Infrared Visual Sensing Detection of Groove Width for Swing Arc Narrow Gap Welding

Abstract

To solve the current problem of poor weld formation due to groove width variation in swing arc narrow gap welding, an infrared passive visual sensing detection approach was developed in this work to measure groove width under intense welding interferences. This approach, called global pattern recognition, includes self-adaptive positioning of the ROI window, equal division thresholding and in situ dynamic clustering algorithms. Accordingly, the self-adaptive positioning method filters several of the nearest values of the arc’s highest point of the vertical coordinate and groove’s same-side edge position to determine the origin coordinates of the ROI window; the equal division thresholding algorithm then divides and processes the ROI window image to extract the groove edge and forms a raw data distribution of groove width in the data window. The in situ dynamic clustering algorithm dynamically classifies the preprocessed data in situ and finally detects the value of the groove width from the remaining true data. Experimental results show that the equal division thresholding algorithm can effectively reduce the influences of arc light and welding fume on the extraction of the groove edge. The in situ dynamic clustering algorithm can avoid disturbances from simulated welding spatters with diameters less than 2.19 mm, thus realizing the high-precision detection of the actual groove width and demonstrating stronger environmental adaptability of the proposed global pattern recognition approach.

Review on the Application of the Attention Mechanism in Sensing Information Processing for Dynamic Welding Processes

“…Infrared sensors and infrared cameras have the ability to record temperature data. One major benefit of these sensors is their resistance to interference from welding noise, making them suitable for monitoring molten pools [26] and detecting defects such as undercuts, humps, and unfused flaws [27]. Ultrasonic sensing [28] and radiography sensing [29] are typical nondestructive testing methods and are currently widely used in weld quality assessment after the welding process.…”

Implementation of a two-stage algorithm for NG-GMAW seam tracking and oscillation width adaptation in pipeline welding

“…With the development of sensor technology, numerous methods have been proposed to accurately set and control the welding torch trajectory and reduce sidewall incomplete defects. In this regard, electromagnetic sensors [11], inductive sensors [12], ultrasonic sensors [13], sound sensors [14], and vision sensors [15][16][17][18] have been proposed so far to conduct welding seam tracking research. Among these sensors, the method of welding seam tracking using a vision sensor has been developed significantly.…”