A method for detect welding quality of component arc weld

A technology of welding quality and detection method, applied in the field of welding quality detection of component arc welding, can solve the problems of large time consumption, labor force, high cost, etc.

Active Publication Date: 2019-02-05
NAT UNIV OF DEFENSE TECH
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Problems solved by technology

[0004] However, there are many deficiencies in the traditional manual detection and classification. Only relying on human eyes and some inspection rules for detection and classification will inevitably lead to many errors and consume a lot of time and labor on repetitive work.
Auxiliary detection methods such as radiographic flaw detection may have higher costs, so efficient and intelligent component welding quality detection and classification methods have become an important demand in current actual production

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  • A method for detect welding quality of component arc weld
  • A method for detect welding quality of component arc weld
  • A method for detect welding quality of component arc weld

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Embodiment Construction

[0042] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings, but the present invention can be implemented in many different ways defined and covered by the claims.

[0043] A component arc welding welding quality detection method, comprising the following steps:

[0044] S1 uses the camera to shoot, collect a certain number of pictures of different types of welding heads of the same component arc welding, and establish a sample library. The samples in the sample library are divided into two parts, one part is used as the training set, and the training set is used to train the machine learning classifier. The other part is used as a test set, which is used to identify its corresponding welding quality type. For example, 70% of the samples in the sample library are used as the training set, and 30% of the samples are used as the test set.

[0045] In order to make the convolutional neural network more efficient ...

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Abstract

A method for detect welding quality of electric arc weld of component features that a plurality of pictures of different types of weld joints of same component are collected and a sample library is established. One part of the sample library is used as training set and the other part as test set. Template Matching Algorithm Based on Spatial Correlation Method Extracts the Pictures of Target WeldedJoint Areas in samples from Training Set and Test Set; Define the welding joint quality types, label the welding joint quality types of all the pictures in the training set manually, build convolution neural network based on tensorflow frame, input the pictures of all the target welding joint areas in the training set into the convolution neural network, and train the machine learning classifier;Finally, the images of the target welded joint area in each sample image of S test set are classified by the trained machine learning classifier, and the classification result is the corresponding quality type of the welded joint. The invention can quickly find out the quality problem of the component arc welding in the picture.

Description

technical field [0001] The invention relates to the technical field of welding quality detection, in particular to a method for detecting welding quality of component arc welding. Background technique [0002] The welding quality inspection of components refers to the inspection of welding results, the purpose is to ensure the integrity, reliability, safety and usability of the welding structure. [0003] Traditional manual inspection needs to be inspected according to welding structure instructions, technical standards, process documents, construction drawings, etc. The inspection of appearance quality mainly includes dimension inspection, geometric shape inspection, and appearance flaw inspection. Radiographic flaw detection and ultrasonic testing can be used Flaw detection, coloring inspection and other methods for auxiliary detection. [0004] However, there are many deficiencies in the traditional manual detection and classification. Only relying on human eyes and some...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/04
CPCG06N3/045G06F18/241G06F18/214
Inventor 刘忠冯旸赫金广垠黄金才程光权马扬梁星星周玉珍王琦
Owner NAT UNIV OF DEFENSE TECH
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