Classification of Arc Welding Joints Images Based on Convolutional Neural Network

Zheng, Peng; Chen, Jiechang; Ye, Shaofeng; Ott, Peter; Wang, Lei

doi:10.1109/icasid.2018.8693214

Cited by 2 publications

(1 citation statement)

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“…In the last years, deep learning have brought revolutionary advances in computer vision and machine learning, outperforming traditional machine learning approaches in many application fields. Recently, vision systems based on Convolutional Neural Networks (CNNs) [11] have also been proposed for quality assessment of arc welding [12], [13]. In both works, CNNs are able to successfully classify welds based on visible images.…”

Section: Introductionmentioning

confidence: 99%

Embedded vision system for monitoring arc welding with thermal imaging and deep learning

Fernandez

Souto

González

et al. 2020

2020 International Conference on Omni-Layer Intelligent Systems (COINS)

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We develop a novel embedded vision system for online monitoring of arc welding with thermal imaging. The thermal images are able to provide clear information of the melt pool and surrounding areas during the welding process. We propose a deep learning processing pipeline with a CNN-LSTM architecture for the detection and classification of defects based on video sequences. The experimental results show that the CNN-LSTM architecture is able to model the complex dynamics of the welding process and detect and classify defects with high accuracy. In addition, the embedded vision system implements an OPC-UA server, enabling an easy vertical and horizontal integration in Industry 4.0.

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Section: Introductionmentioning

confidence: 99%