as an example; the size of the T-joint skin sample used is 150mm×50mm×1.5mm, and the rib plate sample size is 150mm×30mm×10mm.

[0046] The specific experimental method is as follows:

[0047] Step 1: Before welding, the surface of the base metal to be welded needs to be cleaned, first using 10% HNO 3 +30%...

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Abstract

The invention discloses a double-optimized BP neural network laser welding seam formation prediction method, which adopts the double-optimized BP neural network method of principal component analysis and genetic algorithm to predict the weld seam formation; compared with the traditional BP neural network method to predict the welding seam Seam forming has the following advantages: using the molten pool feature quantity and welding process parameters as model input at the same time, the model can predict the weld seam shape with higher accuracy; using principal component analysis to reduce the repetition of model input layer information, eliminating the difference between features The overlapping of information between neural networks reduces the input dimension of the neural network and improves the model operation speed to meet the needs of real-time monitoring; the genetic algorithm is used to improve the initial weight and threshold of the model to improve the inherent defects of the BP neural network and avoid the model from converging locally. Optimum value to prevent large errors in prediction results.

Description

technical field [0001] The invention relates to a double optimization-based BP neural network laser welding seam shape prediction method, which belongs to the technical field of material processing engineering. Background technique [0002] With the development of national defense science and technology, the requirements for the structure and process performance of aerospace equipment are more stringent; the upgrading of aerospace equipment has put forward higher requirements for the manufacturing process, service performance, service life, reliability and automation manufacturing level of structural materials. Requirements; skin grid structure is a typical hollow structure design form, this structure can reduce the weight of structural parts very well, and is widely used in the manufacture of lightweight high-strength components; titanium alloy skin grid structure weldments are large There are many weldment paths, and the requirements for weld quality and stability are high...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/00
CPCB23K26/702B23K26/21
Inventor 雷正龙郭亨通
Owner KUSN BAOJIN LASER TAILOR WELDED
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