Narrow gap deviation detection in Keyhole TIG welding using image processing method based on Mask-RCNN model

Chen, Yunke; Shi, Yonghua; Cui, Yanxin; Chen, Xiyin

doi:10.1007/s00170-020-06466-5

Cited by 20 publications

(7 citation statements)

References 22 publications

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“…However, Faster-RCNN also has a shortcoming, that is, selective search is very time-consuming. Chen et al (2021) [22] proposed to use Mask-RCNN to accurately extract the center of the keyhole entrance, and the welding deviation fluctuation range was ± 0.133mm. Based on Mask-RCNN, this research optimizes the problem of slow training speed.…”

Section: Deep Learning Image Recognitionmentioning

confidence: 99%

Tennis Robot Design via Internet of Things and Deep Learning

Wang

2021

IEEE Access

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To efficiently integrate deep learning (DL) and Internet of Things (IoT) with tennis robot research, the concept and characteristics of mask region-convolutional neural network (Mask R-CNN) under DL are analyzed. Then, the IoT radio frequency identification (RFID) is introduced and the suitable RFID structure is established. Moreover, the real-time intelligent image recognition tennis robot is designed, and simulation experiment on the robot is performed. The results show that when the positioning label is eight, the convergence speed of the optimized algorithm is improved relative to the unoptimized one, and the error is reduced by 0.82. The positioning algorithm proposed in this research has high convergence speed and small system error. The positioning accuracy of the tennis robot is improved by at least 6%, the positioning targets are close to the target to be located, and the tennis robot has the best shortest path effect. In addition, the tennis recognition algorithm based on Mask R-CNN can accurately distinguish dense tennis balls with high accuracy. It shows that the tennis robot positioning algorithm and target recognition algorithm proposed in this research are of practical adoption value.

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Section: Deep Learning Image Recognitionmentioning

confidence: 99%

Tennis Robot Design via Internet of Things and Deep Learning

Wang

2021

IEEE Access

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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%

“…Li et al [ 21 ] employed an infrared camera to detect the weld deviation for the rotation arc process by calculating the relative distance of the gravity center of the arc to the sidewall to which the arc rotates, without involving spatter and fume disturbances. Chen et al [ 22 ] visually identified the weld deviation for narrow gap K-TIG welding by comparing the weld centerline to the keyhole center; this TIG process usually leads to little spatter. In practice, passive visual sensing approaches are also involved in other welding processes [ 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 ].…”

Section: Introductionmentioning

confidence: 99%

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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“…Shao et al designed an image processing algorithm based on the particle filter method to track the seam [15]. Chen et al achieved welding seam tracking based on the Mask-RCNN to segment the molten pool area, and a Hough line transformation was used to fit the seam line [16]. The current research on passive vision welding seam tracking involves performing line fitting on the seam part directly, then performing image segmentation on the weld pool or arc part.…”

Section: Introductionmentioning

confidence: 99%

A Seam Tracking Method Based on an Image Segmentation Deep Convolutional Neural Network

Yang

Chen

et al. 2022

Metals

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Vision-based welding seam tracking is an important and unique branch of welding automation. Active vision seam tracking systems achieve accurate feature extraction by using an auxiliary light source, but this will introduce extra costs and the real-time performance will be affected. In contrast, passive vision systems achieve better real-time performance and their structure is relatively simple. This paper proposes a passive vision welding seam tracking system in Plasma Arc Welding (PAW) based on semantic segmentation. The BiseNetV2 network is adopted in this paper and online hard example mining (OHEM) is used to improve the segmentation effect. This network structure is a lightweight structure allowing effective image feature extraction. According to the segmentation results, the offset between the welding seam and the welding torch can be calculated. The results of the experiments show that the proposed method can achieve 57 FPS and the average error of the offset calculation is within 0.07 mm, meaning it can be used for real-time seam tracking.

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Narrow gap deviation detection in Keyhole TIG welding using image processing method based on Mask-RCNN model

Cited by 20 publications

References 22 publications

Tennis Robot Design via Internet of Things and Deep Learning

Tennis Robot Design via Internet of Things and Deep Learning

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

A Seam Tracking Method Based on an Image Segmentation Deep Convolutional Neural Network

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