Tian Long See
- Automotive Engineering top 5%
- Mechanical Engineering top 5%
- Additive Manufacturing Materials and Processes 5
- Computational Mechanics top 5%
- Laser Material Processing Techniques 18
- Surface Roughness and Optical Measurements 5
- Surfaces, Coatings and Films top 10%
- Surface Modification and Superhydrophobicity 4
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- Advanced Surface Polishing Techniques 6
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- Adhesion, Friction, and Surface Interactions 6
- Laser-induced spectroscopy and plasma 4
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- Diamond and Carbon-based Materials Research 4
Tian Long See
26 papers receiving 567 citations
Hit Papers
Peers
Comparison fields: 5 of 50
- Automotive Engineering 196
- Mechanical Engineering 415
- Computational Mechanics 166
- Surfaces, Coatings and Films 51
- Industrial and Manufacturing Engineering 49
Countries citing papers authored by Tian Long See
This map shows the geographic impact of Tian Long See'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 Tian Long See with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tian Long See more than expected).
Fields of papers citing papers by Tian Long See
This network shows the impact of papers produced by Tian Long See. 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 Tian Long See. The network helps show where Tian Long See may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Tian Long See, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 1 | |
| 2 | 2024 | 2 | |
| 3 | 2024 | 3 | |
| 4 | 2022 | 11 | |
| 5 | A comprehensive review on laser powder bed fusion of steels: Processing, microstructure, defects and control methods, mechanical properties, current challenges and future trendsbreakdown → | 2022 | 303 |
| 6 | 2022 | 11 | |
| 7 | 2022 | 14 | |
| 8 | 2021 | 7 | |
| 9 | 2021 | 42 | |
| 10 | 2021 | 28 | |
| 11 | 2020 | 11 | |
| 12 | 2020 | 14 | |
| 13 | 2020 | 4 | |
| 14 | 2020 | 14 | |
| 15 | 2017 | 7 | |
| 16 | 2015 | 17 | |
| 17 | 2014 | 12 | |
| 18 | 2014 | 29 | |
| 19 | 2013 | 12 | |
| 20 | 2012 | 5 |
About Tian Long See
Tian Long See is a scholar working on Computational Mechanics, Surfaces, Coatings and Films and Mechanics of Materials, having authored 26 papers that have together received 602 indexed citations. Recurring topics across this work include Laser Material Processing Techniques (18 papers), Advanced Surface Polishing Techniques (6 papers), Adhesion, Friction, and Surface Interactions (6 papers), Additive Manufacturing Materials and Processes (5 papers), Surface Roughness and Optical Measurements (5 papers), Laser-induced spectroscopy and plasma (4 papers), Diamond and Carbon-based Materials Research (4 papers) and Surface Modification and Superhydrophobicity (4 papers). The work is most often cited by research in Automotive Engineering (196 citations), Mechanical Engineering (415 citations) and Computational Mechanics (166 citations). Tian Long See has collaborated with scholars based in United Kingdom, Australia and India. Frequent co-authors include Paul J. Scott, Wenhan Zeng, Shan Lou, Xiangqian Jiang, Shubhavardhan Ramadurga Narasimharaju, Zicheng Zhu, Stefan Dimov, Manuela Pacella, Lin Li and Zhu Liu. Their work appears in journals such as Langmuir, Applied Surface Science and Surface and Coatings Technology.
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.