Weld pool and keyhole geometric feature extraction in K-TIG welding with a gradual gap based on an improved HDR algorithm

Wang, Zishun; Shi, Yonghua; Hong, Xiaobin; Zhang, Baori; Chen, Xiyin; Zhan, Aiwen

doi:10.1016/j.jmapro.2021.11.004

Cited by 12 publications

(2 citation statements)

References 22 publications

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“…These methods have the obvious advantages of low computational complexity and high efficiency, which make it possible to obtain stable and accurate segmentation results under certain tasks. However, during the K-TIG welding process, the size, shape, and position of the weld pool and keyhole are affected by different welding process parameters, which leads to difficulty in the segmentation of the molten pool image within the actual welding environment [14]. Furthermore, the segmentation methods mentioned above have the problem of poor segmentation performance due to the interference from the complex reflection characteristics of the molten pool's surface, the intense light intensity of the arc light, and the other noise sources in the environment.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Recognition of Molten Pools Based on Improved DeepLabV3+ in Keyhole Tungsten Inert Gas Welding Applications

Yang,

Dai,

Cui

et al. 2024

Electronics

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During the Keyhole Tungsten Inert Gas (K-TIG) welding process, a significant amount of information related to the weld quality can be obtained from the weld pool and the keyhole of the topside molten pool image, which provides a vital basis for the control of welding quality. However, the topside molten pool image has the unstable characteristic of strong arc light, which leads to difficulty in contour extraction. The existing image segmentation algorithms cannot satisfy the requirements for accuracy, timing, and robustness. Aiming at these problems, a real-time recognition method, based on improved DeepLabV3+, for identifying the molten pool more accurately and effectively was proposed in this paper. First, MobileNetV2 was selected as the feature extraction network with which to improve detection efficiency. Then, the atrous rates of atrous convolution layers were optimized to reduce the receptive field and balance the sensitivity of the model to molten pools of different scales. Finally, the convolutional block attention module (CBAM) was introduced to improve the segmentation accuracy of the model. The experimental results verified that the proposed model had a fast segmentation speed and higher segmentation accuracy, with an average intersection ratio of 89.89% and an inference speed of 103 frames per second. Furthermore, the trained model was deployed in a real-time system and achieved a real-time performance of up to 28 frames per second, thus meeting the real-time and accuracy requirements of the K-TIG molten pool monitoring system.

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Section: Introductionmentioning

confidence: 99%

Real-Time Recognition of Molten Pools Based on Improved DeepLabV3+ in Keyhole Tungsten Inert Gas Welding Applications

Yang,

Dai,

Cui

et al. 2024

Electronics

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“…Wang et al investigated the dynamic behavior of the weld pool and weld formation. They found that the weld formation of the welding direction from a large gap to a small gap was much worse than that from a small gap to a large gap in the case of the same gap change rate [ 13 ]. In addition, the influence of air gap on the glass-to-glass welding using picosecond laser technology was studied, and laser welding with good performance was achieved with an air gap of 2 μm [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

Effect of Butt Gap on Stress Distribution and Carrying Capacity of X80 Pipeline Girth Weld

Zhu

Jia²,

et al. 2022

Materials

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An unstable assembly gap is detrimental to the formation and performance of the pipeline butt girth weld joint. Therefore, a numerical model of an 18.4 mm-thick X80 pipeline girth weld by a homogeneous body heat source was established to investigate the effect of the butt gap on the joint temperature and stress field, and carrying capacity. The accuracy of the simulation results was verified by measuring the welding thermal cycle with a thermocouple. The investigation results showed that the weld pool, heat-affected zone (HAZ) width, and maximum circumferential stress of the joint rose with the increase in the butt gap. The tensile stress unfavorable to the joint quality was mainly distributed in the weld metal and partial HAZ, and the distribution areas gradually expanded as the gap increased. The Von Mises stress peak value of the joint appeared in the order of 3 mm > 2 mm > 1 mm > 0 mm gap, reaching the maximum of 467.3 MPa (3 mm gap). This variation trend is directly related to the improvement in welding heat input with increasing butt gaps. The maximum Von Mises stress of the joint was positively correlated with the carrying capacity of the pipeline, which diminished as the butt gap enlarged. The pipeline carrying capacity reached 17.8 MPa for the joint with no butt gap, and dropped to 13.1 MPa for the joint with a 3 mm gap. The relationship between the carrying capacity (P) and butt gap (C) was described by P = −0.125C2 − 1.135C + 17.715, through which the pipeline carrying capacity with other butt gaps can be predicted.

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Early-Warning System for Copper Alloy Abnormal Molten Pool in Wire-Arc Additive Manufacturing via Convolutional Neural Network Method

Zhang

Wang²,

Xu³

et al. 2023

J. of Materi Eng and Perform

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Weld pool and keyhole geometric feature extraction in K-TIG welding with a gradual gap based on an improved HDR algorithm

Cited by 12 publications

References 22 publications

Real-Time Recognition of Molten Pools Based on Improved DeepLabV3+ in Keyhole Tungsten Inert Gas Welding Applications

Real-Time Recognition of Molten Pools Based on Improved DeepLabV3+ in Keyhole Tungsten Inert Gas Welding Applications

Effect of Butt Gap on Stress Distribution and Carrying Capacity of X80 Pipeline Girth Weld

Early-Warning System for Copper Alloy Abnormal Molten Pool in Wire-Arc Additive Manufacturing via Convolutional Neural Network Method

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