Kai Yu

112 papers receiving 1.6k citations

Peers

Kai Yu
Comparison fields: 5 of 132
  • Industrial and Manufacturing Engineering 509
  • Automotive Engineering 441
  • Computer Graphics and Computer-Aided Design 75
  • Mechanical Engineering 777
  • Management of Technology and Innovation 93
Replace Gang Zhao with:
Gang Zhao China
Suk‐Hwan Suh South Korea
F. Kimura Japan
Judy M. Vance United States
James Millar Ritchie United Kingdom
Sanjay Joshi United States
Mark Price United Kingdom
Qiang Huang United States
Sara McMains United States
Chih‐Hsing Chu Taiwan
Kai Yu relative to Gang Zhao China Gang Zhao's profile →
Citations per field
00.5×2.8×
Gang Zhao · 1×
Citations per year

Countries citing papers authored by Kai Yu

Since Specialization
Citations

This map shows the geographic impact of Kai Yu'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 Kai Yu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kai Yu more than expected).

Fields of papers citing papers by Kai Yu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kai Yu. 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 Kai Yu. The network helps show where Kai Yu may publish in the future.

Co-authors

The 25 scholars most cited alongside Kai Yu, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Kai Yu Line = papers co-authored together Kai Yu links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20250
2 20254
3 20251
4 20251
5 20240
6 20246
7 20231
8 202216
9 202246
10 20213
11 20203
12 20201
13 20150
14
MICROSTRUCTURE OF DIRECTLY EXTRUDED Mg-1Zn-1Ca ALLOY AND ITS CORROSION BEHAVIOR IN SBF SOLUTION
20151
15 20132
16
Deep Learning of invariant features via tracked video sequences
20122
17 20104
18 20072
19 20076
20 20021

About Kai Yu

Kai Yu is a scholar working on Industrial and Manufacturing Engineering, Computer Graphics and Computer-Aided Design, Automotive Engineering, Management of Technology and Innovation and Computational Mechanics, having authored 119 papers that have together received 1.7k indexed citations. Recurring topics across this work include Manufacturing Process and Optimization (37 papers), Advanced Numerical Analysis Techniques (17 papers), Additive Manufacturing and 3D Printing Technologies (16 papers), Injection Molding Process and Properties (13 papers), Advanced machining processes and optimization (12 papers), 3D Shape Modeling and Analysis (11 papers), Computer Graphics and Visualization Techniques (8 papers) and Semantic Web and Ontologies (8 papers). The work is most often cited by research in Industrial and Manufacturing Engineering (509 citations), Automotive Engineering (441 citations), Computer Graphics and Computer-Aided Design (75 citations), Mechanical Engineering (777 citations) and Management of Technology and Innovation (93 citations). Kai Yu has collaborated with scholars based in Hong Kong, China and United States. Frequent co-authors include K.M. Au, Charlie C. L. Wang, Suet To, W.K. Chiu, C.K. Kwong, Yunbo Zhang, Yu Wang, Zhanwen Sun, C.K.M. Lee and Wing Kong Chiu. Their work appears in journals such as The International Journal of Advanced Manufacturing Technology, Computer-Aided Design, Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture, Computers in Industry and Journal of Materials Processing 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.

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