| Real-time sensing of gas metal arc welding process–A literature review and analysis Y Cheng, R Yu, Q Zhou, H Chen, W Yuan, YM Zhang Journal of manufacturing processes 70, 452-469, 2021 | 77 | 2021 |
| Machine learning of weld joint penetration from weld pool surface using support vector regression R Liang, R Yu, Y Luo, YM Zhang Journal of Manufacturing Processes 41, 23-28, 2019 | 67 | 2019 |
| Application of wavelet-packet transform driven deep learning method in PM2. 5 concentration prediction: A case study of Qingdao, China Q Zheng, X Tian, Z Yu, N Jiang, A Elhanashi, S Saponara, R Yu Sustainable Cities and Society 92, 104486, 2023 | 66 | 2023 |
| Virtual reality robot-assisted welding based on human intention recognition Q Wang, W Jiao, R Yu, MT Johnson, YM Zhang IEEE Transactions on Automation Science and Engineering 17 (2), 799-808, 2019 | 60 | 2019 |
| Detecting dynamic development of weld pool using machine learning from innovative composite images for adaptive welding Y Cheng, Q Wang, W Jiao, R Yu, S Chen, YM Zhang, J Xiao Journal of Manufacturing Processes 56, 908-915, 2020 | 58 | 2020 |
| Real-time recognition of arc weld pool using image segmentation network R Yu, J Kershaw, P Wang, YM Zhang Journal of Manufacturing Processes 72, 159-167, 2021 | 52 | 2021 |
| Deep learning based real-time and in-situ monitoring of weld penetration: where we are and what are needed revolutionary solutions? R Yu, Y Cao, H Chen, Q Ye, YM Zhang Journal of Manufacturing Processes 93, 15-46, 2023 | 49 | 2023 |
| A real-time constellation image classification method of wireless communication signals based on the lightweight network MobileViT Q Zheng, S Saponara, X Tian, Z Yu, A Elhanashi, R Yu Cognitive Neurodynamics 18 (2), 659-671, 2024 | 44 | 2024 |
| How to accurately monitor the weld penetration from dynamic weld pool serial images using CNN-LSTM deep learning model? R Yu, J Kershaw, P Wang, YM Zhang IEEE Robotics and Automation Letters 7 (3), 6519-6525, 2022 | 41 | 2022 |
| Modeling of human welders’ operations in virtual reality human–robot interaction Q Wang, W Jiao, R Yu, MT Johnson, YM Zhang IEEE Robotics and automation letters 4 (3), 2958-2964, 2019 | 38 | 2019 |
| Hybrid machine learning-enabled adaptive welding speed control J Kershaw, R Yu, YM Zhang, P Wang Journal of Manufacturing Processes 71, 374-383, 2021 | 33 | 2021 |
| Digital twin implementation of autonomous planning arc welding robot system X Wang, Y Hua, J Gao, Z Lin, R Yu Complex System Modeling and Simulation 3 (3), 236-251, 2023 | 20 | 2023 |
| Prediction of weld penetration using dynamic weld pool arc images W Jiao, Q Wang, Y Cheng, R Yu, Y Zhang Weld J 99 (11), 295-302, 2020 | 17 | 2020 |
| Do we need a new foundation to use deep learning to monitor weld penetration? E Mucllari, R Yu, Y Cao, Q Ye, YM Zhang IEEE Robotics and Automation Letters 8 (6), 3669-3676, 2023 | 11 | 2023 |
| Robotizing double-electrode GMAW process through learning from human welders R Yu, Y Cao, J Martin, O Chiang, YM Zhang Journal of Manufacturing Processes 109, 140-150, 2024 | 9 | 2024 |
| Deep-learning based supervisory monitoring of robotized DE-GMAW process through learning from human welders R Yu, Y Cao, J Martin, O Chiang, YM Zhang Welding in the World 68 (4), 781-791, 2024 | 6 | 2024 |
| Real-time recognition of arc weld pool using image segmentation network. J Manuf Process 72: 159–167 R Yu, J Kershaw, P Wang, Y Zhang | 6 | 2021 |
| Intelligent welding robot path planning XW Wang, YP Shi, R Yu, XS Gu Proceedings of the 2015 Chinese Intelligent Automation Conference …, 2015 | 4 | 2015 |
| Monitoring of backside weld bead width from high dynamic range images using CNN network R Yu, J Kershaw, P Wang, YM Zhang 2022 8th International Conference on Control, Decision and Information …, 2022 | 3 | 2022 |
| Monitoring Weld Penetration by Training A Deep Learning Model Using Inaccurate Labels R Yu, Y Chen, J Zhang, Q Ye, YM Zhang Automation, Robotics & Communications for Industry 4.0/5.0, 17, 2023 | 1 | 2023 |