Weld fatigue behavior of gas metal arc welded steel sheets based on porosity and gap size

Kim, Dong-Yoon; Kim, Gwang-Gook; Yu, Jiyoung; Kim, Dong Cheol; Kim, Young‐Min; Park, Junhong

doi:10.1007/s00170-022-10567-8

Cited by 2 publications

(2 citation statements)

References 22 publications

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“…Although not as critical as cracks, porosity should be minimized. NDT techniques can detect porosity, but their sensitivity varies [ 61 , 62 ].…”

Section: Methodsmentioning

confidence: 99%

Deep convolutional neural network for weld defect classification in radiographic images

Palma-Ramírez,

Ross-Veitía,

Font-Ariosa

et al. 2024

Heliyon

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“…Although not as critical as cracks, porosity should be minimized. NDT techniques can detect porosity, but their sensitivity varies [ 61 , 62 ].…”

Section: Methodsmentioning

confidence: 99%

Deep convolutional neural network for weld defect classification in radiographic images

Palma-Ramírez,

Ross-Veitía,

Font-Ariosa

et al. 2024

Heliyon

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“…The weld quality directly impacts the integrity, durability, and safety of the fabricated components. Traditional methods to assess the quality of weld joints involve destructive testing, such as confirming weld shape [1][2][3][4] and evaluating mechanical properties through tensile testing [4][5][6] and fatigue testing [7][8][9][10]. However, these methods only facilitate a partial inspection of the weld joints and indirectly represent the welding quality of the overall structure [11,12].…”

Section: Introductionmentioning

confidence: 99%

Application of Convolutional Neural Networks for Classifying Penetration Conditions in GMAW Processes Using STFT of Welding Data

Kim,

Lee,

et al. 2024

Applied Sciences

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For manufacturing components with thick plates, such as in the heavy equipment and shipbuilding industries, the gas metal arc welding (GMAW) process is applied. Among the components that apply the thick plate GMAW process, there are groove butt joints, which are fabricated through multi-pass welding. Various welding qualities are managed in multi-pass welding, and the root-pass weld is controlled to ensure complete joint penetration (CJP). Currently, the state of complete joint penetration during root-pass welding is managed visually, making it difficult to confirm the penetration condition in real time. Therefore, there is a need to predict the penetration condition in real time. In this study, we propose a convolutional neural network (CNN)-based prediction model that can classify penetration conditions using welding current and voltage data from the root pass of V-groove butt joints. The root gap of the joints was varied between 1.0 and 2.0 mm, and the wire feed rate was adjusted. During welding, the current and voltage were measured. The welding current and voltage are transformed into a short-time Fourier transform (STFT) representation depicting the arc and wire extension lengths. The transformed dynamic resistance STFT information serves as the input variable for the CNN model. Preprocessing steps, including thresholding, are applied to optimize the input variables. The CNN architecture comprises three convolutional layers and two pooling layers. The model classifies penetration conditions as partial joint penetration (PJP), CJP, and burn-through, achieving a high accuracy of 97.8%. The proposed method facilitates the non-destructive evaluation of the root-pass welding quality without expensive monitoring equipment, such as vision cameras. It is expected to be immediately applied to the thick plate welding process using readily available welding data.

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Weld fatigue behavior of gas metal arc welded steel sheets based on porosity and gap size

Cited by 2 publications

References 22 publications

Deep convolutional neural network for weld defect classification in radiographic images

Deep convolutional neural network for weld defect classification in radiographic images

Application of Convolutional Neural Networks for Classifying Penetration Conditions in GMAW Processes Using STFT of Welding Data

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