Research output per year
Research output per year
Personal profile
Personal profile
Yu Xinghua received his PhD in Welding Engineering from Ohio State University in 2012. After his PhD, he joined the Material Connection Group of Oak Ridge National Laboratory until October 2018. He has nearly 11 years of study and work experience in high-level universities and research institutions in the United States. During the period, he published more than 20 academic papers including Acta Materialia, the top journal of metal science, and his scientific research achievements won the global top 100 R&D Award R&D100. Presided over and participated in nearly 10 projects of energy source Ministry and enterprise. The applicant is also a U.S. Department of Energy grant reviewer, co-chair of the MST International Conference, and reviewer of several journals in the field of metallurgy and welding.
Research Interests
Welding Metallurgy, Welding Automation, Additive Manufacturing, Intelligent Inspection, Machine Learning
Education
2010.04-2012.09 Ohio State University, PhD, Welding Engineering
2007.09-2010.04 Ohio State University, Master, Welding Engineering
2003.09-2007.09 Harbin Institute of Technology, Bachelor, Welding Technology and Engineering
2007.09-2010.04 Ohio State University, Master, Welding Engineering
2003.09-2007.09 Harbin Institute of Technology, Bachelor, Welding Technology and Engineering
Professional Experience
2018.12-present, Beijing Institute of Technology, Professor
2015.2-2018.10 Researcher, Oak Ridge National Laboratory
2012.10-2015.2 Postdoctoral Fellow, Oak Ridge National Laboratory
2015.2-2018.10 Researcher, Oak Ridge National Laboratory
2012.10-2015.2 Postdoctoral Fellow, Oak Ridge National Laboratory
Expertise related to UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
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Collaborations and top research areas from the last five years
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Automated Welding Defect Detection using Point-Rend ResUNet
Zhang, B., Wang, X., Cui, J. & Yu, X., Mar 2024, In: Journal of Nondestructive Evaluation. 43, 1, 11.Research output: Contribution to journal › Article › peer-review
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Enhancing Weld Inspection Through Comparative Analysis of Traditional Algorithms and Deep Learning Approaches
Zhang, B., Wang, X., Cui, J., Wu, J., Xiong, Z., Zhang, W. & Yu, X., Jun 2024, In: Journal of Nondestructive Evaluation. 43, 2, 38.Research output: Contribution to journal › Article › peer-review
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Impact of annotation quality on model performance of welding defect detection using deep learning
Cui, J., Zhang, B., Wang, X., Wu, J., Liu, J., Li, Y., Zhi, X., Zhang, W. & Yu, X., Apr 2024, In: Welding in the World, Le Soudage Dans Le Monde. 68, 4, p. 855-865 11 p.Research output: Contribution to journal › Article › peer-review
1 Citation (Scopus) -
Mechanical performance of 22SiMn2TiB steel welded with low-transformation-temperature filler wire and stainless steel filler wire
Lin, Z. D., Song, K. J., Sun, Z., Zhu, Z. Q., Zhao, X. F., Goulas, C., Ya, W. & Yu, X. H., Apr 2024, In: Journal of Iron and Steel Research International. 31, 4, p. 967-981 15 p.Research output: Contribution to journal › Article › peer-review
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Zoom in on the target network for the prediction of defective images and welding defects' location
Wang, X., Zhang, B. & Yu, X., Apr 2024, In: NDT and E International. 143, 103059.Research output: Contribution to journal › Article › peer-review
1 Citation (Scopus)