맞대기 V형 그루브의 GMA 초층용접에서 합성곱 신경망을 이용한 이면비드 발생 예측 모델 개발
- Title
- 맞대기 V형 그루브의 GMA 초층용접에서 합성곱 신경망을 이용한 이면비드 발생 예측 모델 개발
- Other Titles
- Convolutional Neural Network Model for the Prediction of Back-Bead Occurrence in GMA Root Pass Welding of V-groove Butt Joint
- Author
- 이승환
- Keywords
- Gas metal arc welding (GMAW); V-Groove; Back-bead; Root pass; Full penetration; Deep learning; Convolutional neural network (CNN); Laser vision
- Issue Date
- 2021-10
- Publisher
- 대한용접접합학회
- Citation
- 대한용접접합학회지, v. 39, NO. 5, Page. 463-470
- Abstract
- Gas metal arc (GMA) welding is widely used in the machinery industry. The quality of a welded joint is affected by the penetration of root pass welding in the V-groove joint. Automation using GMA welding is continuously re- quired, and root pass welding automation is required to automate the entire welding process. In particular, the devel- opment of a prediction model that can ensure full penetration back-bead is required for the automation of root pass welding. In this study, a convolutional neural network (CNN) model was applied to predict the occurrence of back-bead in V-groove butt joint GMA root pass welding. The bead profile was measured using a laser vision sensor system and it was used as the input data for the prediction model, and the bead occurrence was used as the output data for the model. A total of 12,873 bead profiles were extracted and pre-processed through cutting, resizing, and thresholding. The CNN model consists of nine layers, and performs three convolution and two pooling operations. The accuracy of the prediction model was 99.5%, and through this study, it was demonstrated that the quality of root-pass welding can be controlled by using convolutional neural network and it can contribute to automation.
- URI
- https://e-jwj.org/journal/view.php?doi=10.5781/JWJ.2021.39.5.1https://repository.hanyang.ac.kr/handle/20.500.11754/177723
- ISSN
- 2466-2232;2466-2100
- DOI
- 10.5781/JWJ.2021.39.5.1
- Appears in Collections:
- COLLEGE OF ENGINEERING[S](공과대학) > MECHANICAL ENGINEERING(기계공학부) > Articles
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