A Study on the Algorithm for Determining Back Bead Generation in GMA Welding Using Deep Learning

김, 민석; 신, 승민; 김, 동현; 이, 세헌

doi:10.5781/jwj.2018.36.2.11

Cited by 14 publications

(7 citation statements)

References 4 publications

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“…Yun [ 13 ] searched the optimal welding process of fiber laser fillet welding with a gradient-based optimization method. Kim [ 14 ] applied the deep learning method to gas metal arc welding.…”

Section: Introductionmentioning

confidence: 99%

Research for the Optimal Flux-Cored Arc Welding Process of 9% Nickel Steel Using Multi Object Optimization with Solidification Crack Susceptibility

Park¹,

Kim

Pyo

et al. 2021

Materials

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The environment of the global shipbuilding market is changing rapidly. Recently, the International Maritime Organization (IMO) has tightened regulations on sulfur oxide content standards for marine fuels and tightened sulfur oxide emission standards for the entire coastal region of China to consider the environment globally and use LNG as a fuel. There is a tendency for the number of vessels to operate to increase significantly. To use cryogenic LNG fuel, various pieces of equipment, such as storage tanks or valves, are required, and equipment using steel, which has excellent impact toughness in cryogenic environments, is required. Four steel types are specified in the IGG Code, and 9% Ni steel is mostly used for LNG fuel equipment. However, to secure safety at cryogenic temperatures, a systematic study investigating the causes of quality deterioration occurring in the 9% Ni steel welding process is required and a discrimination function capable of quality evaluation is urgent. Therefore, this study proposes a plan where the uniform quality of 9% Nickel steel is secured by reviewing the tendency of the solidification crack susceptibility among the quality problems of cryogenic steel to establish the criteria for quality deterioration and to develop a system capable of quality discrimination and defect avoidance.

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Section: Introductionmentioning

confidence: 99%

Research for the Optimal Flux-Cored Arc Welding Process of 9% Nickel Steel Using Multi Object Optimization with Solidification Crack Susceptibility

Park¹,

Kim

Pyo

et al. 2021

Materials

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“…In the realm of research incorporating ANNs into welding practices, the advent of the error back-propagation algorithm in the mid-1980s marked the beginning of the ANN’s extensive application across various welding methodologies. This includes utilizing ANNs for predicting the quality of welds based on control parameters, recommending optimal welding control parameters to achieve specific weld characteristics, deriving profiles from laser vision sensing images, and employing vision sensors for quality and seam tracking in welding processes [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ]. Furthermore, machine learning techniques have been extensively adopted in diverse welding procedures.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, machine learning techniques have been extensively adopted in diverse welding procedures. These applications encompass leveraging deep learning and reinforcement learning for controlling laser welding, utilizing deep learning for the prediction of weld quality in arc welding, and implementing Support Vector Machine (SVM), a specific machine learning algorithm, for the classification of weld quality [ 11 , 12 , 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

Virtual Sensor for On-Line Hardness Assessment in TIG Welding of Inconel 600 Alloy Thin Plates

Górka,

Jamrozik,

Wyględacz

et al. 2024

Sensors

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Maintaining high-quality welded connections is crucial in many industries. One of the challenges is assessing the mechanical properties of a joint during its production phase. Currently, in industrial practice, this occurs through NDT (non-destructive testing) conducted after the production process. This article proposes the use of a virtual sensor, which, based on temperature distributions observed on the joint surface during the welding process, allows for the determination of hardness distribution across the cross-section of a joint. Welding trials were conducted with temperature recording, hardness measurements were taken, and then, neural networks with different hyperparameters were tested and evaluated. As a basis for developing a virtual sensor, LSTM networks were utilized, which can be applied to time series prediction, as in the analyzed case of hardness value sequences across the cross-section of a welded joint. Through the analysis of the obtained results, it was determined that the developed virtual sensor can be applied to predict global temperature changes in the weld area, in terms of both its value and geometry changes, with the mean average error being less than 20 HV (mean for model ~35 HV). However, in its current form, predicting local hardness disturbances resulting from process instabilities and defects is not feasible.

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“…However, since the welding quality in the arc welding processes depends on the skill of the operator, there is a limit to ensuring good welding quality and improving productivity. To solve the inevitable working environment problems in arc welding process such as harmful gas, dust, and strong arc, research on the construction of an automated welding system using a robot or other welding equipment has been actively conducted recently [2][3][4][5][6][7] . The back-bead refers to a weld bead formed on the back side of the weld joint.…”

Section: Introductionmentioning

confidence: 99%

Weldability of Aluminum Alloy and Hot Dip Galvanized Steel by AC Pulse MIG Brazing

Kwon

Sol-Choi

Yoon

et al. 2021

Journal of Welding and Joining

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An automated welding system is essential to ensure a stable and good welding quality and improve productivity in the gas metal arc welding (GMAW) process. Therefore, various studies have been conducted on the establishment of smart factories and the demand for good weldability in the fields of production and manufacturing. In shipbuilding welding and pipe welding, the uniformly generated back-bead is an important criterion for judging the mechanical properties and weldability of the welded structure, and is also an important factor that enables the realization of an automated welding system. Therefore, in this study, the welding current signal measured in real-time in the GMAW process was pre-processed by a short time Fourier transform (STFT) to obtain a time-frequency domain feature image (spectrogram). Based on this, a back-bead generation detection algorithm was developed. To accelerate the training speed of the proposed convolution neural network (CNN) model, we used non-saturating neurons and a highly efficient GPU implementation of the convolution operation. As a result of applying the proposed detection model to actual welding process, the detection accuracies with and without the back-bead regions were 95.8% and 94.2%, respectively, which confirmed the excellent classification performance for back-bead generation.

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A Study on the Algorithm for Determining Back Bead Generation in GMA Welding Using Deep Learning

Cited by 14 publications

References 4 publications

Research for the Optimal Flux-Cored Arc Welding Process of 9% Nickel Steel Using Multi Object Optimization with Solidification Crack Susceptibility

Research for the Optimal Flux-Cored Arc Welding Process of 9% Nickel Steel Using Multi Object Optimization with Solidification Crack Susceptibility

Virtual Sensor for On-Line Hardness Assessment in TIG Welding of Inconel 600 Alloy Thin Plates

Weldability of Aluminum Alloy and Hot Dip Galvanized Steel by AC Pulse MIG Brazing

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