Development of gap sensing system for narrow gap laser welding

Yamazaki, Yosuke; Abe, Yohei; Hioki, Yukio; Nakatani, Mitsuyoshi; Kitagawa, Akikazu; Nakata, Kohei

doi:10.1080/09507116.2016.1142195

Cited by 2 publications

(2 citation statements)

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“…In narrow gap welding, the groove width and weld central position usually vary due to the groove processing error, assembling error and welding thermal deformation, which leads to uneven sidewall penetrations and inconsistent bead surface [ 15 , 16 , 17 ]. To avoid poor weld formation due to groove variation, several passive visual sensing detection methods have been proposed [ 18 , 19 , 20 , 21 , 22 ]. Yamazaki et al [ 18 ] used a CMOS camera to capture infrared images of the welding zone and detected the width and central position of the narrow gap laser welding groove using brightness distribution analysis, but it is difficult to adaptively determine a threshold for the gradient of the brightness distribution curve with the occurrence of laser plume and welding spatter.…”

Section: Introductionmentioning

confidence: 99%

“…To avoid poor weld formation due to groove variation, several passive visual sensing detection methods have been proposed [ 18 , 19 , 20 , 21 , 22 ]. Yamazaki et al [ 18 ] used a CMOS camera to capture infrared images of the welding zone and detected the width and central position of the narrow gap laser welding groove using brightness distribution analysis, but it is difficult to adaptively determine a threshold for the gradient of the brightness distribution curve with the occurrence of laser plume and welding spatter. Zhu et al [ 19 ] proposed a local pattern recognition algorithm for the groove edge position to detect the weld deviation from infrared images in the SA-NGW process, but this algorithm is readily trapped in local optima.…”

Section: Introductionmentioning

confidence: 99%

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

Wang

et al. 2022

Sensors

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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.

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