Sheng Yang

1.9k total citations
60 papers, 1.3k citations indexed

About

Sheng Yang is a scholar working on Industrial and Manufacturing Engineering, Automotive Engineering and Mechanical Engineering. According to data from OpenAlex, Sheng Yang has authored 60 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 32 papers in Industrial and Manufacturing Engineering, 25 papers in Automotive Engineering and 23 papers in Mechanical Engineering. Recurrent topics in Sheng Yang's work include Additive Manufacturing and 3D Printing Technologies (25 papers), Manufacturing Process and Optimization (24 papers) and Additive Manufacturing Materials and Processes (11 papers). Sheng Yang is often cited by papers focused on Additive Manufacturing and 3D Printing Technologies (25 papers), Manufacturing Process and Optimization (24 papers) and Additive Manufacturing Materials and Processes (11 papers). Sheng Yang collaborates with scholars based in Canada, China and United States. Sheng Yang's co-authors include Yaoyao Fiona Zhao, Yunlong Tang, Ying Zhang, Fantahun M. Defersha, Ahmed Naser, Ibrahim Deiab, Tom Page, Xun Xu, Guoying Dong and Hai Qu and has published in prestigious journals such as Environmental Science & Technology, ACS Nano and Advanced Functional Materials.

In The Last Decade

Sheng Yang

57 papers receiving 1.2k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Sheng Yang Canada 20 761 630 560 173 111 60 1.3k
Ian Campbell United Kingdom 16 935 1.2× 515 0.8× 773 1.4× 157 0.9× 240 2.2× 52 1.5k
Thomas Vietor Germany 20 770 1.0× 484 0.8× 568 1.0× 259 1.5× 209 1.9× 168 1.4k
Deon de Beer South Africa 18 507 0.7× 243 0.4× 334 0.6× 78 0.5× 210 1.9× 82 923
Zhifeng Liu China 22 289 0.4× 243 0.4× 666 1.2× 129 0.7× 79 0.7× 76 1.2k
Iñigo Flores Ituarte Finland 19 590 0.8× 270 0.4× 497 0.9× 90 0.5× 160 1.4× 56 1000
Harry Bikas Greece 15 1.2k 1.6× 648 1.0× 976 1.7× 180 1.0× 328 3.0× 44 1.7k
Shailendra Kumar India 21 488 0.6× 459 0.7× 797 1.4× 212 1.2× 372 3.4× 118 1.5k
Megan Kreiger United States 10 1.4k 1.9× 490 0.8× 464 0.8× 515 3.0× 492 4.4× 15 1.8k
Giuseppe Ingarao Italy 24 531 0.7× 396 0.6× 1.4k 2.5× 139 0.8× 136 1.2× 83 1.9k
Ahmed Jawad Qureshi Canada 24 840 1.1× 579 0.9× 843 1.5× 134 0.8× 339 3.1× 106 1.7k

Countries citing papers authored by Sheng Yang

Since Specialization
Citations

This map shows the geographic impact of Sheng Yang's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Sheng Yang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sheng Yang more than expected).

Fields of papers citing papers by Sheng Yang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sheng Yang. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Sheng Yang. The network helps show where Sheng Yang may publish in the future.

Co-authorship network of co-authors of Sheng Yang

This figure shows the co-authorship network connecting the top 25 collaborators of Sheng Yang. A scholar is included among the top collaborators of Sheng Yang based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Sheng Yang. Sheng Yang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Li, Junfei, et al.. (2025). A novel digital twins-driven mutual trust framework for human–robot collaborations. Journal of Manufacturing Systems. 80. 948–962. 4 indexed citations
2.
Yang, Sheng, et al.. (2025). Live digital twin with virtual reality for accessible and immersive manufacturing education. The International Journal of Advanced Manufacturing Technology. 136(7-8). 3577–3590.
3.
Wang, Pei, et al.. (2024). Time series prediction for production quality in a machining system using spatial-temporal multi-task graph learning. Journal of Manufacturing Systems. 74. 157–179. 12 indexed citations
4.
Yang, Sheng, et al.. (2024). Synchronization evaluation of digital twin for a robotic assembly system using computer vision. Manufacturing Letters. 41. 1257–1263. 1 indexed citations
5.
Yang, Sheng, et al.. (2024). Evaluation of digital twin synchronization in robotic assembly using YOLOv8. The International Journal of Advanced Manufacturing Technology. 134(1-2). 871–885. 5 indexed citations
6.
Wang, Pei, et al.. (2024). Machining parameter optimization for a batch milling system using multi-task deep reinforcement learning. Journal of Manufacturing Systems. 78. 124–152. 4 indexed citations
7.
Yang, Sheng, et al.. (2024). Decision support tools for product customisation: an in-depth review. International Journal of Manufacturing Research. 19(1). 62–97. 1 indexed citations
8.
Zhu, Xiyu, Minghui Duan, Lin Zhang, et al.. (2023). Liquid Metal‐Enabled Microspheres with High Drug Loading and Multimodal Imaging for Artery Embolization. Advanced Functional Materials. 33(18). 30 indexed citations
9.
Zhu, Xiyu, Minghui Duan, Lin Zhang, et al.. (2023). Liquid Metal‐Enabled Microspheres with High Drug Loading and Multimodal Imaging for Artery Embolization (Adv. Funct. Mater. 18/2023). Advanced Functional Materials. 33(18). 2 indexed citations
10.
Wang, Pei, et al.. (2023). Production quality prediction of multistage manufacturing systems using multi-task joint deep learning. Journal of Manufacturing Systems. 70. 48–68. 33 indexed citations
11.
Naser, Ahmed, Fantahun M. Defersha, Eujin Pei, Yaoyao Fiona Zhao, & Sheng Yang. (2023). Toward automated life cycle assessment for additive manufacturing: A systematic review of influential parameters and framework design. Sustainable Production and Consumption. 41. 253–274. 14 indexed citations
12.
Yang, Sheng, et al.. (2023). Energy Consumption Modeling of 3D-Printed Carbon-Fiber-Reinforced Polymer Parts. Polymers. 15(5). 1290–1290. 9 indexed citations
13.
Yang, Sheng, Tom Page, Ying Zhang, & Yaoyao Fiona Zhao. (2020). Towards an automated decision support system for the identification of additive manufacturing part candidates. Journal of Intelligent Manufacturing. 31(8). 1917–1933. 52 indexed citations
14.
MacGowan, B. J., O. L. Landen, D. T. Casey, et al.. (2019). Understanding 3D Asymmetries In X-ray Drive At The National Ignition Facility Using a Simple View Factor Metric. APS Division of Plasma Physics Meeting Abstracts. 2019. 1 indexed citations
15.
Yang, Sheng, et al.. (2019). Automated Candidate Detection for Additive Manufacturing: A Framework Proposal. Proceedings of the ... International Conference on Engineering Design. 1(1). 679–688. 13 indexed citations
16.
17.
Ji, Shiqi, et al.. (2014). Physical Model Analysis During Transient for Series-Connected HVIGBTs. IEEE Transactions on Power Electronics. 29(11). 5727–5737. 26 indexed citations
18.
Liu, Yi, Sheng Yang, & Jining Chen. (2012). Modeling Environmental Impacts of Urban Expansion: A Systematic Method for Dealing with Uncertainties. Environmental Science & Technology. 46(15). 8236–8243. 20 indexed citations
19.
Yang, Sheng, et al.. (2008). Estimation of Incident Delays on Arterial Streets. Transportation Research Board 87th Annual MeetingTransportation Research Board. 1 indexed citations
20.
Yang, Sheng, Cheng Wu, & S. Jack Hu. (2000). Modeling and analysis of multi‐stage transfer lines with unreliable machines and finite buffers. Annals of Operations Research. 93(1-4). 405–421. 19 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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