Chenhui Shao

2.2k total citations
93 papers, 1.7k citations indexed

About

Chenhui Shao is a scholar working on Mechanical Engineering, Industrial and Manufacturing Engineering and Electrical and Electronic Engineering. According to data from OpenAlex, Chenhui Shao has authored 93 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 58 papers in Mechanical Engineering, 20 papers in Industrial and Manufacturing Engineering and 14 papers in Electrical and Electronic Engineering. Recurrent topics in Chenhui Shao's work include Welding Techniques and Residual Stresses (21 papers), Non-Destructive Testing Techniques (15 papers) and Industrial Vision Systems and Defect Detection (14 papers). Chenhui Shao is often cited by papers focused on Welding Techniques and Residual Stresses (21 papers), Non-Destructive Testing Techniques (15 papers) and Industrial Vision Systems and Defect Detection (14 papers). Chenhui Shao collaborates with scholars based in United States, China and Japan. Chenhui Shao's co-authors include Yuquan Meng, Yuhang Yang, Jionghua Jin, J. Patrick Spicer, Hui Wang, Tae Hyung Kim, S. Jack Hu, Jeffrey A. Abell, Siyuan Chen and Jingjing Li and has published in prestigious journals such as SHILAP Revista de lepidopterología, ACS Applied Materials & Interfaces and Applied Energy.

In The Last Decade

Chenhui Shao

85 papers receiving 1.6k citations

Peers

Chenhui Shao

IME

EEE

CSE

Emad Abouel Nasr Saudi Arabia

M. Brezočnik Slovenia

Guofu Yin China

Róbert Čep Czechia

Shuyou Zhang China

Kanak Kalita India

Qasim Zeeshan Cyprus

Mohammed Asmael Cyprus

Yingjie Zhang China

S. Kumanan India

Emad Abouel Nasr Saudi Arabia

Chenhui Shao

548 ×0.7

419 ×1.2

IME

262 ×1.1

EEE

189 ×0.8

CSE

129 ×0.6

Citations per year, relative to Chenhui Shao Chenhui Shao (= 1×) peers Emad Abouel Nasr

Countries citing papers authored by Chenhui Shao

Since Specialization

Citations

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

Fields of papers citing papers by Chenhui Shao

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Chenhui Shao

This figure shows the co-authorship network connecting the top 25 collaborators of Chenhui Shao. A scholar is included among the top collaborators of Chenhui Shao 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 Chenhui Shao. Chenhui Shao is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Shao, Chenhui, et al.. (2026). Sensor and feature selection for cost- and time-efficient online monitoring of ultrasonic metal welding. Journal of Manufacturing Processes. 160. 498–508.

Jackson, William Henry, et al.. (2025). Scalable control of extraneously induced defects in in-field additive manufacturing. Journal of Manufacturing Processes. 141. 919–933. 2 indexed citations

Wang, Zihan, et al.. (2025). Operational resilience of additively manufactured parts to stealthy cyberphysical attacks using geometric and process digital twins. Journal of Manufacturing Systems. 83. 626–651.

Chen, Siyuan, et al.. (2025). RLGBS: Reinforcement Learning-Guided Beam Search for process optimization in a paper machine dryer section. Computers in Industry. 172. 104351–104351.

Geng, Peihao, Yu-Jun Xia, Bo Pan, et al.. (2025). Machine learning applications in welding processes: Progresses and opportunities. International Journal of Machine Tools and Manufacture. 213. 104344–104344. 1 indexed citations

McGregor, Davis J., et al.. (2024). Automatic detection of hidden defects and qualification of additively manufactured parts using X-ray computed tomography and computer vision. Manufacturing Letters. 41. 1216–1224. 3 indexed citations

King, William P., et al.. (2024). Deep learning of 3D point clouds for detecting geometric defects in gears. Manufacturing Letters. 41. 1324–1333. 1 indexed citations

Meng, Yuquan, et al.. (2024). Federated domain generalization for condition monitoring in ultrasonic metal welding. Journal of Manufacturing Systems. 77. 1–12. 7 indexed citations

Dawson, Michelle, et al.. (2024). Hybrid physics-guided data-driven modeling for generalizable geometric accuracy prediction and improvement in two-photon lithography. Journal of Manufacturing Processes. 110. 202–210. 8 indexed citations

10.

Shao, Chenhui, et al.. (2024). Filtered kriging for improved interpolation of periodic manufacturing surfaces. Journal of Manufacturing Processes. 131. 1–12. 2 indexed citations

11.

Wei, Sheng, et al.. (2024). Continuing minimal-defect production under material integrity cyberattacks. Manufacturing Letters. 40. 54–57. 1 indexed citations

12.

McGregor, Davis J., et al.. (2024). Detecting and classifying hidden defects in additively manufactured parts using deep learning and X-ray computed tomography. Journal of Intelligent Manufacturing. 36(5). 3465–3479. 6 indexed citations

13.

McGregor, Davis J., et al.. (2024). Geometry repeatability and prediction for personalized medical devices made using multi-jet fusion additive manufacturing. SHILAP Revista de lepidopterología. 9. 100200–100200. 6 indexed citations

14.

Zheng, Zhuoyuan, Bo Pan, Yuquan Meng, et al.. (2023). Corrosion of Al-Fe self-pierce riveting joints with multiphysics-based modeling and experiments. Journal of Manufacturing Processes. 95. 434–445. 7 indexed citations

15.

Meng, Yuquan, et al.. (2023). Explainable few-shot learning for online anomaly detection in ultrasonic metal welding with varying configurations. Journal of Manufacturing Processes. 107. 345–355. 22 indexed citations

16.

Chen, Siyuan, et al.. (2023). A federated learning approach to mixed fault diagnosis in rotating machinery. Journal of Manufacturing Systems. 68. 687–694. 40 indexed citations

17.

Yang, Yuhang, Davis J. McGregor, Sameh Tawfick, William P. King, & Chenhui Shao. (2022). Hierarchical data models improve the accuracy of feature level predictions for additively manufactured parts. Additive manufacturing. 51. 102621–102621. 15 indexed citations

18.

McGregor, Davis J., et al.. (2022). Using machine learning to predict dimensions and qualify diverse part designs across multiple additive machines and materials. Additive manufacturing. 55. 102848–102848. 34 indexed citations

19.

Zhao, Hanyang, Longnan Li, Xiao Yan, et al.. (2020). Extreme Antiscaling Performance of Slippery Omniphobic Covalently Attached Liquids. ACS Applied Materials & Interfaces. 12(10). 12054–12067. 72 indexed citations

20.

Meng, Yuquan, Manjunath C. Rajagopal, Hanyang Zhao, et al.. (2020). Multi-objective optimization of peel and shear strengths in ultrasonic metal welding using machine learning-based response surface methodology. Mathematical Biosciences & Engineering. 17(6). 7411–7427. 25 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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