Classified identification and positioning of metal workpiece welding defects based on fluorescent magnetic powder

Abstract

magnetic powder inspection is widely used in bearing welding crack detection, but the defect still requires manually visual inspection. In view of longitudinal welding defects (such as cracks, pores,slag inclusion or the like) generated by a metal workpiece in a welding process, a magnetic mark image on the surface of the metal workpiece is captured by using an industrial camera, the features ofa suspected defect area are extracted by using the digital image processing technology, and then the automatic identification and defect location positioning of cylindrical surface cracks are completed on the basis of these features by using the neural network classifier (MLP) technology. Experimental results show that the method not only has a relatively high identification rate for true and false defects such as welding defects, metal cutting, metal heat treatment and the like, but also improves the detection efficiency, thereby having certain application research significance.

Description

technical field [0001] The invention relates to the field of intelligent detection, and is an intelligent defect identification system based on fluorescent magnetic powder, which enables the software system to complete the classification, identification, detection and defect location of welding defects of metal workpieces in the factory environment, and belongs to the field of machine identification technology. Background technique [0002] Welding is a highly linear, multivariable, strongly coupled, and complex process with a large number of random uncertain factors, so it is difficult to detect. With the rapid development of my country's manufacturing industry, welding skills are used more and more widely, and the level of welding skills is also increasing. Higher and higher. New welding skill methods are constantly emerging, and professional welding technology is also developing in the direction of digitalization and intelligence. The traditional human eye judgment can no ...

AI Technical Summary

Problems solved by technology

New methods of welding skills are constantly emerging, and professional welding technology is also developing in the direction of digitalization and intelligence. The traditional human eye judgment can no longer meet the needs of social development, and there are many defects

[0003] Due to the continuous development of detection technology and intelligent technology, people began to explore some automatic detection methods. Some people used statistical analysis methods to study welding defects through the characteristics of random uncertainty in welding defects, and established a system based on statistics. On-line detection method of welding defects based on scientific method, some people have proposed online detection method of welding defects based on ray detection and ultrasonic detection technology, and some people have proposed based on wavelet theory, etc., although these methods have promoted detection technology However, there are also some shortcomings of their own: high detection cost, complicated process, low detection efficiency, difficult to realize automation, etc.

Fuzzy logic has the ability to imitate human thinking. On this basis, the fuzzy neural network produced by the combination of fuzzy logic and neural network has made a lot of progress in welding process control and welding defect identification. There are self-organizing feature maps (SCM) in China. ) The neural network automatically detects welding defects in the GTAW process. By observing the distribution characteristics of the probability density of parameters in each time interval during the welding process, it ...

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