Embedded device and method for detecting welding quality of lockhole TIG welding in real time
An embedded device and welding quality technology, applied in welding equipment, welding accessories, arc welding equipment, etc., can solve the problems of real-time identification and remote monitoring of difficult welding quality, and achieve wide application range, low cost, and high recognition rate accuracy high effect
Active Publication Date: 2021-12-07
SOUTH CHINA UNIV OF TECH
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Problems solved by technology
It is difficult to apply real-time identification and remote monitoring of welding quality on long welds on curved surfaces that need to move or crawl
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[0062] Such as figure 1 As shown, the embedded device for real-time detection of keyhole TIG welding welding quality includes:
[0063] The CMOS camera is connected to the AI core board, and is used to collect the welding scene image of the keyhole TIG welding in real time, and transmit the image data to the AI core board; the CMOS camera has the function of software trigger and setting the exposure time, and the exposure time range meets 100us- 50ms, dynamic range greater than 60dB, frame rate greater than 60 frames / s;
[0064] The AI core board performs data communication with other components of the embedded device, and integrates keyhole TIG welding welding quality real-time detection software package; the PCB size of the AI core board is 40mmx55mm, adopts RK1808K wide temperature AI chip, and has a memory of 2GB DDR4 SDRAM, stored as 8GBeMMC 1 channel, equipped with LVDS LCD screen interface, 1 channel Gigabit Ethernet interface, 1 channel USB3.0Host interface, 1...
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The invention discloses an embedded device and method for detecting the welding quality of lockhole TIG welding in real time. The embedded device comprises a CMOS camera, an AI core board, a wireless module, a welding current sensing module, a welding voltage sensing module, a crawling robot controller and a penetration lockhole TIG welding power source. A calibration exposure sequence is determined through test welding calibration, a CMOS camera multi-exposure mode is set, and a welding area is monitored through a multi-exposure fusion technology. According to the method, the AI core board is utilized, a CNN technology is used for segmenting a lockhole and a molten pool, size and Hu invariant moment morphological characteristics are extracted, welding current and voltage characteristics are combined to serve as input, and then a built BP neural network is used for recognizing the penetration state in real time. The image, the welding state and the early warning information are uploaded to a remote server through the wireless module. The embedded device and method are suitable for lockhole TIG welding of a long welding seam on the curved surface of a medium-thickness plate, the embedded device can be installed on the crawling robot, and real-time identification and remote monitoring of the welding quality are achieved.
Description
technical field [0001] The invention relates to the technical field of welding monitoring and image processing, in particular to an embedded device and method for real-time detection of keyhole TIG welding welding quality. Background technique [0002] In recent years, with the development of advanced manufacturing technology and the continuous improvement of the domestic market environment, the heavy machinery industry, which occupies an extremely important position in the manufacturing industry, has developed rapidly. bigger and bigger. At present, medium and thick plate welded structures are widely used in industrial sectors such as shipbuilding, large bridges, petrochemicals, boiler containers, and heavy machinery. However, these large weldments have a large number of long welds with large curved surfaces, and most of them can only use manual multi-layer multi-pass welding, which has many auxiliary processes, poor weld quality stability, low pass rate of one-time weldin...
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IPC IPC(8): B23K31/12B23K9/095B23K9/32B23K9/167
CPCB23K31/125B23K9/095B23K9/32B23K9/167
Inventor 石永华陈熙引王子顺
Owner SOUTH CHINA UNIV OF TECH