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BP neural network-based giant magnetoresistance eddy current testing method for welding defect

A technology of BP neural network and giant magnetoresistance, which is applied in the direction of measuring devices, material magnetic variables, and material analysis through electromagnetic means, can solve the problems of less detection features, strong noise of welding eddy current electromagnetic signals, etc., and achieve fast response speed , good real-time performance and low cost

Inactive Publication Date: 2014-04-30
TIANJIN UNIV
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  • Application Information

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Problems solved by technology

However, there are very few features for eddy current detection of welding defects
Especially due to the complex texture and structure of the welding surface, the welding eddy current electromagnetic signal usually has relatively strong noise
How to distinguish noise signals from defect signals poses new challenges to eddy current testing

Method used

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  • BP neural network-based giant magnetoresistance eddy current testing method for welding defect
  • BP neural network-based giant magnetoresistance eddy current testing method for welding defect
  • BP neural network-based giant magnetoresistance eddy current testing method for welding defect

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Embodiment Construction

[0041] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0042] see figure 1 with figure 2 , the detection device comprises a rectangular excitation coil 2 (the size of the rectangular coil in this embodiment is 60mm long, 30mm wide, and 25mm high), an excitation voltage signal generating circuit 3, four identical giant magnetoresistance sensor chips 4 (fourth in this embodiment) The distance between two giant magnetoresistance chips is 2mm), signal conditioning circuit 6, data acquisition module 7, analysis and calculation module 8. The output of the excitation voltage generation circuit 3 is connected to the wires of the rectangular coil 2; the four giant magnetoresistance sensors 4(1), 4(2), 4(3), and 4(4) are located on the same straight line and are respectively fixed on the rectangular coil 2; the outputs of the four giant magnetoresistances are respectively connected to the signal conditioning circu...

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Abstract

The invention relates to a BP neural network-based giant magnetoresistance eddy current testing method for welding defect, belonging to the technical field of non-destructive testing. The testing method comprises the following steps: selecting a plurality of welding samples of different types as training samples; measuring four giant magnetoresistance sensor output signals of each training sample at the same moment, and extracting a peak-to-peak value, a variance and a slope change as training sample characteristic quantity; respectively establishing a neural network model aiming at the training samples of different types, wherein each neural network model trains one type of sample data, the training sample characteristic quantity of the type of sample is at an input end, and the expected output of the type of samples is at an output end; measuring and extracting to-be-tested sample characteristic quantity; carrying out weld seam quality testing by combining the trained neural network models. The testing method has the advantages of being high in response speed, good in real-time property, simple for measurement process, and easy to implement.

Description

technical field [0001] The invention belongs to the technical field of nondestructive testing, in particular to an eddy current testing method for welding quality. Background technique [0002] Welding technology is widely used in energy, petrochemical, nuclear industry, transportation vehicle manufacturing and some other industrial processes [1-3]. During the welding process, detecting and estimating welding defects can reduce the scrap rate and improve production efficiency; during the operation of the equipment, detecting early defects caused by temperature, pressure and external influence at the welding place can avoid catastrophic accidents[ 4]. Common welding defects include pores, incomplete penetration, cracks, etc. The detection and classification of welding defects can provide a basis for judgment on subsequent repairs and replacements. [0003] Eddy current testing is an important non-destructive testing method [5]. For eddy current testing of welding defects, ...

Claims

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Application Information

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IPC IPC(8): G01N27/90G01N27/904
Inventor 王超王立玢高鹏支亚
Owner TIANJIN UNIV