Back bead control of the one-side robotic welding with visual sensor-cooperative control of current-waveform and torch motion for change of gap and welding position

Eguchi, K.; Yamane, Satoshi; Sugi, H.; Kubota, Takefumi; Oshima, Kenji

doi:10.1109/iecon.1998.724267

Cited by 4 publications

(2 citation statements)

References 2 publications

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“…The back-bead refers to a weld bead formed on the back side of the weld joint. Back-bead formation is considered as one of the important factors in determining the mechanical properties and weldability of the welded structure 8) . The shape of a back-bead also varies greatly depending on the welding motion, and sometimes a back-bead may not be formed even though the welding is performed under the same welding conditions.…”

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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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

View full text Add to dashboard Cite

show abstract

“…In butt joint GMAW, the backbead is typically not uniformly generated on the back side of the welds because of differences in penetration depth or gaps between the workpieces of each welded section. Lack of backbead uniformity adversely affects the mechanical properties and weldability of the welded structure [2]. Consistently generating a back-bead improves productivity by reducing the work required to repair the welds.…”

Section: Introductionmentioning

confidence: 99%

Prediction Model for Back-Bead Monitoring During Gas Metal Arc Welding Using Supervised Deep Learning

Jin

Shin

et al. 2020

IEEE Access

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Creating and consistently maintaining the weld shape during gas metal arc welding (GMAW) is vital for ensuring and maintaining the specified weld quality. However, the back-bead is often not uniformly generated owing to the change that occurs in the narrow gap between the base metals during butt joint GMAW, which substantially influences weldability. Automating the GMAW process requires the capability of real-time weld quality monitoring and diagnosis. In this study, we developed a convolutional neural network-based backbead prediction model. Specifically, scalogram feature image data were acquired by performing Morlet wavelet transform on the welding current measured in the short-circuit transform mode of the GMAW process. The acquired scalogram feature image data were then analyzed and used to develop labeled weld quality training data for the convolutional neural network model. The model predictions were compared with welding data acquired through additional experiments to validate the proposed prediction model. The prediction accuracy was approximately 93.5%, indicating that the findings of this study could serve as a foundation for the future development of automated welding systems. INDEX TERMS Gas metal arc welding, back-bead monitoring, automated weld quality control, supervised deep learning, time-frequency analysis

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Sensing and digital control of weld pool with visual welding robot

Sharif

Yamane²,

Yusuke³

et al.

2000 26th Annual Conference of the IEEE Industrial Electronics Society. IECON 2000. 2000 IEEE International Conference on Indus

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Back bead control of the one-side robotic welding with visual sensor-cooperative control of current-waveform and torch motion for change of gap and welding position

Cited by 4 publications

References 2 publications

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

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

Prediction Model for Back-Bead Monitoring During Gas Metal Arc Welding Using Supervised Deep Learning

Sensing and digital control of weld pool with visual welding robot

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